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The maintenance of cellular proteins in a biologically active and structurally stable state is a vital endeavor involving multiple cellular pathways . One such pathway is the ubiquitin-proteasome system that represents a major route for protein degradation , and reductions in this pathway usually have adverse effects on the health of cells and tissues . Here , we demonstrate that loss-of-function mutants of the Caenorhabditis elegans proteasome subunit , RPN-10 , exhibit moderate proteasome dysfunction and unexpectedly develop both increased longevity and enhanced resistance to multiple threats to the proteome , including heat , oxidative stress , and the presence of aggregation prone proteins . The rpn-10 mutant animals survive through the activation of compensatory mechanisms regulated by the conserved SKN-1/Nrf2 and ELT-2/GATA transcription factors that mediate the increased expression of genes encoding proteasome subunits as well as those mediating oxidative- and heat-stress responses . Additionally , we find that the rpn-10 mutant also shows enhanced activity of the autophagy-lysosome pathway as evidenced by increased expression of the multiple autophagy genes including atg-16 . 2 , lgg-1 , and bec-1 , and also by an increase in GFP::LGG-1 puncta . Consistent with a critical role for this pathway , the enhanced resistance of the rpn-10 mutant to aggregation prone proteins depends on autophagy genes atg-13 , atg-16 . 2 , and prmt-1 . Furthermore , the rpn-10 mutant is particularly sensitive to the inhibition of lysosome activity via either RNAi or chemical means . We also find that the rpn-10 mutant shows a reduction in the numbers of intestinal lysosomes , and that the elt-2 gene also plays a novel and vital role in controlling the production of functional lysosomes by the intestine . Overall , these experiments suggest that moderate proteasome dysfunction could be leveraged to improve protein homeostasis and organismal health and longevity , and that the rpn-10 mutant provides a unique platform to explore these possibilities . The content and quality of the cellular proteome reflects a balance between the synthesis , folding and refolding , and degradation of individual proteins [1] . Within this framework , the ubiquitin-proteasome system ( UPS ) plays a key role in maintaining the abundance of cellular proteins via the controlled degradation of selected proteins , and in maintaining the quality of the cellular proteome via the removal of abnormal or damaged proteins [2–4] . The UPS consists of the proteasome , which is a large multi-protein complex made up of two 19S regulatory caps and a 20S catalytic core , and the small 76 amino acid protein ubiquitin . The attachment of ubiquitin to specific lysine residues in a target protein via the sequential actions of ubiquitin-activating enzymes ( E1 ) , ubiquitin-conjugating enzymes ( E2 ) , and then ubiquitin ligases ( E3 ) serves to target the protein for destruction in the proteasome . The selectivity of the proteasome for ubiquitinated proteins is conferred in part by the 19S subunit that controls access to the 20S catalytic core and has specific subunits that recognize the ubiquitin chains conjugated to proteins [5 , 6] . After these subunits bind to the ubiquitin chains , the 19S subunit promotes the deubiquitination and unfolding of the target protein , and then transfers the protein into the 20S core particle for destruction via proteolytic cleavage [7–11] . This proteolytic cleavage proceeds until the protein is cleaved into small peptides of 2–24 amino acids that can diffuse out of the proteasome , and then be degraded by cytoplasmic peptidases [12 , 13] . The liberated amino acids can then be either recycled for use in new protein synthesis or be metabolized via intermediary metabolism . Aging , environmental stress , and a number of disease states are characterized by proteasome dysfunction , when the reserve of proteasome capacity is insufficient to meet cellular needs [14 , 15] . The resulting accumulation of mis-folded and damaged proteins could be a direct cause of specific age-related diseases , such as Alzheimer’s disease , and could also be a proximal cause of the aging process [16–19] . Consistent with the potentially grave consequences resulting from the loss of proteostasis , several cellular mechanisms are known to be triggered when the UPS is inhibited , including the activation of the cap’n’collar family transcription factors , such as skn-1 , Nrf1 , and Nrf2 , that control the expression of proteasome subunits , the production of proteasome-associated proteins , and the activation of autophagy [20] . The activation of specific cap’n’collar transcription factors is an evolutionarily conserved mechanism to balance the expression level of proteasome subunits to changes in proteasome activity . In C . elegans the skn-1 , in Drosophila the Nrf2 , and in vertebrates the Nrf1 transcription factor promotes the expression of multiple proteasome subunits in response to reductions in proteasome activity [18 , 21 , 22] . Often in parallel to the increased expression of proteasome subunits , UPS dysfunction leads to the expression of one or more proteasome-associated proteins that bind directly to the 26S proteasome to either increase its catalytic activity , promote proteasome assembly , or relax substrate specificity [20 , 23] . For example , in C . elegans and vertebrates , reductions in proteasome activity lead to the production of the AIP-1 and AIRAP proteins that bind to the proteasome and enhance the removal of damaged proteins from the cell [24 , 25] . Interestingly , the expression of both proteasome subunits and aip-1 is under the control of skn-1 in C . elegans , which suggests the existence of a coordinated response to proteasome dysfunction with at least one goal being the rapid compensatory increase in total proteasome capacity [22 , 24 , 26] . Many of the studies examining the responses to proteasome dysfunction have relied upon the use of chemical proteasome inhibitors or RNAi to produce rapid and marked reductions in proteasome activity . While these treatments produce robust effects , the changes in proteasome activity during aging or the development of age-related disease are , in contrast , likely gradual and only partial . To examine the organismal responses to chronic proteasome dysfunction , we sought to develop a model that would retain some level of proteasome activity and be amenable to genetic and RNAi studies . Here we describe the use of the C . elegans rpn-10 mutant , which lacks the worm ortholog of the Rpn10/PSMD4 proteasome subunit , as a model of chronic proteasome dysfunction [27 , 28] . The 19S subunits Rpn10 and Rpn13 act as receptors that recognize the ubiquitin moieties attached to proteins targeted for degradation [29–31] . As in yeast , rpn-10 is not essential for viability of C . elegans except when the rpn-12 subunit is also removed [32] . However , the rpn-10 mutant does show an accumulation of ubiquitinated proteins and reduced fertility due to feminization of the normally hermaphrodite germline resulting from the failure to degrade the TRA-2 protein via the UPS [27] . We find that this mutant shows evidence of proteasome dysfunction , and as a result of the adaptive response to the reduction in proteasome activity , also unexpectedly becomes long-lived and resistant to threats to the proteome such as heat , oxidative stress , and unstable proteins . To investigate these effects , we use a combination of gene expression studies and transgenic animals to investigate the downstream pathways affected by the rpn-10 mutation . The C . elegans rpn-10 gene encodes the worm ortholog of the Rpn10 proteasome subunit from yeast and the PSMD4 proteasome subunit from vertebrates [28] , and worms lacking the rpn-10 gene are viable but show feminization of the germline [27] . This tissue-specific phenotype observed in the rpn-10 mutant could suggest that the RPN-10 protein is either expressed in a limited number of tissues in the worm , or that perhaps other proteins or subunits can compensate for the absence of RPN-10 . To examine the tissue distribution of RPN-10 , we constructed transgenic animals that express an RPN-10::GFP fusion protein from a fosmid clone that has been modified by recombineering to fuse GFP to the C-terminus of RPN-10 [33] . Since the fosmid contains the genomic coding sequence and native promoter , as well as any possible splice variants , the fusion protein is likely both expressed in the proper developmental stages and tissues and trafficked to the correct subcellular locations . The RPN-10::GFP fusion protein was expressed in multiple tissues with the strongest expression seen in the pharynx , intestine , hypodermis , and spermatheca ( Fig 1A and 1B ) . We also observed expression at lower levels in a broader expression pattern , including the excretory cell , body wall muscle , vulva , and somatic gonad , suggesting that the RPN-10 protein might be ubiquitously expressed ( S1 Fig ) . The expression in the spermatheca is consistent with the prior defects noted in sperm development in the rpn-10 mutant [27] . Additionally , the RPN-10::GFP fusion protein is visible in both the nucleus and cytoplasm ( Fig 1A and S1 Fig ) , suggesting that the proteasome in C . elegans may play roles in both the cytoplasm and the nucleus as has been noted in other systems [34] . We confirmed that the GFP signal is produced by an RPN-10 fusion protein because treatment of the transgenic worms with rpn-10 RNAi reduces the expression of GFP in all tissues except for the pharynx ( Fig 1B and 1C ) . The persistent expression of GFP in the pharynx could reflect greater stability of the RPN-10 protein in this tissue compared to others . Despite the efficient knockdown of RPN-10 expression produced by the rpn-10 RNAi , we failed to see any gross developmental phenotypes in the treated animals ( Fig 1B ) . Similarly , we noted few developmental phenotypes for the rpn-10 ( ok1865 ) mutant other than the previously described decline in fertility and a mild increase in time needed to reach adulthood [27] . The rpn-10 ( ok1865 ) mutation is an 1166 base pair deletion that removes most of the 5’ UTR , the entire first , second , and third exons , and a small portion of the fourth exon of the rpn-10 gene . This deletion entirely removes the coding sequence for von Willebrand factor type A domain ( VWA ) and the highly conserved DNSE ( Asp-Asn-Ser-Glu ) sequence , which together are critical for interactions with other proteasome subunits , and the first of two ubiquitin-interacting motifs ( UID ) that mediate interactions with ubiquitinated target proteins ( Fig 1D ) . Hence the mutation is expected to be a null or strong loss-of-function [35] . Western blotting has shown that mutants with the rpn-10 ( tm1180 ) allele , a smaller deletion that removes the third exon and produces a C-terminal truncated protein , accumulate poly-ubiquitinated proteins , which is a phenotype indicating disruption of the UPS [27] . To simultaneously determine whether the rpn-10 ( ok1865 ) allele impairs UPS activity and to examine the sites of UPS dysfunction , we utilized transgenic animals expressing a UbV::GFP fusion protein under the control of the broadly expressed sur-5 promoter [36] . This fusion protein is normally rapidly degraded by the UPS , but decreases in UPS activity lead to the accumulation of the fusion protein and result in visible fluorescence [36] . To control for changes in the activity of the sur-5 promoter or changes in protein translation , the transgenic animals also express a sur-5p::mCherry transgene . When crossed into the rpn-10 ( ok1865 ) mutant we observed strong accumulation of the UbV::GFP fusion protein compared to wild-type animals ( Fig 1E and 1F ) , which is consistent with a reduction of UPS activity in this mutant . In contrast to the marked increase in UbV::GFP levels , we saw little to no change in the expression of mCherry , which makes a global effect on transcription or translation unlikely to account for the increase in UbV::GFP ( S2 Fig ) . Additionally , experiments comparing the rpn-10 ( ok1865 ) mutation to RNAi affecting the 20S proteasome subunits suggests that the impairment of proteasome function shows important distinctions ( S3 Fig ) . Specifically , RNAi knockdown of the 20S catalytic subunit genes pas-6 , pbs-6 , or pbs-7 starting from egg-hatching result in a robust increase in UbV::GFP expression during larval development compared to the rpn-10 mutant which peaks later . Further the worms treated with pas-6 , pbs-6 , or pbs-7 RNAi either arrest later in development or show harmful effects in the adult animal that are absent in the rpn-10 mutant [26] . The later accumulation of UbV::GFP and lack of the severe detrimental phenotypes suggests that the rpn-10 mutant produces a graded reduction in proteasome function compared to the inhibition of the 20S catalytic subunit . Despite the broad expression pattern of the RPN-10 protein , we observed the accumulation of the UbV::GFP fusion protein selectively in the intestine ( Fig 1G ) . Moreover , within the intestine we observed regional differences with the group of three cells at the proximal end of the intestine showing little accumulation compared to other intestinal cells , despite the expression of mCherry from the sur-5p::mCherry transgene at this site ( Fig 1E and 1G ) . The reasons accounting for the tissue and cell-specific accumulation of UbV::GFP are not clear , but this could reflect either greater demands for proteasome activity or lower proteasome expression in these areas . To determine if UPS dysfunction could be occurring in tissues besides the intestine , despite the lack of visible UbV::GFP fusion protein accumulation , we examined the expression of the aip-1p::GFP reporter gene . The aip-1 gene encodes an inducible subunit of the proteasome which is selectively activated in the setting of UPS dysfunction produced by a variety of causes [24–26 , 37] . We found that the aip-1p::GFP reporter was activated in a larger range of tissues in the rpn-10 ( ok1865 ) mutant , including expression in the pharynx , hypodermis , excretory cell , body wall muscle , intestine , and somatic gonad , which suggests that the limited accumulation of the UbV::GFP fusion protein underestimates the degree of UPS dysfunction ( Fig 1H and 1I and S4 Fig ) . Prior work has demonstrated a role for the UPS system in the response to proteostasis threats including oxidative stress and the expression of mis-folded proteins [38–46] . Given the UPS disruption observed in the rpn-10 ( ok1865 ) mutant , we tested the ability of this mutant to withstand thermal stress , oxidative stress , and the expression of an aggregation-prone polyglutamine fusion protein . We found that the rpn-10 mutant showed enhanced survival during a 35°C heat-shock compared to the N2 wild-type control ( Fig 2A ) . This finding is consistent with the increased survival of yeast treated with proteasome inhibitors to survive a subsequent heat shock due to the enhanced expression of heat shock factor proteins [47] . To determine if the rpn-10 mutant showed a differential resistance to thermal stress compared to oxidative stress , we treated the wild-type animals and the rpn-10 mutant with tert-butyl hydroperoxide ( tBHP ) . While we expected the mutant to be hypersensitive to tBHP , we instead found that the rpn-10 mutant animals showed a marked increase in survival when exposed to this source of oxidative stress ( Fig 2B ) . Together these findings suggest that the rpn-10 mutant is better able to resist acute threats to proteostasis compared to a wild-type animal . To investigate whether the resistance extended to long-term proteostasis threats , we used a transgene to express a Q35::YFP fusion protein in the body wall muscles . Previous work has shown this fusion protein to undergo age-dependent aggregation , which can be modified by changes in proteostasis activity in the cell [41] . Particularly , the inhibition of several proteasome subunits via RNAi is known to increase the aggregation of this protein [41] . In contrast to the effects of acute reductions in proteasome activity , we found that the Q35::YFP fusion protein formed fewer aggregates in the rpn-10 mutant compared to wild-type animals ( Fig 2C and 2D and S5 Fig ) . Furthermore , the aggregates observed tended to be smaller in the rpn-10 mutant when observed with fluorescent microscopy ( Fig 2E ) . Since the intestine showed greater evidence of UPS dysfunction than the muscle , we then explored the effects of the rpn-10 mutant on the aggregation of a Q44::YFP fusion protein that is expressed in the intestine with a transgene [48] . We found that the rpn-10 mutation served to protect animals from developing polyglutamine aggregates even in the intestine ( Fig 2F and 2G and S6 Fig ) . These last two phenotypes are in contrast to the effects of proteasome inhibition via RNAi , which could reflect either a difference in the degree of UPS disruption in the rpn-10 ( ok1865 ) mutant , the activation of one or more compensatory pathways , or perhaps a novel role for rpn-10 outside of the proteasome . While the reduced aggregation of these polyglutamine-repeat proteins suggests an improvement in proteostasis in the rpn-10 mutant , a limitation of this experimental approach is that both of the reporters express a non-native protein in the worms . To examine whether the improvement in proteostasis extended to native proteins , we examined the function of the metastable UNC-54 protein that is expressed by the unc-54 ( e1157 ) mutant [49] . This protein functions normally at the permissive temperature of 16°C whereas at the non-permissive temperature of 25°C , the protein becomes non-functional , presumably due to protein misfolding , and results in the animals becoming paralyzed . We found that the rpn-10 mutation also prevented the loss of UNC-54 activity when larval animals are acutely shifted to the non-permissive temperature ( Fig 2H ) , perhaps by promoting the stability or folding of the UNC-54 protein . Together these data suggest that the rpn-10 mutation enhances cellular proteostasis in multiple tissues of the worms . To explore why the rpn-10 ( ok1865 ) mutant shows increased resistance to oxidative and thermal stress , we examined the expression of the hsf-1 and skn-1 transcription factors that control responses to these stresses via the use of Nanostring . We found that the expression of skn-1 was unchanged whereas the expression of hsf-1 is increased in the rpn-10 mutant ( Fig 3A ) [50 , 51] . To examine whether the activity of either transcription factor is changed in the rpn-10 mutant animals , we tested effects of the mutation on GFP reporters which are known to be triggered by each of the stressors . The gst-4 gene encodes a member of the glutathione-S-transferase family and was identified as being differentially expressed in worms following exposure to oxidative stress [52] . A GFP reporter controlled by the gst-4 promoter has similarly been shown to respond to oxidative stress [53] . We found that this reporter is induced in the rpn-10 mutant even in the absence of oxidative stress , which could suggest that oxidative stress response pathways are activated even in unstressed animals ( Fig 3B and 3C ) . Since we found hsf-1 to be up-regulated in the rpn-10 mutant animals , we measured the expression of several heat-shock protein genes controlled by hsf-1 through the use of Nanostring , and we found a trend towards an increase in the expression of these genes in the rpn-10 mutant , but the differences failed to reach statistical significance ( Fig 3D ) . We also examined the heat-shock response using GFP reporters for the hsp-16 . 2 and hsp-70 genes , which respectively encode an α-crystalline and the inducible isoform of HSP-70 [54 , 55] . We found little difference in the expression of either hsp-16 . 2 ( Fig 3E and 3F ) or hsp-70 ( Fig 3G and 3H ) in the rpn-10 ( ok1865 ) mutant animals compared to control animals . However , we did see increased expression of both reporters in the rpn-10 mutant compared to wild-type animals during the recovery from a one hour heat shock ( Fig 3E , 3F , 3G and 3H ) , which is consistent with the rpn-10 mutant serving to prime the heat-shock response . The cause of this priming is unclear but might reflect the increased expression of hsf-1 or perhaps the delayed clearance of unfolded proteins . The enhanced proteostasis , oxidative stress responses , and heat shock responses exhibited by the rpn-10 mutant suggested that these animals could also exhibit an increase in lifespan . However , RNAi studies have demonstrated that the inhibition of most proteasome subunits has a clear detrimental effect on the adult lifespan of C . elegans [16] . We initially examined the lifespan of the rpn-10 mutant at 20°C but saw only modest effects , so we then repeated the studies at 25°C based on the observation that the over-expression of the rpn-6 . 1 subunit only shows a beneficial effect on lifespan at 25°C [56] . At 25°C we observed a consistent increase in the lifespan of the rpn-10 mutant compared to wild-type animals ( Fig 4A and S1 Table ) with up to almost a 30% increase in mean lifespan observed . This finding suggests that while reductions in proteasome activity typically have an adverse effect on aging , the net effect of the changes in proteasome activity and the subsequent adaptive responses produced by the rpn-10 mutation can slow aging and enhance longevity [16 , 18 , 57] . To identify additional genes controlled by changes in proteasome function , we extracted RNA from three independent samples of rpn-10 ( ok1865 ) mutants and wild-type animals and used the RNA to identify differentially expressed genes through whole transcriptome sequencing ( RNA-seq ) . From these studies , we identified 19638 distinct RNA transcripts , with 111 genes being differentially up-regulated in the rpn-10 mutant and 60 genes down-regulated in the mutant ( S2 Table ) . Using the DAVID program to identify themes within the up-regulated and down-regulated genes , we found that proteasome subunits were over-represented among the up-regulated genes ( 50–70 fold enrichment , p<0 . 001; S3 Table ) . This finding was not unexpected as a conserved “bounce-back” response seeks to restore proteasome function in worms or vertebrate cells through the production of additional proteasome complexes [21 , 22 , 58] . Analysis of the down-regulated genes with DAVID did not identify any over-represented gene classes . To complement the analysis using the DAVID program , we also tested if specific gene sets showed differential expression in the rpn-10 mutant through the use of gene set association analysis ( GSAA ) [59] . With GSAA we found that 26/32 proteasome subunits showed differential expression in the rpn-10 mutant , and these increases were readily apparent when the expression of individual genes in the RNA-seq data set where examined ( Fig 4B and S4 Table ) . Additionally , our experiments identified enrichment , in the rpn-10 mutant , of genes previously found to be differentially expressed in worms exposed to oxidative stress produced by hyperbaric oxygen treatment ( Fig 4C ) [60] . This finding is consistent with the elevated expression of the gst-4::GFP reporter we observed and suggests that the activation of oxidative stress responses extends to a larger number of genes ( Fig 3A ) . To better visualize the effects of the rpn-10 mutation on the oxidative stress and heat-shock responses , we examined the expression changes for individual genes involved in each response within the RNA-seq results , and we found increased expression of multiple gene classes within each group ( S4 Table ) . For example within the heat-shock proteins , we again saw the increased expression of hsf-1 and multiple hsp-16 proteins while hsp-1 and most hsp-12 genes showed little change in expression in the rpn-10 mutant ( S4 Table ) . Manual inspection of the differentially expressed genes also revealed possible insights into the response of the mutant worms to the reduction in proteasome activity . The up-regulation of proteasome subunits was clearly present with pas-3 , pas-6 , pas-7 , pbs-3 , pbs-6 , rpt-4 , rpn-8 , rpn-9 , and dss-1 , which is the C . elegans ortholog of the yeast proteasome regulatory cap protein SEM1 , all being overexpressed [61] . We also found that the worm ortholog of NEDD8 , ned-8 , was up-regulated in the rpn-10 mutant . NEDD8 is a small protein with a highly similar amino acid sequence to ubiquitin ( ~60% sequence identity ) [62 , 63] . However , NEDD8 differs functionally from ubiquitin in that NEDD8 is covalently attached selectively to cullin proteins where it promotes the formation of the E2–E3 ligase complex , which then catalyzes the ubiquitination of proteins [64 , 65] . However , under conditions of proteasome dysfunction when free ubiquitin levels are low due to the accumulation of ubiquitinated proteins , NEDD8 can also be activated by the same pathways that act to attach ubiquitin to target proteins [66 , 67] . The role of NEDD8 in this setting is unclear as this could simply reflect NEDD8 being aberrantly utilized by the ubiquitin activating enzymes due to the low levels of ubiquitin . Alternatively , the use of NEDD8 may constitute part of a response pathway to the reduced cellular proteasome activity [67] . Another notable gene found to be up-regulated in the rpn-10 mutant was atg-16 . 2 , which is one of two worm orthologs of ATG16L1 , and participates in autophagy via the recruitment of an ATG5-ATG12 complex to the nascent autophagosome [68 , 69] . Autophagy and the UPS are known to represent parallel pathways by which protein degradation can occur in the cell , so the responsiveness of atg-16 . 2 to changes in proteasome function could represent a point of cross-talk between the pathways . Also , among the up-regulated genes is prmt-1/epg-11 which encodes an arginine methyltransferase that acts to methylate specific cargo receptor proteins and is essential for their clearance of protein aggregates by autophagy during development [70] . We also saw further evidence of an increase in the expression of autophagy genes when the expression of individual genes was queried using the RNA-seq data with 14 out of 21 genes selected showing an increase ( S4 Table ) . Among the down-regulated genes , we identified cpi-1 , which encodes one of the worm cystatin genes [71] . An important role for cystatins is the inhibition of cathepsins , which are lysosomal proteases that degrade proteins brought to the lysosome via endocytic or autophagic transport pathways [71 , 72] . Hence , the down-regulation of cpi-1 may suggest changes in lysosomal activity that might facilitate the ultimate degradation of proteins that are engulfed by macro or selective-autophagy . The RNA-seq experiments suggested that both proteasome subunit and oxidative stress response genes are up-regulated in the rpn-10 mutant . Consistently , we found increased expression of the gst-4p::GFP oxidative stress reporter in the rpn-10 mutant ( Fig 3B and 3C ) . To examine the regulation of proteasome subunit expression , we used an rpn-7p::GFP reporter that expresses GFP under the control of the rpn-7 promoter . The rpn-7 gene encodes the worm ortholog of the human PSMD6 protein , which is a subunit of the 19S regulatory cap [28] . In wild-type worms , the rpn-7p::GFP reporter was expressed in multiple tissues including the intestine , pharynx , and hypodermis ( Fig 5A ) , and the level of GFP expression in these tissues was globally increased in the rpn-10 mutant ( Fig 5A and 5B ) . Hence despite the focal accumulation of UbV::GFP in the rpn-10 mutant , multiple tissues in the animal appear to sense changes in proteasome activity and up-regulate the expression of other subunits . In C . elegans , the control of oxidative stress responses and the up-regulation of proteasome expression following acute reductions in proteasome activity are both coordinated by the cap’n’collar transcription factor skn-1 , which is the ortholog of the Nrf1 and Nrf2 transcription factors from vertebrates [22 , 50 , 73] . We have previously shown that an additional aspect of the response to proteasome dysfunction is the induction of the aip-1/AIRAP gene , which encodes an inducible proteasome subunit that enhances proteasome activity and relaxes substrate specificity when bound to the 19S cap , and that this induction requires both skn-1 and hsf-1 [24–26] . Hence we sought to determine if skn-1 and/or hsf-1 is required for the activation of proteasome subunit expression in the rpn-10 mutant . However , when we treated the rpn-10 mutant with hsf-1 and skn-1 RNAi , we unexpectedly found that inhibition of skn-1 resulted in animals that were small , sickly , and developmentally arrested prior to adulthood ( Fig 5C ) . The effect of the skn-1 RNAi was particularly acute for L3 and L4 larval stage animals because rpn-10 mutant animals treated with skn-1 RNAi from egg hatching appeared similar to the control RNAi treated animals on the second day of treatment but then exhibited the detrimental effects on the third day of treatment ( S7 Fig ) . This could reflect the time needed for full effect of the skn-1 RNAi or the presence of a critical developmental period when skn-1 activity is essential . In contrast , treatment with hsf-1 RNAi had no effect on worm development ( Fig 5C ) . This finding demonstrates that while skn-1 is usually not essential for larval development , skn-1 is essential for development in the rpn-10 mutant . To determine if the essential role played by skn-1 could be mediated via the control of proteasome subunit expression , we treated wild-type N2 and rpn-10 mutant animals with control and skn-1 RNAi , and then measured the expression of several proteasome subunit genes using Nanostring analysis [74] . For these studies , we prepared RNA from the RNAi treated animals at 48 hours after synchronization , which is a timepoint prior to the appearance of any visual phenotypes due to skn-1 RNAi ( S7 Fig ) . We found that all of the proteasome subunits , for which we probed , were up-regulated in the rpn-10 mutant compared to N2 , and that treatment with skn-1 RNAi largely prevented this up-regulation ( Fig 5D and S5 Table ) . To determine if SKN-1 activation was sufficient for the increased expression of proteasome subunits , we also treated N2 with wdr-23 RNAi . The wdr-23 gene encodes a WD40 repeat protein that binds to skn-1 and inhibits its transcriptional activity [75] . We found that wdr-23 RNAi potently increased the expression of oxidative stress response genes ( Fig 5E and S5 Table ) , but had little effect on the expression of proteasome subunits . Conversely , the rpn-10 mutant showed a greater increase in the expression of proteasome subunits , with a more moderate effect on the activation of oxidative stress response genes ( Fig 5D and 5E ) . Together , these findings suggest that skn-1 is necessary but not sufficient for the activation of proteasome subunit expression , and that skn-1 is capable of mounting distinct responses to oxidative stress and proteasome dysfunction . The ability of skn-1 to independently control oxidative stress response genes and proteasome subunit expression suggested that additional transcription factors could act in parallel to skn-1 and contribute to this specificity . To identify such transcription factors , we screened two separate RNAi libraries consisting of subsets of transcription factors drawn from the Ahringer and Vidal RNAi libraries for clones that produced developmental phenotypes in the rpn-10 mutant that are similar to those produced by skn-1 RNAi ( S6 Table ) . From the two independent screens , we identified elt-2 , which is a GATA transcription factor and is essential for the expression of most genes expressed in the intestine [76–78] . In addition to its developmental role , elt-2 is required for innate immunity , contributes to the response to heavy metal exposure , and contributes to the beneficial effects of changes in daf-2/IGFR signaling on worm lifespan [79–82] . In the rpn-10 mutant , we found that inhibiting elt-2 with RNAi mirrored the effects of skn-1 knock-down and produced small , sickly , animals that often arrested during larval development ( Fig 6A ) . To determine if elt-2 cooperates with skn-1 to control the expression of either oxidative stress response genes or proteasome subunit genes in the rpn-10 mutant , we used Nanostring analysis to measure the expression of these genes in rpn-10 mutants treated with control or elt-2 RNAi . In contrast to skn-1 RNAi , we found that elt-2 RNAi did not block the activation of either group of genes ( Fig 5D and 5E , and S5 Table ) . Hence , while elt-2 is similarly required for the viability of the rpn-10 mutant , elt-2 likely acts via a distinct mechanism than skn-1 , and is discussed further below . As discussed previously , our RNAi-seq studies identified the autophagy genes atg-16 . 2 and prmt-1/epg-11 as being up-regulated in the rpn-10 mutant ( S2 and S4 Tables ) . This observation is consistent with the activation of autophagy observed when proteasome function is potently reduced via the expression of a dominant-negative proteasome subunit or is chronically reduced in cultured neural cells by long-term exposure to proteasome inhibitors [83 , 84] . To determine if autophagy is activated in the rpn-10 mutant , we examined the expression and subcellular localization of the LGG-1 protein in wild-type and rpn-10 mutant animals via the use of a transgene expressing a GFP::LGG-1 fusion protein [85] . The LGG-1 protein is the worm ortholog of LC3 and has been shown to play an analogous role in the formation of autophagosomes via integration into the autophagosome membrane [85] . We initially found that the expression of the GFP::LGG-1 fusion protein is increased in the rpn-10 mutant compared to wild-type animals ( Fig 6B and 6C ) , and this increase in lgg-1 expression occurs in part at the transcriptional level and in conjunction with the transcriptional up-regulation of the worm beclin ortholog bec-1 ( S8 Fig ) [85] . The up-regulation of LGG-1 expression has also been seen in worms with activated autophagy resulting from either removal of the germline via a glp-1 mutation or the inhibition of let-363/TOR with let-363 RNAi treatment [86] . Based on this and other work , the increased expression of LGG-1 has been therefore suggested to indicate the activation of autophagy [87] . To seek additional evidence of enhanced autophagy in the rpn-10 mutant , we looked for the presence of GFP::LGG-1 puncta , which are produced by the integration of LGG-1 into the membrane of developing autophagosomes [85] . We introduced a GFP::LGG-1 reporter into the rpn-10 mutant and observed the effects in the intestine and seam cells of the wild-type and rpn-10 mutant transgenic animals [86] . We saw an increase in GFP-positive puncta in both the seam cells and intestine of the rpn-10 mutant animals compared to the wild-type controls , which suggests an increase of the activity of the autophagy-lysosome pathway in these animals ( Fig 6D , 6E and 6F ) . The increase in autophagy could be involved in the adaptation to the changes in UPS activity in the rpn-10 mutant and could also contribute to some aspect of the beneficial effects of this mutation on proteostasis . To examine these possibilities we tested the effects of autophagy inhibition on both the development and improved proteostasis of the rpn-10 mutant . For these studies we focused on the epg-1/atg-13 , prmt-1/epg-11 , and atg-16 . 2 genes based upon either their identification in our RNA-seq studies ( S2 Table ) or identified role in the clearance of protein aggregates via autophagy [70 , 88] . During development , we found that loss of the atg-13 gene greatly impaired the development of the rpn-10 mutant as exhibited by the significantly delayed development of an rpn-10; atg-13 mutant compared to either mutant alone ( Fig 6G ) . Notably , almost 20% of the rpn-10; atg-13 mutants appeared to be permanently arrested during development and failed to reach adulthood even after five days ( Fig 6G ) . We also tested the role of autophagy in the enhanced resistance to the accumulation of polyglutamine-repeat protein aggregates in the intestine of animals expressing a Q44::YFP transgene by inhibiting the prmt-1/epg-11 , atg-16 . 2 , and atg-13 genes via the use of RNAi starting on day 1 of adulthood . We began RNAi treatment at this time point to prevent any adverse effects of RNAi treatment from occurring during development . We found that the knock-down of either atg-13 or atg-16 . 2 produced an increase in the percentage of animals with aggregates compared to the control RNAi treated rpn-10 mutant animals ( Fig 6H ) . In contrast , these RNAi treatments only modestly increased the percentage of wild-type animals with aggregates ( S9 Fig ) . To further explore the effects of these RNAi treatments , we also counted the number of aggregates in each worm in a separate trial . We again found that atg-13 and atg-16 . 2 RNAi treatment had a greater effect on the rpn-10 mutants compared to the wild-type animals , and additionally now prmt-1 RNAi produced a selective increase in polyglutamine aggregation in the rpn-10 mutant ( S9 Fig ) . Together these findings show a vital role for autophagy in both promoting the normal development of the rpn-10 mutant animals , and contributing to the effects of the rpn-10 mutation on proteostasis . The increased autophagic activity seen in the rpn-10 mutant could act to shuttle proteins to the lysosome for degradation via lysosomal proteases . To examine the role of the lysosomes in the rpn-10 mutant , we visualized the intestinal lysosomes through staining with both the Lysotracker fluorescent dye , which concentrates in lysosomes due to their low pH , and the use of the Magic Red cathepsin B and cathepsin L substrates [89] . The cathepsin B and L substrates are cell-permeable cresyl violet-conjugated peptides containing either the Arg-Arg or Phe-Arg sequence cleaved by the respective cathepsin inside of the lysosome . These cleavage events relieve the intramolecular quenching of the cresyl violet fluorophore and produces red fluorescence . Our work represents the first application of the Magic Red substrates in C . elegans research as a novel approach to identify lysosomes and quantify their activity . The use of the cathepsin substrates was particularly attractive because the location and degree of fluorescence are directly related to the activity of the cathepsin enzymes [89] . With the Lysotracker dye , we saw staining of intestinal lysosomes , and a similar pattern was observed when the animals were stained with either the cathepsin B or cathepsin L substrates ( Fig 7A ) . Consistent with both Lysotracker and the Magic Red substrates acting to label lysosomes , we observed a high-degree of co-localization when wild-type animals were stained with both Lysosensor Green and the cathepsin B substrate , as evidenced by Pearson’s correlation score or Mander’s overlap score which averaged greater than 0 . 9 ( Pearson average 0 . 92 , n = 15 and Mander average 0 . 94 , n = 15 ) ( S10 Fig ) [90] . When the staining of the wild-type and rpn-10 mutant animals were compared , we observed an overall decline in both lysosome volume and in the activity of each of the cathepsins as evidenced by reduced fluorescence in the mutant ( Fig 7A and 7B ) . The decline in overall fluorescence was likely due to a reduction in lysosome number and volume in the rpn-10 mutant ( Fig 7C , 7D , 7E and 7F ) . Together our observations could suggest that the cellular lysosome pool is being consumed by an increase in autophagy via the fusion of the lysosomes with the enlarged pool of autophagosomes . To test the importance of lysosome function in the rpn-10 mutant , we treated worms with RNAi to inhibit vha-15 , which is a part of the vacuolar proton-translocating ATPase and acts to promote the acidification of the lysosomes [91] . If the rpn-10 mutant relied upon the autophagosome-lysosome pathway to compensate for the declines in proteasome activity , we expected these mutants to show enhanced sensitivity to lysosome inhibition with vha-15 RNAi compared to wild-type animals . Consistently , we found that the rpn-10 mutant shows a decrease in body size and developmental rate compared to N2 animals treated in parallel with vha-15 RNAi ( Fig 7G and 7H ) . To determine if these effects could result from changes in lysosome pH , we treated worms with NH4Cl which accumulates in lysosomes and neutralizes the normally acidic pH of the organelle , producing a decrease in proteolytic activity [92] . Consistent with the effects of vha-15 RNAi , we observed the rpn-10 mutant animals to develop slowly following treatment with increasing concentrations of ammonium chloride , while N2 worms treated in parallel showed a lesser effect ( Fig 7I and 7J ) . These data suggest that the rpn-10 mutant both exhibits an increase in autophagy and has become dependent on the activity of the autophagy-lysosome pathway for normal development as evidenced by the selective vulnerability of the mutant to lysosome inhibitors . To identify the pathway that could be controlled by elt-2 and contribute to the survival of the rpn-10 mutant , we explored whether elt-2 might be involved in the generation of lysosomes in the intestine . Initially , we utilized an existing gene expression dataset which was generated using Serial Analysis of Gene Expression , SAGE , to compare gene expression differences in RNA prepared from L1 larvae that either lacked elt-2 due to the elt-2 ( ca15 ) mutation or contained the mutation as well as a rescuing transgene expressing elt-2 [77] . In this dataset , we noted that the expression of multiple genes associated with lysosomes , including the lysosome membrane protein lmp-1 , vacuolar proton-translocating ATPase subunits , and cathepsins , all showed decreased expression in the elt-2 ( ca15 ) mutants compared to the elt-2+ larvae ( Table 1 ) . To determine if these changes in gene expression affected lysosome size , number , or function , we stained worms grown on control RNAi or elt-2 RNAi with the Magic Red cathepsin B substrate . This staining demonstrated a reduction in both fluorescence intensity and the number of lysosomes present in the RNAi treated worms ( Fig 8A , 8B and 8C ) . The results are consistent with either a reduction in lysosome production or a reduction in the proteolytic activity of the lysosome following elt-2 RNAi treatment . While the inhibition of either elt-2 or skn-1 in the rpn-10 mutant is harmful , it was less clear to what extent either transcription factor normally acts to promote the growth and survival of the rpn-10 mutant . To determine if enhancing the function of either gene is beneficial , we used transgenes to over-express either elt-2 or skn-1 in the rpn-10 mutant . During the construction of these strains , we observed that the transgenic animals appeared to develop and reproduce faster than the non-transgenic worms . To directly test whether the development of the rpn-10 mutant was at least somewhat normalized by the over-expression of either gene , we performed development assays on synchronized animals using successfully reaching reproductive adulthood as the outcome . We found that the over-expression of either transcription factor led to more rapid development with elt-2 over-expression perhaps having a somewhat stronger effect than skn-1 ( Fig 9A ) . This finding suggests that the activities of elt-2 and skn-1 are limiting in the rpn-10 mutant and likely contribute to the slight developmental delay exhibited by these animals . Hence , enhancing the activity of either transcription factor leads to the increased activity of downstream targets and enhances the development of the rpn-10 mutant . While both elt-2 and skn-1 both contribute to the development of the rpn-10 mutant , it is unclear if either contribute to the improved proteostasis and increased longevity exhibited by the rpn-10 mutant . We first tested whether the inhibition of skn-1 , elt-2 , or hsf-1 affected the reduction in Q44::YFP aggregates seen in the rpn-10 mutant animals by using RNAi to knock-down each gene only in adult animals . We took this approach due to the adverse effects that both elt-2 and skn-1 had during development . We found that neither skn-1 nor hsf-1 was required for the reduction in protein aggregates ( Fig 9B ) . The Q44::YFP transgene was controlled by the vha-6 promoter , and this promoter appears to be regulated by elt-2 because we observed a dramatic decline in YFP fluorescence in either wild-type or rpn-10 mutant transgenic animals treated with elt-2 RNAi . We then examined the role of skn-1 and elt-2 in the increased longevity exhibited by the rpn-10 mutant by treating adult wild-type or rpn-10 mutant worms with control , skn-1 , or elt-2 RNAi and then measuring the effects on lifespan . We found that the increased lifespan of the rpn-10 mutant required skn-1 but not elt-2 ( Fig 9C and 9D ) . Lastly we examined the role of skn-1 in the enhanced oxidative stress resistance exhibited by the rpn-10 mutant . We were unable to obtain adult rpn-10 mutant animals following treatment with skn-1 RNAi , so we shifted to using larval animals that had been treated with control or skn-1 RNAi for 48 hours . Treatment of these animals with 100 μM juglone revealed that the skn-1 RNAi treatment markedly reduced the oxidative stress resistance of the rpn-10 mutant ( S11 Fig ) . Together these data suggest that skn-1 is essential for development , oxidative stress resistance , and longevity but not improved proteostasis of the rpn-10 mutant , while elt-2 is only essential for development . The role of elt-2 in proteostasis is difficult to judge since many promoters active in the intestine are elt-2 target genes [78] . The inhibition of the majority of the proteasome subunits in C . elegans is clearly detrimental , resulting in phenotypes such as developmental arrest or a marked reduction in body size [26] . Similarly , essentially all of the proteasome subunits with deletion alleles are homozygous lethal and need to be maintained as a balanced heterozygote ( see Wormbase for details ) . Therefore , it was somewhat surprising that two independent viable deletion alleles for rpn-10 have been isolated . This could have been due to the expression of rpn-10 in a limited pattern in the animal , or the presence of a redundant subunit . Instead , we find that rpn-10 is broadly expressed and produces aspects of proteasome dysfunction in multiple tissues when removed . Despite these findings , the viability of this rpn-10 deletion mutant could imply that the worm proteasome is similar to those of yeast , which are also less dependent on the analogous Mcb1/RPN10 subunit , or that sufficient compensatory pathways can be activated in the worm rpn-10 mutant to cope with the reductions in proteasome activity [29] . Our work suggests that the activation of compensatory mechanisms , such as the up-regulation of other proteasome subunits via the actions of skn-1 and the increased use of autophagy and lysosomes possibly for protein degradation are an important aspect of the response to the chronic reduction in proteasome activity seen in the rpn-10 mutant ( Fig 9E ) . Within this response elt-2 may play a supportive role by maintaining an adequate pool of intestinal lysosomes ( Fig 9E ) . The pathway leading to changes in the expression of autophagy genes and autophagic activity are currently unclear but recent work has shown that the RPN-10 protein serves as an adapter to facilitate the clearance of the proteasome via autophagy in Arabidopsis [93] . Perhaps the loss of RPN-10 could stimulate autophagic activity via the loss of some form of negative feedback from the proteasome . Alternately , the 19S cap proteasome subunits have been shown to dissociate from the 20S subunit in the setting of proteasome dysfunction and then bind to protein aggregates . When localized to the aggregates , the deubiquitinase activity of RPN-11/Poh1 releases ubiquitin chains from the aggregated protein , and these ubiquitin chains then serve to activate autophagy via the HDAC6 protein [94 , 95] . It could be possible that the loss of RPN-10 destabilizes the 26S proteasome or via other mechanisms promotes the association of RPN-11 and other subunits with cellular protein aggregates to then promote their removal via autophagy . Finally , proteomic studies comparing wild-type and long-lived daf-2 mutants in C . elegans have made the unexpected finding that the daf-2 mutants have increased amounts of insoluble protein during aging compared to wild-type animals [96] . Further , the insoluble fraction includes increased amounts of small heat-shock proteins , which could suggest that the controlled aggregation of proteins is an important mechanism for enhancing proteostasis . Perhaps , the rpn-10 mutant might act in a similar manner , especially in light of the enhanced heat-shock response observed , with the shunting of UPS substrates into a protected insoluble protein compartment in the cell . Future work could test this possibility via the analysis of amounts and types of insoluble proteins present in the rpn-10 mutant . However , it is also possible that the spectrum of UPS substrates that fail to be degraded in the rpn-10 mutant differs in an important way from those that accumulate when other subunits are removed via RNAi or genetic mutations . In the Mcb1/RPN10 yeast mutant , UPS substrates that are degraded via the N-end rule pathway are degraded to a similar extent as seen in wild-type yeast , whereas the substrates degraded via the ubiquitin fusion pathway are no longer degraded normally [29] . These differential effects on substrate degradation could reflect the presence of multiple ubiquitin binding proteins in the 19S proteasome subunit or the ability of the 20S proteasome to independently degrade some UPS substrates [30 , 31 , 97 , 98] . Future work can explore this question via the use of proteomic approaches , such as ubiquitin-remnant profiling , to determine if differences in substrate degradation are also observed in C . elegans . In addition , these experiments could determine the identities of substrates that might account for the phenotypic differences observed between the rpn-10 mutant and mutations or RNAi affecting other subunits [66 , 99 , 100] . An unexpected finding in our work was the increased lifespan and improved responses of the rpn-10 mutant to proteostasis threats including heat , oxidative stress , and the expression of metastable , unstable or aggregation-prone proteins . Based on prior work in the field , we hypothesized that these animals would be sensitive to proteostasis threats [38–46] . However , these previous experiments generally relied upon potent inhibition of proteasome activity via the use of chemical inhibitors or RNAi approaches , so there may be important differences in proteasome activity between the rpn-10 mutant and these other interventions , or in the types of compensatory pathways involved in the response to each . Furthermore , our findings are consistent with prior work showing that either cultured cells or yeast exposed to proteasome inhibitors show elevated expression of heat shock proteins , and increased resistance to heat shock [47 , 101 , 102] . We also find that genes involved in the response to oxidative stress are up-regulated in the rpn-10 mutant so these two compensatory responses may contribute to the ability of the rpn-10 mutant to better survive the exposure to heat or oxidative stress . The reasons accounting for the improved ability of the rpn-10 mutant to prevent the aggregation of the unstable polyglutamine repeat proteins are still somewhat unclear . Data from vertebrate cells suggest that proteasome inhibition may occur prior to the development of polyglutamine protein aggregates , so some aspect of the compensation to proteasome dysfunction , as opposed to proteasome activity alone , may play a key role in determining the timing and levels of protein aggregation [103] . In addition to the UPS , autophagy has been identified as an important pathway for the degradation of polyglutamine repeat proteins . Consequently a cellular environment where there is a high level of autophagic activity may result in low levels of protein aggregation regardless of the level of UPS activity [104] . Our data suggest that this could be at least partially true , because we find that the increased flux in the autophagosome-lysosome pathway in the rpn-10 mutant contributes to at least some of the reduction in aggregation by facilitating the removal of the polyglutamine repeat proteins . Alternately , the increased expression of oxidative stress response genes and priming of the heat-shock response which occur in the rpn-10 mutant could also act to reduce protein mis-folding and aggregation . An important response to the reduction in UPS activity in the rpn-10 mutant is the activation of the skn-1/Nrf2 transcription factor which then promotes the expression of proteasome subunits , and accessory factors like aip-1/AIRAP , as part of a “bounce-back” response [22 , 25 , 26] . Importantly we find that the rpn-10 mutant not only uses this response , but also actually requires the continuous activity of this response in order to develop normally and survive long-term in the presence of chronic reductions in proteasome activity . We also identify a novel role for the elt-2 GATA transcription factor in promoting the survival of the rpn-10 mutant despite the presence of chronic proteasome dysfunction . This effect could occur through a basal role in determining the level and activity of lysosomes in the worm intestine , or elt-2 activity could somehow be enhanced in the setting of proteasome dysfunction . Consistently , elt-2 has been identified as a UPS substrate , and the UPS-mediated degradation of ELT-2 contributes to the killing of worms by the bacterial pathogen Burkholderia pseudomallei [105] . Furthermore , the vertebrate GATA-1 and GATA-2 transcription factors are known UPS substrates [106–108] . Under normal conditions , GATA-2 is usually rapidly degraded by the UPS , and this event determines the ratio of GATA-1 and GATA-2 bound to target gene promoters [106–108] . In worms , we failed to observe significant changes in the expression or localization of an ELT-2::GFP fusion protein in the rpn-10 mutant , but elt-2 activity could be altered in other ways in response to changes in UPS activity , such as through post-translational modifications or associations with specific binding partners . Recently several groups have shown that the elevated expression of specific proteasome subunits leads to increased proteostasis and enhanced longevity in both Drosophila and C . elegans [18 , 56 , 98 , 109] . However , one recent study demonstrated that the expression of the over-expression of a single subunit , pbs-5 , in C . elegans with a transgene unexpectedly also leads to the increased expression of the endogenous genes encoding other proteasome subunits [98] . Furthermore , the over-expression of pbs-5 leads to the increased expression of the gst-4p::GFP reporter , suggesting an increase in skn-1 activity in this mutant . Consistent with a role for skn-1 acting downstream of the increase in PBS-5 expression , the increased lifespan observed in these transgenic worms required the activity of skn-1 [98] . One possible model to account for both the requirement for skn-1 and the elevated expression of non-transgene encoded proteasome subunits would be for the initial imbalanced expression of PBS-5 to disrupt rather than enhance the assembly of active proteasomes , which could trigger at least some of the compensatory mechanisms also utilized by the rpn-10 mutant in an effort to promote the formation of active proteasomes and maintain proteostasis . The authors examined the expression of proteasome subunit genes following the treatment of the transgenic worms with skn-1 RNAi and did see small , but non-significant decreases in subunit expression [98] . These findings could suggest that skn-1 only plays a minor role in controlling proteasome subunit expression following increased PBS-5 expression , and that the expression of other skn-1 target genes , such as the oxidative stress response or other novel pathways , may be more important in an aging context . It will be interesting to explore whether elt-2 , autophagy , or lysosome activity play roles in the effects of this transgene on stress resistance and aging phenotypes . If so , our work using the rpn-10 mutant could identify molecular pathways that could be exploited to improve proteostasis via either the augmentation or graded inhibition of proteasome activity . The strains CB1157 ( unc-54 ( e1157 ) ) , CL2166 ( dvIs19[pAF15 ( gst-4::GFP::NLS ) ] ) [53] , CL2070 ( dvIs70[hsp-16 . 2::GFP; rol-6 ( su1006 ) ] ) [54] , DA2123 ( adIs2122 [lgg-1p::GFP::lgg-1 + rol-6 ( su1006 ) ] ) [110] , HZ1688 ( atg-13 ( bp414 ) ) [88] , MAH236 ( sqIs13 [lgg-1p::GFP::lgg-1 + odr-1p::RFP] ) [86] , SJ4001 ( zcIs1[aip-1::GFP] ) [37] , and VC1369 ( rpn-10 ( ok1865 ) ) were obtained from the Caenorhabditis Genetics Center which is funded by NIH Office of Research Infrastructure Programs ( P40 OD010440 ) . The rrIs1[elt-2::lacZ::GFP] transgene which expresses nuclear localized GFP in the intestine has been previously described [111] . The rpn-10 ( ok1865 ) mutant was outcrossed against N2 five times , and one resulting outcrossed homozygous line was used for all subsequent crosses and experiments . The presence of the rpn-10 ( ok1865 ) allele was determined by single-worm PCR using oligonucleotides designed to amplify the wild-type rpn-10 allele ( F 5’-AAGAGAACAACGCGCATCTT-3’; R 5’-GTGTGCCCCTTTGAGGAGTA-3’ ) and to detect the deletion present in the rpn-10 ( ok1865 ) allele ( F 5’-CCCATTCCAATTGTTGCTCT-3’; R 5’-TGCACCAACAACTCCACATT-3’ ) . The strain AM140 ( rmIs132[unc-54p::Q35::YFP] ) was kindly provided by Dr . Richard Morimoto and previously described by our group [26 , 112] . The strain AM446 ( rmIs223[phsp70::gfp]; pRF4[rol-6 ( su1006 ) ] ) was kindly provided by Dr . Richard Morimoto [55] . The strain PP608 ( hhIs64[unc-119 ( + ) ; sur-5::UbV-GFP]; hhIs73[unc-119 ( + ) ; sur-5::mCherry] ) was kindly provided by Dr . Thorsten Hoppe [36] . The strain BC14890 ( sIs14010[rpn-7::GFP] ) was kindly provided by Dr . David Baillie [113] . The strain OG412 ( drIs20[vha-6p::Q44::YFP + rol-6 ( su1006 ) ] ) was kindly provided by Dr . Todd Lamitina [48] . The strain JM168 ( elt-2 ( ca15 ) ; caIs20[elt-2p::elt-2::GFP + unc-119 ( + ) ] ) was kindly provided by Dr . James McGhee . This strain was produced by gamma irradiation of JM73 , which carried the transgene as an extrachromosomal array , to integrate the array into the genome followed by backcrossing to remove extraneous mutations [114] . Strains containing transgenes , genetic mutations , and the rpn-10 ( ok1865 ) mutation were generated by standard crossing and genotyped by PCR . Worms expressing a RPN-10::GFP fusion protein were generated via biolistic bombardment [115] . The 6236103120536928 H12 fosmid clone which contains the entire rpn-10 coding sequence in fosmid WRM0618DC02 fused to GFP at the C-terminus as well as >10 kilobases of 5’ and 3’ flanking sequences was requested from the TransgenOme project , and the presence and location of the GFP insert was confirmed by PCR and sequencing [33] . The fosmid was purified from E . coli and used to generate transgenic animals via bombardment using the DP38 ( unc-119 ( ed3 ) ) strain as previously described [115 , 116] . Transgenic animals were identified via rescue of the mobility and body size defects of the unc-119 mutant . This resulted in the isolation of the ALF85 ( bafEx85 ) transgenic strain , which was outcrossed with N2 and then used for further study . The hsf-1 , pas-6 , pbs-6 , pbs-7 , rpn-12 , skn-1 , and wdr-23 RNAi clones were previously described [26] . The atg-13 , atg-16 . 2 , elt-2 , prmt-1 , vha-15 , and rpn-10 RNAi clones were retrieved from the Ahringer RNAi library and confirmed by sequencing [117] . For RNAi treatment , NGA plates containing 50μg/ml carbenicillin and 0 . 2% β-lactose ( in place of IPTG for dsRNA induction ) were spotted with overnight cultures of RNAi bacteria inoculated from discrete individual colonies [118] . Due to the adverse developmental effects of the elt-2 and vha-15 RNAi , these clones were typically diluted 1:10 with bacteria containing the empty vector control RNAi clone prior to spotting on the plates . Unless otherwise noted , eggs isolated by hypochlorite treatment were then placed on spotted RNAi plates and incubated at 20°C . Approximately 50–100 eggs isolated via hypochlorite treatment were placed on NGA plates spotted with E . coli strain OP50-1 and grown to adulthood at 20°C . Digital images of day 1 adult worms were either captured with an Olympus BX51 upright microscope and DP70 camera as previously described or with a Nikon Eclipse Ti inverted microscope with a 14-bit CoolSNAP HQ2 ( Photometrics ) CCD camera and Nikon Elements software [26 , 119] . Fluorescence intensity of the respective reporters was then quantified using ImageJ and statistical analysis of the resulting image data was completed in Prism6 ( GraphPad Software , San Diego , CA ) [120] . To perform heat-shock studies with the hsp-16::GFP and hsp-70::GFP reporters , approximately 20 day 1 adult animals drawn from the same synchronized populations used for the initial baseline imaging were transferred to fresh , spotted NGA plates . These worms were incubated at 35°C for one hour and allowed to recover at 20°C overnight ( approximately 14 hours ) before capturing the post-heat shock images . To assess effects of the rpn-10 mutation on GFP::LGG-1 expression the adIs2122 [lgg-1p::GFP::lgg-1 + rol-6 ( su1006 ) ] transgene was outcrossed into N2 and rpn-10 mutant animals via standard crosses . Synchronized day 1 adult animals were mounted and GFP fluorescence was measured via the analysis of digital images with ImageJ . To assess changes in autophagic activity , the sqIs13 [lgg-1p::GFP::lgg-1 + odr-1p::RFP] transgene was outcrossed into N2 and rpn-10 mutant animals via standard crosses . Synchronized day 1 adult animals were mounted and the animals were photographed using a GFP filter set . GFP::LGG-1 puncta in individual seam cells were counted and then analyzed for mean and statistical significance using Prism6 ( GraphPad Software , San Diego , CA ) . GFP::LGG-1 puncta in the intestine were counted by using the “Find Maxima” function in ImageJ , and the puncta counts were then analyzed for mean and statistical significance using Prism6 . Lifespan assays were conducted at 25°C using either NGA or RNAi plates containing 50 μM FUDR as previously described [119] . All worms were synchronized by hypochlorite treatment , hatched , and grown to adulthood at 20°C on NGA plates supplemented with streptomycin ( 0 . 2 mg/mL ) and spotted with E . coli strain OP50-1 . They were transferred to plates containing FUDR ( and RNAi when appropriate ) on the first day of adulthood and placed at 25°C . The worms were transferred to a second FUDR plate on the second day and left at 25°C for the remainder of the assay . Lifespan assays without RNAi treatment were conducted on NGA plates containing streptomycin ( 0 . 2 mg/mL ) as well as FUDR ( 50 μM ) and spotted with OP50-1 . RNAi treatment lifespans were conducted on NGA plates supplemented with FUDR , carbenicillin ( 50 mg/mL ) , and isopropyl β-d-thiogalactopyranoside ( IPTG , 1 mM ) . These plates were spotted with OP50 ( xu363 ) bacteria , which is an OP50-derived bacterial strain that can deliver RNAi to worms , that had been transformed with RNAi-expressing plasmids [121] . Prism6 ( Graphpad Software ) was used to generate graphs and perform log-rank testing for curve comparisons . STATA 8 was used to create lifetables and calculate mean survival . The worms were synchronized by hypochlorite treatment and eggs were plated on NGA plates spotted with E . coli strain OP50-1 and grown to adulthood at 20°C . Starting at 48 hours after synchronization , worms were scored for development to adulthood by microscopy every 8–16 hours until the entire population had reached adulthood . Three plates of approximately 100 worms each were scored for each genotype . For heat stress assays , N2 and outcrossed rpn-10 ( ok1865 ) worms were synchronized via hypochlorite treatment and grown to the L4 stage on E . coli OP50-1-spotted NGA plates at 20°C . Forty L4 individuals of each strain were then transferred to fresh plates in duplicate and incubated at 35°C . Scoring for survival was performed every hour beginning six hours after initiation of heat stress , with dead worms being identified by lack of responsiveness to gentle prodding with a pick . Oxidative stress assays using tert-butyl hydroperoxide ( tBHP ) were performed as described with 40 L4 animals being transferred to E . coli OP50-1-spotted NGA plates containing 7mM tBHP and scored three times per day using the parameters detailed above until all animals died or were censored [122] . A minimum of two trials with comparable results were performed for each assay . Oxidative stress assays using 5-Hydroxy-1 , 4-naphthoquinone ( juglone ) were performed using WT and rpn-10 worms which were treated with control and skn-1 RNAi from egg hatching for 48 hours . The worms were then washed from plates and suspended in M9 buffer . Juglone was added to a final concentration of 100 μM from a 100X stock made fresh in 100% ethanol , and the worms were then exposed to juglone for one hour with nutation . The worms were then washed twice with S-basal and returned to NGA . They were scored for survival 48 hours later . The aggregation of the muscle-expressed Q35::YFP fusion protein was assessed by adding eggs isolated from rmIs132[unc-54p::Q35::YFP] and rpn-10 ( ok1865 ) ; rmIs132[unc-54p::Q35::YFP] animals via hypochlorite treatment to NGA plates , and then incubating the plates at 23°C for 72 hours [112] . After this time essentially all of the animals were gravid adults . The number of individual Q35::YFP aggregates were scored using a fluorescent stereomicroscope as previously described , and digital images were captured at 6X magnification using a Nikon Eclipse inverted compound microscope equipped with epifluorescence illumination and a Nikon Endow GFP filter cube [26] . The aggregation of the intestine-expressed Q44::YFP fusion protein was assessed by adding eggs isolated from drIs20[vha-6p::Q44::YFP + rol-6 ( su1006 ) ] and rpn-10 ( ok1865 ) ; drIs20[vha-6p::Q44::YFP + rol-6 ( su1006 ) ] animals via hypochlorite treatment to NGA plates , and then incubating the plates at 23°C for 72 hours [48] . At this point ~100 worms were transferred to either NGA plates containing 50 μM FUDR or RNAi plates containing 50 μM FUDR and spotted with the indicated RNAi clone , and the plates were returned to 23°C for 4 days before scoring . The drIs20 worms contained large numbers of aggregates at this point , so the percentage of animals with any aggregates present in the intestine was measured by scoring with a fluorescent microscope . For S9 Fig , we also counted individual aggregates in the drIs20 worms , and to enhance the aggregate numbers in the rpn-10 mutant animals , we incubated the plates at 23°C for 5 days instead of 4 days . The aggregate numbers were scored using a fluorescent microscope after briefly incubating the plates on ice to reduce worm activity . A previously described protocol for assessing the phenotypic effects of shifts from permissive to non-permissive temperature on the function of the meta-stable UNC-54 protein in the unc-54 ( e1157 ) mutant was adapted for the analysis of larval animals [49] . Briefly , synchronized L1 larval populations of unc-54 ( e1157 ) , and rpn-10 , unc-54 animals were spotted on NGA plates and grown at 16°C ( permissive temperature ) for 24 hours . At this time , a total of approximately 300 worms per genotype were transferred to three separate fresh NGA plates , and shifted to 25°C ( non-permissive temperature ) for 20 hours . The plates were then allowed to equilibrate at room temperature for 20 minutes before being scoring for paralysis by prodding with a platinum worm-pick . Worms that failed to respond to touch were scored as paralyzed . Two transcription factor RNAi libraries were independently screened to identify transcription factors that permitted survival of the rpn-10 ( ok1865 ) mutant . These libraries were ( 1 ) a subset library sold by Source Bioscience which was created from Ahringer RNAi library clones , and ( 2 ) a library created from clones in the Ahringer and Vidal RNAi libraries ( S6 Table ) . For each screen , individual wells of 24-well plates containing NGA with 50μg/ml carbenicillin and 0 . 2% β-lactose were spotted with 20μL of overnight culture for a clone in the transcription factor library and allowed to dry at room temperature . Each well was then seeded with 70–100 rpn-10 ( ok1865 ) ; zcIs1[aip-1::GFP] L1 larvae , which had hatched from eggs that were isolated by hypochlorite treatment and then placed in S-basal to arrest the progeny at the L1 larval stage . Each plate was then incubated at 20°C and visually screened for phenotypic effects after three days . Clones that caused developmental arrest or sickness were then re-screened , and clones that were again found to produce these phenotypes in the second round were used to treat larger populations of zcIs1[aip-1::GFP] animals both with and without the rpn-10 ( ok1865 ) allele in order to find genes required for the development and survival of mutant but not wild-type animals . In this manner clones determined to be hits from the initial screen were sequentially narrowed down to those that consistently impaired normal development only in the rpn-10 ( ok1865 ) background . Code sets that recognize the indicated genes were synthesized by Nanostring Technologies ( Seattle , WA ) and used with the Nanostring nCounter system to measure the levels of each transcript in 100 ng aliquots of total RNA . The resulting nCounter data were analyzed using the Nanostring nSolver data analysis software with normalization to the geometric mean of the level of the cdc-42 , pmp-3 , and Y45F10D . 4 transcripts in each sample [123] . The normalized expression data were then exported to MS Excel for further analysis . RNA for Nanostring studies was isolated from wild-type ( N2 ) or rpn-10 ( ok1865 ) animals grown from egg hatching on control , skn-1 , or 1:10 diluted elt-2 RNAi for 48 hours . This time point was selected because no visible differences in worm morphology were observed in any of the treatment groups , so any changes in gene expression likely occurred before the animals became ill due to RNAi treatment . After washing the animals from the plates in S-basal , the worm pellet was then suspended in QIAzol lysis reagent and frozen at -80°C . Total RNA was isolated using the Qiagen miRNeasy kit . The yield and quality of each RNA sample was evaluated using a Nanodrop spectrophotometer and also by running an aliquot on an Agilent Bioanalyzer . For each genotype-RNAi treatment pair , six biological replicates were performed . Three independent populations of N2 control and rpn-10 ( ok1865 ) mutant worms were synchronized via the use of hypochlorite treatment and grown on E . coli OP50-1 spotted NGA plates at 20°C for 3 days . The worms were then washed from the plates and washed twice with milliQ-purified water . The worm pellet was then suspended in QIAzol lysis reagent and frozen at -80°C . Total RNA was isolated using the Qiagen miRNeasy mini kit , and the RNA yield was measured by spectrophotometry . Total RNA was sent to Expression Analysis ( Durham , NC ) for analysis including Agilent Bioanalyzer electrophoresis to ensure RNA quality followed by library preparation using the Illumina TruSeq RNA sample prep kit . The resulting library was subjected to high-throughput 50 nucleotide paired end sequencing using an Illumina sequencer at a depth of 17 million reads per sample . The resulting sequence data were analyzed as previously described [119] . Briefly , the sequence reads were clipped using internally developed software by Expression Analysis and matched to the C . elegans genome using RSEM [124] . The resulting transcript counts were then normalized using the upper quartile normalization approach [125] . Differentially expressed genes were then identified through the use of serial t-testing coupled with Benjamini-Hochberg correction and genes with an adjusted p-value score less than 0 . 05 were considered to be differentially expressed . This led to the identification of 171 genes as being differentially expressed ( 111 up-regulated and 60 down-regulated ) between rpn-10 ( ok1865 ) and wild-type N2 ( S2 and S3 Tables ) . Over-represented gene classes were identified in the up-regulated and down-regulated genes through the use of DAVID [126] . Lysosomes were stained via two complementary approaches . The first approach utilized the Lysotracker Red stain ( Life Technologies #L7528 ) and Lysosensor Green stain ( Life Technologies #L7535 ) , which concentrate in the low pH environment of the lysosome , while the second utilized the Magic Red cathepsin B and cathepsin L substrates ( ImmunoChemistry Technologies #938 and #942 ) [89] . These cathepsin B and L substrates are cell-permeable cresyl violet-conjugated peptides containing either the Arg-Arg or Phe-Arg sequence cleaved by the respective cathepsin in the lysosome , and this cleavage event relieves an intramolecular quenching of the cresyl violet fluorophore and produces red fluorescence . Lysotracker Red and Lysosensor Green staining were both performed by spotting NGA plates with an aliquot of dye from a 1mM working stock diluted in S-basal to produce a final concentration of 2 μM [127] . The spotted plates were allowed to dry for one hour at room temperature before L4 larval animals were added . The worms were stained overnight at 20°C , and then transferred to unspotted NGA plates for one hour to clear residual dye from the intestinal lumen . The animals were then mounted on slides and imaged using a Nikon Eclipse Ti inverted microscope using a Y-2E/C filter cube . Images were captured at 20X magnification using a CoolSNAP HQ2 ( Photometrics ) CCD camera and Nikon Elements software . Fluorescence intensity was measured using ImageJ [120] . We did not stain the control and elt-2 RNAi treated animals with Lysotracker Red because preliminary experiments demonstrated greater penetration of Lysotracker into the elt-2 RNAi treated animals . Particularly , we observed the staining of tissues , like the hypodermis , that are not seen in animals stained on control RNAi or NGA , which suggested that the absorption or distribution of the dye is not similar between the RNAi treatments thus precluding reliable comparisons . Magic Red staining was performed by spotting NGA , control RNAi , or elt-2 RNAi plates with an aliquot of dye from a 260X stock , prepared by dissolving the powdered dye in DMSO following the manufacturer’s instructions , to give a final 1X concentration . To facilitate spreading of the dye on the plate , the aliquot was mixed with water to produce a final volume of 20 μL prior to pipetting onto the plate . To conserve dye , we performed these experiments in 12 well plates containing 3 mL of agar per well . The plates were allowed to dry for one hour at room temperature before L4 larval animals were added . The animals were incubated at 20°C overnight , and then cleared of residual dye and imaged as described above .
Proteins are complex molecules assembled from individual amino acids that are linked head to tail in a linear chain . Once assembled , the proteins play vital roles in the structure and function of every cell in the body . However , for these proteins to work properly , they must be assembled correctly and resist damage from stresses originating either from inside the body or from the environment . One way that proteins are safeguarded is through the careful removal and destruction of damaged or unwanted proteins via a molecular machine termed the proteasome , which cleaves the protein chain and releases the individual amino acids back into the cell . Usually a loss of proteasome activity leads to a loss of the quality control mechanisms for cellular proteins and can contribute to aging or the development of diseases , such as Alzheimer’s disease . Here we find that when proteasome activity is only partially reduced , several other protein quality control mechanisms are activated , and this actually leads to a net increase in protein quality . This effect could be utilized to help prevent diseases and aspects of aging caused by the accumulation of damaged proteins .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "cell", "death", "lysosomes", "medicine", "and", "health", "sciences", "autophagic", "cell", "death", "rna", "interference", "oxidative", "stress", "gene", "regulation", "regulatory", "proteins", "cell", "processes", "dna-binding", "proteins", "transcription", "factors", "epigenetics", "cellular", "structures", "and", "organelles", "digestive", "system", "genetic", "interference", "proteins", "gene", "expression", "proteasomes", "gastrointestinal", "tract", "biochemistry", "rna", "protein", "complexes", "cell", "biology", "nucleic", "acids", "anatomy", "genetics", "biology", "and", "life", "sciences" ]
2016
Graded Proteasome Dysfunction in Caenorhabditis elegans Activates an Adaptive Response Involving the Conserved SKN-1 and ELT-2 Transcription Factors and the Autophagy-Lysosome Pathway
Plasmodium falciparum infection results in adhesion of infected erythrocytes to blood vessel endothelium , and acute endothelial cell activation , together with sequestration of platelets and leucocytes . We have previously shown that patients with severe infection or fulminant cerebral malaria have significantly increased circulatory levels of the adhesive glycoprotein von Willebrand factor ( VWF ) and its propeptide , both of which are indices of endothelial cell activation . In this prospective study of patients from Ghana with severe ( n = 20 ) and cerebral ( n = 13 ) P . falciparum malaria , we demonstrate that increased plasma VWF antigen ( VWF∶Ag ) level is associated with disproportionately increased VWF function . VWF collagen binding ( VWF∶CB ) was significantly increased in patients with cerebral malaria and severe malaria ( medians 7 . 6 and 7 . 0 IU/ml versus 1 . 9 IU/ml; p<0 . 005 ) . This increased VWF∶CB correlated with the presence of abnormal ultra-large VWF multimers in patient rather than control plasmas . Concomitant with the increase in VWF∶Ag and VWF∶CB was a significant persistent reduction in the activity of the VWF-specific cleaving protease ADAMTS13 ( ∼55% of normal; p<0 . 005 ) . Mixing studies were performed using P . falciparum patient plasma and normal pooled plasma , in the presence or absence of exogenous recombinant ADAMTS13 . These studies demonstrated that in malarial plasma , ADAMTS13 function was persistently inhibited in a time-dependent manner . Furthermore , this inhibitory effect was not associated with the presence of known inhibitors of ADAMTS13 enzymatic function ( interleukin-6 , free haemoglobin , factor VIII or thrombospondin-1 ) . These novel findings suggest that severe P . falciparum infection is associated with acute endothelial cell activation , abnormal circulating ULVWF multimers , and a significant reduction in plasma ADAMTS13 function which is mediated at least in part by an unidentified inhibitor . In spite of the significant mortality associated with P . falciparum infection , the molecular mechanisms involved in its pathophysiology remain poorly understood . However , sequestration of P . falciparum-infected erythrocytes ( IE ) in the microvasculature of vital organs including the brain and placenta plays a key role in this process [1] . Previous studies have demonstrated that sequestration involves adhesion of IE to endothelial cell ( EC ) surfaces . This process is mediated by various parasite-related ligands , including P . falciparum erythrocyte membrane protein 1 ( PfEMP1 ) , expressed on the surface of IE [2] . Furthermore , a number of specific receptors expressed on EC surfaces are important in regulating IE adhesion , including thrombomodulin , CD36 , thrombospondin , intercellular adhesion molecule-1 ( ICAM-1 ) , vascular adhesion molecule-1 ( VCAM-1 ) , P-selectin and E-selectin . Expression of these receptors varies significantly between different vascular beds , and can be regulated in response to inflammatory cytokines ( e . g . TNF and interleukin-1 ) [3] , [4] . Consequently , EC activation plays a critical role in regulating IE cytoadherence [5] . Von Willebrand factor ( VWF ) is a large plasma glycoprotein that plays a critical role in primary haemostasis by mediating the adhesion of platelets to sites of vascular injury [6] . In vivo VWF biosynthesis is limited to EC and megakaryocytes [7] . VWF synthesised within EC is either constitutively secreted into the plasma , or alternatively stored within specific intracellular organelles known as Weibel-Palade ( WP ) bodies [8] . Following EC activation by a variety of secretagogues including thrombin , fibrin and histamine , VWF and its propeptide are secreted in equimolar concentrations from the WP bodies [9] . We recently reported marked increased plasma VWF and VWF propeptide levels in severe P . falciparum infection , consistent with acute EC activation [10] . Indeed , children with cerebral malaria ( CM ) had VWF propeptide levels exceeding those typically observed in fulminant vascular diseases such as thrombotic thrombocytopenic purpura ( TTP ) [11] . Subsequently , a study of 14 healthy volunteers infected with P . falciparum showed that the increased plasma VWF and VWF propeptide levels develop soon after the onset of blood stage infection [12] . Consequently , acute EC activation constitutes an early feature of P . falciparum malaria infection , and may therefore be important in the pathogenesis of progression to severe or cerebral malaria respectively . Plasma VWF plays a critical role in primary haemostasis by mediating the adhesion of platelets to sites of vascular injury [6] , [8] . Following endothelial disruption , VWF binds to exposed collagen in the subendothelial matrix . This anchored VWF undergoes marked conformational changes in response to shear stress exerted by the circulating blood , and can then tether platelets through specific binding of the platelet Gp Ib-IX-V receptor [9] , [13] . Accumulating evidence suggests that platelet adhesion and aggregation may play important roles in facilitating cytoadhesion of P . falciparum IE to activated EC [14]–[16] . However , it remains unclear if the increased plasma VWF levels play any direct role in mediating this process , or whether they merely serve as a marker of acute EC activation . Nevertheless , elegant studies using a novel llama-derived nanobody have demonstrated that a significant proportion of circulating plasma VWF in P . falciparum infected patients is present in an active platelet GpIb-binding conformation [12] . Furthermore , plasma VWF∶Ag levels in patients with malaria inversely correlate with platelet count [12] , and we have previously shown that plasma VWF propeptide levels correlate with other established biochemical markers of malaria severity , including plasma lactate [10] . To further elucidate the mechanism responsible for quantitative and qualitative variations in plasma VWF levels in malaria , we collected plasma samples from a cohort of children with laboratory confirmed severe P . falciparum infection , or full-blown cerebral malaria . We demonstrate herein that severe P . falciparum malaria is associated not only with increased plasma VWF antigen ( VWF∶Ag ) levels , but an even more marked increase in VWF activity as determined by collagen binding assay ( VWF∶CB ) , due to the presence of abnormal circulating ultra-large VWF ( ULVWF ) multimers . In addition , we also demonstrate that the presence of ULVWF is associated with a significant reduction in plasma levels of the VWF cleaving protease ADAMTS13 ( A Disintegrin And Metalloproteinase with ThromboSpondin type-1 repeats ) , and an unidentified inhibitor of ADAMTS13 activity present in the plasma of children with severe P . falciparum . Patients were recruited from those presenting with severe malaria to the Komfo Anokye Teaching Hospital in Kumasi , Ghana , as previously described [10] . Subjects were children aged between 6 months and 6 years , recruited after written informed consent had been obtained . Ethical approval was granted by the committee on human research , publications and ethics ( CHRPE ) , School of Medical Sciences , University of Science and Technology , Kumasi , and also by the Liverpool School of Tropical Medicine research ethics committee . For each subject , clinical details were obtained at presentation , and P . falciparum infection confirmed on thick blood films . Venous blood samples were collected , before standard anti-malarial treatment was commenced in all patients . Cerebral malaria was defined as a Blantyre coma score of two or less in a child with malarial parasitaemia , and without any other cause of coma ( e . g . hypoglycaemia or meningitis ) [17] . For the cohort of children with cerebral malaria ( n = 13 ) , follow-up samples were also collected at 24 and 72 hours respectively , following admission and after commencement of treatment . Non-cerebral severe malaria was defined according to standard World Health Organization criteria ( WHO , 2000 ) , which included severe anaemia ( <5 g/dl ) , prostration , convulsions , and respiratory distress ( n = 20 ) , but which did not meet the criteria for cerebral malaria . Finally , healthy controls ( n = 25 ) were recruited from children attending for immunisation , surgery , or outpatient surgical review . From each patient and control , 1 . 2 ml of venous blood was collected into 3 . 2% citrate ( 1∶9 vol/vol ) , and immediately placed on ice . After centrifugation at 3000 g for 20 min at 4°C , plasma aliquots were stored at −80°C . Plasma VWF∶Ag levels were performed as previously described [10] , [18] . VWF∶CB levels were determined using a commercial ELISA method ( Technoclone , UK ) as before [19] . VWF multimer analysis was performed according to Ruggeri et al with minor modifications [20] . Sodium dodecyl sulfate ( SDS ) -agarose gel electrophoresis was performed using 1 . 5% agarose gels , and VWF multimer composition , visualised using HRP-labelled polyclonal rabbit anti-human VWF ( Dako , Glostrup , Denmark ) . For objectively quantifying differences in VWF multimer composition , densitometry was performed using ImageJ software ( Image Processing and Analysis in Java ) . ADAMTS13 activity was determined by FRETS-VWF73 assay ( Peptides International , Kentucky , USA ) , and ADAMTS13 antigen levels by ELISA using murine monoclonal antibodies ( kind gift of Dr H . Feys , Washington University , Saint Louis , USA ) as previously reported [21] . To investigate ADAMTS13 inhibition , individual malaria plasmas ( n = 4 ) were mixed in different proportions ( 25%∶75% or 50%∶50% ) with pooled normal plasma . ADAMTS13 activity using the FRETS-VWF73 assay ( Peptides International , Kentucky , USA ) , was assessed immediately following mixing , and after incubation at 37°C for 15 min or 30 min respectively . Recombinant human ADAMTS13 ( cDNA kind gift of Dr R . Montgomery , Medical College of Wisconsin , USA ) was stably expressed in HEK293 cells , and purified as previously described [18] . Recombinant ADAMTS13 was adjusted to normal pooled plasma activity level ( 1 U/ml ) and then added to P . falciparum-infected ( n = 4 ) , or control plasma samples ( n = 4 ) , and ADAMTS13 activity timecourse assessed as above ( FRETS-VWF73 assay ) . Finally , plasma concentrations of IL-6 ( Abcam , Cambridge , UK ) and TSP-1 ( R&D systems , Minneapolis , USA ) were measured using commercial ELISA kits , in accordance with the manufacturer's instructions , and plasma haemoglobin levels were measured using a Sysmex XE 5000 analyser . No red cells were detected in the platelet poor plasma preparations . All statistical analyses were performed using the SPSS statistics package ( version 4 . 02 , SPSS Inc ) , and statistical significance was assigned at a value of p<0 . 05 . Normally distributed data are presented as mean +/− SEM , and differences between patients and controls analyzed using the two sample Student's t test . For nonparametric data , medians and ranges were calculated and nonparametric tests for statistical significance were performed using Mann-Whitney test . In children with cerebral ( CM ) and non-cerebral severe ( SM ) P . falciparum malaria , plasma VWF∶Ag levels were significantly elevated at presentation ( medians 3 . 1 and 3 . 4 IU/ml; p<0 . 05 ) ( Fig 1A ) . In addition , plasma VWF activity levels ( VWF∶CB ) were also markedly increased ( medians 7 . 6 and 7 . 0 IU/ml; p<0 . 05 ) . In the CM cohort , plasma VWF∶Ag levels remained elevated during the 72 hours following admission ( Fig 1B ) . In contrast , VWF∶CB activity decreased progressively over this same time period ( Time 0 hrs – 7 . 6 IU/ml; 24 hrs – 6 . 1 IU/ml; 72 hrs – 5 . 0 IU/ml respectively , p = 0 . 03 ) , in a manner similar to that previously reported for VWF propeptide levels [10] . Although plasma VWF∶Ag and VWF∶CB were both increased in children with CM or SM at initial presentation , the observed increase in VWF∶CB was much greater compared to that in plasma VWF∶Ag levels , so that the ratio of CB∶Ag was consistently increased compared to that observed in healthy control children ( Fig 1C ) . Moreover , in the cohort of children with CM , despite the progressive reduction in plasma VWF∶CB activity following admission to hospital and commencement of anti-malarial therapy ( Fig 1B ) , the ratio of VWF∶Ag to VWF∶CB still remained skewed even 72 hours after initiation of anti-malarial therapy ( data not shown ) . It is well-established that the VWF∶CB is particularly sensitive to circulating high molecular weight multimer ( HMWM ) VWF [22] . VWF multimer analysis and densitometry confirmed that abnormal ultra-large ( UL ) VWF multimers were present in the plasma of children with either CM or SM respectively ( Fig 2 ) . Furthermore , these abnormal multimers were not observed in the plasma of normal Ghanaian children . Cumulatively , these data further support the hypothesis that acute endothelial cell activation constitutes a hallmark of severe P . falciparum infection , but also confirm the presence of abnormal circulating ULVWF multimers in malarial plasma . ADAMTS13 regulates normal plasma VWF multimer composition and thereby activity , through cleavage at the Tyr 1605- Met 1606 bond within the VWF A2 domain [9] , [23] . To determine the molecular mechanism ( s ) responsible for the markedly increased VWF∶CB and circulating ULVWF in severe malaria infection , we investigated plasma ADAMTS13 levels in children with CM , SM and normal healthy controls . Although plasma ADAMTS13 antigen and activity levels were previously shown to be markedly reduced in neonates [21] , normal ADAMTS13 levels have not previously been described for older African children . In our normal control children ( minimum age 6 months ) , median ADAMTS13 activity levels were 1 . 07 U/ml ( children aged 6–12 months; n = 10 ) and 1 . 23 U/ml ( children 1–6 years; n = 15 ) . These levels were not significantly different ( p = 0 . 45 ) , and fell well within our normal adult plasma ADAMTS13 activity range . In addition , plasma ADAMTS13 activity levels were not significantly influenced by gender . These findings are consistent with another recent study that determined plasma ADAMTS13 levels in a cohort of U . K . children [24] . ADAMTS13 antigen levels were significantly lower in children with severe malaria at presentation than in controls ( Fig 3A ) . Similarly , plasma ADAMTS13 activity levels were also significantly reduced in both CM ( median = 0 . 63 U/ml; p<0 . 001; Mann Whitney ) and SM ( median = 0 . 56 U/ml; p<0 . 001 ) at initial presentation ( Fig 2B ) . Furthermore , in the cohort of children with CM , ADAMTS13 activity remained significantly reduced during the subsequent 72 hour follow-up period . Although ADAMTS13 antigen and ADAMTS13 activity levels were reduced to comparable degrees in most children with SM and CM , four patients were identified with reduced activity∶antigen ratios ( <0 . 7 ) . These novel data are consistent with a recent report of acquired ADAMTS13 deficiency in paediatric patients with other causes of severe sepsis [25] , but contrast with the previous findings of de Mast et al , who reported normal plasma ADAMTS13 activity by FRETS-VWF73 assay during the early stages of P . falciparum infection [12] . To investigate whether the reduction in ADAMTS13 activity in malaria plasma might be attributable to the presence of an inhibitor , we performed mixing studies of malaria and normal plasma respectively . Following immediate mixing , no ADAMTS13 inhibition was apparent ( Fig 4A ) . However following incubation at 37°C , clear evidence of a time-dependent inhibitory effect was observed ( Fig 3B ) . Indeed , ADAMTS13 activity in normal plasma was reduced by approximately 60% after pooled normal plasma was incubated in a 75%∶25% mix with malarial plasma for 30 mins . Similar levels of inhibition were observed using four different malaria plasma samples , but not after similar incubation with normal control plasmas . This ability of malaria plasma to directly inhibit ADAMTS13 activity was confirmed by spiking malarial plasmas with recombinant human ADAMTS13 ( Fig 3C ) . Once again , significant time-dependent inhibition of FRETS-VWF73 activity was observed in the plasmas of children with severe P . falciparum ( p<0 . 001 ) , but not in normal control plasmas . Proteins suggested to regulate the ability of plasma ADAMTS13 to cleave full-length VWF include interleukin 6 ( IL-6 ) [26] , thrombospondin-1 ( TSP-1 ) [27] , thrombin and plasmin [19] , free plasma haemoglobin [28] , and most recently reduced factor VIII ( FVIII ) levels [29] . We found that plasma IL-6 levels were significantly elevated at presentation in children with either CM ( mean 240 pg/ml; p<0 . 001; Student's t-test ) or SM ( mean 217 pg/ml; p = 0 . 01 ) compared to normal controls ( Fig 4A ) . However , although plasma IL-6 levels were increased in these children , the levels did not approach those previously reported necessary to inhibit ADAMTS13 activity [26] . Furthermore , in spiking experiments we found no inhibitory effect of IL-6 ( final concentration range 0 . 01–10 µg/ml ) in a static ADAMTS13 activity assay ( Fig 4B ) . Disseminated intravascular coagulopathy ( DIC ) is associated with enhanced thrombin generation , and consumption of circulating FVIII , both of which can inhibit VWF proteolysis by ADAMTS13 [19] , [29] . However , in children with CM or SM respectively , we observed significantly increased plasma FVIII∶C levels ( Fig 4C ) , confirming previous reports suggesting that fulminant DIC is a rare complication of severe malaria [30] , [31] . Intravascular haemolysis is a recognised complication of malarial infection . Moreover , increased plasma haemoglobin has previously been described to inhibit ADAMTS13 activity [28] , [32] . In children with CM or SM , we observed only minor increased plasma haemoglobin concentrations ( Fig 4D ) , well below that previously described to significantly inhibit ADAMTS13 activity [28] . Finally , in contrast to the increased plasma levels of IL-6 and FVIII∶C , we found that plasma TSP-1 levels were not significantly elevated in children with either CM or SM compared to pooled normal plasma ( data not shown ) . VWF undergoes complex post-translational modification within EC prior to secretion , including two distinct polymerization steps [7] . Dimerisation occurs within the ER , through the formation of inter-subunit C-terminal disulphide bonds . Subsequently in the Golgi , VWF dimers are formed into multimers through another round of N-terminal disulphide bond formation . Consequently , VWF is constitutively secreted into the plasma as multimers of varying size [7] , [9] . In contrast , VWF stored within WP bodies exists predominantly as ULVWF multimers that are released into plasma in response to EC activation [33] , [34] . The multimeric composition of VWF is a critical determinant of its functional activity . Larger VWF multimers bind collagen and activated platelets with ∼100 fold higher affinities compared to monomers , and are thus more efficient in inducing platelet aggregation [9] , [13] . In this study , we demonstrate that severe P . falciparum malaria is associated with an accumulation of hyper reactive ULVWF multimers in the plasma , and thereby a marked increase in plasma VWF activity ( Fig 1 ) . The mechanism ( s ) responsible for this accumulation of abnormal ULVWF remains unclear . However we recently demonstrated that severe P . falciparum malaria results in fulminant , acute EC activation together with marked exocytosis of WP bodies , which are responsible for the majority of the increase in plasma VWF levels [10] . Following secretion , ULVWF multimers released from WP bodies normally undergo rapid , partial proteolysis on the endothelial cell surface by a VWF-specific cleaving protease termed ADAMTS13 [35] , [36] . ADAMTS13 cleaves the Y1605/M1606 peptide bond in the VWF A2 domain , thereby preventing the accumulation of ULVWF multimers in normal plasma , and thus also regulating VWF functional activity [23] , [36] . However , it remains largely unclear how VWF proteolysis by ADAMTS13 is regulated in-vivo , although recent studies have identified some putative mechanisms of cofactor enhancement [29] and inhibition [19] respectively . In this study , we demonstrate that plasma ADAMT13 antigen and activity levels are both significantly reduced in children with CM or SM at presentation ( Fig 3 ) . This finding differs from early P . falciparum malaria , where plasma ADAMTS13 levels were previously reported to be within the normal range [12] . Decreased plasma ADAMTS13 antigen level may be partly attributable to a reduction in hepatic ADAMTS13 synthesis . However , increased consumption of ADAMTS13 in the setting of sustained , systemic release of ULVWF has also been previously described [37] . Interestingly , ADAMTS13 antigen and activity levels did not improve significantly during the 72 hours following commencement of anti-malarial therapy in children with CM ( Fig 3 ) . In contrast , we observed a significant fall in VWF collagen binding activity ( and VWF propeptide ) over this time period , but not in plasma VWF antigen levels . Cumulatively , these data suggest that acute EC activation and ongoing WP body secretion are essential in order to maintain circulating ULVWF in P . falciparum malaria . Although ADAMTS13 antigen and activity were both significantly reduced in children with severe P . falciparum malaria compared to normal controls , absolute plasma ADAMTS13 levels remained above 50% ( median 0 . 63 U/ml or 63% ) . These novel data are in keeping with those of Nguyen et al . , who reported a similar reduction in ADAMTS13 activity ( mean 57 . 4% ) in children with non-malarial severe sepsis [25] . Furthermore , Sosothikul et al also observed comparable reductions in plasma ADAMTS13 activity in a cohort of paediatric patients with Dengue virus [38] . The correlation between these respective data is noteworthy , given that different methods ( FRETS-VWF75; full-length VWF cleavage; and flow chamber assay ) were used in the three studies to determine plasma ADAMTS13 activity . Nevertheless , whether this modest reduction in plasma ADAMTS13 plays an important role in mediating the ULVWF accumulation in severe malaria remains unclear . Previous studies have suggested that ADAMTS13 activity levels above 10% are sufficient to maintain normal plasma VWF multimer composition , at least in the absence of any associated acute EC activation [39] . Consequently , it seems likely that in-vivo inhibition of plasma ADAMTS13 activity may also be occurring in children with CM or SM respectively . To further investigate the mechanism ( s ) responsible for the persistence of ULVWF in the presence of reduced but significant residual ADAMTS13 , we investigated ADAMTS13 activity inhibition in malarial plasma . Using a FRETS-VWF73 assay to quantify residual ADAMTS13 activity , moderate time-dependent inhibition of the ADAMTS13 activity in normal pooled plasma was observed following incubation with an equal volume of malarial plasma . Furthermore , similar inhibition was clearly apparent when recombinant human ADAMTS13 was spiked into malarial plasma , but not into normal plasma ( Fig 3 ) . Cumulatively , these findings support the hypothesis that severe P . falciparum plasma may contain an inhibitor of ADAMTS13 activity . However , current in vitro ADAMTS13 assays are performed under non-physiological conditions ( low ionic strength buffer containing barium and urea ) . Consequently , it is difficult to reliably extrapolate in vitro results to VWF processing in vivo , and consequent translational significance . Although the mechanisms underlying the physiological regulation of ADAMTS13 enzymatic activity are not well-defined , previous studies have identified several putative inhibitors , including IL-6 , TSP-1 , thrombin , free plasma haemoglobin , and reduced FVIII ( FVIII∶C ) levels [19] , [26]–[29] , [39] . In keeping with previous reports , we observed significantly elevated plasma IL-6 levels in children with both CM and SM respectively ( Fig 5 ) [40] , [41] . Plasma haemoglobin levels were also slightly increased in both groups of children . However , the absolute plasma concentrations of both IL-6 and haemoglobin were well below those previously reported to significantly ADAMTS13 activity in-vitro [26] , [28] . Whether these inhibitors might interact synergistically , or indeed whether their true in-vivo inhibitory capacity is accurately reflected in an ex-vivo ADAMTS13 functional assay remains unclear [39] . Finally , and again in keeping with previous studies , consumption of coagulation factor VIII ( a recently described ADAMST13 cofactor [29] ) was not a feature of severe P . falciparum malaria . Thus the mechanism responsible for ADAMTS13 inhibition in malarial plasma remains unknown , and cannot be explained by quantitative variation in any of the previously reported plasma ADAMTS13 inhibitors . In conclusion , based upon our findings we propose that the presence of hyper-reactive ULVWF multimers in the plasma of children with severe P . falciparum malaria is the result of ( i ) acute EC activation and release of ULVWF from WP bodies; ( ii ) significantly reduced plasma ADAMTS13 antigen levels ( iii ) a circulating but unidentified inhibitor of human ADAMTS13 activity . Further studies will be required in order to determine the relative importance of each of these three components , and to characterize the molecular mechanisms responsible for ADAMTS13 inhibition . Although we have demonstrated that ULVWF and ADAMTS13 deficiency are both associated with CM and SM , it remains unclear whether these abnormalities constitute epiphenomena , or whether they play active direct roles in mediating the pathophysiology of the condition . However , it is well established that abnormal ULVWF multimers are also present in the circulation in patients with thrombotic thrombocytopenic purpura ( TTP ) [23] , [36] . This rare life-threatening condition is characterized by the development of pathological platelet-rich thrombi in the microvasculature , which in turn results in end-organ dysfunction , principally involving the brain and kidneys . Although inherited or acquired deficiencies of ADAMTS13 have been implicated in the pathogenesis of many cases of TTP [36] , [42] , recent evidence suggests that ADAMTS13 deficiency is not by itself sufficient to trigger acute TTP . In particular , ADAMTS13 −/− mice are viable , exhibit normal survival , and only develop TTP-like symptoms after specific additional insults ( e . g . shigatoxin challenge ) . Nevertheless , in view of the critical role played by VWF in mediating platelet adhesion/aggregation , and the accumulating evidence suggesting that platelet adhesion/aggregation also facilitate cytoadhesion of IE [43] , it seems entirely plausible that ULVWF multimers may indeed be involved in mediating the pathophysiology of severe P . falciparum malaria .
Malaria is caused by infection of red blood cells ( erythrocytes ) with protozoan parasites of the genus Plasmodium . Infected erythrocytes adhere to and disrupt the inner lining , or endothelium , of small blood vessels , especially those of the brain , resulting in blockage and subsequent cerebral malaria . We have studied the effect of Plasmodium falciparum infection on the endothelial cell activation marker , the multimeric adhesive protein von Willebrand factor ( VWF ) in a cohort of patients with severe infection or cerebral malaria . We demonstrate that malarial infection in these patients is associated with abnormally high levels of ultra-large VWF in blood plasma , and that VWF functional ability as measured by collagen binding is disproportionately increased as compared to normal plasmas . Circulating levels of the VWF-specific cleaving enzyme ADAMTS13 is reduced to ∼55% of normal in patients , and plasma mixing studies demonstrate the presence of an inhibitor of ADAMTS13 function . Thus , severe P . falciparum infection results in disruption of the endothelium , causing release of ultra-large VWF . Together with reduced ADAMTS13 levels , and an unidentified inhibitor of ADAMTS13 , this may contribute to the pathophysiology of malaria .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "infectious", "diseases/tropical", "and", "travel-associated", "diseases", "hematology/coagulation", "disorders" ]
2009
Severe Plasmodium falciparum Malaria Is Associated with Circulating Ultra-Large von Willebrand Multimers and ADAMTS13 Inhibition
Cutaneous leishmaniasis ranks among the tropical diseases least known and most neglected in Libya . World Health Organization reports recognized associations of Phlebotomus papatasi , Psammomys obesus , and Meriones spp . , with transmission of zoonotic cutaneous leishmaniasis ( ZCL; caused by Leishmania major ) across Libya . Here , we map risk of ZCL infection based on occurrence records of L . major , P . papatasi , and four potential animal reservoirs ( Meriones libycus , Meriones shawi , Psammomys obesus , and Gerbillus gerbillus ) . Ecological niche models identified limited risk areas for ZCL across the northern coast of the country; most species associated with ZCL transmission were confined to this same region , but some had ranges extending to central Libya . All ENM predictions were significant based on partial ROC tests . As a further evaluation of L . major ENM predictions , we compared predictions with 98 additional independent records provided by the Libyan National Centre for Disease Control ( NCDC ) ; all of these records fell inside the belt predicted as suitable for ZCL . We tested ecological niche similarity among vector , parasite , and reservoir species and could not reject any null hypotheses of niche similarity . Finally , we tested among possible combinations of vector and reservoir that could predict all recent human ZCL cases reported by NCDC; only three combinations could anticipate the distribution of human cases across the country . Leishmaniasis remains one of the major public health problems in the Mediterranean Basin . In Libya , two forms of leishmaniasis occur: visceral leishmaniasis ( VL ) , and cutaneous leishmaniasis ( CL ) . VL has been reported in the country since 1904; however , little information is available on leishmaniasis epidemiology as regards the insect vector species and vertebrate reservoirs involved in transmission [1–3] . VL was identified from northeastern Libya and southern Saharan and sub-Saharan areas [1 , 4 , 5] . CL is most prevalent in the northwestern part of the country [2 , 6 , 7] . CL is caused by two species of Leishmania: Leishmania major Yakimoff & Schokhor , 1914 and L . tropica Wright , 1903 ( Kinetoplastida: Trypanosomatidae ) . Leishmania major is the etiological agent of zoonotic CL ( ZCL ) , where the parasite is thought to circulate in small-mammal reservoirs ( Meriones libycus Lichtenstein , 1823 ( Rodentia: Muridae ) , Gerbillus gerbillus Olivier , 1801 ( Rodentia: Muridae ) , Psammomys obesus Cretzschmar , 1828 ( Rodentia: Muridae ) , M . shawi Duvernoy , 1842 ( Rodentia: Muridae ) ) and is transmitted by the sand fly Phlebotomus papatasi ( Scopoli ) , 1786 ( Diptera: Psychodidae ) [2 , 7 , 8] . Leishmania tropica is the causative organism for anthroponotic CL ( ACL ) ; zoonotic foci have also been reported from rock hyrax in Kenya , and Israel [9 , 10] , and gerbil in Egypt [11] , where the disease is transmitted by the sand fly P . sergenti Parrot , 1917 ( Diptera: Psychodidae ) [10 , 11] . In Libya , seasonal wadis provide potential suitable conditions of climate and vegetation for vertebrate populations to maintain transmission [12] . The sand fly P . papatasi has a wide geographic distribution , from northern Africa to India [13]; it is considered as a proven vector of ZCL in North Africa [11] . In most field surveys in the country , P . papatasi and P . sergenti were the most abundant species [7 , 14 , 15]; however , P . papatasi was most frequent in the northern part of the country . Recently , the World Health Organization identified four possible transmission systems of ZCL , based on associated mammal reservoirs: Ps . obesus , Meriones spp . , Rhombomys opimus Lichtenstein , 1823 ( Rodentia: Muridae ) , and Mastomys spp . Thomas , 1915 ( Rodentia: Muridae ) [16] . Limited epidemiological studies have been carried out in the country to characterize the roles of several species of reservoir hosts in maintaining CL in Libya . Leishmania major was identified from Meriones libycus [12] , and M . shawi [16] in endemic areas of the northwestern part of the country . Early studies revealed Ps . obesus as the potential natural reservoir host of L . major in many North African countries including Libya [12 , 17]; Ps . obesus was most prevalent along wadi edges from Sahara to the Middle East , where high density of this species is associated with abundant vegetation and halophilic plants [17] . Meriones spp . are thought to play an important role in ZCL outbreaks by maintaining the parasite in nature in the long term . Meriones shawi and M . libycus have been found repeatedly to be naturally infected with L . major in Libya [12] , Tunisia [18–20] , Morocco [21] , and Algeria [22] . Most ZCL outbreaks in North African countries have been tied to epidemiological modifications and environmental changes [23 , 24] , highlighting the importance of understanding the epidemiology of ZCL in this region . This study represents a first effort to understand the ecology and geography of ZCL using remote-sensing data across Libya to predict ZCL risk areas . We used ecological niche modeling approaches to identify the distribution of sand fly vector species , mammal reservoirs , and the pathogens to test their patterns of overlap in environmental space , which illuminate details of the local ZCL cycle in Libya where these species coexist . Libya is situated in North Africa on the Mediterranean coast between Egypt and Tunisia . The country lies between 18 and 33° N latitude and 8 and 25° E longitude . Dominant climate conditions include hot-summer Mediterranean and hot desert climates [25]: coastal lowlands have very hot summers and mild winters , while the desert interior has long , hot summers and high diurnal temperature ranges , with very dry conditions . Precipitation declines rapidly to the interior with distance from the coast . Libya lacks large rivers and streams , and extended droughts are frequent; however , the government has constructed a network of dams for water management [26] . Based on the leishmaniasis surveys in Libya , ZCL is endemic in the northwestern regions of the country . We collected records for all organisms involved in the ZCL transmission cycle including the pathogen ( L . major ) , vector ( P . papatasi ) , and potential mammal reservoirs ( Ps . obesus , M . libycus , M . shawi , G . gerbillus ) . We retrieved vector and pathogen data from our own surveillance , and the PubMed database using keywords of species’ names and Libya . When L . major was identified at the coarser district level ( e . g . [2] ) , NCDC provided details for the exact locations of these cases for the purpose of this study . Leishmania major records based on clinical features only were excluded from analysis to avoid possible diagnostic errors in species identification; we included all records identified rigorously by either zymodeme analysis ( i . e . MON-25 ) of 16 enzymatic loci [27] or restriction fragment length polymorphisms of the ribosomal internal transcribed spacer 1 ( ITS1 ) region [2] . Host and vector species included in the study were identified by reference to previously published morphological keys [28–30] . Data were included if a geographic reference was linked to any of the six species ( geographic coordinates or textual descriptions ) . Other records of Ps . obesus , M . libycus , M . shawi , and G . gerbillus were obtained from the Global Biodiversity Information Facility ( www . gbif . org ) , VertNet ( http://www . vertnet . org/ ) , and our own field surveillances across the country . When geographic references were textual in nature , we assigned longitude-latitude coordinates via reference to Google Earth ( https://www . google . com/earth/ ) . All occurrence data were filtered to eliminate duplicate records and longitude-latitude coordinates falling from outside Libya . Environmental data sets by which to characterize environmental landscapes across Libya were obtained from three sources . ( 1 ) Advanced Very High Resolution Radiometer ( NOAA-AVHRR ) satellite imagery was obtained from the European Distributed Institute of Taxonomy ( EDIT; http://bit . ly/1TDsUQM ) . These data comprise monthly mean Normalized Difference Vegetation Index ( NDVI ) coverage from 1982 to 2000 , rescaled to a range of 1 to 255; we calculated mean , maximum , minimum , median , and range across the 12 monthly NDVI layers . ( 2 ) Climatic data layers representing 35 variables were obtained from global climatologies in CliMond ( https://www . climond . org/; S1 File ) . ( 3 ) Digital elevation model were obtained from the Shuttle Radar Topography Mission ( SRTM; http://srtm . usgs . gov/ ) at 1 km spatial resolution . All variables were resampled in ArcGIS 10 . 2 ( Environmental Systems Resource Institute , Redlands , California ) to a spatial resolution of 10 x 10' ( ≈20 x 20 km ) . The particular environmental variables were chosen for modeling in light of their likely importance in shaping the geographic distributions of the species of interest in this study [25 , 31] . We selected historical NDVI and climatic data to cover the same time interval as when most records were obtained for the species . NDVI has been identified in previous epidemiological studies as an important variable by which to convey seasonality resulting from changing temperature or moisture availability , and to understand broad-scale patterns of land use and land cover and their effects on pathogen populations and transmission [32] . NDVI is significantly correlated also with details of soil conditions , including type of soil , water content , and soil moisture [33–36] . Principal components analysis ( PCA ) was applied to the environmental variables to reduce multicollinearity and dimensionality . We used the first 10 principal components , which summarized more than 95% of the overall variance , to summarize environmental variation across Libya . The MaxEnt algorithm [37] was used to estimate the fundamental ecological niche of the six species in this study . The fundamental ecological niche is defined as the set of environmental conditions under which a species is able to maintain populations without immigrational subsidy [38] . Correlational ecological niche models ( ENMs ) estimate niches by relating known occurrences to environmental values to identify conditions associated with the species presence . We calibrated ENMs within the districts where sampling was most detailed , and then transferred the model across all of Libya . MaxEnt was specified to conduct 100 bootstrapping replicates for each species . We used medians across the replicates as a final niche estimate for each species . All ENMs were converted to binary maps using a least training presence ( i . e . lowest probability value of the occurrence points used in calibration of the models ) thresholding approach adjusted to permit 5% omission in light of some probably erroneous records likely remaining in our data set [39] . To test the robustness of the ENMs in predicting the occurrences of the species accurately across unsampled areas of Libya , a partial receiver operating characteristic ( ROC ) approach was used [39] . This approach potentially allows differential weighting of omission ( i . e . , false negatives , leaving out actual distributional area ) and commission errors ( i . e . , false positives , including unsuitable areas in prediction ) and concentrates attention on parts of error space most relevant to niche modeling [39] . We selected 50% of the occurrence points of each species at random to test the ENMs by comparing the reduced threshold-independent area under the curve to null expectations: the dataset was bootstrapped , and probabilities obtained by direct count . AUC ratios were calculated via a software partial ROC available as a visual basic application at http://bit . ly/1JusDwz , based on 100 iterations and an E = 5% omission threshold . An additional independent 98 records from the Libyan National Centre for Disease Control ( NCDC ) were used to test the model’s ability to predict the distribution of new ZCL cases across Libya . These samples were identified in the NCDC laboratory based on PCR protocols from previous studies ( e . g . [2 , 11] ) . We checked these records to remove any occurrences matching these used in calibrating ENMs , but none coincided with those used in model calibration . We used a one-tailed cumulative binomial probability distribution that assessed the probability of obtaining the observed level of correct prediction by a chance alone given the background expectation of correct predictions and based on the proportional coverage of the region by the thresholded model prediction . Niche breadth was estimated for each species based on the inverse concentration measure in ENMTools ( http://enmtools . blogspot . com/ ) . For successful ZCL transmission , pathogen , vector , and host species should overlap spatially and ecologically [31 , 40] . Here , ZCL transmission requires presence of L . major , P . papatasi , and at least one of the mammal reservoir species . We used background similarity tests [41] to assess similarity between pairs of estimated niches . We first estimated the accessible area ( M ) for each species in the study [42]; the accessible area for L . major was identified based on the distribution of that species across the country , where the species occurs only in the northwestern part . M estimates for the other species included all or at least a subset of the northern parts of the country depending on the species’ current distributions . To test the null hypothesis of niche similarity between each pair of niches , we used D-statistics and Hellinger's I implemented in ENMTools [41] . Niche similarity was tested with respect to all environmental variables used to develop the ENM for each species . The background similarity test is based on generating random points from across the accessible area of one species in numbers equal to the numbers of real occurrence data available for that species in the study , with 100 replicate samples , and comparing an ENM based on these “background” points to the ENM of the other species . The null hypothesis of niche similarity was rejected if the D or I values fell below the 5th percentile in the random-replicate distribution of similarity values [41] . We assumed that areas could be considered as at risk of ZCL transmission when all necessary elements for transmission co-occur [40] . We used the ENMs for P . papatasi and the four candidate ZCL reservoirs to identify areas of overlap between the vector and each of the possible hosts . These grids were obtained by multiplying the binary ENM of P . papatasi with the binary grid for the host species . We used a one-tailed cumulative binomial test to assess the relationship between the areas of vector-reservoir overlap and independent leishmaniasis human case records from the NCDC . We collected a total of 348 occurrences for P . papatasi , L . major , and four candidate reservoir species across Libya . Occurrence records are fully and openly available via Figshare repository ( https://dx . doi . org/10 . 6084/m9 . figshare . 1613478 ) . These data were concentrated along the northern coast of Libya ( Fig 1 ) . Phlebotomus papatasi was recorded from 84 localities , whereas L . major was characterized by 50 localities . Meriones libycus was the most commonly recorded mammal reservoir ( 104 sites ) followed by Ps . obesus ( 48 ) , G . gerbillus ( 32 ) , and M . shawi ( 30 ) . ENMs developed for these six species are illustrated in Fig 1; ENMs calibrated across the country ( for comparison ) are presented in the Supporting Information ( S2 File ) . ENMs predicted most of the species to range across the northern coast of Libya; however , three species had broader potential distributions extending south to central Libya ( M . libycus , M . shawi , Ps . obesus ) . The ENM for L . major predicted highest suitability in a belt between 30–33° N . These areas included many western provinces ( e . g . , Nalut , Yafran , Nuqat Al Khams , Al Jifarah , Sabratah , Misrata , Al Marqab , Gharyan , Babratah , Az Zawiya , Tajura , Tarhunati , Bani Walid , and Sirte ) , but also some eastern provinces ( e . g . , Al Jabal Al Akhdar , Al Qubbah , Al Hizam Al Akhdar , and Ajdabiya ) . The potential distribution of P . papatasi extended across the northern coast of Libya , but also in a disjunct area in central east Libya . All ENMs calibrated for these species were significantly robust based on partial ROC tests , with AUC ratios uniformly above 1 ( P < 0 . 001; Table 1 ) . In the most recent CL outbreaks across Libya , NCDC identified L . major in cases from 98 sites . The L . major ENM predicted 98 out of 98 of these additional independent data , which is statistically better than random expectations ( P < 0 . 001 ) . These additional independent data thus corroborated the L . major ENM , and the ability of that model to anticipate all recent cases of ZCL identified ( Fig 2 ) . Niche breadth was least in L . major and P . papatasi , and greater in the mammal species; indeed only G . gerbillus had niche breadth similar to L . major ( S3 File ) . We visualized the environmental conditions where these species occur: L . major and P . papatasi were at low elevations , and mostly under a maximum temperature of 25°C–37°C ( Fig 3 ) . The other species had similar responses to environmental conditions; however , they tend to be distributed along a broader environmental range ( except G . gerbillus; S4 File ) . The background similarity tests comparing the ENMs of parasite , vector , and possible reservoirs were uniformly unable to reject the null hypothesis of niche similarity between these species ( P > 0 . 05; Fig 4 & S5 File ) . This result indicates the niche estimate for L . major could not be distinguished from those of the vector or the four potential reservoirs . We used NicheA to visualize overall overlap between the species based on three dimensions of PCAs ( Fig 5 ) , which revealed broad overlap in environmental conditions used by six species . Finally , we combined the modeled distribution of the vector P . papatasi with those of each of the potential reservoirs as hypotheses of system that could support zoonotic transmission of CL across Libya ( Fig 6 ) . Results revealed that P . papatasi-M . libycus , P . papatasi-M . shawi , and P . papatasi-Ps . obesus systems predicted recent ZCL well ( P < 0 . 01 ) ; the first two predicted 100% of the cases reported to the NCDC , but Ps . obesus identified only 85 . 7% of these cases ( 84 out of 98 ) . The P . papatasi-G . gerbillus map was able to predict only 29 of 98 records , not better than null expectations ( P > 0 . 05 ) . Numerous recent studies have attempted to map potential distributions of key species involved in leishmaniasis transmission in several countries in Europe and the Americas [43 , 44] . Africa , however , has seen only a few efforts to map vector populations [31 , 45–47] . Libya sees many CL cases [3]; for example , 6284 cases were identified there in 2006 alone ( S6 File ) . CL case rates are still underestimated owing to inefficient infrastructure for early notifications of cases , and lack of public awareness among doctors and patients [3] . For the national control program to be successful , all organisms associated with leishmaniasis transmission should be identified and understood in detail ( i . e . vectors , reservoirs , and pathogens ) . We developed this mapping exercise across Libya for several reasons . ( 1 ) Most prominently , we wished to map the potential distribution of ZCL cases across the country . ( 2 ) We strove to map the potential distribution of 5 other organisms potentially associated with the disease’s dynamics in Libya . ( 3 ) We wished to test niche similarity among the set of species involved . Finally , ( 4 ) we tested the possible reservoir-vector combinations that could allow better prediction of ZCL cases . All of these analyses will help to understand the disease risk areas across the country and guide possible control programs . Our models identified risk areas across both the western and eastern portions of the north coast of the country . Although all previous studies in Libya had found CL cases only in the western provinces , some recent reports have provided evidence of CL occurrence in eastern sites as well ( e . g . Ajdabiya , and Al Jabal Al Akhdar; [48] ) . Although this report [48] is the only one to place CL at these sites , most CL surveillance has concentrated in western Libya [2 , 6 , 7] , so this results is perhaps expected . Our ENMs found suitability of both regions for ZCL transmission , benefitting from higher-resolution environmental data , and consideration of areas that were sampled and accessible to each species [42 , 49] . The risk of ZCL transmission in North Africa appears to be determined by the joint dynamics of vectors and mammal reservoir populations [16] . When we visualized the environmental conditions suitable for the species examined in this study , they were most prevalent in a maximum temperature range of 25°C–37°C , similar to other recent reports across North Africa and Middle East [50–52] . These latter studies reported that P . papatasi was abundant in semi-arid and arid steppe zones , and that low and high temperatures are key in limiting its distribution and activity [50–52] . For example , P . papatasi in Morocco was less active at temperatures of 11–20°C and 37–40°C [52] . The distributional patterns of L . major and P . papatasi estimated in this study concords with these latter reports [51 , 52] . Northern coastal regions of Libya are characterized by a Mediterranean climate , whereas the rest of the country has hot , dry desert climates that are unfavorable for these species , with maximum summer temperatures over 40°C apparently . Previous studies have shown that water is a major limiting factor for sand flies and for leishmaniasis abundance and spread , respectively [53] . Phlebotomus papatasi cannot tolerate the extreme conditions of temperature and low humidity associated with the rare rainfall in the south , although the species is well established in other deserts where conditions are more mesic ( e . g . , Negev Desert [53] ) . Our study identified an interesting prediction of the presence of suitable environmental conditions in central Libya , associated with construction of new water resources [26] and raised concerns for changes in the eco-epidemiology of leishmaniasis across the country as water resources ( S7 File ) and agricultural activities are established in southern parts of the country . These important anthropogenic changes will be key factors in affecting distributions of vectors and reservoir hosts of ZCL across Libya; for example , in other studies in the region , soil moisture was an important variable in determining vector and reservoir abundance [54 , 55]; anthropogenic disturbance was also identified as favoring conditions for vector and larger host populations in Israel [56] . The effects of these two factors may be reflected among some of environmental variables included in our study , but their absence in explicit terms still marks a limitation to our study; a more detailed picture of ZCL transmission risk in the region will need to consider their possible effects on long-term sand fly and rodent abundances . Most recent ZCL cases occurred at relatively low elevations; the areas near Al Jabal Al Gharbi alone accounted for most cases ( S7 File ) [2 , 57] . Similar observations were reported for L . major , P . papatasi , and wild mammals in Morocco [58] . Elevation and temperature are not the only factors influencing the distribution of ZCL cases: precipitation has also been shown to play a role [45] . Low-elevation northern areas , where L . major and P . papatasi species occur in high densities , are characterized by the highest precipitation in the country [45] . Although testing niche similarity among species was unable to distinguish among hypotheses of ZCL hosts , as was possible in our previous analyses in Egypt [31] , our analyses of possible species combinations excluded G . gerbillus as a main reservoir across Libya . In our previous analysis in Egypt , however , we found marked niche similarity between P . papatasi and L . major , but none between L . major and P . sergenti in terms of geographic distribution and ecological niche [31] , supporting the idea that carefully constructed ENMs are able to predict disease risk based on models of vectors and reservoir hosts in a complex transmission system like leishmaniasis . In this study , 85 . 7% of cases were predicted successfully , focusing on areas where Ps . obesus co-occurred with the vector . WHO had reported that Ps . obesus was likely the main reservoir of ZCL in Libya [16]; however , our results more strongly supported the two Meriones spp . –P . papatasi system , which were able to anticipate all recent human cases . Evidence for this association has also been found in the form of high infection rates with L . major in Meriones tristrami in the most recent ZCL foci documented in Israel [59]; these observations provide mounting evidence that jird play a major role in disease transmission across the region . This study took Libya as a target population for illuminating the identity and distribution of reservoir hosts in the complex ZCL cycle . Indeed , simply the definition of “reservoir host” remains unresolved [60–62]; early studies defined reservoir host as the “ecological system in which the infection agent survives indefinitely” [60] , but later studies focused on definition in reference to a specific target population [61] . Certainly , some confusions in reservoir definition still exist; the specification of particular target populations emphasizes the importance of geographic and ecological associations in defining reservoir hosts , which underlines the approach in this study . As a result , we urge development of similar studies regarding other target populations to examine spatial and temporal relationships of these hosts , and characterize differences in ZCL dynamics among regions . The study of the association among these organisms in both spatial and temporal dimensions is of great added values to map the ZCL risk areas across Libya , guide the control program across the country , and provided the first detailed maps for the potential distributions of organisms associated with the zoonotic transmission cycle across Libya . An early study shed light on disease ecology and possible host-pathogen associations [63] , discussing criteria of host geographic distribution , pathogen range within the host range , regional distributions of organisms in different biomes and habitats , relative prevalence of the pathogen among host subpopulations , temporal and fine-scale spatial pattern of host-pathogen dynamics , and integrative time- and place-specific predictive models . These criteria were discussed as major steps to promote understanding of pathogen-host associations in complex transmission cycles . This study applied most of these criteria to the complex ZCL cycle in Libya but we note knowledge gaps in Libya regarding the prevalence of L . major among different host subpopulations , and the dynamics and potential distribution of host and parasite at finer scales across the country . Filling these gaps as regards the disease system in Libya will promote a more detailed picture both for its ecology and for control programs . Leishmaniasis control programs should consider our findings by applying integrated approaches to combating ZCL by considering the environmental risk factors that we have explored . That is , if a particular combination of host and vector species is necessary for leishmaniasis transmission , then strategies by which to interrupt that transmission can focus on removing the pathogen , the vector , or key hosts from the system . Such measures may be implemented via educational programs in risk areas , mass drug administration in infected communities , and host or vector control programs . Our future work will focus on possible hotspots in the less-well-known areas of the country via intensive disease surveillance and sampling of all relevant organisms . More deeply , we plan to consider socioeconomic variables in tandem with the physical environmental variables for a more universal model that links physical , biological , and human factors in this complex disease system .
Zoonotic cutaneous leishmaniasis ( ZCL ) represents a major public health problem in North Africa where Leishmania major is the potential etiological agent associated with all ZCL cases . In many countries across North Africa , L . major is transmitted by the sand fly Phlebotomus papatasi , with rodents as likely reservoir hosts . In Libya , ZCL cases are underestimated for lack of reporting , insufficient information about the distribution of ZCL , and interactions between local environmental conditions and different disease components . This situation worsened with recent political and socio-economic changes in the country , with expansion and rapid increases in numbers of cases across the country . For management and planning of leishmaniasis control , predicting the potential geographic distribution of risk of infection with the disease is important to guide such programs . We introduced ecological niche model as a tool for risk-mapping of both ZCL cases and distributions of associated species . Our models were able to anticipate areas of highest risk with statistical significance , lending confidence that they were successful in identifying areas of transmission risk .
[ "Abstract", "Introduction", "Materials", "and", "Methods", "Results", "Discussion" ]
[ "invertebrates", "body", "weight", "ecology", "and", "environmental", "sciences", "medicine", "and", "health", "sciences", "ecological", "niches", "pathology", "and", "laboratory", "medicine", "geographical", "locations", "tropical", "diseases", "vertebrates", "parasitic", "diseases", "parasitic", "protozoans", "animals", "mammals", "diptera", "protozoans", "physiological", "parameters", "leishmania", "obesity", "neglected", "tropical", "diseases", "africa", "libya", "infectious", "diseases", "zoonoses", "phlebotomus", "protozoan", "infections", "pathogenesis", "insects", "arthropoda", "people", "and", "places", "leishmania", "major", "ecology", "host-pathogen", "interactions", "physiology", "leishmaniasis", "biology", "and", "life", "sciences", "organisms" ]
2016
Coarse-resolution Ecology of Etiological Agent, Vector, and Reservoirs of Zoonotic Cutaneous Leishmaniasis in Libya
Viroporins are small transmembrane proteins with ion channel activities modulating properties of intracellular membranes that have diverse proviral functions . Hepatitis C virus ( HCV ) encodes a viroporin , p7 , acting during assembly , envelopment and secretion of viral particles ( VP ) . HCV p7 is released from the viral polyprotein through cleavage at E2-p7 and p7-NS2 junctions by signal peptidase , but also exists as an E2p7 precursor , of poorly defined properties . Here , we found that ectopic p7 expression in HCVcc-infected cells reduced secretion of particle-associated E2 glycoproteins . Using biochemical assays , we show that p7 dose-dependently slows down the ER-to-Golgi traffic , leading to intracellular retention of E2 , which suggested that timely E2p7 cleavage and p7 liberation are critical events to control E2 levels . By studying HCV mutants with accelerated E2p7 processing , we demonstrate that E2p7 cleavage controls E2 intracellular expression and secretion levels of nucleocapsid-free subviral particles and infectious virions . In addition , our imaging data reveal that , following p7 liberation , the amino-terminus of p7 is exposed towards the cytosol and coordinates the encounter between NS5A and NS2-based assembly sites loaded with E1E2 glycoproteins , which subsequently leads to nucleocapsid envelopment . We identify punctual mutants at p7 membrane interface that , by abrogating NS2/NS5A interaction , are defective for transmission of infectivity owing to decreased secretion of core and RNA and to increased secretion of non/partially-enveloped particles . Altogether , our results indicate that the retarded E2p7 precursor cleavage is essential to regulate the intracellular and secreted levels of E2 through p7-mediated modulation of the cell secretory pathway and to unmask critical novel assembly functions located at p7 amino-terminus . Hepatitis C virus ( HCV ) infection is a major cause of chronic liver diseases worldwide . With 180 million people persistently infected , chronic HCV infection induces liver diseases such as liver cirrhosis and hepatocellular carcinoma . Although new direct antiviral agents are now able to eradicate the virus in most patients , no protective vaccine currently exists against HCV and it remains major challenges in basic , translational and clinical research [1 , 2] . HCV is a plus-strand RNA enveloped virus . Its genome is translated as a single polyprotein that is processed by cellular and viral proteases in 10 mature viral proteins [3] consisting of: i ) an assembly module ( core-E1-E2-p7-NS2 ) encompassing the capsid protein ( core ) as well as the E1 and E2 surface glycoproteins that are incorporated in viral particles , and the p7 and NS2 proteins that support virion assembly , and ii ) a replication module encompassing the nonstructural proteins NS3 , NS4A , NS4B , NS5A and NS5B that are sufficient to support viral RNA replication but that also contribute to virion production through ill-defined processes . As HCV proteins arise from a shared polyprotein , several post-translational modifications control their expression rates within infected cells . In addition , at least three precursors , i . e . , tandem proteins with delayed cleavage , are also detected and may implement functions different than their cognate individual proteins . They consist of immature core protein , associated to the D3 trans-membrane peptide , whose removal allows core targeting to lipid droplets ( LDs ) [4 , 5]; NS4B-5A , which promotes the formation of replication vesicles [6]; and E2p7 [7–11] , whose properties are explored in this report . Assembly of viral particles occurs at endoplasmic reticulum ( ER ) -derived membranes in close proximity to LDs and virus replication complexes [12] , with NS2 and p7 being key players in gathering virion components [13–17] . Particularly , NS2 associates with E1E2 glycoproteins and NS3 as well as with NS5A , which interacts with HCV RNA and core [18–20] and promotes genome encapsidation . Virion assembly begins with the formation of a nucleocapsid , formed by core/RNA complex , and is coupled with its envelopment and acquisition of the E1E2 glycoproteins as well as lipids and apolipoproteins [18 , 21] . The pathway of secretion of virions remains to be elucidated and may occur through a non-canonical route [22 , 23] . Of note , HCV produces different types of particles in addition to infectious virions , including nucleocapsid-free subviral particles [24 , 25] , E2-containing exosome vesicles [26 , 27] , naked nucleocapsids [28] , and a range of more or less lipidated infectious viral particles [29]; yet , the regulation of their production is poorly understood . The p7 protein is a small , 63 amino-acid-long protein , consisting of a “hairpin-like” topology involving three helices inducing two trans-membrane segments connected by a hydrophilic , positively-charged cytosolic loop [30–32] , though alternative folds and topologies have been proposed [33–35] , e . g . , with the p7 C-terminus exposed to the cytosol [33] . As it is able to form an ion channel in either hexameric or heptameric form [30 , 35 , 36] exhibiting a funnel- or flower-like shape [35 , 37] , it was classified as a viroporin , like M2 of influenza virus [38] . Importantly , p7 is dispensable for replication but essential for both assembly and secretion of infectious particles [11] . First , p7 modulates the formation of NS2 complexes with E2 , NS3 and NS5A [9 , 13 , 14 , 16 , 39] , allowing clustering of assembly components and regulation of early assembly events . Second , p7 allows , in concert with NS2 , the regulation of core localization at lipid droplets vs . ER-derived membranes [17] , from where viral particles are released in the secretory pathway . Third , p7 modulates the envelopment of nascent virions [40] . Fourth , p7 may regulate the pH of some intracellular compartments , which could be essential for the protection and secretion of infectious particles [41 , 42] . A recent study indicated that residues of the first helix of p7 that are predicted to point toward the channel pore are important for assembly [10] . Noteworthy , viroporin ion channel activities modulate properties of intracellular membranes and , thereby , impacts several fundamental biological processes such as trafficking , ion fluxes as well as connections and exchanges between organelles [38 , 43] . While several biophysical studies showed that p7 can change ionic gradients in reconstituted membrane assays in vitro [30 , 44–47] , few reports have addressed the relevance of such properties in cellulo [41 , 42 , 48 , 49] , although , by analogy with viroporins from alternative viruses , this may have diverse proviral functions [38] . For example , the 2B viroporin from coxsackievirus modulates calcium homeostasis , which leads to the suppression of apoptotic host cell responses [50] . Likewise , p7 may promote immune evasion by antagonizing the antiviral IFN function [51] . Interestingly , while p7 is released from the viral polyprotein through cleavage at E2-p7 and p7-NS2 junctions by the cellular signal peptidase [7 , 52] , it also exists in infected cells as an E2p7 precursor of poorly defined properties [7–11] . Intriguingly , virus mutants that exhibit either only E2p7 precursor ( i . e . , using a point mutation in E2-p7 cleavage site ) or , conversely , no E2p7 expression ( i . e . , using an IRES sequence between E2 and p7 ) are both impaired for production of infectious particles [9 , 14 , 39 , 53] , suggesting that timely liberation of E2 and/or p7 are critical events for assembly/release of HCV particles . Here , we explored how the retarded cleavage between E2 and p7 could regulate their functions associated to virion assembly and/or perturbation of cellular membrane processes . We demonstrate that p7 is able to regulate the cell secretory pathway , which induces intracellular retention of HCV glycoproteins , and to control release of nucleocapsid-free subviral and infectious viral particles . Specifically , through biochemical , imaging and functional analysis of a series of mutant viruses with modified E2-p7 junction as well as through p7 transcomplementation assays , our data uncover different mechanisms by which p7 regulates the proportion of different types of secreted HCV particles and determines their specific infectivity . As HCV p7 is released through inefficient E2p7 cleavage , we first sought to address its function by increasing its expression levels in HCV-infected cells . We found that co-expression of individual p7 with JFH1 HCVcc RNAs in Huh7 . 5 hepatoma cells decreased the levels of extracellular particle- associated E2 proteins , resulting in ca . 3-fold reduced secretion ( Fig 1A ) . Since some viroporins from alternative viruses alter the canonical secretory pathway [38 , 43] , we then asked whether p7 could impact the secretion of VSV-G tsO45 ( VSV-Gts ) , a temperature-dependent folding mutant of VSV-G glycoprotein commonly used as model cargo of protein secretion [54] . At 40°C , this protein remains unfolded , resulting in its accumulation in the ER , whereas its folding can be restored at 32°C , which allowed its transfer from the ER to the Golgi and then the plasma membrane ( Fig 1C ) . We transfected in Huh-7 . 5 cells VSV-Gts with p7 constructs from different HCV strains and using different signal peptide configurations , which resulted in ca . 60% of cells co-expressing both proteins among the transfected cells ( Fig 1B ) . As monitored by flow cytometry analysis , p7 co-expression significantly reduced the kinetics and levels of VSV-Gts cell surface expression at permissive temperature of 32°C ( Fig 1C ) . Next , to address how p7 alters traffic through the secretory pathway , we measured the resistance of intracellular VSV-Gts to endoH digestion , used as a marker of ER-to-Golgi traffic [54 , 55] . While at 0h , all VSV-Gts glycans remained endoH-sensitive , reflecting ER retention at 40°C , they progressively became resistant to endoH cleavage upon 1-3h incubation at 32°C ( Fig 1D–1F ) , underscoring VSV-Gts transfer to the Golgi apparatus . Importantly , p7 co-expression resulted in dose-dependent decrease of the kinetics of VSV-Gts endoH-resistance ( Fig 1D–1F ) , in a manner similar to influenza virus M2 ( Fig 1E ) , indicating that p7 slowed down the rate of VSV-Gts ER-to-Golgi traffic or , alternatively , favored its retention in the ER . We noticed that p7 proteins from different HCV genotypes/strains mediated this effect ( Fig 1E ) with that of H77 strain appearing less efficient for inhibiting Golgi transfer . We also found that p7 associated to its own signal-peptide , i . e . , the E2 amino-terminus ( ∆E2p7 construct ) , induced the strongest VSV-Gts ER retention . We then thought that p7-mediated alteration of the secretory pathway could induce the retention of HCV glycoproteins at ER membranes , which may favor assembly of HCV particles [21] . Thus , we expressed E1E2 glycoproteins , alone or with p7 , to promote their secretion in the cell supernatant as subviral particles ( SVP ) , i . e . , nucleocapsid-free enveloped particles [24] , that peaked at a buoyant density of ca . 1 . 05–1 . 06 g/ml ( Fig 1G ) . As detected by E2 immunoblots from the pellets after ultracentrifugation of the cell supernatants ( Fig 1H and 1I ) , we found that p7 co-expression induced up to 60% increase of E2 intracellular expression and concomitant 55% decrease of SVP release ( Fig 1H ) . This resulted in a ca . 2–3 fold reduced E2 secretion ( Fig 1I ) , which , combined with the results of Fig 1A , indicates that p7 can induce the retention of HCV glycoproteins . By controlling the release of free p7 , E2p7 cleavage efficiency may adjust the levels of p7 expression , which could regulate the extent by which p7 slows down the cell secretory pathway and , concomitantly , the traffic and thus secretion of HCV E2 glycoproteins ( Fig 1A , 1H and 1I ) . Hence , we introduced mutations at the E2-p7 junction in Jc1 and/or JFH1 viruses in order to design HCVcc mutants that exhibit increased E2p7 cleavage scores ( Fig 2A ) , as predicted by SignalP method ( http://www . cbs . dtu . dk/services/SignalP/ ) . This strategy was preferred over the use of an IRES sequence between E2 and p7 , as reported before [9 , 10 , 14] , because it induces a natural , i . e . , signal peptidase-mediated liberation of p7 from the HCV polyprotein . First , we inserted linkers of various sizes at the N-terminus of p7 ( HAHALp7 and ASGGSp7 viruses ) , which left p7 sequence unchanged; the former linker consisting of a double HA tag allowing the detection of p7 [56] . Second , we introduced a substitution at position 2 of p7 ( p7-L2S ) . Third , we generated p7 mutants having insertions of a single residue , either a threonine ( p7-T2 ) at position 2 [8] or an alanine ( Ap7 ) at position 1 of p7 ( Fig 2A ) . None of these mutations–termed hereafter p7 ATMI ( Amino-Terminus Membrane Interface ) mutants , introduced before the first p7 helix ( shown as grey box in Fig 2A ) [30 , 35 , 36] , are expected to change p7 structure or opening of its channel pore , as shown by molecular modeling ( S1 Fig ) . Of note , we could not identify mutations at E2 carboxy-terminus that accelerated E2p7 cleavage . Finally , we also introduced a control mutation abrogating E2p7 cleavage ( E2-A367R; Fig 2A ) [9] . Next , we investigated the rate of E2p7 cleavage by treating lysates of Huh-7 . 5 cells expressing mutant virus RNAs with EndoH to remove E2 glycans , which improved E2 vs . E2p7 electrophoretic separation ( Fig 2B and 2C ) . Except for the E2-A367R mutant that was not cleaved , as expected , all mutants displayed almost complete E2p7 cleavage , which compared to the ca . 40% and 50% uncleaved E2p7 precursor detected with parental Jc1 and JFH1 viruses , respectively . Using a HA-tag antibody to reveal the HAHALp7 protein , we confirmed that the accelerated cleavage detected for the JFH1 HAHALp7 and Jc1 HAHALp7 viruses induced the release of p7 at the expected molecular size with poor if not undetectable E2p7 ( i . e . , E2HAHALp7 ) expression ( Fig 2D ) , though such analysis could not be extended to the other p7 ATMI mutants owing to the unavailability of antibodies against native p7 . In addition , as previously reported [8 , 11] , we also detected small amounts of E2p7NS2 precursor for both wt and mutant viruses ( Fig 2D; S2A Fig ) . Interestingly , when we investigated the infectivity of these mutant viruses , we found that , relative to parental viruses , the p7 ATMI mutant viruses had decreased extracellular infectivity , by ca . 3-fold to over 100-fold ( Fig 2E ) , depending on the p7 modifications and the virus backbones ( Fig 2A ) . Particularly , the Ap7 insertion and the p7-L2S substitution mutants in JFH1 virus induced ca . 3- and 10-fold decreased infectivity , respectively , whereas the p7-T2 , ASGGSp7 , and HAHALp7 JFH1 insertion mutants exhibited complete loss of infectivity ( Fig 2E ) . Likewise , the Jc1 HAHALp7 mutant virus displayed a 10–20 fold reduced infectivity , as compared to parental virus ( Fig 2E ) . Similar defects were observed for intracellular infectivity ( Fig 2E ) , indicating that these p7 ATMI mutant viruses were not impaired at the stage of secretion of viral particles . Finally , we found that co-expression of wt p7 ( though not mutant p7 such as HAHALp7 ) restored the production of both extracellular and intracellular infectious particles to levels detected for wt viruses ( Fig 2F and 2G; S2B Fig ) . Hence , since all p7 ATMI mutants displayed nearly complete E2p7 cleavage ( Fig 2B and 2C ) and since HAHALp7 co-expressed with mutant viruses did not restore infectivity ( Fig 2G ) , these results indicated that the integrity of the N-terminal end of p7 itself is crucial for assembly of infectious particles . Thus , in the subsequent experiments , we compared more particularly the Jc1 HAHALp7 virus to its parental Jc1 counterpart since this mutant retained some infectivity levels , which allowed us to further characterize this novel phenotype; yet , the most salient results described below could be extended to the other p7 ATMI mutant viruses ( see supplemental figure set ) . Since p7 ATMI mutant viruses displayed augmented E2p7 cleavage rates ( Fig 2 ) , we wondered whether they had altered E2 expression levels . As compared to wt viruses , we observed a 2–3 fold increased intracellular expression of total E2 ( i . e . , free E2 + E2p7 ) for all mutant viruses exhibiting increased E2p7 cleavage ( Fig 3A–3C; S3A Fig ) . No modification of E2 half-life could be detected for mutant vs . wt virus ( S3C Fig ) . Specifically , taking into account that ca . 40% E2 was detected as E2p7 precursor for wt JC1 virus ( Fig 2B and 2C ) , we estimated that the actual ratio of free E2 expression for mutant vs . wt viruses is ca . 4–5 fold . We also found that core expression was increased by ca . 1 . 5–2 fold ( Fig 3B and 3D; S3B Fig ) , whereas expression levels of NS2 and NS5A non-structural proteins ( Fig 3B and 3D ) were unchanged . As similar results were obtained for all p7 ATMI mutant viruses , this indicated that these mutations modulated the expression levels of structural proteins without altering viral replication and/or translation . Importantly , co-expression of either wt p7 or HAHALp7 did not revert E2 expression to wt levels ( Fig 3B and 3C ) , which indicated that increased E2p7 cleavage ( rather that p7 N-terminus modification per se ) induce up-regulation of E2 glycoproteins . Next , we hypothesized that the increased expression of structural proteins could be due to a blockage of their secretion , which may also explain the losses of mutant virus infectivity ( Fig 2E–2G ) . Thus , we quantified the total secretion of virion components , i . e . , E2 glycoproteins , core and viral RNAs , in the supernatants of cells expressing p7 ATMI mutant virus relative to wt virus ( Fig 4 ) . Using immuno-precipitation ( IP ) assays of cell supernatants with GNA lectins , which bind glycans present on HCV E1E2 glycoproteins [57] , we detected a ca . 4–5 fold increased secretion of E2 protein ( Fig 4A ) , which matched the 4–5 fold elevated levels of intracellular free E2 ( Fig 3C , combined with Fig 2B and 2C ) . Although the ratio of extracellular vs . total intracellular E2 expression levels indicated a 2–3 fold difference between wild-type and mutant viruses ( Fig 4D ) , taking into account that 40% of intracellular E2 species were in the form of non-secreted E2p7 , we deduced identical ratios of extracellular vs . intracellular free E2 for wt and mutant viruses . Similar results were obtained for the other p7 ATMI mutants ( S4A Fig ) , suggesting that the increased E2 secretion from cells expressing the p7 ATMI mutant viruses is directly linked to the augmented intracellular E2 expression . As co-expression of p7 in trans did not significantly restore E2 expression ( Fig 3C ) and secretion ( Fig 4A and 4D ) to wt levels , this indicated that the delayed cleavage of E2p7 is essential for the control of intracellular and extracellular E2 levels . Strikingly , in contrast to E2 , we observed that the p7 ATMI mutant viruses had decreased secretion of both core and viral RNAs in the cell supernatants , by ca . 2–5 fold ( Fig 4B and 4C; S4B and S4C Fig ) . Furthermore , we found that wt p7 restored normal secretion levels of nucleocapsid components ( Fig 4B and 4C; S4D Fig ) though not those of E2 glycoproteins ( Fig 4A ) . This indicated that viruses harboring p7 ATMI mutations display differential alterations of pathways leading to trafficking and/or secretion of viral glycoproteins vs . nucleocapsid components , i . e . , HCV core and RNA . Finally , since mutant p7 , e . g . , HAHALp7 , co-expression did not restore normal secretion levels of core and RNA ( Fig 4B and 4C ) , these results indicated that p7 itself , rather than its cleavage from E2 , modulates the secretion of viral nucleocapsids . Altogether , our results suggest that altered p7 expression , as induced by accelerated cleavage and release from E2 as well as by its N-terminal modification , influence the proportion of secreted virion components . Indeed , relative to core and/or viral RNAs , a 15–25 fold higher expression of HCV glycoproteins was detected in the supernatants of cells infected with Jc1 HAHALp7 virus as compared to wt virus ( Fig 4E and 4F ) . Interestingly , we found that the p7 ATMI mutant viruses exhibited strongly decreased specific infectivity relative to their content in core protein or viral RNA , from 4–5 fold for Jc1 HAHALp7 virus ( Fig 4H and 4I ) to over 50-fold for other mutants ( S5A and S5B Fig ) . Likewise , relative to E2 glycoprotein , the specific infectivity of the Jc1 HAHALp7 virus was decreased by ca . 35-fold , as compared to the parental virus ( Fig 4G ) . Importantly , co-expression of wt p7 , but not of ATMI mutant p7 , restored ( though not completely ) , the specific infectivity of the mutant viruses ( Fig 4G–4I; S5C Fig ) . Altogether , this pointed out to altered ratios of different secreted forms of HCV-derived particles incorporating these components or , alternatively , altered composition of the viral particles themselves . To demonstrate if the viral components were secreted in particulate forms , we centrifuged the supernatants of infected-cells in conditions allowing sedimentation of particles . We detected in the pellets a ca . 3-fold increase of E2 levels ( Fig 5A; S6A Fig ) and a 2–3 fold decrease of both core and RNA ( Fig 5C ) while comparing Jc1 HAHALp7 mutant vs . parental viruses . This augmentation of secreted particle-associated E2 levels could be detected for all p7 ATMI mutant viruses ( S6B Fig ) . Interestingly , we also observed an increased secretion of E1-containing particles ( S6A Fig ) , likely owing to secretion of HCV glycoproteins as E1E2 heterodimers . Furthermore , ectopic expression of wt p7 restored wt levels of particle-associated HCV glycoproteins , core and RNA ( Fig 5A and 5C ) . Since ectopically-expressed ATMI mutant p7 did not rescue the above levels ( Fig 5C ) , this indicated that p7 N-terminus modifications rather than accelerated E2p7 cleavage induced changes in secretion levels of mutant viral particles , perhaps by altering the ratios between SVPs and ( infectious ) viral particles . Then , we aimed at characterizing the different types and proportions of secreted particles for wt vs . p7 ATMI mutant viruses . First , to confirm that E2 detected in the pellets of ultracentrifuged cell supernatants was secreted as particles , we treated the supernatants of virus-expressing cells with Triton-X100 before ultracentrifugation . We found that such treatment decreased the presence of E2 in the pellets for both wt and mutant viruses ( Fig 5D; S6C Fig ) , hence indicating that a substantial part of secreted E2 proteins were in a sediment form , likely vesicular . Second , since the p7 ATMI mutant viruses secreted higher E2 amounts with poorer infectivity compared to wt viruses ( Fig 4G ) , we quantified the association of their glycoproteins with other virion components , i . e . , core and RNA . When we pulled down E2 using GNA lectins , we found a 5–6 fold decreased association of core and RNA with E2 ( Fig 6A ) . Moreover , ectopic expression of wt p7 , though not mutant p7 , restored , though partially , the association of E2 with viral core and genome ( Fig 6A ) . Finally , we investigated the degree of envelopment of the secreted core proteins within a lipid bilayer . Hence , we treated the supernatants of virus-expressing cells with proteinase K and subsequently determined the amounts of proteinase K-resistant core , which indicates its full protection by a lipid membrane or , conversely , its secretion as naked or badly enveloped core particles . Interestingly , as compared to their corresponding parental viruses , we detected decreased amounts of membrane-protected core for Jc1 HAHALp7 virus and , more dramatically , for JFH1 HAHALp7 virus ( Fig 6B ) , which correlated with their respective losses of infectivity ( Fig 2E–2G ) . Furthermore , we found that co-expression of wt p7 , though not mutant p7 , restored the lipid membrane envelopment of their secreted core proteins to almost wt levels ( Fig 6B ) . Altogether , these results pointed out to a disruption induced by p7 amino-terminus changes of the degree of association between HCV glycoproteins , viral envelopes and nucleocapsids , which could be due to an excess of E2 vs . core and RNA forms secreted independently , such as SVPs vs . naked/partially enveloped core particles , respectively , or , alternatively , to an increased density of E2 glycoproteins on the surface of secreted viral particles . To investigate this further , we separated virus sub-populations using buoyant density-gradient fractionation . Jc1 and JFH1 HCVcc physical particles had more than 90% of viral RNA and 95% of core protein in fractions of densities of 1 . 10–1 . 15 g/ml , with a peak at 1 . 11 g/ml ( Fig 6C; S7A–S7C Fig ) . As shown before [58–62] , core and RNA could also be detected at higher densities ( to up to 1 . 36 g/ml ) and at lower densities , until 1 . 02 g/ml ( S7 Fig ) . Jc1 HCVcc particles had more than 80% of their infectivity in fractions of densities of 1 . 08–1 . 13 g/ml , with a peak at 1 . 11 g/ml ( Fig 6C; S7A and S7B Fig ) , in agreement with recent reports [57 , 60–64] . Lastly , we found that E2 glycoproteins were detected in lower density fractions , with ca . 90% in fractions of densities of 1 . 03–1 . 08 g/ml and a peak at 1 . 05–1 . 06 g/ml ( Fig 6C; S7A Fig ) representing SVPs ( Fig 1G ) . Importantly , less than 10% of E2 could be detected at densities of 1 . 08–1 . 15 g/ml , in which physical and infectious particles were prominent and corresponded to enveloped viral particles . Interestingly , the density profile of Jc1 HAHALp7 virus was qualitatively similar to that of wt virus ( Fig 6C; S7A and S7B Fig ) and did not reveal any alteration of the distribution of the different types of particles along the gradient . However , quantitatively , we found the same alterations of the ratios of E2 , core , RNA and infectivity in the different fractions for the mutant vs . parental virus ( S7A Fig ) , as compared to unfractionated viral particles ( Fig 5A and 5C ) . Specifically , 2–3 fold augmented E2 levels were detected in the SVP fractions of 1 . 03–1 . 08 densities ( S7A Fig ) . Likewise , 2–3 fold reduced core or RNA levels were detected in all fractions whereas infectious titers were decreased by ca . 10-fold in these fractions ( S7A Fig ) . Furthermore , we found that , whatever the density , the co-expression of Jc1 HAHALp7 virus with ectopic p7 restored wt infectivity concomitantly to restoration of the wt levels of core and RNA ( S7B Fig ) . Finally , we found that the loss of infectivity was accentuated for the JFH1 HAHALp7 virus , which displays a stronger phenotype than the Jc1 HAHALp7 virus ( Fig 2E–2G ) , in fractions of densities of 1 . 08–1 . 15 g/ml containing most viral particles ( Fig 6C; S7C Fig ) . Particularly , while core levels were reduced by ca . 2–3 fold for both Jc1 HAHALp7 and JFH1 HAHALp7 , 6- and over 100-fold reductions of infectivity levels were detected for the Jc1 HAHALp7 and JFH1 HAHALp7 mutant viruses , respectively , relative to parental viruses ( Fig 6D ) . Altogether , these results suggested that the p7 N-terminus controls both the secretion and the specific infectivity of secreted virus particles , likely via a process involving the completion of their envelopment , as indicated by the PK-sensitivity of core from secreted p7 ATMI mutant particles . We then aimed at dissecting how p7 modulates the composition of viral particles by addressing HCV assembly mechanisms , from intracellular clustering of virion components to their envelopment . First , since the HCV virion assembly rate is linked to core subcellular localization [17 , 65–67] and since p7 alters this event in concert with NS2 [17 , 40] , we investigated by confocal microscopy analysis whether core from p7 ATMI mutant viruses could be relocated from lipid droplets to ER membranes , where envelopment and release of viral particles occur [3 , 21] . While individually expressed Jc1 core had predominant distribution around lipid droplets , as previously described [17] , its co-expression with either wt p7 or HAHALp7 protein induced full targeting at ER membranes ( S8A Fig ) . Similar results were obtained with full-length viruses , since no difference between Jc1 and Jc1 HAHALp7 viruses could be detected regarding the prevalent ER-localization of their core proteins ( S8B Fig ) . These data indicated that the p7 ATMI mutations and/or the accelerated E2p7 cleavage did not prevent p7-mediated early assembly events leading to core targeting to the ER membrane , but rather , impaired later assembly events . Using HA-tag antibodies , we confirmed that HAHALp7 and core co-localized at the ER in cells infected with the Jc1 HAHALp7 virus ( Fig 7A ) , as reported before [56] . Interestingly , we found that HAHALp7 and core co-localization could also be detected in infected cells treated with 5μg/ml digitonin ( Fig 7A and 7C ) , which permeabilizes plasma but not ER membranes [68 , 69] . Similar results were also obtained with JFH1 HAHALp7 virus as well as with HAHALp7 expressed individually , with or without signal peptide ( S9A–S9C Fig ) . Since E2/core co-localization could be detected in Triton-treated cells but not in digitonin-treated cells ( Fig 7B and 7C; S9A–S9C Fig ) , as expected owing to the luminal exposition of E2 ectodomain , these results indicated that the N-terminus of HAHALp7 points towards the cytosol . Note that these results do not exclude that p7 may also adopt the reverse topology , i . e . , with N- and C-termini exposed toward the luminal side of the ER [70] . We then investigated the sites of HCV assembly , which are represented by ER-derived areas where structural and non-structural viral proteins co-cluster with HCV RNA [66] . As compared to parental virus , we did not find significantly altered clustering of core , E2 , NS4B , and NS5A for the Jc1 HAHALp7 virus ( Fig 8A and 8B ) , suggesting identical rates of early assembly events . Likewise , we did not observe strong differences in the number of core structures co-localizing at assembly sites with HCV positive strand RNA for wt vs . mutant viruses ( Fig 8C and 8D ) . Altogether , these results indicated that the initiation of early assembly events , i . e . , allowing clustering of the HCV structural components at the ER membrane , were not impaired by p7 ATMI mutations . Next , since the NS2 non-structural protein is thought to serve as a scaffold for gathering virion assembly components through its interaction with E1E2 [9 , 13–16 , 71] and since p7 regulates this association [9 , 14 , 39] , we tested if our p7 ATMI mutants had impaired E1E2/NS2 association . Using confocal microscopy , we did not detect altered co-localization of core , E2 and NS2 ( Fig 9A and 9B ) , further highlighting that the assembly sites of viral particles were not grossly changed . However , when we analyzed E1 and NS2 co-immuno-precipitation with E2 antibodies , although the E1/E2 association was unchanged ( Fig 9C ) , we detected a 3–4 fold decreased association of E2 with NS2 for p7 ATMI mutant vs . wt viruses ( Fig 9E; S10A Fig ) . These results indicated that , relative to wt virus , the higher amounts of intracellular HCV glycoproteins detected for the p7 ATMI mutant viruses ( Fig 3B and 3C; S3 Fig; S7 Fig ) were not associated to NS2 . This implied that only a fraction of the up-regulated E2 levels interact with the NS2 assembly platform , and suggested that part of the pool of HCV glycoproteins not associated to NS2 or to assembly sites induce the formation of SVPs . Thus , we performed the reverse co-immuno-precipitation with NS2 antibodies to more directly address interactions between assembly proteins at assembly sites . Strikingly , when we analyzed E1 and E2 co-immuno-precipitation with NS2 , we found ca . 2–3 fold increased E1E2 association to NS2 in cells expressing Jc1 ( Fig 9F ) or JFH1 ( Fig 9D; S10B Fig ) p7 ATMI mutant viruses , as compared to parental viruses . Furthermore , these altered interactions with NS2 were reversed upon ectopic expression of wt p7 ( Fig 9D; S10B Fig ) . Thus , since ectopically-expressed p7 did not restore wt intracellular E2 expression ( Fig 3C ) and since NS2 expression was unchanged for the mutant virus compared to wt ( Fig 3D ) , this indicated that p7 N-terminus modulates NS2 association with HCV glycoproteins . Finally , since NS5A interacts with HCV RNA [72 , 73] and core [18 , 19] as well as with NS2 [13 , 15] , which likely transfers core and HCV RNA to assembly sites or to nascent viral particles [18–20] , we investigated NS2 association with NS5A . No significantly altered co-localization of core , E2 , NS2 and NS5A could be detected while comparing mutant vs . parental viruses ( Fig 10A–10D ) , again underscoring the proximity of these different factors at assembly sites that appeared unaltered qualitatively . However , as compared to parental virus , we found a reduced NS5A co-immuno-precipitation with NS2 in cells expressing the Jc1 HAHALp7 or other p7 ATMI mutant viruses ( Fig 10E; S9C Fig ) , which was restored upon co-expression with wt p7 ( Fig 10E ) . Altogether , these results suggested that the p7 amino-terminus determines the fine-tuning of the interactions between HCV glycoproteins , NS5A and NS2 required for envelopment of viral particles at assembly platforms . Because of their intrinsic capacity to be routed to the cell surface , owing to their localization in the cell secretory pathway , the traffic and distribution of envelope glycoproteins need to be controlled , by e . g . , retention in specific intracellular compartments , in order to avoid immune detection of infected cells . Moreover , as these glycoproteins are synthesized in the ER lumen , counteracting ER stress activation in infected cells is important to avoid subsequent cell death . Since HCV proteins are expressed from a polyprotein , implying that all proteins are initially expressed at the same rate , its well-ordered cleavage may regulate the stability and functions of some proteins , such as for E2 and p7 that coexist with a E2p7 precursor ( Fig 2 ) of ill-defined functions [7–11] . Here , by designing mutants at E2-p7 junction , we show that the augmentation of E2p7 cleavage , which is mediated by signal peptidase [7 , 52] , induced an up-regulation of the levels of E2 in infected cells , by ca . 4–5 fold . This original phenotype is not caused by increased rates of replication or translation of such mutant viruses , judging from similar levels of viral RNAs or of non-structural proteins , respectively , for mutant vs . wt viruses . That both short peptide extensions and single alanine insertion before p7 structure induced E2p7 increased cleavage and E2 up-regulation at similar levels ( Figs 2C and 3C; S3A Fig ) argued against the possibility that p7 N-terminal modifications could per se change E2 expression . Consistently , co-expression of wt p7 with these mutant viruses did not restore wt E2 intracellular levels ( Fig 3C ) . Different possibilities may explain how E2p7 processing could modulate E2 expression . On the one hand , liberated E2 could be stabilized by its partners , such as e . g . , E1 [74 , 75] or SPCS1 [71] . On the other hand , E2 and E2p7 could activate or block different degradation pathways , such as autophagy or proteasome/lysosome . Indeed , HCV glycoproteins are known to be activators of the unfolded protein response ( UPR ) in HCV-infected cells [76] . Finally , it is also possible that the amounts of free p7 , which are likely higher for the p7 ATMI mutant viruses , may regulate these degradation pathways . In support of these assumptions , a previous study [77] indicated that a mutation of p7 reported to block cleavage between E2 and p7 ( p7-R ( K ) GR33-35AAA ) [9 , 11 , 13] induced E2 degradation . Likewise , mutants abrogating E2p7 cleavage , such as E2-A367R ( Fig 2 ) as well as p7-A1W , p7-E3W , p7-K4W , p7-A10W or p7-S12W [10] , exhibited poor E2p7 expression despite wt rates of replication , relative to parental viruses . Further studies will be necessary to clarify this issue . Regulation of the intracellular quantities of surface glycoproteins as well as their recruitment at virion assembly sites is crucial for production of infectious particles , which require optimal E1E2 incorporation levels to mediate entry into cells . Several cellular factors promoting the different steps of HCV assembly have been identified [3] and include factors allowing initial core and NS5A targeting at the LDs [78–82] , HCV particle assembly [71 , 83] , or fission of enveloped nucleocapsids [84 , 85] . As for viral factors , NS2 gathers assembly components at ER-localized sites near LDs and replication complexes [13 , 15 , 16 , 86] , at detergent-resistant membranes ( DRM ) areas [9 , 39] . Specifically , NS2 interaction with E2 and E1 as well as with other viral factors—p7 , NS3 and NS5A , is believed to be key for virion biogenesis [9 , 13–15 , 71 , 84] . Yet , independent of this capture mechanism , E1E2 glycoproteins have the intrinsic capacity to induce SVP formation ( Fig 1 ) [24] , which implies a competition for their recruitment at the assembly sites of infectious particles . Noteworthy , E2/NS2 interaction depends on SPCS1 , one of the 5 subunits of signal peptidase , and abrogation of E2/NS2/SPCS1 triple interaction via SPCS1 silencing markedly reduced HCV assembly [71] . Our co-IP assays indicated that increasing E2p7 cleavage , concomitantly with augmented E2 expression and SVP formation , stimulated the interaction between E1E2 and NS2 ( Fig 9D and 9F; S10B Fig ) . A first , simple possibility to explain this stronger E1E2/NS2 association may involve the increase of E2 intracellular expression , which , incidentally , would lead to greater opportunities for E2/NS2 association . A second possibility could involve either a concentration of SPCS1 at the vicinity of E2 and NS2 , as a result of SPCS1 recruitment by the signal peptidase complex during E2p7 and p7NS2 processing , or , alternatively , of a preferential interaction of cleaved , liberated E2 with SPCS1 and hence , with NS2 . However , both possibilities would be difficult to reconcile with the finding that wt p7 co-expression , which did not restore wt E2 intracellular levels ( Fig 3C ) , restored normal levels of E1E2/NS2 interaction ( Fig 9D and 9F; S10B Fig ) . A third possibility is that the alteration of p7 N-terminus in our E2-p7 junction mutants may affect NS2 capacity to interact with some of its other partners , as discussed below . Unexpectedly , our results indicated that the increased E1E2/NS2 interaction correlated with reduced formation of infectious particles , in agreement with lowered secretion of nucleocapsids ( Fig 5C ) . In this respect , it is likely that a loss of viral particle formation would translate in an increase of E2 density on NS2 platforms ( Fig 9D and 9F ) because E2 would not be consummated in assembled and released virions . Our results indicate that the p7 N-terminus also determines HCV infectivity by controlling the secretion of enveloped vs . naked/partially enveloped core particles ( Fig 6B and 6D ) . This is in agreement with a previous report showing that p7 regulates the envelopment of nascent viral particles [40] . A recent study indicated that the first helix of p7 harbors a key determinant of HCV infectivity ( e . g . , V6 , H9 , S12 ) , as underscored by mutagenesis of these residues pointing toward the p7 channel pore [10] . Intriguingly , we reveal here for the first time , a novel determinant at the extreme amino-terminal end of p7 , i . e . , before its first helix ( S1 Fig ) , that strongly modulates infectivity ( Fig 2 ) and the relative amounts of enveloped vs . non/partially-enveloped core particles ( Fig 6B ) . Furthermore , we demonstrate that changes in this amino-terminal p7 determinant ( p7 ATMI mutants ) , via e . g . , short peptide extensions , single amino-acid insertions or substitutions that did not alter p7 structure ( S1 Fig ) , induced a stronger reduction of infectivity ( Fig 2 ) than of secretion of enveloped core particles ( Fig 6D ) , resulting in reduced specific infectivity of the mutants , by 4- to over 50-fold depending on ATMI mutant types ( Fig 4G–4I; S5 Fig . Envelopment of viral particles pertains to a series of events that likely occur rapidly once two components , i . e . , surface glycoproteins and nucleocapsids , encounter at assembly sites following mobilization from their respective storage pools , i . e . , respectively , NS2 platforms apposed to LDs [9 , 39] and LDs/replication complexes [12] . Such events are difficult to catch experimentally , because they are transient by nature as they lead to quick release of assembled viral particle from such assembly sites within the ER lumen . How HCV core and RNA are transferred from LD surface to ER assembly sites to initiate the release of infectious , enveloped viral particles remains poorly defined [3 , 87] , although it involves concerted actions of p7 and NS2 [17] and of NS5A [18 , 20] . Accordingly , previously described assembly-defective mutants , such as p7-KR33/35QQ and core-C69-72A [40] , ∆p7 [65] or ∆E1E2 and NS5A-∆2328–2435 [20] , display strong core-LD accumulation , which correlates with their loss of infectivity . Strikingly , in contrast to these previous assembly mutants but similar to parental viruses , our Jc1 p7 ATMI mutants readily targeted core at the ER membrane ( S8 Fig ) despite reduced infectivity and did not significantly alter the co-clustering of structural and non-structural proteins with HCV RNA ( Figs 8–10 ) , both of which events previously shown to be critical for achieving efficient assembly [17 , 65 , 66] . Moreover , as shown by others , p7 regulates NS2 subcellular localization at punctate sites near LDs and its association with DRMs along with other viral proteins , including core , E2 , and NS3 [9 , 16 , 39 , 86]; yet , while other assembly-defective virus mutants , such as the p7-KR33/35QQ and p7-KR33/35AA in these previous reports , disrupted NS2 localization and/or E2/NS2 association , the p7 ATMI mutants displayed increased E1E2/NS2 interaction , compared to parental viruses . Along with the finding that our mutants exhibited wt capacity to slow down the cell secretory pathway ( Fig 1E ) , this underscores that the ATMI class of p7 mutants retains most p7 properties and inhibits viral assembly though a novel mechanism . Our results imply an envelopment defect caused by inadequate mobilization and/or transfer of core and RNA at E1E2-containing NS2 assembly platforms . This is reflected by our findings that such p7 ATMI mutants exhibited altered E1E2/NS2 and NS2/NS5A interactions ( Figs 9 and 10 ) but also that failure to mediate correct particle envelopment resulted in secretion of partially enveloped , proteinase K-sensitive core particles ( Fig 6B ) . Since co-expression of our mutant viruses with wt p7 restored the above alterations to almost normal levels , this questions about the role of p7 N-terminus in this mechanism and raises the possibility that it regulates core and RNA transfer to assembly sites and/or to assembling viral particles ( Fig 11 ) . Interestingly , a previous report suggested that p7 genetically interacts with some regions of NS2 as well as of NS5A [88] , strengthening the notion of a functional dialog between p7 , NS2 and NS5A . Our findings that the N-terminus of HA-tagged p7 points towards the cytosol ( Fig 7 ) support the likelihood that it mediates critical interactions with cytosolic factors promoting assembly . A possibility is that modifications of p7 N-terminal surface , before the first p7 helix ( S1 Fig ) , disrupted such interactions ( Fig 11 ) . In this respect , co-localization and association of NS2 with NS5A , which is decreased upon p7 deletion or alterations [13 , 16] , is thought as a crucial event mediating core/RNA transfer to assembly sites [18 , 20 , 66] . Thus , since our mutant viruses did not display altered co-localization of these assembly factors , it is likely that NS2 complexes formed with p7 ATMI mutants failed to efficiently mediate the encountering of nucleocapsids with NS2-bound viral surface components and/or to induce the release of fully enveloped , infectious viral particles . Why such failure resulted in a proportional release of partially enveloped core particles ( Fig 6B vs . Fig 6D ) , which could be similar to those that have been detected in the serum of infected patients [28] , is intriguing . It raises the possibility that the transfer of core and RNA to E1E2 glycoproteins liberated from NS2 complexes at assembly sites is associated to the recruitment of a mechanism that closes nascent particles ( Fig 11 ) . Such a mechanism , which likely involves components of the ESCRT pathway , as previously described for HCV [84 , 85] , could be disrupted by p7 ATMI mutants is such a way that , rather than being correctly closed , the budding membrane capsule would detach , allowing escape of imperfectly enveloped nucleocapsids in the ER lumen , which could explain their reduced specific infectivity . Indeed , as p7 ATMI mutants impair the interaction between NS2 and NS5A , this may prevent the recruitment of HRS , an ESCRT-0 component that interacts with p7 , NS2 and NS5A [84] and subsequently , of all ESCRT components required for correct envelopment . Our data indicate that the regulation of the amounts of free p7 could modulate the production of viral particles . Indeed , we found that p7 , which localizes at the ER ( Fig 7 ) [89] slows down the cell secretory pathway in a dose-dependent manner , likely at the stage of ER-Golgi transport ( Fig 1 ) . While this property is not intuitive , given that HCV , like other Flaviviridae , is thought to exit the cells through the secretion pathway [90] , this could either induce the concentration of its glycoproteins at virion assembly sites in the ER lumen through their active retention or reflect the cooptation of another pathway of secretion for HCV particles [22 , 23] . Furthermore , as HCV glycoproteins can be secreted as SVPs independently of other viral proteins ( Fig 1G ) [24] , this feedback loop may ensure that excess glycoproteins , arising from their release upon E2p7 cleavage , could be appropriately controlled so as to prevent activation of immune responses . Additionally , as indicated by the delayed transport of VSV-Gts used as a model cargo ( Fig 1B–1F ) , it is possible that p7 expression could alter the secretion of cellular proteins such as , e . g . , immune effectors , as shown for viroporins of other viruses [43 , 91] . The mechanism used by p7 to slow down the secretion of glycoproteins needs further investigation . Viroporins of alternative viruses have previously been involved in modulation of the secretory pathway , though through a variety of mechanisms [38] . For example , the M2 protein from influenza virus has a direct effect on late steps of plasma membrane delivery by delaying late Golgi transport , which indirectly affects the efficiency of earlier transport steps by altering the ionic content of the Golgi apparatus and the endosomes [92 , 93] . Alternatively , Coxsackievirus 2B proteins modify ER membranes , which inhibits protein processing and sorting by decreasing calcium homeostasis in ER and Golgi [43] . Likewise , p7 can change ionic gradients in both reconstituted membrane assays in vitro [30 , 44–47] and in cellulo [41 , 42] , which could affect anterograde transport and/or modify intracellular compartments . In conclusion , our report underscores the function of E2p7 delayed processing in modulating i ) the intracellular E2 levels , ii ) the retention of E2 through the slowing down of the secretion pathway , and iii ) the unmasking of functions of p7 amino-terminus in assembly and envelopment . Huh7 . 5 cells ( kind gift of C Rice , Rockefeller University , New York , USA ) and 293T kidney ( ATCC CRL-1573 ) cells were grown in Dulbecco’s modified minimal essential medium ( DMEM , Invitrogen , France ) supplemented with 100U/ml of penicillin , 100μg/ml of streptomycin , and 10% fetal bovine serum . pFK-JFH1wt_dg , pFK-JFH1/J6/C-846_dg plasmids encoding full-length JFH1 and Jc1 HCV [94] were kind gifts from R Bartenschlager ( Heidelberg University , Germany ) . pFK-JFH1J6XbaIC-846HAHA-L-p7_dg encoding a Jc1 virus with the HAHALp7 linker peptide between E2 and p7 [56] was kindly provided by T Pietschmann ( Twincore , Germany ) . JFH1 virus-derived constructs encoding the p7-T2 , p7-L2S , Ap7 and ASGGSp7 , HAHALp7 , and E2-A367R mutants were derived from the pFK-JFH1wt_dg plasmid . The E1 glycoprotein was also point-mutated in the pFK-JFH1wt_dg and pFK-JFH1 HAHALp7 constructs to introduce the A4 epitope , resulting in plasmids encoding JFH1 E1 ( A4 ) and JFH1 E1 ( A4 ) HAHALp7 viruses , respectively [57] . Constructs were created by PCR mutagenesis ( oligonucleotide sequences are available upon request ) . The plasmid pEGFP-N3-VSV-Gts was a kind gift from K Konan ( Albany Medical College , USA ) . The plasmids encoding noSPp7 ( JFH1 ) , ΔE2p7 ( JFH1 ) , ΔCp7 ( JFH1 ) , ΔCp7 ( H77 ) , ΔE2p7 ( J6 ) , ΔE2HAHALp7 ( JFH1 ) , and noSPHAHALp7 ( JFH1 ) allow individual expression of wt , variant or mutant p7 under different signal peptide configurations . The plasmids pTG 13077-HCV-ΔC-E1-E2-J6 , pTG 13077-HCV-ΔC-E1-E2-JFH1 and pTG 13077-HCV-ΔC-E1-E2-p7-JFH1 contain retroviral vector genomes encoding E1E2 and/or E1E2p7 proteins from J6 and JFH1 viruses . All constructs were expressed in Huh7 . 5 cells using procedures reported before [17] . Mouse anti-actin ( clone AC74 , Sigma-Aldrich ) , mouse anti-E1 A4 ( kind gift from HB Greenberg ) , rat anti-HA ( clone 3F10 , Roche ) , mouse anti core C7-50 ( Thermo Fisher Scientific ) , rat anti-E2 clone 3/11 ( kind gift from J McKeating ) , mouse anti-NS2 6H6 and mouse anti-NS5A 9E10 ( kind gift from C Rice ) , rabbit anti-NS2 ( kind gift from B Lindenbach ) , human anti-E2 AR3A ( kind gift from M Law ) , mouse anti-GFP ( Roche ) , anti-VSV-G 41A1 , mouse anti-E2 antibody AP33 ( kind gift from A Patel ) were used according to the providers’ instructions . Huh7 . 5 cells were seeded 16h prior to transfection with pEGFP-N3-VSV-Gts and p7-encoding plasmids using GeneJammer transfection reagent ( Agilent ) . Medium was changed 4h post-transfection and cells were incubated overnight at 40°C . 24h post-transfection , cells were chased at 32°C . For western blot analysis , cells were lysed at indicated time points in wells cooled on ice before clarification and western blot analysis . For flow cytometry analysis , cells were harvested and put in suspension at 32°C . At indicated time points , cells were fixed with 3% paraformaldehyde . The plasmid popol-ΔE1sE2 ( JFH1 ) -H6 ( kind gift from Epixis SA ) encoding soluble E2 ( JFH1 ) with a 6xHis tag was used to purify E2 in order to assess the sensitivity of E2 quantifications by western blots . 293T cells grown in 10 cm-plates were transfected with 15μg of popol-ΔE1sE2 ( JFH1 ) -H6 . 16h post-transfection , the medium was replaced by OptiMEM . 24h and 48h later , supernatant was harvested and purified using a HisTrap column . Fractions were pooled and then dialyzed . A sample was analyzed by SDS-PAGE . Concentration of sE2 was obtained by measurement of OD at 280nm and purity was analyzed by LC-MS/MS . HEK293T cells were seeded 24h prior to transfection with VSV-G plasmid , pTG-5349 packaging plasmid , and either pTG 13077-HCV-ΔC-E1-E2-JFH1 , pTG 13077-HCV-ΔC-E1-E2-J6 or pTG 13077-HCV-ΔC-E1-E2-p7-JFH1 plasmids using calcium phosphate precipitation . Medium was replaced 16h post-transfection . Vector supernatants were harvested 24h later , filtered through a 0 . 45 μm filter , and were titrated by flow cytometry using AP33 antibody against E2 . Lentiviral vectors were used to transduce Huh7 . 5 cells ( MOI = 2 ) . 72h post-transduction , cell supernatants were centrifuged at 25 , 000 rpm for 4h at 4°C using SW41 rotor and Optima L-90 centrifuge ( Beckman ) . Pellets were suspended in PBS prior to use for western blot analysis . For gradient analysis , 1 ml of supernatant concentrated 40x by Vivaspin columns ( MW cut-off 100-kDa ( Sartorius ) ) was loaded on iodixanol density gradients . 12 fractions were collected from the top and used for refractive index measurement and precipitation of proteins before western blot analysis . Methods for in vitro transcription of HCV RNA and its electroporation into Huh-7 . 5 cells have been described [17 , 61] . When p7 was co-expressed with viral RNA , 2μg of plasmid DNA encoding p7 or control DNA were co-electroporated with 10μg of viral RNA . To determine the percentage of HCV-positive producer cells following electroporation , cells were fixed and stained using Cytofix/Cytoperm ( BD ) according to manufacturer's instructions . NS5A staining was achieved with 9E10 antibody ( kind gift from C . Rice , Rockefeller University , New York , USA ) and cells were analyzed using MacsQuant VYB ( Milteny Biotech ) . Electroporated cells were counted and 100 , 000 cells were lysed in lysis buffer ( 20 mM Tris [pH 7 . 5] , 1% Triton X-100 , 0 . 05% sodium dodecyl sulfate , 150 nM NaCL , 5‰ Na deoxycholate ) supplemented with protease/phosphatase inhibitor cocktail ( Roche ) and clarified from the nuclei by centrifugation at 13 , 000×g for 10 min at 4°C for quantitative western blot analysis ( see below ) . HCV core protein was also quantified by CMIA—Chemiluminescent Microparticle ImmunoAssay ( Architect , Abott ) . The extracellular E2 protein was quantified by Western Blot after precipitation of E2-containing cell supernatants with Galanthus Nivalis lectins ( GNA ) bound to agarose beads ( Vector Laboratories ) . The extracellular HCV RNAs were quantified as described previously [61] . Infectivity titers were determined as focus-forming units per milliliter [17] . Serial dilutions of supernatants were used to infect Huh7 . 5 cells and focus-forming units were determined 3 days post-infection by counting NS5A-immunostained foci . For determining intracellular infectivity , electroporated cells were washed with PBS , harvested with Versene and centrifuged for 4 min at 400xg . Cell pellets were suspended in medium and subjected to 4 cycles of freeze and thaw , using liquid nitrogen . For purification of particles , supernatants were harvested and filtered through a 0 . 45μm filter and centrifuged at 25 , 000 rpm for 1h45 at 4°C with a SW41 rotor and Optima L-90 centrifuge ( Beckman ) . Pellets were resuspended in PBS prior to use for western blot to quantify E2 or for quantification of core and RNAs . Viral supernatants were i ) left untreated , ii ) treated with Proteinase K ( PK , 50 μg/mL ) in 10x PK buffer as described in [84] for 1h on ice , or iii ) pre-treated with Triton X-100 5min at room temperature prior to treatment with PK . PK activity was stopped by adding 10 mM PMSF and protease inhibitors cocktail ( Roche ) . The core protein was quantified with CMIA . 1mL of viral supernatant was loaded on top of a 3–40% continuous iodixanol gradient ( Optiprep , Axis Shield ) . Gradients were centrifuged for 16h at 4°C in Optima L-90 centrifuge ( Beckman ) . 16 fractions of 750 μl were collected from the top and used for refractive index measurement infectivity titration , core quantification and RNA quantification , as described above . For E2 protein analysis , HCV particles were produced in OptiMEM ( Invitrogen ) and concentrated 40x by Vivaspin molecular weight cutoff 100-kDa columns ( Sartorius ) . 1 ml of concentrated virus suspension was loaded on density gradients . 12 fractions were collected from the top and used for refractive index measurement , titration , core quantification and RNA quantification , as described above . The remaining volumes of fractions were used for protein precipitation with 4 volumes of acidified acetone/methanol buffer and left at -20°C overnight . Proteins were pelleted at 16 , 000xg for 15min and dried before resuspension in lysate buffer , denaturation in Laemmli buffer , and Western Blot analysis . Endoglycosidase Hf ( Endo-Hf; NEB ) treatment was performed according to the manufacturer's recommendations . Briefly , protein samples were mixed to denaturing glycoprotein buffer and heated at 100°C for 5 min . Subsequently , 1 , 000 units of Endo-Hf were added to samples in a final volume of 25 μl and the reaction mixtures were incubated for 1 h at 37°C , before western blot analysis . Proteins obtained in total lysates or after digestion or immunoprecipitation , were denatured in Laemmli buffer at 95°C for 5min and were separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis , then transferred to nitrocellulose membrane and revealed with specific primary antibodies , followed by the addition of IRdye secondary antibodies ( Li-Cor Biosciences ) , followed by imaging with an Odyssey infrared imaging system CLx ( Li-Cor Biosciences ) . For NS2/E2 interaction , 1 million electroporated cells were lysed with buffer ( 50 mM Tris-Cl ( pH 7 . 5 ) , 150 mM NaCl , 1% Nonidet P-40 , 1% sodium deoxycholate , and 0 . 1% SDS ) . Lysates were cleared by centrifugation at 16 , 000xg for 10 min at 4°C and were incubated overnight at 4°C with AR3A antibody against HCV E2 or with rabbit NS2 antibody . Protein A/G-coated agarose beads were added to samples for 2h at room temperature . Immune complexes were then washed and eluted with Laemmli buffer for 5 min at 95°C before western blot analysis . For NS2/NS5A interaction , 1 million electroporated cells were cross-linked with 1mM dithiobis ( succinimidyl propionate ) ( DSP ) ( ThermoFisher ) 30min at room temperature . Tris ( pH 7 . 5 ) was added up to 200 mM to quench unreacted DSP . Cells were resuspended in lysis buffer ( 50mM Tris pH 7 . 4 , 150mM NaCl , 1mM EDTA , 0 . 5% n-dodecyl-β-maltoside ) and treated as for NS2/E2 interaction with incubation with rabbit NS2 antibody . Experimental procedures were previously described [66] . Briefly , Huh7 . 5 cells grown on glass coverslips and were infected at MOI of 0 . 2 . 72h post-infection , cells were washed with PBS , fixed with 3% paraformaldehyde in PBS for 15min , quenched with 50mM NH4Cl and permeabilized with 0 . 1% Triton X-100 . Fixed cells were then incubated with primary antibodies in 1% BSA/PBS , washed and stained with the corresponding fluorescent Alexa-conjugated secondary antibody ( Alexa-488 , Alexa-555 and Alexa-647 , Molecular Probes ) in 1% BSA/PBS . LDs were stained with 10μg/mL Bodipy 493/503 ( Molecular Probes ) according to the manufacturer’s instructions . Cells were washed with PBS , stained for nuclei with Hoechst ( Molecular Probes ) and mounted with Mowiol 4–88 ( Sigma-Aldrich ) prior to image acquisition with LSM-710 ( Zeiss ) confocal microscope . When stated , the combined detection of HCV RNA by FISH and viral proteins was done as previously described [66] . For digitonin permeabilization , the staining procedure was the same except that cells were permeabilized with 5μg/ml Digitonin ( Sigma-Aldrich ) for 10 min . Cells permeabilized with Triton X-100 were acquired first; then , cells permeabilized with Digitonin were acquired in order to use the same laser settings . Images were analyzed and quantified with the ImageJ software as previously described [66] . The Pearson’s and Manders’ correlation coefficients were calculated by using the JACoP plugin [95] . For the Digitonin vs . Triton permeabilization experiments , the relative fluorescence intensity of each channel was quantified by using the integrated density measurement of ImageJ software . Three-dimensional homology models of p7 hexamers and their mutants were constructed using the NMR/MD p7 model of Chandler and colleagues [36] and the NMR p7 structure of OuYang and colleagues [35] ( PDB accession number 2M6X ) as templates . Models of p7 were constructed with the Swiss-Model automated protein structure homology modeling server ( http://www . expasy . org/spdbv/ [96] ) using the HCV JFH1 strain p7 sequence as input . JFH1 p7 homology model derived from OuYang et al . was directly obtained as a hexamer by the automated procedure . For JFH1 p7 homology model derived from Chandler et al . , raw amino-acid sequence of p7 from strain JFH1 was first loaded in Swiss-PdbViewer software [96] and fitted to the NMR/MD p7 hexamer model [36] before submission for model building to Swiss-Model using the SwissModel Project Mode . All p7 JFH1 mutants were constructed using the latter protocol , i . e . , fitting of the raw amino-acid sequence of p7 mutants to wild type hexamer models from the JFH1 strain . Coordinates of homology models derived from the automated model building were used without further minimization or manual manipulation . For mutants Ap7 and ASGGSp7 exhibiting N-terminal extensions , additional residues were added manually assuming a random conformation and were minimized using Swiss-PDB Viewer tools . Significance values were calculated by applying the paired t-test using the GraphPad Prism 6 software ( GraphPad Software , USA ) . For confocal analysis , a two-tailed , unpaired Mann-Whitney test was applied . P values under 0 . 05 were considered statistically significant and the following denotations were used: **** , P≤0 . 0001; *** , P≤0 . 001; ** , P≤0 . 01; * , P≤0 . 05; ns ( not significant ) , P>0 . 05 .
Viroporins are small transmembrane viral proteins with ion channel activities modulating properties of intracellular membranes , which impacts several fundamental biological processes such as trafficking , ion fluxes as well as connections and exchanges between organelles . Hepatitis C virus ( HCV ) encodes a viroporin , p7 , acting during assembly , envelopment and secretion of viral particles . HCV p7 is produced by cleavage from the HCV polyprotein but also exists as an E2p7 precursor , of poorly defined properties . In this study , we have explored how the retarded cleavage between E2 glycoprotein and p7 viroporin could regulate their functions associated to virion assembly and/or perturbation of cellular membrane processes . Specifically , we demonstrate that p7 is able to regulate the cell secretory pathway , which induces the intracellular retention of HCV glycoproteins and favors assembly of HCV particles . Our study also identifies a novel assembly function located at p7 amino-terminus that is unmasked through E2p7-regulated processing and that controls the infectivity of different types of released viral particles . Altogether , our results underscore a critical post-translational control of assembly and secretion of HCV particles that governs their specific infectivity .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]
[ "protein", "transport", "medicine", "and", "health", "sciences", "immune", "physiology", "pathology", "and", "laboratory", "medicine", "intracellular", "pathogens", "hepacivirus", "pathogens", "immunology", "cell", "processes", "microbiology", "viral", "structure", "viruses", "physiological", "processes", "rna", "viruses", "antibodies", "immune", "system", "proteins", "viral", "core", "proteins", "medical", "microbiology", "microbial", "pathogens", "hepatitis", "c", "virus", "hepatitis", "viruses", "virions", "biochemistry", "protein", "secretion", "cell", "biology", "flaviviruses", "virology", "physiology", "viral", "pathogens", "secretion", "biology", "and", "life", "sciences", "organisms" ]
2017
The amino-terminus of the hepatitis C virus (HCV) p7 viroporin and its cleavage from glycoprotein E2-p7 precursor determine specific infectivity and secretion levels of HCV particle types
The response of a neuronal population over a space of inputs depends on the intrinsic properties of its constituent neurons . Two main modes of single neuron dynamics–integration and resonance–have been distinguished . While resonator cell types exist in a variety of brain areas , few models incorporate this feature and fewer have investigated its effects . To understand better how a resonator’s frequency preference emerges from its intrinsic dynamics and contributes to its local area’s population firing rate dynamics , we analyze the dynamic gain of an analytically solvable two-degree of freedom neuron model . In the Fokker-Planck approach , the dynamic gain is intractable . The alternative Gauss-Rice approach lifts the resetting of the voltage after a spike . This allows us to derive a complete expression for the dynamic gain of a resonator neuron model in terms of a cascade of filters on the input . We find six distinct response types and use them to fully characterize the routes to resonance across all values of the relevant timescales . We find that resonance arises primarily due to slow adaptation with an intrinsic frequency acting to sharpen and adjust the location of the resonant peak . We determine the parameter regions for the existence of an intrinsic frequency and for subthreshold and spiking resonance , finding all possible intersections of the three . The expressions and analysis presented here provide an account of how intrinsic neuron dynamics shape dynamic population response properties and can facilitate the construction of an exact theory of correlations and stability of population activity in networks containing populations of resonator neurons . Integration and resonance are two operational modes of the spiking dynamics of single neurons . These two modes can be distinguished from each other by observing the neuron’s signal transfer properties: how features in its input current transfer to features in its output spiking . The traditional approach to investigating neuronal transfer properties is to measure the stationary response: the time-averaged rate of firing of spikes as a function of the mean input current , or fI-curve . In Hodgkin’s classification [1] , Type I membranes can fire at arbitrarily low rates , while the onset of firing in Type II membranes occurs only at a finite rate . This distinction arises naturally from the topology of the bifurcations that a neuron can undergo from resting to repetitive spiking [2] . In many central neurons , it is fluctuations rather than the mean input current that drive spiking , putting them in the so-called fluctuation-driven regime [3] . Many dynamical phenomena are nevertheless tightly linked to excitability type . For example , Type II neurons exhibit rebound spikes , subthreshold oscillations and spiking resonance ( e . g . mitral cells , [4–6] , respectively ) . The qualitative explanation for these phenomena is that the dynamical interplay of somatic conductances endow some neurons with a voltage frequency preference , i . e . a subthreshold resonance . This preference can contribute to a superthreshold resonance in the modulation of their output spiking [7] . How dynamic response properties of spiking dynamics such as resonance emerge can be directly assessed by considering the neuron’s dynamic gain . Dynamic gain , first treated by Knight [8] , quantifies the amount by which features at specific frequencies in the input current to a neuron are amplified or attenuated in its output spiking . It can accurately distinguish functional types and unveil a large diversity of phenomena shaping the response to dynamic stimuli [9–18] . Dynamic gain and response are also essential ingredients for theoretical studies of network dynamics in recurrent circuits [8 , 12 , 13 , 18–49] . First , they determine the stability of the population firing rate dynamics [21 , 25 , 26] . Second , they determine how input correlations between a pair of cells are transferred to output correlations [28 , 42 , 44–49] , from which self-consistent relations for correlations in recurrent circuits can be obtained . Experimental studies have started over the past years to use dynamic gain measurements to investigate the encoding properties of cortical neuron populations [9–18] . Although theoretical studies have investigated many neuron models , very few models are known for which dynamical response can be explicitly calculated . One basic reason for this lies in the fact that Fokker-Planck equations for neuron models with two or more degrees of freedom are not solvable in general [50] . For Type II neuron models that require at least two degrees of freedom , no solvable model is known . The simplest model capable of subthreshold resonance was introduced by Young [51] in the early theories of excitability . Later , Izhikevich formulated a structurally similar neuron [52] . Richardson and coworkers performed the first calculation of the linear response function of a neuron model capable of resonance , the Generalized Integrate-and-Fire ( GIF ) neuron [22 , 29] . Only in the limit of relatively slow intrinsic current time constant can analytical expressions for the GIF response be obtained , however . The distinct transfer properties of resonant vs . non-resonant dynamics leads to different information transfer properties . While this has been demonstrated in the mean-driven regime [53 , 54] , no such results exist for the fluctuation-driven regime , in part due to a lack of exact analytical expressions for even the linear dynamic gain . Type II excitability and dynamic response thus are representative of the more general challenge posed by response properties of neurons with complex intrinsic dynamics . In the current study , we derive and analyze the linear response function in the fluctuation-driven regime of a neuron model capable of resonance . We express it as a filter cascade from current to voltage to spiking . It is valid across all relevant input frequencies and over all relevant values of the intrinsic parameters . In particular , we apply to the GIF neuron model the Gauss-Rice approach in which the voltage reset after a spike is omitted . The methods generalize to additional intrinsic currents and to the full nonlinear response with spike generation . To understand how subthreshold features interact to determine a neuron’s filter characteristics , including resonance , we provide a two-dimensional representation of the response properties that completely characterizes all possible filter types . For this idealized model , we determine analytically and numerically a wide and biologically-relevant regime of validity of the derived expression . The paper begins with the definition of the model and its numerical implementation . We then derive a general expression for the linear response in the mean channel of a Gauss-Rice neuron . In the next section , the analytical results for the response properties of the Gauss-Rice GIF neuron model are obtained . The final section then presents an analysis of the expression . For the sake of mathematical clarity , most calculations appear in the main text; the rest , including an exposition of model assumptions , are contained in the Methods . We consider the most simple hard-threshold , no-reset , GIF-type neuron capable of exhibiting resonator dynamics , whose response properties have been partially studied in [25] . A reset version of this model is treated in [29] , where the population spiking response properties were calculated assuming large intrinsic time constant . In the Methods , we present a more detailed exposition of the model assumptions , and justify an additional simplification of the voltage reset after a spike . The feature that distinguishes the GIF model from the classical Leaky Integrate-and-Fire ( LIF ) model is that the dynamics of the voltage , V , is coupled to an intrinsic activity variable , w , τ V V ˙ = − V − g w + I s y n τ w w ˙ = V − w , ( 1 ) where g is a relative conductance and τV and τw are the respective time constants of the dynamics . The notation x ˙ denotes the derivative with respect to time of the variable x . Spikes are emitted at upward crossings of a threshold , θ . Synaptic current modeled by Isyn drives the model whose dynamics are kept stable by keeping g > −1 . When g < 0 , w is depolarizing . When g > 0 , it is hyperpolarizing and can lead to resonant voltage dynamics . Response theory captures the population response to input signals with arbitrary frequency content and so we now turn to it , and linear response theory in particular , in the pursuit of understanding the population firing rate dynamics of the GIF neuron model . The formal , implicit definition of the linear response function , ν1 ( ω ) , arises from a weak oscillatory modulation of amplitude A and frequency ω in the mean input , and an expansion of the response , ν ( t ) , in powers of A , ν ( t ) = ν 0 + ν 1 ( ω ) A e i ω t + O ( A 2 ) , ( 9 ) where ν0 is the stationary response , Eq ( 6 ) . In the Methods , we restate how the linear response can be obtained ( Eq ( 59 ) ) directly from the spike times using the complex response vector , r ( ω ) ≔〈 e−iωtm 〉m . Below , we show the classic formulation that shows that it can also be obtained from the voltage dynamics . In this section , we take the general result of the previous section , Eq ( 23 ) , and go through its explicit calculation for a population of Gauss-Rice GIF neurons to obtain the result Eq ( 41 ) . A work taking a similar approach , partly inspired by this work , though with with less intermediate analysis has recently appeared [25] . Our novel findings arise from an exhaustive characterization of the parameter dependence across the phase diagram of the voltage response , Fig 3 . We calculate the current-to-voltage filter , expressing it in each of the three representations listed in Table 1 , Eqs ( 25 , 26 and 27 ) respectively . We show ( Fig 4 ) how the low pass component of the filter undergoes a qualitative change from second-order low pass to first-order low pass to resonant as QL is increased . We find the voltage resonance condition , ω L τ w > Q L - 2 - 1 , where the resonance has a contribution from slow adaptation and from the frequency , Ω . Either can exist without the other ( Fig 5 ) . We then compute the voltage correlation function , Eq ( 37 ) ) , whose envelope depends on the relaxation time , τr ( Fig 6 ) . From this , the variances are calculated and an expression for the differential correlation time , τs , Eq ( 39 ) is obtained . We show a characteristic dependence on the ratio τw/τI ( Fig 7 ) . Finally , we show in Fig 8 how the stationary firing rate has unimodal dependence on the time constants , τV and τI , monotonic rise with input variance , σ I 2 , and monotonic decay with intrinsic frequency , Ω . In this section , we characterize the qualitative features of the response function , Eq ( 41 ) , again with a focus on completeness . We first show that the high and low input frequency limits of the response constrain the parameter sets that can achieve high and low pass behavior and we give an expression , Eq ( 45 ) , of the critical stationary rate separating these two regions in terms of the other parameters . We then reparametrize the expression for the response , Eq ( 47 ) , using the height of the response at its center frequency , νωL and high frequency limit , ν∞ , both relative to its low frequency limit . The two-dimensional shape parameter space give responses with a peak , dip or step at ωL whose width varies with QL . The additional high or low pass nature of the filter give six classes of filter shape . The constraint of stable voltage dynamics restricts the area accessible to the model to νωL ≥ QL ( 1 + ν∞ ) . Neuron models with hard-thresholds , such as the LIF and GIF , have been unexpectedly successful in modeling cortical neurons [58] . They are obtained from more complex models by a series of reductions . In Methods , we gave a rationale for the reduction to a no-reset , hard-threshold model , where we state the additional limitations imposed by lifting the voltage reset . First , these models do not apply to mean-driven situations and so do not cover phenomena such as the masking of a subthreshold resonance by a resonance at the firing rate [29] . Second , to avoid extremely bursty spike patterns , we extend previous work [19] and argue that the correlation time of the input , τI , and the correlation time of the voltage statistics , τs , can not be too different . This precludes analysis involving white current noise but implies that satisfaction depends additionally on intrinsic parameters through their dependence on τs . For example , since τs ≤ τV , the rough validity condition 1 ∼ τs/τI ≲ τV/τI so that the timescale of the input fluctuations , τI , should not be much slower than the membrane time constant , τV . Third , for correspondence with threshold models the voltage relaxation time , τr , should fall within the average inter-spike interval , ν0τr ≪ 1 . Last , these models should only be considered in the irregular firing regime , ν0τs ≪ 1 . We found that τr ≤ τs for τw > τI , so that this last constraint is in fact implied by the combination of the second and third . To verify the validity of the no-reset model within the prescribed range , we made a direct , quantitative comparison to a canonical model with an active-spike generating mechanism . The dynamic gain of the two models coincides up to the high frequency limit , flimit , beyond which the low pass effect of the finite action potential rapidness dominates . Thus , all of the 6 distinct types of response shapes are altered by additional low pass behavior at high frequencies . For a previously used value of the rapidness , the intermediate frequency behavior is affected , while for a higher , and perhaps more accurate value it is not , and the artificially flat high frequency response is brought down by the realistic finite onset rapidness . In summary , these results show that the simplification to a no-reset , hard threshold is an adequate approximation when response features are slower than the speed of action potential onset . A topic of related future work regards the possibility of accelerated kinetics of auxiliary currents during a spike [59] . To study such a scenario , one could numerically compute the gain for a model where the auxiliary current , w , undergoes a jump at spike times . In this study of the Gauss-Rice GIF neuron and a previous on the Gauss-Rice LIF [42] , exponentially-correlated Gaussian noise was used as an example of a Gaussian input statistics with non-trivial temporal correlations . These input statistics will not in general produce self-consistent firing statistics . It is therefore important to note that the approach to the linear response taken here admits arbitrary temporal correlations in the input , so long as their effect on the short-delay features of the temporal correlation of the voltage can be calculated , since that is what determines τs and thus the effect of temporal correlations on the response properties . We also note that since the voltage correlation affects the response properties only through τs , there is an equivalence class structure over the space of input correlation functions based on how they influence τs . Excitable membranes are classified by the type of bifurcation that they undergo from resting to spiking , with Type I and II referring to super and sub critical Hopf bifurcation , respectively . The respective set of eigenvalues around the resting state are real and complex , with the imaginary part of the latter providing an intrinsic frequency . In this case , the voltage impulse response exhibits decaying oscillations and the voltage response function can exhibit a resonant peak near the intrinsic frequency . The mean-driven stationary spiking response rises continuously from 0 for Type I while firing in Type II neurons begins only at a finite frequency . The dynamic gain of the spiking response of Type II neurons can exhibit a superthreshold resonance arising from such subthrehsold resonance . Frequency-sweeping ZAP input currents have revealed resonant responses from neurons in the inferior olive [60 , 61] , thalamus [62] , hippocampus [63] , and cortex [64] . Consistent with the type classification , these cells often display Type II membrane excitability properties such as subthreshold oscillations with power at similar frequencies as the spiking resonance ( for a review , see ref . [7] ) . Type II stationary spiking responses have been measured in cortical interneurons [65] . Direct measurements of the dynamic gain of resonator neurons are lacking , however . Moreover , these existing measurements used the mean input to drive the neurons to spike . Resonator response properties in the in vivo fluctuation-driven regime remain unmeasured . Numerical simulations of resonator models containing the minimally required currents can nevertheless reproduce the peaked voltage and ZAP response and bimodal ISI distributions in both mean and fluctuation-driven regimes [66–68] . Inspired in part by the research presented here , Tchumatchenko and Clopath [25] used similar methods as those used here on excitatory and inhibitory GIF networks where they investigated the role of subthreshold resonance and electrical synapses on the emergence of network oscillations for a particular choice of model parameters , in which they also confirm the correspondence between the response properties with and without voltage reset . The remaining few analytical results for the stationary and linear response have so far been restricted to the long intrinsic time constant limit , τw ≫ 1 [22 , 29] . In this paper , we are able to obtain exact results for the stationary and linear response for all values of τw , something not possible in ref . [22] due to the difficulty of the analytics of the Fokker-Planck approach used there . For large τw and the fluctuation-driven regime , our results qualitatively match their high noise results , where σI ∼ 0 . 1 − 1 . Since Gauss-Rice models apply only to the fluctuation-driven regime , there is no meaningful mean-driven , deterministic limit attained in the limit of vanishing noise strength with which to compare to the mean-driven results of [22] , such as the shift in the resonant frequency with increasing , small amounts of noise . Their phase diagram of subthreshold behavior is essentially the same as ours , up to reparametrization . We also note that the low frequency limit will differ slightly between the models due to the slightly differing slopes of their fI-curves . These small quantitative discrepancies between idealized models should not , however , be emphasized over their ability to provide a qualitative explanation of the phenomena . Finally , we note the GIF Spike-Triggered Averaged can be obtained from our expression for dynamic gain . It has also been computed through other methods [69] . Explicit expressions for the linear response , such as Eq ( 41 ) obtained above , are essential ingredients for the analysis of the collective states in recurrent networks . First , they are the key quantity in the evaluation of population stability [21] . The dynamics of the population firing rate linearized around one of its fixed points is defined by the linear response function . Second , knowledge of the response function additionally reveals the correlation gain in the mapping of input current correlations to output spiking correlations . Recurrent networks exhibiting such gain will generate self-consistent patterns of inter-neuron correlations [47 , 49 , 70] . In the Gauss-Rice approach used here , the linear response providing the population stability and correlation gain is tractable for arbitrary Gaussian input current . Many networks generate such input statistics , most prominently balanced networks [71 , 72] . We expect that the correlation gain and population firing rate stability of these networks can be theoretically investigated using the expressions for the linear response derived here . One target application area is in understanding the connection between circuit oscillations and single cell excitability . Subthreshold resonance is often neglected in modeling studies of the PING and ING mechanisms for population oscillations [73] . This is despite the ample suggestive evidence of phase locking between subthreshold oscillations and gamma band population oscillations [7] . This connection has been studied in the olfactory bulb where mitral cells display a host of resonator properties such as subthreshold oscillations [5 , 6] , rebound spikes [74] , and Type II phase resetting curves [75] . The role of this resonance in sustaining the population oscillation has not been directly assessed in detailed network models of resonating mitral cells [76] , though it should play a role in either of two existing hypotheses for the origin of the oscillations [77] . Combining subthreshold and PING mechanisms has been studied in other contexts [78] . The demonstrated subthreshold resonance in inhibitory interneurons in cortex likely also contributes to the population oscillation observed there ( as suggested by the numerical results of [79] and [78] ) and could be investigated using the expression for dynamic gain that we provide . A first of such studies inspired by an unpublished version of the work presented here has already appeared [25] , where the Gauss-Rice GIF response gain was also derived . Finally , an ad hoc dynamic response filter of the same form as the one derived here [80] has been successful in modeling responses of cortical neurons ( personal communication O . Shriki ) . The explicit dependence in our derived expression on the parameters of an underlying neuron model can be used to extend those studies , in particular , by inferring from the fitted values the properties of the intrinsic dynamics of the measured cells . The differential correlation time , τs , was used in a variety of ways throughout this paper . First , it appeared in expressions for other important quantities in the theory . It appears most prominently in our expression for the fluctuation-driven voltage autocorrelation function for exponentially-correlated Gaussian input current . The result for a Type II GIF , Eq ( 37 ) , gives exponentially enveloped , oscillatory decay , with a decay constant equal to the relaxation time of the model and oscillation frequency given by the intrinsic frequency , Ω . Despite these oscillations , we find that the dynamic gain depends only on the initial falloff behavior away from 0-delay , a feature that can be shown to define , τs . From the perspective of the response then , voltage correlation functions differ only insofar as they exhibit different τs . The characteristic time , τc , and thus also the attenuation of the spiking filter scales linearly with τs , influencing the high or low pass nature of the filter accordingly . Second , τs appears in the validity conditions for the model . Namely , the range of valid firing rates for all Gauss-Rice neurons must lie below τ s - 1 . Third , model parameters such as the intrinsic time scale , τw , have an effect on dynamic response features , such as the high and low frequency limits , only through τs . The analysis of their effect on τs provides insight as to their role in sculpting the response properties . In Fig 7c for example , τs grows with τw , and for large τw saturates at τs , GIF → τs , LIF = τV , so that τs can only be made shorter , not longer , than the membrane time constant , τV , by intrinsic and synaptic current parameters . The central role of τs could be tested by applying a variety of input correlation functions with significant differences only away from the fall-off at 0-delay so that they provide the same τs . Our model predicts no significant change in the response properties . Such a large number of experiments could be performed by methods of high-throughput electrophysiology currently under development . We re-expressed the response expression , Eq ( 20 ) , using the center and high frequency response relative to the low frequency response , νωL and ν∞ respectively . We find six qualitatively distinct filter shapes distributed around ( 1 , 1 ) in the ( ν∞ , νωL ) plane , with the value of QL determining which of the six are accessible . Depending on the region there is a peak , dip or step at ωL whose width is determined by QL . We summarize below the constraints on the accessible shapes set by QL . For QL < 1/2 , all six filters shapes are possible for fast relative spiking ( τc < τw ) . There are no high pass resonating shapes in the limit of vanishing QL for slow relative spiking ( τc > τw ) . For Q L > ( - 1 + 5 ) / 2 ∼ 0 . 7 all accessible shapes have elevated response at the center frequency , νωL > 1 . For QL > 1 , all allowed filter shapes are resonating , that is νωL > ν∞ . There are no low pass resonating filters for slow relative spiking and so a sharp resonance , i . e . a high QL , is only possible when the overall filter is high pass . Neither voltage nor spiking resonance strictly imply the other in this model . First , there can be voltage resonance with no spiking resonance because the spiking high pass pulls up the response in the high input frequency range above the elevated response around the intermediate-range resonant input frequency . The high frequency limitation of the approach ( e . g . Fig 12 ) implies that the elevated response extends up to the speed of the action potential , leaving a broad resonant band at high input frequencies . Second , there can be spiking resonance with no voltage resonance because of a low frequency attenuation by the spiking high pass filter of a low pass current-to-voltage filter . In addition , neither voltage nor intrinsic resonance strictly imply the other . First , the existence of an intrinsic frequency does not imply voltage resonance in general because the response at ωL where Ω becomes finite is QL = 1/2 and is thus still attenuated relative to the response at low input frequencies . This response only becomes resonant at QL = 1 . Second , there can be a voltage resonance with no intrinsic resonance for the same reason that a high pass with low characteristic frequency ( this time from relatively slow intrinsic dynamics ) can sculpt a peak from the low pass component of the full filter . Finally , we found that the strength of the spiking resonance ( ∼νωL ) is composed of a contribution from the intrinsic timescale , τw and from the intrinsic frequency , Ω . Nevertheless , νωL is dominated by the attenuation at low input frequencies associated with the high pass effect of large τw , while the unique effect of Ω is to sharpen this resonance . The effect of spiking in the Gauss-Rice formulation of the response is as an explicit first-order high pass filter of the voltage dynamics ( see Eq ( 20 ) ) . We note that this high pass behavior associated with spiking is distinct from that discussed in the literature as arising from sodium channel inactivation [81] . This has nothing to do with the Gauss-Rice high pass arising in this paper . In this work , we always consider the threshold fixed . Closed form expressions are thus obtained for the low frequency limit and characteristic time of this filter in terms of the parameters of the model . When the characteristic frequency is high , the filter has the effect of flattening an otherwise decaying voltage response . The flattening effect is physiologically meaningful up to frequencies at which the spike-generator cut-off appears . It thus sculpts a plateau of constant response at high frequencies that can be elevated or depressed relative to the low frequency response . On the other hand , when the characteristic frequency is low , the resulting effect is a low frequency attenuation that carves out a resonant peak . The high pass characteristics are then also dependent on the intrinsic timescales . Here , we detail how one arrives at a model like the one used in this paper from simplifications made to the synaptic , subthreshold , spiking , and spiking reset currents of a Hodgkin-Huxley type neuron model for the dynamics of the somatic transmembrane voltage potential , V ( here measured in mV ) , C V ˙ = I m e m + I s y n ( 48 ) where C is the membrane capacitance , Imem is the sum of all membrane currents and Isyn is the total synaptic current arriving at the soma . Our exposition of the reductions to synaptic and subthreshold currents is standard . To the exposition of the reductions of spiking currents we add analysis determining the high frequency limit , flimit , below which the approximation to a hard threshold is valid . To the exposition of the reduction of reset currents , we add more detailed consideration of the mechanisms through which the no reset approximation breaks down . When the eigenvalues of the solution to the voltage dynamics are complex , we can re-express the denominator of Eq ( 25 ) using the intrinsic frequency , i . e . the imaginary part of the eigenvalues of the voltage solution . We first obtain the eigenvalues . For the linear matrix evolution operator B = - 1 τ V - g τ V 1 τ w - 1 τ w ( 55 ) the eigenvalues are obtained via the identity λ ± = tr B 2 ± 1 2 tr B 2 - 4 det B ( 56 ) = - 1 ± 1 - ( ω L τ r ) 2 τ r . ( 57 ) where tr B 2 = - τ r - 1 = - 1 2 ( 1 τ V + 1 τ w ) as the negative reciprocal of the harmonic mean of the two time constants , τr , and det B = ω L 2 = 1 + g τ w τ V where ωL is the center frequency of the voltage filter . When ωLτr < 1 , the magnitude τ r | λ ± | = | - 1 ± 1 - ( ω L τ r ) 2 | . When ωLτr > 1 , the eigenvalues are complex with r : = - τ r - 1 as the real part . We define the imaginary part that plays the role of the intrinsic frequency , Ω > 0 , via λ± = r ± iΩ , so Ω = ω L 2 - r 2 and now the magnitude is | λ ± | = r 2 + Ω 2 = ω L . We can substitute the expression for ωL , obtaining the relation between g and Ω , Eq ( 3 ) , τ V τ w Ω 2 = g - g c r i t ( 58 ) where g > g c r i t = ( τ V - τ w ) 2 4 τ w τ V is the condition for complex eigenvalues ( see Fig 1 ) . Here we rederive the linear relationship between the vector strength and the linear response . ν1 ( ω ) from Eq ( 9 ) can be expressed using the complex response vector , rk ( ω ) =1nk∑jnke−iωtjk=1nk∫−T2T2∑jnkδ ( t−tjk ) e−iωtdt≈1ν0T∫−T2T2∑jnkδ ( t−tjk ) e−iωtdt , where in the last step we use nk ≈ ν0T , good when T is made much larger than ν 0 - 1 . Taking the ensemble average , 〈 rk ( ω ) 〉=1ν0T 〈 ∫−T2T2∑jnkδ ( t−tjk ) e−iωtdt 〉=1ν0T ∫−T2T2〈 ∑jnkδ ( t−tjk ) 〉e−iωtdt=1ν0T∫−T2T2ν ( t ) e−iωtdt≈1ν0T ∫−T2T2 ( ν0+ν1 ( ω ) Aeiωt ) e−iωtdt=Aν1 ( ω ) ν0 ∫−T2T2dtT〈 r ( ω ) 〉=Aν1 ( ω ) ν0 . Using the decomposition of the response into its gain and phase , ν1 ( ω ) = |ν1 ( ω ) |eiΦ ( ω ) , the dynamic gain is thus obtained from the norm of the ensemble-averaged response vector , called the vector strength , |ν1 ( ω ) |=ν0A| 〈 eiωtm 〉m | , ( 59 ) where here we have simplified the notation by having m run over all the spikes from the full ensemble . We computed this expression using the spike times obtained directly from numerical simulations of the stochastic dynamics generated by the neuron model . We use the result to confirm the validity of the analytical gain function derived below , whose utility goes far beyond the numerical result because it provides the explicit dependence on the model parameters . Rearranging the expression for ν0 and then substituting in the σI-dependent expression for the voltage fluctuations , σ , we have ν 0 = 1 2 π τ s e - 1 2 σ - 2 σ V 2 = - θ 2 2 log 2 π ν 0 τ s - 1 J 2 σ I 2 τ V τ I + 1 · 1 + α w τ w τ I τ V τ e f f α I + τ w τ I = log 2 π ν 0 τ s - 1 σ I 2 = - θ 2 2 τ V τ I + 1 J 2 · τ V τ e f f α I + τ w τ I 1 + α w τ w τ I log 2 π ν 0 τ s . ( 60 ) When we study the model’s behavior we will use this relation to set the input variance for a chosen output firing rate so that the dimensions of the parameter space to be explored are the four time scales in the problem , ( τV , τeff , τw , 1/ν0 ) and when Ω exists , ( τV , 1/Ω , τw , 1/ν0 ) . The firing rate response derived in this paper allows us to compute the response to any weak signal and we demonstrate that in this section where we derive the response to step-like input . The time-domain version of linear frequency response , ν1 ( t ) , is the impulse response function , which when convolved with any input times series gives the corresponding response time series , ν ( t ) = ν 0 + ∫ ν 1 ( t ) I ( t - t ′ ) d t ′ , where ν 1 ( t ) = F - 1 [ ν 1 ( ω ) ] has units of [Time]−2[Current]−1 . If there is an accessible frequency representation of the input , the interaction can be made in the frequency domain and then the result transformed back to the time domain , ν ( t ) = ν 0 + F - 1 [ ν 1 ( ω ) I ( ω ) ] . ( 61 ) We used this definition to study the response to step-like input , I ( t ) = AΘ ( t ) , with step height , A , and with frequency domain expression for the Heaviside theta function , Θ ( ω ) = π δ ( ω ) - i ω . ( 62 ) Applying the inverse Fourier transform to the product of this with the linear frequency response gives the expression for the response . The relative response is then , ν ( t ) - ν 0 ν 0 = A D sgn ( t ) | λ ± | 2 + Θ ( t ) λ + - λ - ∑ j = + , - ( 1 + τ c λ j ) ( 1 + τ w λ j ) λ j e ( λ j t ) j ( i π 2 ) , ( 63 ) with D = θ σ V 2 τ V τ w . We can express Eq ( 63 ) in terms of r and Ω , ν ( t ) - ν 0 ν 0 = A D sgn ( t ) r 2 + Ω 2 + Θ ( t ) e r t 2 Ω R + e i ( Ω t + ϕ + + π 2 ) + R - e - i ( Ω t + ϕ - - π 2 ) , ( 64 ) where R ± ∈ R and ϕ ± ∈ R depend on the parameters . Taking the limit t → 0+ , the relative instantaneous jump height is A D τ w τ c = A θ σ V - 2 τ c τ V = A θ 3 π 2 τ s 2 τ c 2 τ c τ V , consistent with the notion that higher characteristic cutoff frequencies , i . e . τ c - 1 , imply stronger instantaneous transmission . The exponent of the subsequent decay is r = - 2 τ ¯ - 1 , providing an envelope that funnels into the relative asymptotic response , A D | λ | 2 , attained in the limit t → ∞ . Since the oscillation amplitude scales as 1/Ω while the asymptotic response scales with 1/Ω2 , there will be a tapering envelope for Ω > 1 . Within this envelope the response oscillates at the intrinsic frequency and with a phase that is explicitly dependent on the neuron parameters , as well as implicitly though τc . This function was used to calculate the step response shown in Fig 2 .
Dynamic gain , the amount by which features at specific frequencies in the input to a neuron are amplified or attenuated in its output spiking , is fundamental for the encoding of information by neural populations . Most studies of dynamic gain have focused on neurons without intrinsic degrees of freedom exhibiting integrator-type subthreshold dynamics . Many neuron types in the brain , however , exhibit complex subthreshold dynamics such as resonance , found for instance in cortical interneurons , stellate cells , and mitral cells . A resonator neuron has at least two degrees of freedom for which the classical Fokker-Planck approach to calculating the dynamic gain is largely intractable . Here , we lift the voltage-reset rule after a spike , allowing us to derive a complete expression of the dynamic gain of a resonator neuron model . We find the gain can exhibit only six shapes . The resonant ones have peaks that become large due to intrinsic adaptation and become sharp due to an intrinsic frequency . A resonance can nevertheless result from either property . The analysis presented here helps explain how intrinsic neuron dynamics shape population-level response properties and provides a powerful tool for developing theories of inter-neuron correlations and dynamic responses of neural populations .
[ "Abstract", "Introduction", "Results", "Discussion", "Methods" ]
[]
2015
Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics
The kinetoplast ( k ) , the uniquely packaged mitochondrial DNA of trypanosomatid protists is formed by a catenated network of minicircles and maxicircles that divide and segregate once each cell cycle . Although many proteins involved in kDNA replication and segregation are now known , several key steps in the replication mechanism remain uncharacterized at the molecular level , one of which is the nabelschnur or umbilicus , a prominent structure which in the mammalian parasite Trypanosoma brucei connects the daughter kDNA networks prior to their segregation . Here we characterize an M17 family leucyl aminopeptidase metalloprotease , termed TbLAP1 , which specifically localizes to the kDNA disk and the nabelschur and represents the first described protein found in this structure . We show that TbLAP1 is required for correct segregation of kDNA , with knockdown resulting in delayed cytokinesis and ectopic expression leading to kDNA loss and decreased cell proliferation . We propose that TbLAP1 is required for efficient kDNA division and specifically participates in the separation of daughter kDNA networks . Kinetoplast ( k ) DNA is one of the hallmarks of the family Kinetoplastea , and is a unique form of mitochondrial ( mt ) DNA . Kinetoplastids are a species-rich group , which includes numerous obligatory parasitic trypanosomatids , such as Trypanosoma brucei , T . cruzi and Leishmania spp . , causative agents of serious human diseases . In all known trypanosomatids , kDNA exists as a single network composed of mutually catenated circular molecules of two categories–maxicircles and minicircles—that are extended and densely packaged into a highly organized discoid structure [1] . The kDNA network is located in the mt lumen adjacent to the basal body of the single flagellum , to which it is physically attached via the tripartite attachment complex ( TAC ) . The TAC is a structure comprised of both exclusion zone and unilateral filaments , which are assembled with a region of non-corrugated inner mt membrane between them [2] . Unlike the mt DNA of most eukaryotes , kDNA divides once per cell cycle . Whilst this occurs immediately prior to nuclear DNA synthesis , replication of kDNA is in synchrony with nuclear division [3] . In T . brucei , kDNA is essential for mt functions , although recently specific mutations have been described that can facilitate viability without kDNA [4] . However , this is not the case for most infective strains found in the field , as treatment with ethidium bromide , which leads to kDNA loss , proves effective in controlling the spread of the parasite among cattle [5] . In the African trypanosome T . brucei , kDNA is composed of ~25 maxicircles , each 23 kb long , and ∼ 5 , 000 minicircles that are ~1 kb in size [6] . All maxicircles have essentially identical sequence and represent homologues of typical mtDNA . Maxicircles contain 18 protein-coding genes , the products of which are mostly subunits of respiratory complexes plus two ribosomal RNAs [6 , 7] . Most of these genes are present in an encrypted form and in order to become translatable , their transcripts require extensive post-transcriptional insertions and deletions of uridines via RNA editing [8–10] . Minicircles are very heterogeneous in sequence and encode hundreds of different guide ( g ) RNAs ( usually one per minicircle ) that provide sequence information for editing the maxicircle transcripts [10 , 11] . Maxicircles and minicircles probably constitute distinct networks that are mutually interlocked within the single kDNA disk [12] . A unique feature of the trypanosomatid kDNA is that its constituents are non-supercoiled open circles , catenated with three neighbors via a single interlock , creating a chainmail-like structure [1 , 13 , 14] . Within this catenated kDNA disk the circles remain closed throughout the cell cycle and only during replication are they decatenated by the action of topoisomerase ( s ) II , and transported into a region between the kDNA disk and the basal body termed the kinetoflagellar zone [15 , 16] . Decatenated minicircles then undergo replication via a θ structure . The replication products of each and every minicircle , distinguished from their pre-replicated neighbors by retaining nicks and gaps , are re-attached into the kDNA disk within two antipodal sites , upon which the strand discontinuities are sealed [17 , 18] . Therefore , for every detached and replicated minicircle , two minicircles re-attach , doubling the size into a catenane of about 10 , 000 minicircles . In T . brucei , progeny minicircles accumulate on opposite sites of the disc at the antipodal sites [15] . Subsequently , the network divides into two by an unknown process requiring at least one of three mt DNA polymerase [19 , 20] and a DNA ligase that closes the nicks [21] . Maxicircle replication occurs within the kDNA network , and many proteins , including multiple helicases and three mt DNA polymerases are also required for this process [18 , 20 , 22] . In T . brucei the accumulation of replicated minicircles at the antipodal sites results in the formation of a dumbbell-shaped network , whereas in other trypanosomatids , such as Crithidia fasciculata , T . cruzi , Leishmania and Phytomonas spp . , minicircles are uniformly distributed around the periphery of the kDNA disk , forming a peripheral ring [23–25] . To explain such distinct replication mechanisms , it has been proposed that in the latter group the kDNA disk rotates , distributing the re-attaching minicircles around the periphery of the kDNA network [26] . In T . brucei , however , the disk remains stationary , and the replicated network divides by an unknown mechanism . It is assumed that this mechanism is unique to T . brucei and its close relatives due to the particular nature of the kDNA segregation process present in this species . Situated in the posterior region of the cell , the kDNA disk is physically linked to the basal and pro-basal bodies by the tripartite attachment complex ( TAC ) [2] . It has been postulated that , at the moment of cell division , the basal bodies align and direct kDNA segregation , a process orchestrated via the TAC [27] . The physical separation of the progeny kDNA networks as observed by electron microscopy , has been associated with the formation of a filament-resembling structure termed the nabelschnur or umbilicus [28] . This structure , so far observed exclusively in T . brucei , constitutes the final physical connection between the newly replicated daughter kDNA networks [28 , 29] . Although the kDNA of T . brucei is one of the best-studied mt genomes , the mechanism ( s ) governing this highly precise division remain largely unknown . Here we characterize the function of a leucyl aminopeptidase , TbLAP1 , in the segregation of dividing kDNA . LAPs are homohexameric metallopeptidases classified into either the M1 or M17 protease families [30] that cleave N-terminal amino acids from proteins , particularly , but not exclusively , L-leucine . Members of the M1 family carry a canonical HEXXH motif in their active site , whereas the M17 family members lack this motif and require two metal ions per monomer for activity [31] . LAPs have diverse subcellular localizations; initially found in the cytosol , they have been subsequently encountered in chloroplasts [32] , on bacterial surfaces [33] , or teguments of parasitic helminths [34] . Moreover , in Escherichia coli LAPs bind DNA [35] , while they are amongst secreted proteins in other bacteria such as Mycoplasma [33] . Despite high sequence conservation , members of the M17 protease family perform a range of moonlighting functions in diverse organisms . Amongst these functions , LAPs regulate meiosis in fungi [36] , are involved in infectivity of various bacteria , yeast and parasitic protists [37–39] , regulate stress responses and signal transduction [40] , act as molecular chaperones that protect proteins from heat inactivation and assist in their refolding in plants [41] and finally , are required for glutathione metabolism and recycling [42] . Regardless of these diverse roles of LAPs , the exact mechanisms of their many moonlighting functions remain to be clarified . In this paper we report that TbLAP1 is a mt protein that dynamically associates with kDNA during the cell cycle ( Fig 1 ) . The protein localizes to kDNA and the nabelschnur , the proteinaceous link connecting progeny kDNAs at late stages of segregation , indicating a role of TbLAP1 in resolving kDNA replication . TbLAP1 ( Tb927 . 8 . 3060 ) is a predicted protein of ~70 kDa . Several algorithms ( PSORTII [43] , iPSORT [44] , MitoProt II [45] and TargetP 1 . 1 [46] ) predict mt localization for TbLAP1 based on the presence of an N-terminal mt targeting signal . Due to this we chose to investigate TbLAP1 further . Indeed , TbLAP1 , in situ tagged at the C-terminus with either green fluorescent protein ( GFP; S6 Fig ) or V5 was throughout the cell cycle exclusively associated with the kDNA disk ( Fig 1 ) . Significantly , TbLAP1 distribution within the kDNA undergoes substantial changes during cell division . During the G1 phase , TbLAP1 co-localizes with the kDNA but not with TAC102 , a component of the TAC , a filamentous structure that connects kDNA with both the basal body and the flagellum ( Fig 1 ) . The onset of mitosis in T . brucei is marked by basal body division followed by duplication of the TAC . Replicating in parallel with the TAC , kDNA initially forms a dumbbell-like structure , where TbLAP1 bifurcates into two lobes that bind each side of the divided but as yet unsegregated kDNA disk ( Fig 1B ) . Shortly afterwards , the kDNA disk commences segregation into two daughter networks , positioned perpendicular to one another ( Fig 1C , 1D and 1E ) . At this stage , the characteristic nabelschnur becomes apparent between the newly replicated kDNA networks [28 , 29] . As the daughter kDNA discs progressively separate , the nabelschnur extends with a small , yet prominent , TbLAP1 focus at its center ( Fig 1C and 1D ) . Once the daughter kDNA disks have completed their realignment , the TbLAP1 signal within the nabelschnur decreases and eventually remains confined to a spot overlaying each newly synthesized kDNA disk ( Fig 1G ) . It is important to highlight that TbLAP1 localization overlaps with kDNA , but is clearly distinct . The division of the basal body was also followed as a landmark for the dynamic localization of TbLAP1 during the division of procyclic T . brucei . This structure has been shown to divide prior to , and promote segregation of the daughter kDNA disks [47] . Immunolocalization of the YL1/2 epitope , specific for mature basal bodies , does not overlap with that of TbLAP1 ( Fig 2 ) , demonstrating that these structures indeed segregate prior to the separation of the kDNA disks ( Fig 2B and 2C ) . Staining of the basal bodies depicts localization of TbLAP1 to kDNA upon division and its dynamic localization along the duplicating disk ( Fig 2C ) . The TbLAP1 signal in Fig 2D and 2E indicates that the nabelschnur emerges coincident with immunostaining of anti-TAC102 antibody ( Fig 1 ) . After segregation of these newly divided disks , the basal bodies align perpendicularly , standing at 180° angle to each other , as both kDNAs separate and align for cytokinesis ( Fig 2E and 2F ) . Localization of in situ V5-tagged TbLAP1 into the kDNA disk was further confirmed by cryosectioned transmission electron microscopy via immunodecoration with colloidal gold-labeled anti-V5 antibodies , with abundant gold particles found exclusively on the electron-dense kDNA disk . Unlike with immunofluorescence , except for the “dumbbell-shaped” kDNA , it is not possible to assess at which stage of cell division these kDNAs are , but based on their sizes these are most likely interphase networks ( Fig 3 ) . The spatial distribution of the signal in the observed kDNAs was evaluated by Ripley’s K function [48 , 49] . The statistical analysis indicates that the anti-V5 antibody signal exists in two modes and either forms clusters ( Fig 3A and 3B ) , or is randomly distributed throughout the kDNA ( Fig 3C and 3D ) . In the analyzed samples ( n = 20 ) , 40% of the gold particles found display cluster formation , while the remaining 60% are randomly distributed ( n = 342; S1 Table ) . RNAi-mediated down-regulation of the TbLAP1 protein induces a growth defect ( Fig 4A and 4D ) , which is nonlethal but causes a delay in cytokinesis . Real-time qPCR analysis confirmed the TbLAP1 transcript is reduced by 80% at 48 and 96 hrs post-induction , but seems to increase at 144 hrs to 50% when normalized to 18S rRNA levels ( Fig 4C ) . At this time a substantial proportion of 2K2N cells was observed ( Fig 4B ) . Propidium iodide ( PI ) staining by FACS analysis indicated that the variability on the fluorescence of this compound associated with the cell population was greater for the RNAi-induced cells than the uninduced cells . Nevertheless , overall fluorescence does not vary between induced and uninduced cell lines , an indication that DNA content is not significantly changed upon silencing of TbLAP1 ( Fig 4E ) . The actual evidence is that cell duplets accumulate in culture , represented by 2K2N cells , in accordance with the DAPI staining results . RNAi-ablated cells display a dividing kDNA , prior to cytokinesis ( Fig 4F ) . Ectopic expression of TbLAP1-HA in procyclic T . brucei induced a significant growth defect ( Fig 5A ) , which was manifested by the accumulation of 0K1N cells ( Fig 5B ) and a defect in mt membrane potential ( Fig 5D ) . Ectopic expression of TbLAP1-HA was monitored by Western blot using anti-HA antibody ( Fig 5C ) . A concomitant loss of kDNA was observed 2 hrs post-induction , as shown by the significantly increased proportion of 0K1N cells , almost complete disappearance of 2K2N cells after 48 hrs , and the increase of 0K2N cells after 72 hrs ( Fig 5B ) . The staining of procyclic trypanosomes expressing TbLAP1-HA with MitoTracker resulted in an uneven , patchy distribution of the probe throughout the reticulated mitochondrion ( S8 Fig ) , suggesting a defect in mt membrane potential . Indeed , we observed a very rapid effect on this parameter , evident after only 2 hrs of induction of TbLAP1-HA . Over-polarization of the mt membrane continued until 72 hrs of induction , after which the mt membrane potential decreased and fell below values in uninduced cells , presumably due to failure of the mt membrane and/or loss of viability ( Fig 5D ) . When TbLAP1-HA expressing trypanosomes were immunodecorated with anti-TAC102 antibody , TAC segregation was heavily affected by the halt in kDNA separation ( Fig 6 ) . Cells expressing TbLAP1-HA are able to duplicate their kDNA , but proper separation fails ( Fig 6B , 6C and 6D ) . Aberrant kDNA segregation was observed as early as 6 hrs post-induction ( Fig 6B ) . Neither the division of the basal body , nor its segregation were significantly affected ( Fig 7 ) . Immunodecoration of the same cells with anti-mtHsp70 antibody , a marker for the mt matrix [50] , demonstrated accumulation of mtHsp70 around the kDNA disk after 48 hrs of induction , with a concomitant loss of the reticulated network structure of the mitochondrion ( Fig 8B ) . Expression of TbLAP1-HA was associated with extensive kDNA loss that peaked after 72 hrs of induction , when over 60% of cells became akinetoplastic ( Fig 8A ) and lost the reticulated mt network , as well as the focal distribution of TbLAP1-HA ( Fig 8B ) . Here , we report the first case of a mitochondrion-localized LAP , namely TbLAP1 in T . brucei , which has a unique and unexpected function . In this compartment , TbLAP1 is present in both the kDNA disk and the nabelschnur , where it undergoes dynamic relocations during kDNA division and segregation . Dynamic repositioning during the kDNA replication is known for a number of kDNA-associated proteins , such as the one observed for DNA polymerase ID [1 , 6 , 9 , 51 , 52] , yet to the best of our knowledge TbLAP1 is the first protein known to be localized within the nabelschnur . This morphologically prominent structure was first observed and defined by transmission electron microscopy under treatment with ethanolic phosphotungstic acid , a technique that highlights highly basic proteins [28] . The nabelschnur is formed by two parallel filaments that form a bridge between the segregating kDNA networks [28] . TbLAP1 has a basic pI of 9 . 8 , which suggests the potential to interact with nucleic acids . It is noteworthy that the localization of TbLAP1 to the kDNA disk usually does not cover the entire network . This is particularly evident at the G1 phase and immediately before cytokinesis , when progeny kDNA networks have completed segregation and the TbLAP1 foci are asymmetric , yet invariably colocalize with the kDNA disk . Cytosolic forms of LAP are present in most eukaryotes , but the expansion into three paralogs in T . brucei , revealed by phylogenetic analysis ( S1 Fig , S2 Fig ) is notable and suggests lineage-specific evolution of these proteins . The division of functions between LAPs following their expansion is apparently unique for trypanosomes and does not inform us about the functions of LAP paralogs in other lineages . In T . cruzi LAP has been described as a cytosolic protein displaying enzymatic activity [53] being orthologous to the protein studied here ( S2 Fig ) . Down-regulation of TbLAP1 resulted in a growth defect , as well as in the accumulation of cell doublets . DAPI counts and PI staining of the TbLAP1-depleted cells suggests delayed cytokinesis . On the other hand , ectopic expression of TbLAP1-HA causes the loss of kDNA and concomitant growth defects . Since the process of kDNA segregation finishes with the onset of cytokinesis , both the effect of down-regulation and ectopic expression of TbLAP1 indicate its clear involvement in segregation of kDNAs . Although our results cannot clearly establish a molecular model for the process driven by TbLAP1 , we propose that this protein is involved in the machinery that orchestrates and possibly times the final stages of cell division , led by the movement of the basal bodies . Similar phenotypes have been observed upon RNAi-mediated depletion of katanins and spastin in bloodstream form T . brucei , with these proteins known to be involved in cytokinesis [54] . However , the down-regulation of TbLAP1 does not seem to cause a defect in cell division , since as long as 8 days post RNAi-induction , no multinucleated cells were observed in the culture . While it may be argued that the tag impairs the function of the ectopic copy in a dominant-negative fashion , we have found that in situ tagging of TbLAP1 , with either short V5 or long GFP tag does not produce the phenotype caused by the ectopic copy , though it exhibits exactly the same localization ( S6 Fig ) . Overexpression of a cytochrome b5 reductase–like protein renders kDNA incapable of duplication prior to cell division [55] , whereas excess levels of two of six kDNA helicases induce a gradual loss of kDNA [27] . Ectopically expressed TbLAP1-HA accumulates around the kDNA disk , inducing a rapid loss of the structure . Several proteins are known to be important for kDNA segregation [56 , 57] or its maintenance [22 , 27 , 58 , 59] , yet none of them display a localization similar to that of TbLAP1 , nor a kDNA loss of this type . Ectopically expressed TbLAP1 disrupted kDNA disk segregation , but division and segregation of the basal bodies were not affected . The basal body has been described to mediate kDNA segregation [60] and the same has been implied for TAC-associated proteins , such as p166 [56] . Moreover , it is well established that nuclear division and segregation are independent of the mechanism of kDNA segregation [47 , 61] . Throughout the cell cycle , the TbLAP1-HA signal dynamically changes , and its movement resembles that of the basal bodies ( Fig 9 ) . The stress experienced by trypanosomes expressing TbLAP1-HA is further reflected by the recruitment of mtHsp70 and its colocalization with TbLAP1-HA . Moreover , the viability of the newly divided cells was seriously hampered , as observed by the accumulation of 0K1N cells . It is likely that the collapse of mt membrane potential is a secondary effect of kDNA loss , as the same phenotype occurs following the depletion of several kDNA polymerases [20] . On the other hand , the down-regulation of TbLAP1 affects the separation of 2K2N cells but does not seem to affect division . These results strongly associate the protein with the segregation process and with the basal bodies as its orchestrators . Moreover , they provide further support for the independence of the division and segregation processes in the parasite . In an attempt to determine proteins interacting with TbLAP1 , we constructed a C-terminal in situ GFP-tagged version of the protein and the corresponding cell line was used to immunoprecipitate TbLAP1-GFP using anti-GFP nanobodies ( S6 Fig ) . Although the eluate did not contain any other proteins than TbLAP1-GFP , we noticed that the protein was not able to enter the polyacrylamide gel . Reversion of this phenomenon upon DNAse treatment and/or sonication suggested binding to kDNA ( S7 Fig ) [53] . Multiple attempts and alternative immunoprecipitation conditions did not yield any TbLAP1-interacting proteins , although both the tagged and untagged proteins were efficiently pulled down , likely a consequence of its capacity to form oligomers , as described in other eukaryotes [65] . We explain this result as a consequence of the short-lived nature of the nabelschnur , which is observed only at the moment of kDNA segregation . Since kDNA replication commences before that of the nucleus and lasts for only a short period of time , at any point in a non-synchronized culture a very small percentage of cells are undergoing the G2/M phase transition , during which the newly synthesized kDNAs separate . While our experiments cannot assert that TbLAP1 binds kDNA directly , the association with the structure is evident . Interaction of LAP with DNA has been observed in other organisms . DNA-associated PepA from E . coli regulates the pyrimidine-dependent repression of the carbamoylphosphate operon transcription [35] and participates in the site-specific Xer recombination system [62 , 63] . It is worth noting that proteolytic activity is not necessary for recombination activity [64] . Analysis of the TbLAP1 sequence indicates that not all of the amino acids involved in DNA binding and recombination activity of the E . coli orthologue are present in the T . brucei protein ( S3 Fig , S4 Fig and S5 Fig ) . Of the nine amino acids required for exclusive binding of PepA to DNA [65] , TbLAP1 contains only two ( S3 Fig ) . Furthermore , TbLAP1 displays extra 100 amino acids that are absent in all the other LAPs assessed , and this sequence does not correspond to any known domain ( S1 Fig , S3 Fig ) . In other organisms such as basidiomycetes , LAP promotes meiosis and may also be involved in DNA repair [36] . DNA binding of LAP has also been observed in human esophageal carcinoma , where it promotes G1/S transition , yet the underlying mechanism remains unknown [66] . The cysteinyl-glycyl activity is a more recently described function of LAPs . Although highly specialized when compared to that of the cleavage of N-terminal peptides , this LAP activity ranging from bacteria to mammals was proposed to recycle cysteinyl-glycyl in the γ-glutamyl cycle [42 , 67] . However , many components of both the γ-glutamyl cycle and the urea cycle with which it is closely associated , are absent from the T . brucei genome [68] . Hence , this enzymatic pathway likely does not take place in trypanosomatids , where glutathione is just one of several precursors for the subsequent formation of trypanothione . In summary , a new function and subcellular localization of LAP has been described in T . brucei , where the protein is involved in a unique mechanism . Dataset for phylogenetic analysis was created from publicly available sources , using BLASTP at an E-value cut-off of 1x10-20 . The dataset was aligned by MUSCLE [69] and informative positions were selected using Gblocks [70] with manual adjustment in Seaview 4 . 6 . 1[71] . Maximum likelihood tree was constructed using RAxML 8 . 2 . 1 [72] with LG+GAMMA model and 1 000 bootstrap replicates . TriTrypDB and Genbank accession numbers for the sequences used are listed as follows: Trypanosoma brucei Tb927 . 8 . 3060 ( TbLAP1 ) , Tb927 . 11 . 6590 and Tb927 . 11 . 2470; Trypanosoma cruzi 1 EAN87580 . 1; Trypanosoma cruzi 2 EAN97960 . 1; Trypanosoma cruzi 3 EAN99056 . 1; Plasmodium falciparum , XP_001348613 . 1; Solanum lycopersicum LAP-A gi|350540058|ref|NP_001233862 . 1|; Solanum lycopersicum LAP-N AAO15916 . 1; Escherichia coli; Macaca mulatta NP_001247627 . 1; Homo sapiens NP_056991 . 2; Bos taurus NP_776523 . 2; Schizosaccharomyces pombe , NP_592993 . 1; Oryza sativa , NP_001066684 . 1; Mus musculus , NP_077754 . 3; Gallus , NP_001026507 . 1; Dictyostelium discoideum , XP_641537 . 1; Danio rerio , NP_001108319 . 1; Arabidopsis thaliana 3 NP_194821 . 1; Arabidopsis thaliana 2 , NP_194820 . 1; Arabidopsis thaliana 1 , NP_179997 . 1 . A TbLAP1 RNAi cell line was constructed by cloning into the p2T7Ti-177 vector [73] a PCR amplified 400 bp-long region of the TbLAP1 gene using primers TTGGTGTTGAGCTTCTGGTG and TTGCCAGACCTTTTCTTTCC . The final construct was linearized with NotI and transfected into procyclic SMOXP9 T . brucei [74] . For overexpression , the full-size TbLAP1 gene , amplified with primers AAAAGTAAAATTCACGGGCCCATGCTCAAGAGAGT and CAGATTTTCGTTTCTGGTACCTCAATTGCCAGACCT ( inserted ApaI and KpnI restriction sites are underlined , respectively ) , was cloned into the HindIII+BamHI pre-digested p2329 vector [75] using the Geneart Seamless Cloning and Assembly kit ( Life Technologies ) . TbLAP1 was in situ tagged using the long PCR approach , with either GFP or V5 tag attached to its C-terminus . For the GFP-tag , the pMOTag 3G vector was used to amplify a PCR product , which was transfected into procyclic stage of T . brucei 427 cells [76] . The following primers were used . For GFP tagging , primers 5’-GAAAAGAAGAGGGTGAAAAAGGCACCTGCGGCCAAGCAGGGTCGCCGGGCAGTGAAGGGGAACCCGAAAGGAAAGAAAAGGTCTGGCAATGGTACCGGGCCCCCCCTCGAG-3’ and 5’-AACCCCCAGTTGTGGGAACTTACGTGTCGAAAAACACCCCTTCCTCACAATACAGAAACGAGCGGCACGGTGGTTCCAATGTGGCGGCCGCTCTAGAACTAGTGGAT-3’ were used [76] . In situ tagging with C-terminal V5-tag was performed using the pPOTv4 vector , in which the yellow fluorescent protein ( eYFP ) was replaced by the V5 tag [77] . The following primers were used: 5’-AGTAATGCGCCACGGGGCTGCATGTAACCCTGTTGATGTCATTGAGAACTATCTGGAGGACAAACTCGATGAAATCGACATATGGGTGGGTACCGGGCCCCCCCTCGAG-3’ and 5’-CACATCCTGATGTGTTGCTTCTCGCCGCACCTAGCACGGTGAAGGCCGTGAGCATGTATGTGTAGTGCAGAAGAGTAAAGAGCGTTTTGGCGGCCGCTCTAGAACTAGTGGAT-3’ . All experiments were conducted SDM79 medium . The procyclic 13–13 and SMOXP9 ( which we refer to as SMOX ) cell lines were used as parental lines for the ectopic expression of TbLAP1-HA ( referred to as TbLAP1-HA throughout the paper ) and RNAi against TbLAP1 , respectively . In situ tagging of TbLAP1 was performed in procyclic 427 and SMOX cell lines . The SMOX cell line was cultivated in SDM79 with puromycin ( 0 . 5 μg/mL ) for the stable expression of tetracycline ( Tet ) repressor and T7 polymerase . The 13–13 trypanosomes were grown with phleomycin ( 5 μg/mL ) for the maintenance of stable expression of pHD13-13-encoded Tet repressor [78] , and the SMOX cell line was cultivated in the presence of puromycin ( 0 . 5 μg/mL ) . Transfections were performed with 10 μg of linearized vector or PCR product and 2 x 107 cells per transfection using a BTX electroporator . Clones were selected as described previously [79] . Induction of RNAi and overexpression was initiated by the addition of 1 μg/mL Tet to the cultures . Cell numbers were monitored using a Beckman Coulter Z2 counter . All growth curves were started with 2 x 106 cells/mL and subcultured to the same cell number every 24 hrs . Ectopically expressed and in situ tagged TbLAP1 was followed by immunofluorescence assay as described elsewhere [80] , with minor modifications . Expression was induced with 1 μg/mL Tet and samples were taken at several time points , with parental cell lines used as controls . Cells were fixed with 4% ( w/v ) paraformaldehyde in phosphate buffered saline ( PBS ) , permeabilized with 0 . 2% ( v/v ) TX-100 in phosphate buffered saline ( PBS ) on microscopy slides and then probed with primary antibodies in PBS/gelatin . Polyclonal anti-TbLAP1 and anti-HA antibodies produced in rabbits ( Sigma-Aldrich ) , and monoclonal anti-mtHsp70 antibody ( kindly provided by Ken Stuart ) were used at 1:1 , 000 dilution . Monoclonal anti-TAC102 and YL1/2 antibodies ( kindly provided by Torsten Ochsenreiter and Keith Gull , respectively ) were used at 1:2 , 500 and 1:500 dilutions , respectively . Rabbit anti-V5 antibody ( Sigma-Aldrich ) was used at a dilution of 1:8 , 000 . As secondary antibodies , Alexa Fluor 488 anti-rabbit and Alexa Fluor 555 anti-mouse ( Life Technologies ) were used . DNA was visualized using ProLong Gold antifade reagent with DAPI ( Life Technologies ) , and DAPI counts were performed as described previously [81] . Immunofluorescence analysis was performed using a Zeiss microscope Axioplan 2 , equipped with an Olympus DP73 digital camera and detection was carried out with cellSens software ( Olympus ) . Image analysis was performed using Magic Montage plugin for ImageJ [82] and FIJI [83] . All zoomed image sections are approximately 1μm x 1μm . Procyclic T . brucei expressing in situ tagged TbLAP1-V5 were fixed in 4% formaldehyde and 0 . 1% glutaraldehyde in 0 . 1 M 4- ( 2-hydroxyethyl ) -1-piperazineethanesulfonic acid buffer ( HEPES ) for 1 h at room temperature . After washing in 10 mM glycine in HEPES , cell pellets were embedded into 10% ( w/v ) gelatin at 37°C , and left on a rotating wheel in 2 . 3 M sucrose at 4°C for 4 days , after which they were frozen by immersion in liquid nitrogen . Ultrathin cryosections were obtained using an ultramicrotome EM FCS equipped with UCT cryochamber ( Leica Microsystems ) . Sections were transferred onto formvar-carbon-coated grids using a drop of 2 . 3 M sucrose/2% methyl cellulose ( 1:1 ) . They were then washed in HEPES , blocked in a solution containing 5% low-fat milk , 10 mM glycine and 0 . 05% ( v/v ) Tween 20 for 1 h and incubated overnight with anti-V5-tag antibody ( 50 μg/ml; Invitrogen ) in the blocking solution at 4°C . After a wash in 2 . 5% ( w/v ) low-fat milk , 5 mM glycine and 0 . 025% ( v/v ) Tween 20 in HEPES , sections were incubated for 1 h in goat anti-mouse IgG conjugated to 10 nm gold particles ( Aurion ) , diluted 1:40 in the washing solution . Sections were then washed in HEPES , distilled water , contrasted and dried using 2% methyl cellulose with 3% aqueous uranyl acetate solution diluted at 9:1 , and examined in either 80 kV JEOL 1010 or 200 kV 2100F transmission electron microscopes . Background labeling was tested by a negative control , in the absence of primary antibody , under the same conditions as those described above . Cell lysates were prepared in NuPAGE LDS sample buffer ( Invitrogen ) using 5 x 106 cells per lane separated on Bolt 4–12% Bis-Tris polyacrylamide gels ( Invitrogen ) and transferred to a Amersham Hybond P PVDF membrane ( GE Healthcare ) , which was subsequently hybridized with monoclonal anti-GFP ( Roche ) , polyclonal anti-HA ( Sigma ) or monoclonal anti-tubulin antibodies at 1:1 , 000 , 1:2 , 000 and 1:10 , 000 dilutions , respectively . After hybridizing with an appropriate secondary antibody conjugated with horseradish peroxidase ( Sigma ) , Clarity ECL substrate ( Bio-Rad ) was used to visualize the proteins . Band densitometry analysis was analyzed using ImageJ software [82] . FACS analysis was performed using a FACS Canto II flow cytometer ( BD Biosciences ) . For mt membrane potential measurement , 5 x 106 cells procyclic trypanosomes overexpressing TbLAP1 were incubated with MitoTracker Red CMXRos or TMRE ( Tetramethylrhodamine ) at 27°C for 20 min in SDM79 , spun , and the pellet was resuspended in 1 mL PBS . Ten thousand events were measured per sample and each set of samples was measured at least three times in independent experiments [84] . Propidium iodide ( PI ) stained cells were prepared as described elsewhere [85] . Briefly , 2 x 107 RNAi-induced cells were collected by centrifugation , washed with PBS , resuspended in 200 μL ice-cold 0 . 5% formaldehyde/PBS and incubated for 5 min on ice . Next , they were fixed with 2 mL of ice-cold 70% ethanol in vortex and allowed to stand 1 hr on ice . To stain cells with PI , the sample was centrifuged at 1500 x g , resuspended in 1 mL PBS and incubated with 50 μg PI and 200 μg of RNase A at 37°C for 1 hr . Data were analyzed using Flowing Software ( Turku Centre for Biotechnology ) . qRT-PCR analysis was performed as previously described [86] . RNA from 2 x 108 T . brucei cells was isolated using TRI Reagent ( Sigma-Aldrich ) and cDNA from 1 μg of total RNA was synthesized using Quantitect Reverse Transcription kit ( Qiagen ) . TbLAP1 RT-qPCR was performed under non-saturating conditions , in triplicate using primers 5’-ATGTGGATAAACACGACGCA-3’ and 5’-GCTCCGGATCCAACAAAATA-3’ for TbLAP1 on a Roche LightCycler 480 ( v1 . 5 ) . Analysis of the data was performed using the Pfaffl method for relative quantification [87] after adjustment of primer efficiency , on 18S rRNA as a standard [88] with LinRegPCR software [89] .
Trypanosomes bear a single mitochondrion with its genome ( kinetoplast , or kDNA ) arranged as a network of circular molecules . kDNA is essential for the Trypanosoma brucei life cycle , as it is required for proper functioning of the mitochondrion and progression through the insect vector . Division of kDNA is synchronized with the replication of nuclear DNA and additional cell cycle events . Though the mechanism of kDNA replication is well understood and proteins mediating this process identified , several key steps , including segregation , remain poorly known , in part due to the absence of characterized proteins specifically functioning at this stage . Here we report leucine aminopeptidase 1 ( LAP1 ) as the first identified component of the nabelschnur , or umbilicus , a structure observed during the separation of daughter kDNA networks , whose expression is required for correct kinetoplast segregation .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]
[ "rna", "interference", "parasitic", "cell", "cycles", "cell", "cycle", "and", "cell", "division", "cell", "processes", "parasitic", "protozoans", "parasitology", "developmental", "biology", "protozoans", "dna", "replication", "cytokinesis", "dna", "epigenetics", "cellular", "structures", "and", "organelles", "research", "and", "analysis", "methods", "sequence", "analysis", "kinetoplasts", "sequence", "alignment", "bioinformatics", "genetic", "interference", "gene", "expression", "life", "cycles", "biochemistry", "rna", "trypanosoma", "cell", "biology", "nucleic", "acids", "database", "and", "informatics", "methods", "genetics", "biology", "and", "life", "sciences", "trypanosoma", "brucei", "gambiense", "organisms", "parasitic", "life", "cycles" ]
2017
A leucine aminopeptidase is involved in kinetoplast DNA segregation in Trypanosoma brucei
Inferior temporal ( IT ) cortex in human and nonhuman primates serves visual object recognition . Computational object-vision models , although continually improving , do not yet reach human performance . It is unclear to what extent the internal representations of computational models can explain the IT representation . Here we investigate a wide range of computational model representations ( 37 in total ) , testing their categorization performance and their ability to account for the IT representational geometry . The models include well-known neuroscientific object-recognition models ( e . g . HMAX , VisNet ) along with several models from computer vision ( e . g . SIFT , GIST , self-similarity features , and a deep convolutional neural network ) . We compared the representational dissimilarity matrices ( RDMs ) of the model representations with the RDMs obtained from human IT ( measured with fMRI ) and monkey IT ( measured with cell recording ) for the same set of stimuli ( not used in training the models ) . Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category . In addition , better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities . Representational geometries were significantly correlated between IT and many of the models . However , the categorical clustering observed in IT was largely unexplained by the unsupervised models . The deep convolutional network , which was trained by supervision with over a million category-labeled images , reached the highest categorization performance and also best explained IT , although it did not fully explain the IT data . Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data . Overall , our results suggest that explaining IT requires computational features trained through supervised learning to emphasize the behaviorally important categorical divisions prominently reflected in IT . Visual object recognition is thought to rely on a high-level representation in the inferior temporal ( IT ) cortex , which has been intensively studied in humans and monkeys [1]–[12] . Object images that are less distinct in the IT representation are perceived as more similar by humans [10] and are more frequently confused by humans [13] and monkeys [6] . IT cortex represents object images by response patterns that cluster according to conventional categories [6] , [7] , [9] , [14]–[16] . The strongest categorical division appears to be that between animates and inanimates . Within the animates , faces and bodies form separate sub-clusters [6] , [7] , [15] . Previous studies have compared the representational dissimilarity matrices ( RDMs ) of a small number of models ( mainly low-level models ) with human IT and some other brain areas [7] , [17]–[19] . One of the previously tested models was the HMAX model [20] , [21] , which was designed as a model of IT taking many of its architectural parameters from the neuroscience literature . The internal representation of one variant of the HMAX model failed to fully explain the IT representational geometry [7] . In particular , the HMAX model did not account for the category clustering observed in the IT representation . This raises the question if any existing computational vision models , whether motivated by engineering or neuroscientific objectives , can more fully explain the IT representation and account for the IT category clustering . IT clearly represents visual shape . However , the degree to which categorical divisions and semantic dimensions are also represented is a matter of debate [22] , [23] . If visual features constructed without any knowledge of either category boundaries or semantic dimensions reproduced the categorical clusters , then we might think of IT as a purely visual representation . To the extent that knowledge of categorical boundaries or semantic dimensions is required to build an IT-like representation , IT is better conceptualized as a visuo-semantic representation . Here we investigate a wide range of computational models [24] and assess their ability to account for the representational geometry of primate IT . Our study addresses the question of how well computational models from computer vision and neuroscience can explain the IT representational geometry . In particular , we investigated whether models not specifically optimized to distinguish categories can explain IT's categorical clusters and whether models trained using supervised learning with category labels better explain the IT representational geometry . Evaluating a computational model requires a framework for relating brain representations and model representations . One approach is to directly predict the brain responses to a set of stimuli by means of the computational models . Because of its roots in the computational neuroscience of early visual areas , this approach is often referred to as receptive-field modeling . It has been successfully applied to cell recording , e . g . [25] , and fMRI data , e . g . [26]–[28] . Here we attempt to test complex network models whose internal representations comprise many units ( ranging from 99 to 2 , 904 , 000 ) . The brain-activity data consist of hundreds of measured brain responses . In this scenario , the linear correspondency mapping between model units and brain responses is complex ( a matrix of number of model units by number of brain responses ) . Estimating this linear map is statistically costly , requiring a combination of substantial additional data ( for a separate set of stimuli ) and prior assumptions ( for regularizing the fit ) . Here we avoid these complications by testing the models in the framework of representational similarity analysis ( RSA ) [17] , [18] , [29] , [30] , in which brain and model representations are compared at the level of the dissimilarity structure of the response patterns . The models , thus , predict the dissimilarities among the stimuli in the brain representation . This approach relies on the assumption that the measured responses preserve the geometry of the neuronal representational space . The representational geometry would be conserved to high precision if the measured responses sampled random dimensions of the neuronal representational space [31] , [32] . The RSA framework enables us to test any pre-trained model directly with data from a single stimulus set . We tested a total of 37 computational model representations . Some of the models mimic the structure of the ventral visual pathway ( e . g . HMAX , VisNet , Stable model , SLF ) [20] , [21] , [33]–[37]; others are more broadly biologically motivated ( e . g . Biotransform , convolutional network ) [38]–[41]; and the others are well-known computer-vision models ( e . g . GIST , SIFT , PHOG , PHOW , self-similarity features , geometric blur ) [42]–[48] . Some of the models use features constructed by engineers without training with natural images ( e . g . GIST , SIFT , PHOG ) . Others were trained in an unsupervised fashion ( e . g . HMAX and VisNet ) . We also tested models that were supervised with category labels . Two of the models ( GMAX and supervised HMAX ) [35] were trained in a supervised fashion to distinguish animates from inanimates , using 884 training images . In addition , we tested a deep supervised convolutional neural network [41] , trained by supervision with over a million category-labeled images from ImageNet [49] . We also attempted to recombine model features , so as to construct a representation resembling IT in both its categorical divisions and within-category representational geometry . We linearly recombined the features in two ways: ( a ) by reweighting features ( thus stretching and squeezing the representational space along its original axes ) and ( b ) by remixing the features , creating new features as linear combinations of the original features ( thus performing general affine transformations ) . All unsupervised and supervised training and all reweighting and remixing was based on sets of images nonoverlapping with the image set used to assess how well models accounted for IT . We analyzed brain responses in monkey IT ( mIT; cell recording data acquired by Kiani and colleagues [6] ) and human IT ( hIT; fMRI data from [7] ) for a rich set of color images of isolated objects spanning multiple animate and inanimate categories . The human fMRI measurements covered the entire ventral stream , so we also tested the models on fMRI data from the foveal confluence of early visual cortex ( EVC ) , the lateral occipital complex ( LOC ) , the fusiform face area ( FFA ) , and the parahippocampal place area ( PPA ) . Internal representations of the HMAX model ( the C2 stage ) and several computer-vision models performed well on EVC . Most of the models captured some component of the representational dissimilarity structure in IT and other visual regions . Several models clustered the human faces , which were mostly frontal and had a high amount of visual similarity . However , all the unsupervised models failed to cluster human and animal faces that were very different in visual appearance in a single face cluster , as seen for human and monkey IT . The unsupervised models also failed to replicate IT's clear animate/inanimate division . The deep supervised convolutional network better captured the categorical divisions , but did not fully replicate the categorical clustering observed in IT . We proceeded to remix the features of the deep supervised model to emphasize the major categorical divisions of IT using maximum-margin linear discriminants . In order to construct a representation resembling IT , we combined these discriminants with the different representational stages of the deep network , weighting each discriminant and layer of the deep network so as to best explain the IT representational geometry . The resulting IT-geometry model , when tested with crossvalidation to avoid overfitting to the image set , explains our IT data . Our results suggest that intensive supervised training with large sets of labeled images might be necessary to model the IT representational space . Among the not-strongly-supervised models , the seven models with the highest RDM correlations with hIT and mIT are shown in Figure 1 ( for other brain regions , see Figure S1 and Table 1 ) . Visual inspection suggests that the models capture the human-face cluster , which is also prevalent in IT . However , the models do not appear to place human and animal faces in a single cluster . In addition , the inanimate objects appear less clustered in the models . All models shown in Figure 1 have small , but highly significant ( p<0 . 0001 ) RDM correlations with hIT and mIT ( Figure 1A , 1B , respectively; for RDM correlation with other brain regions see Figure S2 for the not-strongly-supervised models , and Figure S3 for the deep supervised model representations ) . Most of the other not-strongly-supervised models also have significant RDM correlations ( Table 1 , Figure 2; inference by randomization of stimulus labels ) . Although often significant , all RDM correlations between not-strongly-supervised models and IT were small ( Kendall τA<0 . 17 for hIT; τA<0 . 26 for mIT ) . Combining features from the not-strongly-supervised models improved the RDM correlations to IT . Model features were combined by summarizing each model representation by its first 95 principal components and then concatenating these sets of principal components . This approach ensured that each model contributed equally to the combination ( same number of features and same total variance contributed ) . The combination of the 27 not-strongly-supervised models ( combi27 ) has a higher RDM correlation with both hIT and mIT than any of the 27 contributing models . Second to the combi27 model , internal representations of the HMAX model have the highest RDM correlation with hIT and mIT . This might reflect the fact that the architecture and parameters of the HMAX model closely follow the literature on the primate ventral stream . In addition to the combi27 , we also tested the combination of untrained models , the combination of unsupervised trained models , and the combination of weakly supervised trained models ( Figure S4 ) . The combi27 explained IT equally well or better than other combinations of the not-strongly-supervised models . In the remaining analyses , we therefore omit the other combinations and consider the combi27 along with each individual model . Monkey IT was significantly better explained by the combi27 than by the second best among the not-strongly-supervised models ( HMAX-C2UT; p = 0 . 02; inference by bootstrap resampling of the stimulus set [50] , not shown ) . This suggests that the models are somewhat complementary in explaining the IT features space . For hIT , the second best model was also a version of HMAX ( HMAX-allUT ) , but it did not explain hIT significantly worse than combi27 ( p = 0 . 261 , not shown ) . Model RDM correlations with mIT tended to be higher than model correlations with the hIT RDM . For example , the dissimilarity correlation of the combi27 with mIT was 0 . 25 , whereas for hIT it is 0 . 17 . This difference is statistically significant ( p = 0 . 001 ) , suggesting that the models were able to better explain the mIT RDM compared to the hIT RDM . This could be caused by a lower level of noise in the mIT RDM ( estimated from cell-recording data ) than in the hIT RDM ( from fMRI data ) . For the human data we were able to estimate a noise ceiling [30] ( Materials and Methods ) , indicating the RDM correlation expected for the true model , given the noise in the data . None of the 28 not-strongly-supervised models reached the noise ceiling ( Figure 2A ) . The combi27 representation came closest , but at τA = 0 . 17 , it was far from the lower bound of the noise ceiling ( τA = 0 . 26 ) . This indicates that the fMRI data capture a component of the hIT representation that all the not-strongly-supervised models leave unexplained . For mIT , we could not estimate the noise ceiling because we had data from only two animals . The main categorical divisions observed in IT appear weak or absent in the best fitting models ( Figure 1 ) . To measure the strength of categorical clustering in each model and brain representation , we fitted a linear model of category-cluster RDMs to each model and brain RDM ( Materials and Methods , Figure S5 ) . The fitted models ( Figure 3 ) descriptively visualize the categorical component of each RDM , summarizing sets of within- and between-category dissimilarities by their averages . The fits for several computational models show a strong human-face cluster , and a weak animate cluster . The human-face cluster is expected on the basis of the visual similarity of the human-face images ( all frontal aligned human faces of the same approximate size ) . The animate cluster could reflect the similar colors and more rounded shapes shared by the animate objects . However , IT in both human and monkey exhibits additional categorical clusters that are not easily accounted for in terms of visual similarity . First , the IT representation has a strong face cluster that includes human and animal faces of different species , which differ widely in shape , color , and pose . Second , the IT representation has an inanimate cluster , which includes a wide variety of natural and artificial objects and scenes of totally different visual appearance . These clusters are largely absent from the not-strongly-supervised models ( Figures 3 , S6 , S7 , S8 ) . In order to statistically compare the overall strength of categorical divisions between IT and each of the models , we computed a categoricality index for each representation . The categoricality index is the proportion of RDM variance explained by categorical divisions . The categoricality index is calculated as the squared correlation between the fitted category-cluster model ( Figure S5 ) and the RDM it is fitted to ( Figure 4 ) . The model RDMs are noise-less . However , the brain RDMs are affected by noise , which lowers the categoricality index . To account for the noise and make the categoricality indices comparable between models and IT , we added noise matching the noise level of hIT to the model representations ( Materials and Methods ) . We then compared the categoricality indices of the 28 not-strongly-supervised models to that of hIT ( Figure 4 ) . Human IT has a categoricality index of 0 . 4 . All of the not-strongly supervised models have categoricality indices below 0 . 16; most of them below 0 . 1 . Inferential comparisons show that the categoricality index is significantly higher for hIT than for any of the models ( inference by bootstrap resampling of the image set ) . We also compared the categoricality indices between models and IT without equating the noise levels . In this analysis , the categoricality index reflects the categoricality of the models without noise . For hIT and mIT , the noise lowers the categoricality estimate . Nevertheless , the hIT categoricality index remains significantly greater than that of any of the models . For mIT , similarly , the categoricality index is significantly greater than for all but three of the models ( Figure S9 ) . We also analyzed the clustering strength separately for each of the categories ( Figure S6 ) . For animates , clustering strength was significant for a few models ( Lab joint color histogram , PHOG , and HMAX-all ) . For human faces , significant clustering was observed for several computational models ( convNet , bioTransform , dense SIFT , LBP , silhouette image , gist , geometric blur , local self-similarity descriptor , global self-similarity descriptor , stable model , HMAX-C1 , and combi27 ) . These significant category clusters reflect the visual similarity of the members of these categories . Inferential comparisons of clustering strength between each of the models and hIT ( Figure S8 ) and mIT ( Figure S8 ) for each of the categories revealed that IT clusters animates , inanimates , and faces ( including human and animal faces ) significantly more strongly in both species than most of the models ( blue bars in Figures S7 and S8 ) . There are only a few cases , in which a model clusters one of the categories more strongly than IT . The finding that categoricality is stronger in IT than in any of the models raises the question of what the models are missing . One possibility is that the models contain all essential nonlinear features , but in proportions different from IT , thus emphasizing the features differently in the representational geometry . In that case reweighting of the features ( i . e . stretching and squeezing the representational space along its original axes ) should help approximate the IT representational geometry . For example , the representation might contain a feature perfectly discriminating animates from inanimates . This single categorical feature would not have been reflected strongly in the overall RDM if none of the other features emphasized this categorical division . The influence of such a feature on the overall representational geometry could be increased either by replicating the feature in the representation or by amplifying the feature values . These two alternatives are equivalent in their effects on the RDM , so we consider only the latter . Another possibility is that all essential nonlinearities are present , but the features need to be linearly recombined ( i . e . performing general affine transformations ) to approximate the IT representational geometry . We therefore investigated whether linear remixing and reweighting of the features of the not-strongly-supervised models could provide a better explanation of the IT representational geometry . So far , we showed that none of the not-strongly-supervised models were able to reproduce the categorical structure present in IT . Most of these models were untrained or trained without supervision . A few of them were weakly supervised ( i . e . supervised with merely 884 training images ) . Their failure at explaining our IT data suggests that computational features trained to cluster the categories through supervised learning with many labeled images might be needed to explain the IT representational geometry . We therefore tested a deep convolutional neural network trained with 1 . 2 million labelled images [52] , nonoverlapping with the set of 96 images used here . The model has eight layers . The RDM for each of the layers and the RDM correlations with hIT and mIT are shown in Figure 6 . The deep supervised convolutional network explains the IT geometry better than any of the not-strongly-supervised models . The RDM correlation between hIT and the deep convolutional network's best-performing layer ( layer 7 ) is τA = 0 . 24 . Layer 7 explains the hIT representation significantly better ( p<0 . 05; obtained by bootstrap resampling of the stimulus set ) than combi27 ( τA = 0 . 17 ) , the best-performing of the not-strongly-supervised models . Monkey IT , as well , is better explained by layer 7 ( τA = 0 . 29 ) than by combi27 ( τA = 0 . 25 ) , although the difference is not significant . Layer 7 is the deep network's highest continuous representational space , followed only by the readout layer ( layer 8 , also known as the “scores” ) . The readout layer is composed of 1000 features , one for each of the 1000 category labels used in training the network . The readout layer has a lower RDM correlation with hIT ( τA = 0 . 13 ) and mIT ( τA = 0 . 18 ) than layer 7 . From layer 1 to layer 7 the RDM correlation with IT rises roughly monotonically ( Figure 7 , Table 2 ) and many of the pairwise comparisons between RDM correlations for higher and lower layers are significant ( Figure 7 , horizontal lines at the top ) . Nevertheless , even the best-performing layer 7 does not reach the noise ceiling ( Figure 7 ) . Although the deep convolutional network outperforms all not-strongly-supervised models , it does not fully explain our IT data . As for the not-strongly-supervised models , we analyzed the categoricality of the layers of the deep supervised model ( Figures 8 , 9 ) . All layers of the deep supervised model , including layer 7 and layer 8 ( the readout layer ) , have significantly lower categoricality indices than hIT and mIT ( Figure 9 ) . This might reflect the fact that the stimulus set was equally divided into animates and inanimates and this division , thus , strongly influences our categoricality index . Importantly , the deep supervised network emphasizes some categorical divisions more strongly and others less strongly than IT ( Figure 8 ) . For example , layer 7 emphasizes the division between human and animal faces and the division between artificial and natural inanimate objects more strongly than IT . However , IT emphasizes the animate/inanimate and the face/body division more strongly than layer 7 . We have seen that the deep supervised model provides better separation of the categories than the not-strongly-supervised models and that it also better explains IT . However , it did not reach the noise ceiling . As for the not-strongly-supervised models , we therefore asked whether remixing the features linearly ( by adding linear readout features emphasizing the right categorical divisions ) and reweighting of the different layers and readout features could provide a better model of the IT representation . The method for remixing and reweighting was exactly the same as for the not-strongly-supervised models ( Figure 5 ) . However , the linear SVM features were based on layer 7 ( instead of combi27 ) and the reweighting involved fitting one weight for each of the layers ( 1–8 ) and one weight for each of the three linear SVM features . As before , the linear SVM features were trained for body/nonbody , face/non-face , and animate/inanimate categorization using the nonoverlapping set of 884 training images . The RDMs for the SVM readout features show strong categorical divisions ( Figure 10 , top row ) . This is consistent with the fact that the layer-7 representation performs well on categorization tasks ( Figures 11 , S11 ) . As before , we used non-negative least square fitting to find the weighted combination of the representations that best approximates hIT . Again , we avoided overfitting to the image set by fitting the weights to random subsets of 88 of the 96 images in a crossvalidation procedure , holding out 8 images on each fold . This procedure yielded a weight for each of the eight layers of the deep network and for each of the three linear SVM readout features ( 11 weights in total; Figure 10 , middle row; Materials and Methods ) . We refer to this weighted combination as the IT-geometry-supervised deep model . Inspecting the RDM reveals the similarity of its representational geometry to hIT and mIT ( Figure 10 , bottom row ) . The model emphasizes the major categorical divisions similarly to IT ( Figure 8 , bottom right ) . In contrast to all other models , this model has a categoricality index matching mIT and not significantly different from either mIT or hIT ( Figure 9 ) . The IT-geometry-supervised deep model explains hIT better than any layer of the deep network ( Figure 7 , horizontal lines at the top ) . It has the highest RDM correlation with hIT ( τA = 0 . 38 ) and mIT ( τA = 0 . 4 ) among all model representations considered in this paper . Importantly , it falls well within the upper and lower bounds of the noise ceiling and , thus , fully explains the non-noise component of our hIT data . Figure 11 shows the animate/inanimate categorization accuracy of linear SVM classifiers taking each of the model representations as their input ( for the face/body dichotomy and the artificial/natural dichotomy among inanimates , see Figure S11 ) . The categorization accuracy for each model was estimated by 12-fold crossvalidation of the 96 stimuli ( Materials and Methods ) . The deep convolutional network model ( layer 7 ) has the highest animate/inanimate categorization performance ( 96% ) , and the combi27 has the second highest performance ( 76% ) . Figure 12 shows that models whose representations were more similar to IT tended to have a higher animate/inanimate categorization performance . The Pearson correlation between the IT-to-model representational similarity ( τA RDM correlation ) and categorization accuracy was 0 . 75 for hIT and 0 . 68 for mIT across the 28 not-strongly-supervised model representations and the seven layers of the deep supervised model . This finding could simply reflect the fact that the categories correspond to clusters in the IT representation and any representation clustering the categories will be well-suited for categorization . Indeed categorization performance is also predicted by the RDM correlation between a model and an animate-inanimate categorical RDM , albeit with a lower correlation coefficient ( r = 0 . 38 , not shown ) . In order to further assess whether it was only the category clustering that predicted categorization accuracy or something deeper about the similarity of the model representation to IT , we considered the within-category dissimilarity correlation between each model and IT as a predictor of categorization accuracy . Models that were more similar to IT in terms of their within-category representational geometry ( dissimilarities among animates and dissimilarities among inanimates ) also tended to have higher categorization performance ( Pearson r = 0 . 45 for hIT , r = 0 . 67 for mIT; p<0 . 01 , p<0 . 0001 , respectively ) . These results may add to the motivation for computer vision to learn from biological vision . If computational feature spaces more similar to the IT representation yield better categorization performance within the present set of models , then it might be a good strategy for computer vision to seek to construct features even more similar to IT . We could not distinguish early visual areas V1 , V2 , and V3 , because stimuli were presented foveally in the human fMRI experiment ( 2 . 9° visual angle in diameter , centered on fixation ) . Instead we defined an ROI for early visual cortex ( EVC ) , which covered the foveal confluence of these retinotopic representations . Several models using Gabor filters ( SIFT , gist , PHOG , HMAX , ConvNet ) and other features ( Geometric blur , local self-similarity descriptor , global self-similarity descriptor , silhouette image ) explained the early visual RDM estimated from fMRI ( Figure S1A , S2A ) . These models not only explained significant dissimilarity variance , but reached the noise ceiling , indicating that they explain the EVC representation to the extent that the noise in our data enables us to assess this . For the HMAX model ( as implemented by Serre et al . [20] ) , we tested several internal representations . The HMAX-C2 layer had the highest RDM correlation with EVC among all models . The HMAX-C2 layer falls within the early stages ( above S1 , C1 , and S2 layers , and below S2b , S3 , C2b , C3 , and S4 layers ) of the HMAX model and its features closely parallel the initial stages of primate visual processing . For the deep supervised model , the RDM correlations of different layers with EVC are shown in Figure S3A . Layers 2 and 3 of the model have the highest RDM correlation with EVC and reach the noise ceiling . However , their correlation with EVC is lower than that of the HMAX-C2 layer . We also compared the model RDMs with brain areas other than IT and EVC ( i . e . FFA , LOC , and PPA ) . Figure S2 shows how well each of the 28 not-strongly-supervised models explained EVC , FFA , LOC , and PPA . The seven not-strongly-supervised models with the highest RDM correlations to these brain regions are shown in Figure S1 . Among the not-strongly-supervised models , the HMAX model showed the highest RDM correlation with EVC and FFA . Specifically , the HMAX-C2 layer had the highest RDM correlation with EVC ( τA = 0 . 22 ) and HMAX-all had the highest RDM correlation with the FFA ( τA = 0 . 13 ) . The combi27 model had the highest RDM correlation with LOC and PPA ( τA = 0 . 14 and τA = 0 . 03 , respectively ) . For the deep supervised model , Figure S3 shows how well different layers explain EVC , FFA , LOC , and PPA . Layers 2 and 3 reached the noise ceiling for EVC . Subsequent layers along the deep network's processing stream exhibited decreasing RDM correlations with EVC and increasing RDM correlations with LOC . Layer 7 gets closest to the LOC noise ceiling , but does not reach it . For FFA , however , layer 6 reaches the noise ceiling . PPA exhibited the lowest RDM correlations with the models , including both the not-strongly-supervised and the deep supervised representations . The only model with a significant RDM correlation with PPA was combi27 ( τA = 0 . 034 , p<0 . 001; Table 1 ) , which was far below the noise ceiling . This somewhat puzzling result might reflect a limitation of our stimulus set for investigating PPA . Konkle and Oliva [53] have shown that a bilateral parahippocampal region that overlaps with PPA responds more strongly to objects that are big than to objects that are small in the real world . Our stimulus set included a limited set of place and scene images and mostly objects that are small in the real world . We used a wide range of computational models to explore many different ways for extracting visual features . We selected some of the well-known biologically motivated object recognition models as well as several models and feature extractors from computer vision . Some of the models need a training phase ( these are shown by a subscript –either ‘ST’ for supervised trained , or ‘UT’ for unsupervised trained ) and some others do not ( models without any subscript ) . For the models with a training phase , we used a new set of 884 training images . Half of the images were animates and the other half were inanimates . Then , all models were tested using the testing stimuli ( the set of 96 images ) . In the training set –similar to the testing set– animate images had subcategories of human/animal faces and human/animal bodies . Inanimate images had subcategories of artificial and natural inanimates . Below is a description for all models used in this study ( see [24] for a more comprehensive explanation of the models ) . For those models that the code was freely available online , we have provided the link . Ten category-cluster RDMs ( Figure S5 ) were created as predictors for a linear model of each RDM . The category clusters were: animate , inanimate , face , human face , non-human face , body , human body , non-human body , natural inanimate , and artificial inanimate . To measure the clustering strength for each of the categories in each brain and computational-model RDM , we fit the category-cluster RDMs to each brain and computational-model RDM minimizing the sum of squared dissimilarity deviations ( Figure 3 ) . The design matrix for the least-squares fitting was created using the ten category RDMs ( each RDM was vectorized to form a column in the design matrix ) with addition of a constant vector of 1 ( confound mean RDM ) . Then the category model RDMs were fitted to object-vision model RDMs . Bars in Figure S6 show the fitted coefficients ( Beta values ) . Standard errors and p values are based on bootstrapping of the stimulus set . For each bootstrap sample of the stimulus set , a new instance is generated for the reference RDM ( e . g . hIT RDM ) and for each of the candidate RDMs ( e . g . model RDMs ) . We did stratified resampling , which means that the proportion of categories was the same across all bootstrapped resamples . Because bootstrap resampling is resampling with replacement , the same condition can appear multiple times in a sample . This entails 0 entries ( from the diagonal of the original RDM ) in off-diagonal positions of the RDM for a bootstrap sample . These zeros are treated as missing values and excluded from the dissimilarities , across which the RDM correlations are computed . The number of bootstrap resamplings used in bootstrap tests was 10 , 000 . The IT-geometry supervised models ( i . e . IT-geometry-supervised combi27 , and IT-geometry-supervised deep convolutional network ) are made by remixing and reweighting of the model features . For the IT-geometry-supervised combi27 , only the not-strongly-supervised models were used for remixing and reweighting; and for the IT-geometry-supervised deep convolutional network , only deep supervised model representations were used for remixing and reweighting . For both of them , as explained before in the context of remixing and reweighting , we trained three SVM classifiers for animate/inanimate , face/nonface , and body/nonbody classification using 884 training images . The SVM classifiers were then fed with the 96 stimuli and we used the SVM decision values as new features . The non-negative least square fitting was then used for finding the optimal weights for different model representations and the SVM discriminant features so as to minimize the sum of squared errors between the RDM of the weighted combination of the features and the hIT RDM . For making the IT-geometry supervised RDM , which is a weighted combination of the model representations and the SVM discriminants , we fit the non-negative weights by cross-validating the stimulus set . Each time we randomly left out 8 stimuli ( 4 animates and 4 inanimates ) from the set of 96 , and learned the optimal weights over the remaining stimuli ( 88 images ) so as to minimize the sum of squared errors between the RDM of the weighted combination of the features and the hIT RDM . Note that the hIT RDM and the model RDMs become 88×88 ( not 96×96 ) because 8 stimuli are left out . The obtained weights were then applied to weight the model feature for the left-out stimuli . The result is an 8×8 weighted RDM that shows the pairwise dissimilarities for the left-out stimuli . This procedure was repeated for several times until a point that we had the cross-validated pairwise dissimilarities for all the 96 stimuli . We calculated the categorization performance of the object-vision models in the following categorization tasks: animates vs . inanimates ( Figure 11 ) , faces vs . bodies ( Figure S11B ) , and artificial inanimates vs . natural inanimates ( Figure S11A ) . For each of the models , a SVM classifier [70] with a linear kernel was trained using k-fold cross validation ( k = 12 ) . The 96 stimuli were randomly partitioned into k = 12 equal size folds . Of the k folds , a single fold was retained as the validation data for testing the model categorization performance , and the remaining k−1 folds were used as training data . The cross-validation process was then repeated k times , with each of the k folds used exactly once as the validation data . The k results from the folds were then averaged . For each of the categorization tasks the SVM was trained in the following way: To see if a model categorization performance significantly differs from chance , we did a permutation test by retraining the models after category-orthogonalized permutation of labels . RSA enables us to relate representations obtained from different modalities ( e . g . computational models and fMRI patterns ) by comparing the dissimilarity patterns of the representations . In this framework representational dissimilarity matrices ( RDMs ) are used for making the link between different modalities . RDM is a square symmetric matrix in which the diagonal entries reflect comparisons between identical stimuli and are 0 , by definition . Each off-diagonal value indicates the dissimilarity between the activity patterns associated with two different stimuli . RDM summarizes the information carried by a given representation from an area in the brain or a computational model . We had 96 stimuli , of which half were animates and the other half were inanimates . To calculate the RDM for a brain region or a computational model , a 96×96 matrix was made in which each cell was filled with the dissimilarity value between the response patterns elicited by two stimuli . For each pair of stimuli , the dissimilarity measure was 1 minus the Pearson correlation between the response patterns elicited by those stimuli in a brain region or a computational model . To judge the ability of a model RDM in explaining a brain RDM , we used Kendall's rank correlation coefficient τA ( which is the proportion of pairs of values that are consistently ordered in both variables ) . When comparing models that predict tied ranks ( e . g . category model RDMs ) to models that make more detailed predictions ( e . g . brain RDMs , object-vision model RDMs ) Kendall's τA correlation is recommended . In these occasions τA correlation is more likely than the Pearson and Spearman correlation coefficients to prefer the true model over a simplified model that predicts tied ranks for a subset of pairs of dissimilarities . For more information in this regard please refer to the RSA Toolbox paper [30] . This is the first toolbox to implement RSA . It is a modular and work-flow based toolbox that supports an analysis approach that is simultaneously data- and hypothesis-driven . There are a set of “Recipe” functions in the toolbox that allow automatic ROI analysis as well as whole-brain searchlight analysis . Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods . Figure 2 shows τA correlation of the hIT/mIT RDM with model RDMs . To estimate significance , randomization and bootstrap tests were used . Randomization tests permute the stimulus labels whereas bootstrap tests bootstrap resample the conditions set . The noise in the brain activity data has imposed limitations on the amount of dissimilarity variance that a model RDM can explain . Therefore an estimation of noise-ceiling was needed to indicate how much variance of a brain RDM –given the noise level– was expected to be explained by an ideal model RDM ( i . e . a model RDM that is able to perfectly capture the true dissimilarity structure of the brain RDM ) . The noise-ceiling in Figure 2A is shown by a gray horizontal bar . The upper and lower edges of this bar correspond to upper- and lower-bound estimates on the group-average correlation with the RDM predicted by the unknown true model . There is a hard upper limit to the average correlation with the single-subject reference-RDM estimates that any RDM can achieve for a given data set . Intuitively , the RDM maximizing the group-average correlation lies at the center of the cloud of single-subject RDM estimates . To find an upper bound , we averaged the rank-transformed single-subject RDMs and used an iterative procedure to find the RDM that has the maximum average Kendall's τA correlation to the single-subject RDMs . This average RDM can be thought of as an estimate of the true model's RDM . This estimate is overfitted to the single-subject RDMs . Its average correlation with the latter therefore overestimates the true model's average correlation , thus providing an upper bound . To estimate a lower bound , we employed a leave-one-subject-out approach . We computed each single-subject RDM's correlation with the average of the other subjects' RDMs . This prevents overfitting and underestimates the true model's average correlation because the amount of data is limited , thus providing a lower bound on the ceiling . For more information about the noise ceiling please refer to the toolbox paper [30] . We did not estimate a noise ceiling for the cell recording data , because our procedure requires several individuals to be measured and we only had data for two monkeys . To compare the categoricality in the models with the categoricality in human IT , we added Gaussian noise to the models to equate the level of noise in the models with that of the fMRI data . To this end , we averaged the pairwise correlation between the IT RDMs of the four human subjects; let's denote the obtained value with ‘q’ . Then to add the same amount of noise to the models , we iteratively and increasingly added noise to the model outputs until they reach the same level of noise as in human IT . The procedure for each model was that , we made new instantiations of that model by adding random Gaussian noise to the model output . We did this four times for each model , therefore having four noisy instantiation for each model . Then we made four model RDMs for each of the noisy model features , and calculated the mean of their pairwise correlation , which we denote by ‘qm’ . If the obtained mean is equal to the mean of the pairwise correlation between the four hIT RDMs , denoted by ‘q’ , ( i . e . ) we stop the iteration , otherwise the procedure is repeated and in each iteration the added noise to the model output is updated . At the end , when the stopping criterion is satisfied , the four model RDMs are averaged and used as the noise-equated model RDM . We used the experimental stimuli from Kriegeskorte et al . [7] . The stimuli were 96 images which half were animates and the other half were inanimates . The animate cluster consisted of faces and bodies , and the inanimate cluster consisted of natural and artificial inanimates . For cell recording data , we had 92 stimuli . To make 92×92 RDMs comparable with 96×96 RDMs , we made a 96×96 RDM from 92×92 RDM by filling the gaps with NaN . The fMRI and cell recording data , which we used here , have been previously described and analyzed to address different questions . See [6] , [7] , [18] for further experimental details . The IT representation has been described as categorical by some authors [7] and as a visual shape space by others [23] . How should a “categorical” representation be defined in this context ? One meaning of categoricality refers to the degree to which categorical divisions are explicit in the representation . The images themselves ( and their retinal representations ) clearly contain category information . However , this information is not explicit . Instead it requires a highly nonlinear readout mechanism commonly referred to as object recognition . An explicit representation is sometimes defined [78] , [80] as one that enables linear readout of the category dichotomy . Since linear readout is a trivial one-step operation in a biological neuronal network , this definition of “explicit” is arguably only slightly broader than requiring single-cell step-like responses encoding the category dichotomy . Linear discriminability does not require that the categories form separate clusters in the representational space . A bimodal distribution in representational space , with two clusters corresponding to the categories and divided by a margin or region of lower density , could be considered to be an even more explicitly categorical representation than one that merely enabled linear readout . Defining categoricality as the degree to which category information is explicit ( as all the above definitions do ) may be useful in some contexts . However , it misses a crucial point . Depending on the nature of the images and categories , “explicit” category representations could be observed in: ( 1 ) pixel images or color histograms , ( 2 ) simple computational features ( e . g . Gabor filters or gist features ) , ( 3 ) more complex unsupervised features ( e . g . HMAX features ) . If the features happen to be sufficiently correlated with a categorical division , these “visual” representations would be considered explicitly categorical by the above definitions . This illustrates the difficulty of drawing a clear line between visual and categorical ( or semantic ) representations . We would rather not refer to the representation as “categorical” when the categories are already separated in the distribution of the sensory input patterns . We therefore suggest a criterion distinct from category explicitness as the defining property of a categorical representation . A representation is “categorical” when it affords better category discriminability than any feature set that can be learned without category supervision , i . e . when it is designed to emphasize categorical divisions . A categorical representation in this sense can be interpreted as serving the function to emphasize behaviorally relevant categorical divisions or semantic dimensions . A category is a discrete semantic variable . A semantic representation could also include continuous variables that describe visual objects . Categorical clusters in the representational space do not require discrete categorical variables . A sufficient prevalence of continuous semantic variables that are correlated with a given categorical division could also produce categorical clusters . Future studies should investigate in greater detail whether the semantic component of the IT representation is better accounted for by categorical or continuous semantic dimensions . Several studies suggested that the IT representation is not purely visual but also semantic [7] , [14] , [22] , [81] . Our study provides additional support for this claim by showing that IT exhibits significantly stronger category clustering than a wide range of unsupervised models . It is impossible to prove that no visual feature model built without category-label supervision can explain the IT representation . However , our current interpretation is that IT reflects knowledge of category boundaries or semantic dimensions , and is thus not purely visual . This finding may appear to contradict a previous study suggesting that the IT representation is better accounted for by visual shape than by semantic category [23] . Note , however , that the representation of visual shape in IT is uncontroversial . A better account on the basis of visual shape does not preclude an additional semantic component . There is clearly a continuum between visual and semantic , between the representation of the appearance and the representation of the behavioral significance of an object . Our working hypothesis is that the function of the primate ventral stream is to achieve this transformation . Intermediate-level features detecting parts of objects ( e . g . eyes , noses , ears ) might provide a stepping stone toward semantics and could lead to clustering of faces and animates [82] , [83] . Recognition requires abstracting from several sources of within-category variation among object images . One source of variation lies in the accidental properties [84] of the appearance of the object , such as its pose , distance , and lighting . Another source of within-category variation are the substantial differences between exemplars . In our study , the winning model was supervised with category-labeled images , learning to abstract from both of these sources of variation . It would be interesting to investigate whether training a representation to abstract from accidental properties only with exemplar-label supervision ( where multiple images of the same particular object have the same exemplar label ) can also produce a representation similar to IT . To our knowledge , however , the previous studies [75] , [76] that investigated accidental property variation in greater detail also required category-label supervision to derive representational geometries resembling that of IT . In this study , we were looking to discover a model of the mechanism of biological object vision . We did not attempt to model the developmental process that builds that mechanism . Creating a viable model of IT appeared to require supervised learning . How might biological development implement this process ? Biologically plausible implementations of backpropagation and related rules for supervised learning have been proposed ( e . g . [85] ) . However , it is unclear what supervision signal such a process would use . What is the equivalent of the category labels in the biological development of the IT representation ? One possibility is that the perceptual and behavioral context provides the equivalent of the supervision signal in natural development . For example , visual images appearing in the same temporal context will often represent the same object in different retinal positions , poses , distances , and sizes . It has been argued that invariance to accidental properties can be learned from temporal proximity in natural experience [86]–[88] . Different visual images in the same temporal context will also tend to represent the same scene . A biological learning mechanism that associates visual inputs that tend to co-occur with similar representational patterns would learn features that are more stable across time , abstracting from rapidly changing aspects of visual appearance . Moreover , objects present in a given scene might tend to be semantically related . Such a mechanism might therefore even learn semantic features . Another way that context might provide a stepping stone toward a semantic representation is through perceptual channels beyond the current retinal image . Natural perception provides a rich multimodal and dynamic stream of information . Distinct visual patterns associated with similar context percepts might come to be represented together in the representational space . For example , visual motion is associated with animacy [89] , so dissimilar shapes associated with the same visual motion patterns might come to be co-located in the representational space . The argument from context can be extended to other sensory modalities ( e . g . the same sound associated with two distinct visual stimuli ) , and to behavioral and social context , which might contain signals correlated with the categories of the objects present in the scene [90] . Visually dissimilar stimuli may be associated with the same linguistic utterances of contemporaries , or with the same physical actions [91] or emotional states . Finally , the cognitive context , including conscious inferences based on our perception of the current scene and behavioral goals , might influence the development of the IT representation through feedback signals from frontal regions that provide an endogenous context to natural visual experience . An unsupervised learning process that receives such context signals alongside the visual input would be expected to cluster percepts that are similar in this more complex multimodal input space . The resulting representational clusters might then persist when the context is removed from the input and only static visual shapes are presented , as in our experiments . The argument from context illustrates how the distinction between supervised and unsupervised learning , which is clearly defined in computer science , is blurred for biological brains . Unsupervised learning from a richly contextualized sensory input might achieve a result similar to that of supervised learning . The ultimate purpose of vision is not to provide a veridical representation of our visual environment , but to support successful behavior . An explanation of the IT representation , then , requires consideration of behavioral affordances . It appears plausible that any primate faced with an unknown object might want to determine whether it is animate with high priority . Similarly , faces are important to recognize because they confer a host of information that renders animates somewhat more predictable . In computational modelling , such behavioral affordances can be brought in by optimizing the representations for particular categorization tasks , using supervised training . Such task-specific performance optimization appears essential to explaining IT . Models with higher recognition accuracy better explained not only the categorical clusters , but also the within-category representational geometries observed in IT . Our results suggest that the IT representation is visuo-semantic . Explaining IT requires consideration of the perceptual and cognitive context and of behavioral affordances . Through phylo- and ontogenesis , IT appears to have learned to emphasize certain behaviorally important divisions that transcend visual appearance and relate to the meaning of objects in the context of the organism's survival and reproduction .
Computers cannot yet recognize objects as well as humans can . Computer vision might learn from biological vision . However , neuroscience has yet to explain how brains recognize objects and must draw from computer vision for initial computational models . To make progress with this chicken-and-egg problem , we compared 37 computational model representations to representations in biological brains . The more similar a model representation was to the high-level visual brain representation , the better the model performed at object categorization . Most models did not come close to explaining the brain representation , because they missed categorical distinctions between animates and inanimates and between faces and other objects , which are prominent in primate brains . A deep neural network model that was trained by supervision with over a million category-labeled images and represents the state of the art in computer vision came closest to explaining the brain representation . Our brains appear to impose upon the visual input certain categorical divisions that are important for successful behavior . Brains might learn these divisions through evolution and individual experience . Computer vision similarly requires learning with many labeled images so as to emphasize the right categorical divisions .
[ "Abstract", "Introduction", "Results", "Materials", "and", "Methods", "Discussion" ]
[ "computer", "and", "information", "sciences", "computer", "vision", "computational", "neuroscience", "biology", "and", "life", "sciences", "computational", "biology", "neuroscience" ]
2014
Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation
The development of morphological traits occurs through the collective action of networks of genes connected at the level of gene expression . As any node in a network may be a target of evolutionary change , the recurrent targeting of the same node would indicate that the path of evolution is biased for the relevant trait and network . Although examples of parallel evolution have implicated recurrent modification of the same gene and cis-regulatory element ( CRE ) , little is known about the mutational and molecular paths of parallel CRE evolution . In Drosophila melanogaster fruit flies , the Bric-à-brac ( Bab ) transcription factors control the development of a suite of sexually dimorphic traits on the posterior abdomen . Female-specific Bab expression is regulated by the dimorphic element , a CRE that possesses direct inputs from body plan ( ABD-B ) and sex-determination ( DSX ) transcription factors . Here , we find that the recurrent evolutionary modification of this CRE underlies both intraspecific and interspecific variation in female pigmentation in the melanogaster species group . By reconstructing the sequence and regulatory activity of the ancestral Drosophila melanogaster dimorphic element , we demonstrate that a handful of mutations were sufficient to create independent CRE alleles with differing activities . Moreover , intraspecific and interspecific dimorphic element evolution proceeded with little to no alterations to the known body plan and sex-determination regulatory linkages . Collectively , our findings represent an example where the paths of evolution appear biased to a specific CRE , and drastic changes in function were accompanied by deep conservation of key regulatory linkages . Recurrence is a widespread phenomenon in evolutionary biology [1] , where similar derived traits have often been found to evolve in parallel . This theme of recurrence extends to the molecular level , as the same genes are often targeted by evolutionary change to generate convergent phenotypes [2] . Illustrative examples include Pitx1 for pelvic reduction in stickleback fish [3] , Oca2 for cavefish albinism [4] , svb for fruit fly larval trichome loss [5] , yellow for fruit fly wing pigmentation spots [6] , Mc1r for vertebrate melanism [7] , [8] , and ATPα for insect [9] and RNASE1 for monkey dietary specializations [10] . These examples of mechanistically biased evolution include gene duplications [9] , [10] , amino acid altering mutations [4] , [7]–[10] , and mutations that modify gene regulatory sequences [6] , [11] , [12] . While the phenomenon of recurrent evolution of regulatory sequences is now well established , a mechanistic understanding of how transcriptional regulatory sequences change function is still in its infancy . Specifically , does bias in the path of evolutionary change extend to the level of individual protein-DNA interactions in the regulatory sequences that influence transcription ? Traits are generated during development through the combined activities of cooperating genes [13]–[15] . Most genes are composed of a coding sequence , and non-coding sequences that include one or more cis-regulatory elements ( CREs ) that control a gene's overall expression pattern [16] . CREs possess binding sites for numerous transcription factor proteins [17] , where each unique transcription factor and binding site ( s ) interaction can be considered a “regulatory linkage” . The types of linkages and their organization form a “regulatory logic” that integrates the regulatory state of a cell , and thereby directs a spatial and temporal output pattern of gene expression [15] . For a given trait , the multitude of genes , their coding and non-coding regions , and CRE regulatory linkages present an abundance of mutational targets to alter the phenotype . Hence , it might be expected that the genetic path of evolution could proceed by many routes and resultantly , would appear unpredictable in retrospect . However , mutations that are pleiotropic often reduce fitness [18] and bear considerable deleterious effects [16] . As a result , evolution may more readily proceed by paths that minimize pleiotropy [19] . It is unclear whether and how pleiotropy constrains the path of regulatory logic evolution: the gain and loss of binding sites for transcription factors . Relatively few cases of CRE evolution have been characterized in sufficient detail [20]–[28] , and often a connection remains to be made between the causative mutations and the molecular mechanisms of evolved activity [6] , [29]–[38] . Furthermore , a small number of studies have investigated the pleiotropic consequences of a CRE's evolution . Thus , an important research goal is to advance a general understanding of the paths by which CRE function evolves . Extant CREs appear to be elegantly built with an intricate regulatory logic of transcription factor binding sites , and yet , when a CRE's function changes , how many steps does it take ? Do the relevant mutations create or destroy binding sites for transcription factors that already interact with the CRE , or do they represent new factor inputs ? If a model exists where independent paths of evolution can be traced in parallel , one could assess the general attributes of successful paths of CRE divergence . One suitable model is the sexually dimorphic abdominal pigmentation exhibited among species within the Sophophora subgenus of Drosophila , which includes the model organism species Drosophila ( D . ) melanogaster . The fruit fly abdomen consists of ten abdominal segments ( annotated A1–A10 ) , the first seven of which are covered by dorsal cuticle plates ( tergites ) . For D . melanogaster , tergite pigmentation is sexually dimorphic , where the male A5 and A6 tergites are completely pigmented ( Figure 1A ) and female pigmentation is typically restricted to a posterior stripe similar to that observed on the more anterior A2–A4 tergites of both sexes ( Figure 1B ) . These sex-specific phenotypes are the outcomes of a regulatory network that includes prominent genes from the body plan and sex determination pathways . The HOX protein ABD-B is expressed in segments A5 and A6 of both sexes [39] , [40] , and positively activates a melanin synthesis enzyme that generates dark color [23] . While ABD-B provides body-plan positional information to activate pigmentation enzymes , their male-limited expression results from the sexually dimorphic expression of the tandem duplicate bab1 and bab2 genes ( collectively bab , Figure S3A ) . These paralogous genes encode the transcription factors Bab1 and Bab2 ( collectively Bab ) that function as repressors of pigmentation development [41] , [42] . In the pupal abdomen , both Bab1 [20] and Bab2 [42] are expressed in the A2–A7 segments of females , whereas male expression is limited to segments A2–A4 . Bab expression in female posterior abdominal segments is controlled by a CRE located in the first intron of bab1 named the dimorphic element ( Figure S3A ) . This CRE contains regulatory linkages with the Hox protein ABD-B and sex-specific DSX protein isoforms through its possession of multiple binding sites for these two transcription factors . Thus , the dimorphic element functions as a sexually dimorphic genetic switch controlling Bab expression . In males , ABD-B and DSXM ( male DSX isoform ) binding to this CRE represses Bab expression in segments A5 and A6; whereas in females , ABD-B and DSXF ( female DSX isoform ) binding activates Bab expression at increasing levels from the A5 segment to the more posterior A7 segment [20] . The bab genes have been implicated in both intraspecific and interspecific pigmentation evolution . Variation in female abdomen pigmentation exists among D . melanogaster populations [43]–[46] and in some cases this variation has been linked to genetic differences at the bab locus [47] , [48] . Within the Sophophora subgenus of Drosophila , large-scale differences in pigmentation have been attributed to altered dimorphic element activity and consequent Bab expression [20] . Furthermore , male-specific pigmentation and underlying dimorphic Bab expression are inferred to be the derived state , evolving from an ancestor with sexually monomorphic Bab expression and pigmentation [23] . This ancestor possessed a CRE orthologous to the dimorphic element that drove Bab expression in the A7 and A8 segments ( presumptive genitalia ) of females [20] , where it presumably regulated the development of other dimorphic traits [41] , [42] . In the lineage of D . melanogaster , the dimorphic element was modified to drive female-specific expression in the more anterior A6 and A5 segments . This expanded Bab expression pattern was essential to limit full tergite pigmentation to the male A5 and A6 segments . Surprisingly , the ancestral dimorphic element was inferred to have possessed both the orthologous Dsx binding sites and 13 of the 14 Abd-B sites found in the D . melanogaster CRE . An amalgam of changes were introduced along an evolutionary path of greater than 30 million years to arrive at the derived activity; including Abd-B binding site number , Dsx site polarity , and the spacing between conserved binding sites [20] . Whether gains and losses of other regulatory linkages were a part of this transition remains unknown . Moreover , the simplicity and multiplicity of the mutations that occurred over this mesoevolutionary timescale [49] , [50] inspired several questions: Do evolutionarily relevant mutations in the dimorphic element occur over microevolutionary time scales ? Have orthologous dimorphic elements been repeatedly functionally modified ? Do commonalities exist between independent cases of dimorphic element evolution ? Here , we implicate alterations in the bab dimorphic element as an underlying cause of the recurrently evolving diversity of female abdomen pigmentation at both the intraspecific and interspecific scales of comparison . Using this system to examine the evolution of regulatory logic along parallel paths , we characterized the mutational paths of dimorphic element divergence responsible for the diversification of intraspecific phenotypes using a gene reconstruction approach [51] . Inferring the ancestral dimorphic element sequence of extant D . melanogaster populations , we found that a small number of functionally-relevant mutations altered the ancestral CRE's regulatory activity to generate derived capabilities . Intriguingly , mutations largely avoided the ancestral ABD-B and DSX regulatory linkages , presumably to preserve the ancestral function of this CRE in the A7 segment and genitalia where it presides over other dimorphic aspects of abdominal development . While not definitive , these results can be viewed to support the notion that evolution can be biased to follow certain paths and such biases can pertain not only to certain genes in a network , or particular CREs , but that bias also permeates in how a CRE's encoded regulatory logic evolves . Bab expression in the female A5 through A8 abdominal segments of D . melanogaster is driven by the dimorphic element . This regulatory activity evolved from an ancestral state limited to the female A7 and A8 segments since the most recent common ancestor of D . melanogaster and D . willistoni , species that diverged over 30 million years ago [20] , [52] . It remained unknown whether the functional evolution of this CRE was limited to mesoevolutionary timescales , or whether recent transitions in activity occurred over microevolutionary timescales to diversify pigmentation patterns . Thus , we surveyed individuals from geographically diverse populations of D . melanogaster to identify those that differ in the extent of dimorphic abdominal pigmentation ( Figure S1 ) . In contrast to the invariant male pigmentation phenotype ( Figure S1 and Figure 1A ) , the extent of pigmentation varied greatly among the female A5 and A6 tergites ( Figure S1 , and Figure 1B–H ) . Phenotypes ranged from unpigmented tergites that bear only a posterior stripe of pigment ( e . g . Figure 1B ) to complete A6 pigmentation ( Figure 1H ) , extending in one instance to the A5 tergite ( Figure 1G ) . We suspected that these “Light” and “Dark” pigmentation phenotypes stem from differences in Bab expression , due to dimorphic element alleles with different regulatory activities . Indeed , sequencing of dimorphic element alleles isolated from twenty seven separate populations revealed many genetic differences ( Figure S2 ) . To test whether the observed genetic variation could cause divergent dimorphic element activities , we tested a subset of these alleles for the ability to drive GFP reporter gene expression ( referred to as regulatory activity ) in transgenic pupae . Relative to a previously characterized dimorphic element allele [20] , we observed female regulatory activities ranging from 182±10% down to 9±2% ( Figure 1B′–1H′ ) , a 20 fold difference between the extreme alleles . Additionally , the level of dimorphic element activity generally correlated with the extent of female pigmentation ( Figure 1 ) , suggesting that this allelic variation is not coincidental but contributes to this variable phenotype . The correspondence between dimorphic element allele activity and pigmentation was suggestive of causation . Hence , we performed a series of genetic tests to further implicate the bab locus , and more importantly , the dimorphic element . First , we sought a genetic association between dimorphic element allele genotype and pigmentation phenotype . Males from a stock that produces a “Light” female pigmentation phenotype ( called Light 1 , Figure 1D and S1A ) were separately crossed to females from two different population stocks that exhibit a “Dark” female pigmentation phenotype ( called Dark 1 , Figure 1G and S1AM; and called Dark 2 , Figure 1H and S1AJ ) . F1 siblings were crossed to derive F2 progeny . The phenotypes of 102 F2 female progeny from the Light 1× Dark 1 cross were evaluated and 25 , 54 , and 23 respectively had Light , Intermediate , and Dark female pigmentation ( Figure 2B–2D ) . This near 1∶2∶1 ratio ( chi square p = 0 . 787 ) is indicative that this variable phenotype is largely due to a single semi-dominant gene . A subset of the F2 progeny were genotyped for a BstXI restriction fragment length polymorphism ( RFLP ) present in the Light 1 dimorphic element allele but not the Dark 1 allele . We found an invariant association between female progeny with the Light ( Figure 2B ) and Dark ( Figure 2D ) phenotypes respectively with homozygous genotypes for the Light 1 and Dark 1 dimorphic element alleles ( Table S1 ) . Moreover , females with an intermediate phenotype were heterozygous for this RFLP . We also found a similar genetic association for the F2 progeny hailing from the cross of Light 1 and Dark 2 ( Table S2 ) . After backcrossing the Dark 1 phenotype into the Light 1 genetic background for ten generations , we found that two independent backcross lines retained a Dark 1 bab locus haplotype ( Figure S3F ) . Thus , the bab locus or something in close linkage causes this strain's Dark phenotype . We performed genetic complementation tests to rule out the possibility that the genotype-phenotype associations were due to a variant linked to the bab locus . Light 1 and Dark 1 individuals were separately crossed to individuals with a bab locus null allele and pigmentation phenotypes were assessed for F1 progeny . Homozygous bab null mutants exhibit phenotypes present in both sexes , including fusion of the TS5 , TS4 , and TS3 leg tarsal segments and ectopic pigmentation on the A2–A4 segment tergites ( Figure 2P and 2H ) , and several phenotypes limited to females . These female phenotypes include male-like pigmentation on the A5 and A6 tergites , posterior to anterior transformations of the A6 , A7 and A8 ( genitalia ) segment morphologies [41] , [42] ( Figure 2H and 2L ) . While the Light 1 , Dark 1 , and Dark 2 bab loci complemented the bab null allele ( bab- ) with respect to the leg , A2–A4 tergite pigmentation , and female A7–A8 segment phenotypes , only the Light 1 locus fully-complemented the bab null allele with respect to female A5 and A6 tergite pigmentation ( compare Figure 2E to 2F and 2G ) . These same patterns of complementation and non-complementation were reproduced when Light and Dark lines were crossed to a deficiency line that included the entire bab locus ( not shown ) , suggesting that the abdominal pigmentation phenotype is not due to mutations in the genetic background of the bab null allele , but rather allelic variation at bab between Light and Dark strains . Collectively , the most parsimonious conclusion from the genotype-phenotype association , genetic mapping , and complementation results is that the genetic basis for these Light and Dark female pigmentation phenotypes reside largely within the bab locus . The failure of Dark lines to complement female A5/A6 phenotypes , whilst otherwise rescuing body-wide phenotypes of the bab null allele , suggested the existence of regulatory mutations underlying this phenotypic variation . Although a small number ( 6 ) of non-synonymous mutations were found that could potentially contribute to variation in abdominal pigmentation by altering Bab protein function ( Figure S4 ) , we pursued the hypothesis that relevant mutations would be located in the dimorphic element since this CRE controls Bab activity in the segments where bab-regulated phenotypes vary among the studied populations . Considering that the phenotypic effects of these naturally occurring dimorphic element alleles and pigmentation phenotypes were restricted to the A6 and to a lesser extent the A5 abdominal segment ( Figure 1 ) , we suspected that mutations in the dimorphic element could cause the observed differences in pigmentation . This hypothesis would be supported by differing levels and/or patterns of Bab expression in the pupal abdominal epidermis for females that develop different pigmentation phenotypes . Thus , we characterized the pattern of Bab expression in the abdominal epidermis at the end of pupal development when tergite pigmentation is being specified . If the regulatory activity for the dimorphic element alleles identified in reporter transgene assays ( Figure 1 ) were indicative of the endogenous Bab expression , then Bab1 and Bab2 expression should be elevated in females with Light tergite pigmentation compared to those with Dark pigmentation . Consistent with this expectation , Bab1 and Bab2 were expressed robustly throughout the A2–A7 abdominal segments of Light 1 females ( Figure 3A and 3F ) , while Bab1 and Bab2 expression were reduced in the A5 and A6 abdominal segments of Dark 1 female pupae ( Figure 3B and 3G , red arrowheads ) . This reduction corresponds with the reduced regulatory activity of this strain's dimorphic element allele ( Figure 1G′ ) and where the pigmentation develops on adult females ( Figure 1G ) . Compared to Dark 1 females that possess expanded pigmentation on the A5 and A6 tergites , expanded pigmentation is limited to the A6 tergite of Dark 2 females ( Figure 1H ) . Consistent with the Dark 2 phenotype , the expression of Bab1 , but not Bab2 , was reduced in the A6 segment and to a lesser extent the A5 segment ( Figure 3C and 3H ) . These patterns of expression are consistent with the finding that the bab1 null pigmentation phenotype is limited to the female A6 tergite , whereas a bab2 null phenotype affects both the A6 and A5 tergite [41] . We also characterized Bab expression in the developing female genitalia and analia that respectively develop from the A8 and A9/A10 segments . In contrast to the reduced expression seen in the A5 and A6 segments epidermis of Dark 1 females , expression in these more posterior structures was comparable to that observed for Light 1 females ( compare Figure 3D and 3I to 3E and 3J ) . Collectively , the genetic and expression data strongly supports the conclusion that the conspicuous Light and Dark female pigmentation phenotypes are due , at least in part , to allelic differences in dimorphic element regulatory activity . We were interested in revealing how these modified regulatory activities evolved . To accomplish this , it was essential to know the ancestral sequence and regulatory state . Ancestral Sequence Reconstruction ( ASR ) has been an effective approach to study the path of protein functional evolution [51] , [53] . This approach , to our knowledge , had been used only sparingly to study CRE evolution in Drosophila [36] , and primates [34] , [54] , presumably due to the fact that CRE sequences evolve at an accelerated rate compared to protein coding sequence [55]–[57] , making reconstruction untenable when comparing organisms of distantly-related taxa . In the case here , the dimorphic element alleles share an ∼98% sequence identity ( Figure S2 ) and a most recent common ancestor of extant Drosophila melanogaster populations that existed ∼60 , 000 years ago [58] . Hence , we suspected that the ancestral sequence for these populations could be reasonably inferred . The dimorphic elements from 27 populations of D . melanogaster were sequenced and aligned to those from several outgroup species . From this alignment ( Figure S2 ) , we used the principle of parsimony to infer the nucleotide state at each position for the most recent common ancestor of the D . melanogaster populations , including 52 polymorphic sites; a sequence that was named the “Concestor element” [59] . For this sequence , the ancestral nucleotide states were unambiguous at 44 of the 52 sites . To test the robustness of this sequence's regulatory activity to the ambiguous eight sites , we tested alternate reconstructions that differed in the nucleotide states for these sites . We determined the regulatory activities for these reconstructions were comparable to that for the Concestor element ( See “Evolutionary Robustness in Dimorphic Element Reconstruction” , Figure S2 and S6 ) . Therefore , we sought to identify which of the 44 unambiguous derived mutations were responsible for the diverse regulatory activities possessed by the Light and Dark alleles . From this point forward , the Concestor element sequence was utilized for the ancestral sequence and regulatory activity state . Several observations were made from a comparison of the Concestor element sequence to the dimorphic element alleles ( Figure 4A–4E ) . First , the Concestor element possessed all of the ABD-B ( 14 ) and DSX ( two ) sites that were characterized for the D . melanogaster CantonS strain sequence [20] . Second , the Light 1 , Light 2 , Dark 1 , and Dark 2 alleles respectively differ from the Concestor element by 20 , 20 , 22 , and 20 derived mutations ( Figure 4A–4E , vertical red lines ) , many of which are common to multiple alleles ( Figure S2 ) . Third , we observed an excess of nucleotide substitutions relative to indel mutations ( Figure 4B–4E , thin versus thick red lines ) . Fourth , of the known binding sites , the only site gain/loss event caused by a derived mutation was ABD-B binding site 10 , which was lost in the Dark 1 and Dark 2 alleles ( caused by mutation “G” , Figure S2 ) . With the dimorphic element alleles differing in regulatory activity by up to 20 fold ( Figure 1 ) , we wanted to evaluate how these activities compare to that of the Concestor element . The regulatory activities were evaluated for the Light 1 , Light 2 , Dark 1 , Dark 2 , and Concestor element in a quantitative reporter transgene assay [60] . The Concestor element drove GFP expression in females throughout the epidermis of the A6 and A7 abdominal segments and the genitalia , and at a comparatively lower level in segment A5 ( Figure 4A′ and 4A″ ) . Compared to the Concestor element's regulatory activity , the Light 1 and 2 alleles' activities were increased in the A6 segment to 184±8% and 220±8% of concestor , respectively ( Figure 4B′ and 4C′ ) . Moreover , the Light 2 activity was increased in the A5 segment and expanded into the posterior region of segment A4 . Conversely , compared to the Concestor element the A6 segment regulatory activities for the Dark 1 and Dark 2 alleles were reduced to 58±4% and 27±3% respectively ( Figure 4D′ and 4E′ ) . Additionally , the range of regulatory activities for the A6 segment was much greater than that for the A7 segment and genitalia ( Figure 4A″–4E″ ) . These results demonstrate that the ancestral dimorphic element for extant D . melanogaster populations drove low , modest , and high levels of bab expression respectively in the female A5 , A6 , and A7–A8 segments ( Figure 4 ) . This ancestral regulatory element was modified by mutation events resulting in derived alleles that include increased , expanded , and reduced activities in the relatively more anterior abdominal segments . We next sought to determine which of the derived mutations were functionally-relevant to the evolved regulatory activities . In order to identify allele sub-regions that possess functionally-relevant mutations , we created a series of chimeric dimorphic elements and quantitatively compared their regulatory activities to that of the Concestor element . Each chimeric element was composed in part of Light 2 or Dark 1 allele sequence and the remaining sequence was from the Concestor element ( Figure S5 ) . For the chimeric elements containing some Light 2 dimorphic element sequence , most of this allele's derived activity was conveyed by the central “core” region that is occupied by the previously characterized binding sites for the ABD-B and DSX transcription factors . The Light 2 core flanked by Concestor element sequences had a regulatory activity of 239±5% , compared to 153±10% when the Concestor element core was within Light 2 flanks ( Figure S5E and S5F ) . A similar outcome was found for the Dark 1 dimorphic element . When this allele's core sequence was flanked by Concestor element sequences , the chimeric element had an activity of 58±5% , whereas the reciprocal swap had no regulatory activity effect ( 106±2%; compare Figure S5J to S5K ) . Thus , for these two derived dimorphic element alleles , their unique regulatory activities principally stem from mutations in the core region . The Light 2 core region has seven derived mutations ( referred to as the “C” , “F” , “H” , “J” , “K” , “L” , and “N” mutations , Figure S2 ) , four of which also reside in the Light 1 core ( C , F , K , and N ) . We individually substituted each of these mutations into the Concestor element in place of the ancestral nucleotide , and then tested whether these substitutions caused measurable effects on regulatory activity ( Figure S6 ) . Large mutational effects were only measured for the C , F , and L mutations; respectively these substitutions increased Concestor element activity to 140±6% , 160±6% , and 215±4% ( Figure S6G , 5I and 5J ) . The C mutation is present in both the Light and Dark alleles being studied ( Figure S2 ) and hence , cannot account for their differences in regulatory activity . When the F and L mutation were substituted together , regulatory activity was measured at a nearly additive 241±9% ( Figure S6S ) . The Light 1 core differs from that of Light 2 by possessing a derived mutation , called “I” and lacking the L mutation . However , the I mutation had no affect on regulatory activity when it was substituted into the Concestor element ( Figure S6M ) . Collectively , the derived regulatory activities of the Light 1 and 2 dimorphic element alleles both require the F mutation ( Figure 5D and 5I ) , and the further increased and spatially expanded activity of the Light 2 allele requires the L mutation ( Figure 5E and 5J ) . The Dark 1 core sequence possesses six derived mutations that include: the “C” , “D” , and “G” mutations , each of which also reside in the Dark 2 allele , and the “M” mutation that is unique to the Dark 1 . This core also has the “H” and “K” mutations that alter the C and T nucleotide expansions , though these occur in the Light alleles and were found not to cause significant regulatory effects ( Figure S6L and S6O ) . Interestingly , the G mutation had no measurable effect on activity ( Figure S6K ) , although it was the only one found to alter a known ADB-B site among the surveyed dimorphic element alleles . We conclude that the diversity of regulatory activities observed did not involve changes to the regulatory linkage between ABD-B and the dimorphic element . Testing the D and M mutations highlighted the functional relevance of the D mutation . When individually substituted into the concestor element , the D and M mutations respectively altered regulatory activity to 68±4% and 118±3% of the Concestor element ( Figure S6H and S6Q ) . Though , when both the D and M mutations were substituted together , the net result was an activity of 68±3% ( Figure S6T ) . Thus , the strong effect of the D mutation is epistatic to the moderate effect of M . As the complete Dark 1 core inserted between Concestor element flanking sequences had a regulatory activity of 58±5% , one or more core mutations must further reduce the Dark 1 allele's activity , either by increments below our capability to detect or through epistatic interactions . However , the D mutation is responsible for most of this allele's reduced regulatory activity ( Figure 5B and 5G ) . We next sought to find mutations underlying the further reduced regulatory activity of the Dark 2 allele . Like Dark 1 , this allele possesses the D mutation , indicating the existence of an additional functionally-relevant mutation ( s ) in the core element . The only mutation unique to the Dark 2 core region was a 9 base pair deletion referred to as the “E” mutation . When the E mutation was substituted into the Concestor element , regulatory activity was reduced to 78±2% ( Figure 5C and 5H ) . Moreover , the Dark 1 allele's activity was 58±4% . The addition of the E mutation to this allele lowered activity to 34±2% , near the 27±3% activity of the Dark 2 allele ( Figure S6U ) . Collectively , the evolutionary paths of the Dark 1 and Dark 2 alleles include one shared functionally-relevant mutation and one that is unique to the Dark 2 allele . The derived E mutation deletes nine base pairs , and the 9th base pair is the first base pair for a DSX binding site ( called Dsx1 , Figure 5C ) , though this mutation creates a sequence that still matches the consensus motif for Dsx binding [61] . Mutational ablation of the Dsx1 site reduced the Concestor element's regulatory activity in the female A6 segment to 67±6% and raised activity in males from 6±2% to 73±5% ( Figure S6Y-S6AA ) . This demonstrated that the Dsx1 site was necessary for robust female-specific regulatory activity . A priori , the E mutation could alter the quality of this Dsx1 site or reduce this allele's activity through other mechanisms . Such alternate mechanisms include: removing a binding site for a neighboring transcriptional activator , the formation of a novel binding site for a repressor , or by placing the Dsx1 site close to an adjacent transcription factor site . To obtain evidence supporting either of these mechanisms , we created and measured the regulatory activities for a set of modified Concestor elements with alterations to ancestral sequence at the E mutation region ( Figure 5K ) . First , we introduced non-complementary transversions in the Concestor element at the 2nd , 4th , 6th , and 8th base pairs of the E mutation ( E Scramble ) . Here , the 9th base pair and hence the consensus DSX binding site was not altered , but the other mutations would seemingly degrade an adjacent transcription factor binding site . This set of mutations did not alter Concestor element activity , indicating the E mutation did not delete a binding site adjacent to that of the DSX site . To disentangle regulatory effects due to the loss of sequence next to the Dsx1 site from loss of the 1st base pair of the DSX site , we created two separate modifications to the Concestor element . One modification was a deletion of the first eight base pairs of the E mutation ( called E8Del ) , and the second removed only the ninth base pair of the E mutation , which is the first of the Dsx1 site ( called E Dsx1 ) . Surprisingly , the 8 base pair deletion modestly increased Concestor activity to 118±3% , indicating that the E mutation's impact was not due to reduced spacing between the Dsx1 site and a more remote transcription factor binding site . The other modification , a deletion of only the 9th base pair of the E mutation , reduced Concestor element activity to 80±3% . This reduction was nearly equal to that induced by the complete E mutation ( Figure 5K ) . Collectively , these results demonstrate that the E mutation rendered the Dsx1 site less functional . One possible mechanism is that the E mutation made a derivative Dsx1 site with reduced affinity for the DSX protein . In order to validate this possibility , we compared the binding of the DSX DNA-binding domain ( DBD ) to the Concestor element , E mutant , and knockout ( KO ) Dsx1 site sequences in gel shift assays ( Figure 5L ) . The Concestor element sequence was bound with high affinity by the DSX protein , and specifically as the KO site sequence is not readily bound ( compare 5L lanes 1–7 to lanes 15–19 ) . In comparison , DSX bound the site with the E mutation with reduced affinity compared to the wild type site ( Figure 5L , lanes 8–14 ) . A shift of the Concestor Dsx1 site was evident with as low as 16 ng of DSX protein , whereas binding of the E mutant site was not detected with this amount of DSX , but was with 32 ng ( compare Figure 5L lane 3 to lanes 10 and 11 ) . From these data , we estimate that the E mutation resulted in a Dsx1 site with ∼50% of the Concestor element site's affinity for the DSX protein . Of the four prominent functionally-relevant mutations identified for the Light and Dark dimorphic element alleles ( Figure 5 ) , only one affects a known regulatory linkage . Specifically , the E mutation weakens the regulatory linkage between DSX and the dimorphic element by creating a lower affinity binding site . The D , F , and L mutations appear unremarkable compared to the other mutations that had no measureable regulatory effects ( Figure S6 ) . Moreover , the D , F , and L mutations caused regulatory effects comparable in magnitude to mutations implicated in the mesoevolutionary expansion of dimorphic element activity into the A6 and A5 segments [20] . Hence , it can be concluded that short mutational paths are sufficient to evolve pronounced alterations in this CRE's activity . This conclusion inspired the hypothesis that changes in female abdominal pigmentation may frequently occur through the alteration of the dimorphic element via similarly short paths . In the oriental lineage of the Sophophora subgenus , males of extant species generally are fully pigmented on the A5 and A6 tergites [23] . Female pigmentation is more variable , ranging from the complete absence of pigmentation like that seen for D . fuyamai , to a more male-like pattern like that seen for D . yakuba ( Figure 6 ) . Bab2 expression was found to be robustly sexually dimorphic for D . fuyamai [42] , and Bab1 expression is reduced in the A5 and A6 segments of females ( Salomone and Williams , unpublished data ) . These observations suggest that differences in Bab expression contribute to these different female pigmentation patterns . Multiple mechanisms could underlie these differences in Bab expression , including a change in the activity of or the expression pattern for a trans-acting regulator of the dimorphic element ( trans-regulatory evolution ) . An alternative mechanism is through changes in orthologous dimorphic elements that result in differing responses to a conserved set of trans-regulators ( cis-regulatory evolution ) . An effective test to distinguish between instances of cis- and trans-regulatory evolution is to compare the activities of CREs in a common genetic background and observe whether reporter expression patterns resemble that of the host species ( trans-regulatory evolution ) or the species from which the CRE was derived ( cis-regulatory evolution ) [62] . We isolated orthologous dimorphic elements from D . yakuba , D . fuyamai , and an outgroup species D . auraria ( from the Sophophora montium group ) that is also sexually dimorphic for pigmentation and Bab expression though limited to the A6 segment [42] . The regulatory activities for these orthologous CREs were evaluated in transgenic D . melanogaster pupae and normalized to the Concestor element ( Figure 6 ) . The D . auraria dimorphic element exhibited an A6 segment regulatory activity of 51±3% of the Concestor element's activity ( Figure 6Q ) . The regulatory activity of the D . fuyamai element was 209±10% ( Figure 6O ) and extended into segments A5-A2 . The A6 regulatory activity for D . yakuba was 62±7% ( Figure 6M ) . These results support a scenario where evolutionary changes in the extents of female posterior abdomen pigmentation for the presented clade ( Figure 6 ) occurred , at least in part , via cis-regulatory evolution that altered the activity of orthologous dimorphic elements . Interestingly , of the 14 ABD-B and two DSX sites typical of the D . melanogaster dimorphic element , the orthologous D . yakuba and D . fuyamai sequences had the same 13 of the 14 ABD-B sites and both DSX sites ( Figure S2B ) . Even the D . auraria dimorphic element , the most distantly related in this comparison , possessed 12 ABD-B sites and both DSX sites . Thus , like the situation for the D . melanogaster dimorphic element alleles , the functional diversification of these orthologous CREs occurred largely , if not entirely , by modifying CRE properties other than the ABD-B and DSX regulatory linkages . The collaborative interactions of genes during development are hierarchically structured through the formation of a gene network at the level of expression [15] . At the top of these networks are patterning genes , prominently transcription factors that can form connections directly with CREs of differentiation genes , or with CRE ( s ) of intermediate level transcription factors that act as “Input-Output switches” [15] , [19] . For the latter , the inputs are converted into a regulatory output that is directed to multiple target genes . On one hand , mutations altering a patterning gene may be sufficient to alter a network's phenotype , but these highly pleiotropic mutations tend to alter other phenotypes too , typically in a deleterious manner [63] . On the other hand , mutations altering the function of a single differentiation gene , while generally less pleiotropic often are insufficient to alter a phenotype . For these reasons , evolution may be biased to target Input-Output genes , an expectation that has been observed for several traits [19] . In the D . melanogaster pigmentation network , the bab genes function as an Input-Output node through the dimorphic element's integration of patterning inputs that include body plan ( ABD-B ) and sex determination ( DSX ) pathway inputs ( Figure 7A ) . These inputs are converted into a female-specific pattern of expression that culminates in the repression of the differentiation genes yellow and tan in females [23] , ( Figure 7C ) . In principle , changes in the expression or activity of a patterning gene , differentiation gene , or the Input-Output gene ( bab ) could alter pigmentation phenotypes . In application though , it is logical that bab expression and dimorphic element encodings were modified as those alterations minimize negative pleiotropic effects while being sufficient to alter the female pigmentation phenotype . For example , ectopic yellow expression failed to create additional melanic pigmentation [64] , [65] , and changes in either DSX or ABD-B expression result in ectopic abdominal pigmentation in addition to several other trait phenotypes [20] , [23] , [66] . Thus , sufficiency for pigmentation is counterbalanced by the negative pleiotropic affects for these genes . In contrast , increased Bab expression in the A5 and A6 segments was sufficient to suppress pigmentation , and ectopic abdomen pigmentation develops in bab heterozygous and homozygous null mutant females ( Figure 2E and 2H ) . Bab though is not dedicated to pigmentation [41] , [42] . In the pupa , Bab expression includes the leg tarsal segments , abdomen epidermis , sensory organ precursor cells , oenocytes , and dorsal abdominal muscles , and each of these expression patterns are governed by a modular CRE ( s ) [20] . Thus , Bab itself is highly pleiotropic , however it's CREs are far less pleiotropic . For this reason , mutations altering female pigmentation would maximize sufficiency and minimize pleiotropy if they occurred in the dimorphic element , an expectation borne out in this study . Pigmentation of the A5 and A6 segments , though , is only one of many traits influenced by the regulatory activity of the dimorphic element . This CRE drives Bab expression in the female A7 and A8 segments , regulating numerous female-specific traits , including the size , shape , trichome density , and bristle morphologies of the resident dorsal tergites and ventral sternites [41] . As expression in these more posterior segments require the ABD-B and DSX regulatory linkages , these regulatory linkages remain highly pleiotropic . For this reason , it seems logical that evolution would disfavor mutations that have deleterious consequences to these linkages and favor mutations that alter other CRE properties . This scenario reflects how dimorphic element function was modified in both the intraspecific and interspecific comparisons presented here as well as the long term conservation of the ABD-B and DSX linkages previously described [20] . Our findings provide a unique contrast with previous investigations of the relationship between CRE conservation and CRE evolution . Although Drosophila non-coding DNA , including CRE sequences , evolves slower than synonymous sites [55] , several well studied CREs were found to undergo substantial sequence evolution without matching regulatory activity evolution . During Drosophila embryonic development , the pair-rule gene even-skipped ( eve ) is expressed in seven stripes along the anteroposterior axis , with the second stripe of eve expression being specified by the stripe 2 element ( S2E ) CRE . In D . melanogaster , the S2E possesses binding sites for four transcription factors that collectively specify the eve expression output [67] , [68] . The orthologous S2E from the species D . pseudoobscura differs in sequence for numerous binding sites , the overall content of binding sites , and spacing between conserved binding sites [69] , [70] , yet the orthologous S2Es function equivalently in vivo [71] . Hence , the S2E is an exemplar as to how selection acting at the level of the character ( eve stripe expression ) can accommodate a surprising amount of CRE evolution . Similarly , CRE sequence evolution without corresponding functional evolution was found between Drosophila species for the sparkling ( spa ) CRE that directs cone cell expression for the dPax2 gene [72] . The content and spatial proximity of binding sites for neurogenic ectoderm enhancers ( NEEs ) evolved in order to conserve expression pattern outputs in response to changing regulatory inputs [24] . These case studies , demonstrate how CRE sequence conservation is not a prerequisite for CRE functional conservation . In contrast , we found little divergence in the content and sequence of known binding sites for the D . melanogaster dimorphic element alleles and orthologous sequences . At the sequence level , these CRE alleles and orthologs respectively posses identities of ∼98% and ∼80% . Indeed , the vast majority of binding sites in the dimorphic element have been conserved for over 30 million years , showing conservation to D . willistoni [20] . At the functional level , these CREs exhibited striking differences in their regulatory activities ( Figure 4 and Figure 6 ) . Thus , in contrast to S2E , spa , and the NEEs , the dimorphic element demonstrates how CREs can derive dramatic changes in function that drive phenotypic divergence , with little-to-no alteration to the characterized pre-existing regulatory linkages . While the regulatory activity of the Light and Dark dimorphic elements alleles correlated with female A5 and A6 pigmentation ( Figure 1 ) , some outcomes suggest that these variant sequences are affected by other features within or perhaps outside of the bab locus . For instance , the Light 2 and Dark 2 alleles exhibit the highest and lowest regulatory activities respectively . Surprisingly , the Light 1 and Dark 1 alleles and their intermediate regulatory activities are associated with the more extreme Light and Dark female pigmentation phenotypes . At the expression level , Bab1 and Bab2 showed similar patterns in females from the Light 1 ( prominent expression in segments A5 and A6 ) and Dark 1 ( reduced expression is A5 and A6 ) strains ( Figure 3 ) . In the Dark 2 strain , Bab1 but not Bab2 expression was reduced in females . Several possible explanations might explain the uncoupled expression of the Bab paralogs in Dark 2 . For example , it is possible that a separate , as of yet unidentified CRE controls Bab2 expression . However , a screen of the entire ∼160 kb locus failed to identify such a CRE [20] . A second possibility is that a mutation ( s ) in the Dark 2 allele has paralog-specific regulatory effects , perhaps by modifying an interaction with the promoter for bab1 but not that of bab2 . Another possible explanation would involve the existence of CREs that coordinate communication between bab1 and bab2 . In such a scenario , the Dark 2 allele could contain mutations that alter interaction with coordinating elements to result in paralog-specific expression patterns in the female A5 and A6 segments . This possibility is consistent with observations of bab locus evolution in another population where females differ in A6 segment pigmentation [47] . For this population , fine-scale genetic mapping found that three disparate non-coding regions of the bab locus collaborate to compose a major effect QTL [48] . One of these regions spans the dimorphic element , though no mutations reside with this CRE's core element . The other two regions include an intergenic sequence between bab1 and bab2 and a large sequence that includes the bab2 promoter . In the future , it will be important to understand what roles these other regions serve , and how they may interact with polymorphisms in the dimorphic element to produce paralog-specific effects on gene expression . With the centrality of CREs and their evolution to the diversification of phenotypic traits [16] , [73] , a major obstacle to reaching this goal is understanding the processes by which CRE regulatory logics were modified to contemporary forms [74] . Often studies of CRE evolution involve comparisons of two divergent derived regulatory states , where one sequence assumes the role of a surrogate for the ancestral function [20] , [21] , [35] , [65] , [74] , [75] . This approach has been successful in making inferences about the ancestral states for regulatory linkages and identifying gains and losses of other key derived transcription factor binding sites . However , it is important to acknowledge a key limitation of this comparative approach; a CRE derived from an outgroup species that serves as a surrogate for the ancestor has also evolved along a unique lineage since divergence . Studies into the evolution of divergent protein activities encountered a similar problem when comparing extant proteins forms [53] . For several cases , key amino acid residues necessary for a derived function were identified . When substituted into the surrogate ancestral protein , these changes were insufficient to impart the derived function and thereby indicating that the paths of evolution were more intricate . As a solution , the reconstruction of ancestral protein sequences , combined with functional testing of inferred ancestral proteins has allowed a more realistic simulation of evolutionary events . As a result , inferences about the paths of protein evolution were made that likely would not have been found from comparisons of extant proteins [51] , [53] . A more ideal research program to study CRE evolution would include reconstruction of ancestral CREs as a starting point to trace the paths of evolutionarily relevant mutations . To our knowledge , few studies have used CRE reconstruction [34] , [36] , [54] . For one study , a novel optic lobe expression pattern for the D . santomea Nep-1 gene occurred via the modification of a CRE that drove an eye field pattern of expression for an ancestor that existed ∼0 . 5 million years ago [36] . Importantly , by reconstructing and evaluating the ancestral CRE , the wrong conclusion - that this optic lobe activity evolved de novo – was avoided and the correct conclusion was found - a latent optic lobe CRE activity was augmented into a robust derived state . In our study , had the Concestor element not been reconstructed , the Dark 1 and Dark 2 dimorphic element sequences would have been considered hypomorphic CRE alleles compared to the robust wild type-like activity of the Light 1 and Light 2 alleles . The Light alleles possessed activities more similar to a previously characterized dimorphic element allele [20] and consistent with the narrative of D . melanogaster being a sexually dimorphic species where females lack posterior abdominal pigmentation . Reconstruction of the dimorphic element revealed a more complex reality , where neither alleles were good surrogates for the ancestral state . Using ancestral sequences as a starting point , we found that the evolutionary paths for these alleles to be short in number of steps ( one to two mutations ) and in time frame ( in the last ∼60 , 000 years ) [58] . Thus , demonstrating how simple and rapid an existing CRE regulatory logic can evolve . The cases of Nep1 optic lobe CRE and the bab dimorphic element evolution demonstrate the utility for reconstructing ancestral CRE states; though it must be pointed out that these cases involved comparisons of very closely-related species/populations . As a result of these short time frames for divergence , the extant CRE forms differ at fewer than two percent of the nucleotide sites . This made possible ancestral sequence reconstruction by the principle of parsimony . However , not all compelling instances of functional CRE evolution occur over similarly short time frames . Therefore , studies will need to reconstruct CREs that existed further in the past and for which the method of parsimony will need to be replaced by methods of maximum likelihood-based inference coupled with the testing of multiple alternate reconstructions [51] . D . melanogaster populations from disparate geographical regions were obtained from the San Diego Drosophila Stock Center and are identified in Figure S1 . Dark 1 stock was obtained from M . Rebeiz [29] , stocks for other species were obtained from S . B . Carroll . Reporter transgenes in Figure 1 were introduced into the attP site VK00006 on the X chromosome [76] , all other reporter transgenes were introduced into the attP2 site on chromosome 3L [77] . Complementation test progeny were obtained by crossing individuals from a D . melanogaster population stock to a line possessing the bab locus null allele babAR07 [41] . The homozygous bab null genotype was a heteroallelic combination of the babAR07 and the deficiency chromosome Df ( 3L ) BSC799 for which the entire bab locus is deleted . bab1 and bab2 protein coding exons from Light 1 and Dark 1 bab loci were amplified by PCR ( Primer details in Table S3 ) , cloned into the pGEMT-Easy vector ( Promega ) , sequenced by the Sanger method ( DNA Analysis LLC ) , and the resulting chromatograms were analyzed using the Staden software package [78] . The Dark 1 female phenotype was introgressed for up 10 generations into the Light 1 genetic background . For each backcross generation , female progeny with a phenotype intermediate to that of the Light 1 and Dark 1 females ( Figure 2C ) were selected and mated to Light 1 males . Following 10 generations of backcrossing , male and female progeny were mated to generate pure lines for which females exhibited the Dark 1 phenotype ( Figure S3F ) . Four bab locus marker genotypes were determined by PCR . These markers include #3 , a BstXI restriction fragment length polymorphism ( RFLP ) , and markers #1 , #2 , and #4 for which the PCR products differ in size when amplified from the Light 1 and Dark 1 stocks . PCR primers and population stock-specific allele sizes are provided in Table S4 . For the RFLP analysis , the BstXI Fwd 1 and BstXI Rvs 1 primers ( Table S4 ) were used to amplify a ∼381 base pair ( bp ) product from F2 progeny genomic DNA . PCR products were purified and digested with the BstXI restriction endonuclease and then size fractioned by agarose gel electrophoresis . PCR products from the Light 1 allele were cut into fragments of 235 and 146 bp , whereas products from the Dark 1 and Dark 2 alleles remained at 381 bp . The to-scale representation of the bab locus shown in Figure S3 was made using the Gene Palette software tool [79] . Genetic association tests were performed by crossing individuals from Dark 1 and separately Dark 2 stocks with individuals from Light 1 stock . F1 progeny were then intercrossed to generate an F2 generation . The abdomens of adult F2 progeny were imaged and then used to extract genomic DNA from ( DNeasy Blood & Tissue Kit , Qiagen ) for genotypic assays . F2 progeny genomic DNAs were then genotyped for the BstXI RFLP . Pupal abdomens were dissected for immunohistochemistry at ∼29 and ∼85 hours after puparium formation ( hAPF ) , the former a time point when Bab1 and Bab2 are expressed in the developing genitalia and analia and the latter a time point when the dimorphic element drives high levels of reporter gene expression in the A5–A7 segments , and downstream targets of bab repression have begun to be expressed in males [23] , [31] . The primary antibodies used were rabbit anti-Bab1 [20] and rat anti-Bab2 [80] at a dilution of 1∶250 and 1∶400 respectively . The secondary antibodies used were goat anti-rat Alexa Fluor 488 ( Invitrogen ) and goat anti-rabbit Alexa Fluor 647 ( Invitrogen ) at a dilution of 1∶500 . The expression patterns presented are consistent with patterns seen in replicate specimens . Thirty one dimorphic element sequences were isolated from twenty seven world-wide populations of D . melanogaster . These sequences were used as an ingroup and aligned to seven outgroup sequences from related species by the Chaos+Dialign alignment tool [81] . From this alignment ( Figure S2 ) , using the parsimony principle we reconstructed the sequence ( named the “Concestor element” ) possessed by the most recent common ancestor of the surveyed D . melanogaster population stocks . This ancestral reconstructed sequence was synthesized ( GenScript ) for use in reporter transgene analyses . Outgroup species relationship were based on a published phylogeny [23] . Polymorphic sites among D . melanogaster population alleles are distinguished in the alignment as red text on a black background . D . melanogaster dimorphic element alleles in the alignment are referred to as mel . ## . # , which refers to the species name , stock number ( from the San Diego Drosophila Species Stock Center ) , and the clone number assigned to the sequence cloned into the BPS3aG vector . Sequence references that include “Ug” , were isolated from chromosome extractions from a Uganda Africa population [29] , [82] . Orthologous dimorphic element sequences for outgroup species are referred to by the species three letter abbreviation and clone number assigned to the sequence when cloned into the BPS3aG vector . Derived mutations in the region where characterized ABD-B and DSX binding sites reside [20] , referred to as the “core” ( Figure S5 ) , are identified by a alphabetic letter designation above the nucleotide position ( Figure S2 ) . Polymorphic sites in regions flanking the core were assigned a numerical designation that is listed above the variable nucleotide position in the alignment . The characterized binding sites for ABD-B ( 14 sites for D . melanogaster ) are indicated by white text on a blue background , whereas the two DSX binding sties ( Dsx1 and Dsx2 sites ) are indicated by black text on a yellow background . The sites were previously found to be bound by these transcription factors in vitro [20] and their sequences respectively match the empirically derived consensus motifs for ABD-B ( TTTAY ) and DSX ( RNNACWAWGTNNY ) [61] , [83] . Ambiguously reconstructed Concestor element nucleotide states are indicated as blue or black text on a gray background . The ggcgcgcc and cctgcagg sequences respectively at the 5′ and 3′ ends of the dimorphic sequences are not part of the endogenous bab sequences , but are respectively AscI and SbfI restriction endonuclease sites that were included by PCR for cloning into the BPS3aG vector . The polymorphic BstXI restriction endonuclease site ( CCANNNNNNTGG ) is indicated by white text on a dark red background ( Figure S2 ) . GFP reporter transgenes were used as a proxy to measure the in vivo gene-regulatory activity of CREs . In brief , CREs are cloned into a vector upstream of the green fluorescent protein ( GFP ) coding sequence forming a “reporter transgene” . Transgenes were individually inserted into the D . melanogaster germline at the same genomic location via site-specific integration methods to avoid confounding position effects , which permits a quantitative comparison of CRE regulatory capabilities [20] , [60] , [77] ( BestGene Inc . ) . All dimorphic element sequences were amplified using the sub1orthoF1 and dimorphic Rvs1 primers that were designed to sequences conserved between species from the most divergent Sophophora lineages ( Table S5 ) . Dimorphic elements were cloned into the AscI and SbfI sites in the vector BPS3aG , a vector derived from the S3aG vector [60] by the inclusion of a 119 bp sequence from the bab2 promoter inserted between the BamHI and XhoI sites . Regulatory activities were determined as the mean GFP intensities and standard error of the mean ( SEM ) for female dorsal abdominal segment A6 expression as previously described [20] , [60] . For each transgene , a preliminary analysis was done for several independent transgenic lines to gauge the level and pattern of activity and variation between replicate specimens . Regulatory activities were then determined using three or more newly acquired specimens that were at the same developmental time point ( 85 hAPF ) . The samples sizes ( n ) for Figure 1A′–1H′ respectively were: 5 , 8 , 9 , 4 , 6 , 8 , 11 , and 4 . The samples sizes ( n ) for Figure 4A′–4E′ respectively were: 34 , 9 , 6 , 6 , and 3 . The samples sizes ( n ) for Figure 5F–5J respectively were: 34 , 10 , 6 , 11 , and 14 . In Figure 5K , the n values for Concestor , E scramble , E8Del , Dsx KO , E Dsx1 , and Concestor+E respectively were: 34 , 15 , 28 , 23 , 22 , and 6 . The samples sizes ( n ) for Figure 6K , 6M , 6O , and 6Q respectively were: 9 , 3 , 6 , and 18 . The samples sizes ( n ) for –S5L respectively were: 6 , 6 , 6 , 10 , 7 , 6 , 6 , 6 , 8 , 14 , and 6 . The samples sizes ( n ) for Figure S6A–S6AA respectively were: 34 , 44 , 6 , 9 , 6 , 3 , 45 , 10 , 6 , 11 , 12 , 14 , 15 , 22 , 23 , 14 , 22 , 12 , 13 , 30 , 21 , 15 , 28 , 22 , 23 , 17 , and 6 . Activities reported in Figure 1 were normalized to an allele from the CantonS strain [20] . All other regulatory activities used the Concestor element transgenic lines for normalization . Derived mutations that alter dimorphic element function were mapped by the construction and transgenic evaluation of chimeric reporter transgenes [74] . In brief , a series of chimeric dimorphic elements were constructed in which a broad region ( s ) from the Concestor element was combined with the complementary region from a Light or Dark dimorphic element allele . Regions of alleles sufficient to impart some of the evolved activity on an otherwise Concestor element were refined to find smaller regions responsible for or contributing to the activity differences . This culminated with tests of individual mutations . Ancestral sequence inferences occur with a certain degree of ambiguity that can result in incorrect evolutionary conclusions . One way to estimate the confidence in a particular reconstruction , is to test the function of other possible ancestral sequences [51] . In the reconstructed Concestor element sequence , we were uncertain of the ancestral nucleotide state at eight sites ( sites 1 , 17 , 19 , H , K , 27 , 30 , and 31; Figure S2A ) . Two of these sites were the “H” and “K” mutations that respectively occur at repeat tracts of C and T nucleotides . The difference in number of nucleotides among the surveyed alleles ranged between 0–7 for the C tract and 0–3 for the T tract ( Figure S2 ) . Length differences occur in the Light 1 allele and both Dark alleles , suggesting these differences would not be responsible for the allele-specific regulatory activities . To test this suggestion , we made two modified Concestor elements , one where four C nucleotides were added to the H mutation site , and the other where three T nucleotides were added to the K mutation site . These alterations had no significant effect on the Concestor element's regulatory activity ( Figure S6L and S6O ) , thus , supporting that this reconstruction was robust to inference uncertainty at these two sites , and ruling out the H and K mutations as being functionally-relevant . We also synthesized an ancestral sequence , called Concestor 2 , which differed from the Concestor element at six sites ( Figure S2; sites 1 , 17 , 19 , 27 , 30 , and 31 ) . While this sequence had an activity of 125±1% of the Concestor element ( Figure S6B ) , this difference was quite modest compared to the activities of the Light and Dark alleles . Moreover , this result supported the evolutionary conclusion that the regulatory activity of the dimorphic element possessed by the most recent common ancestor of the surveyed population stock alleles was intermediate to the alleles with reduced and increased activity in the female A6 segment . Chimeric constructs and tests of derived mutations were done using the Concestor element sequence . Gel shift assays used the DSX DNA-binding domain proteins and wild type and mutant Dsx1 sites as previously published [20] . Sequences for oligonucleotides used for gel shift assay probes are presented in Table S6 . Reverse complementary oligonucleotides were synthesized ( Integrated DNA Technologies ) that contain the Concestor element , E mutation variant , and a null mutation for Dsx1 site sequence , each flanked by endogenous dimorphic element sequence . Each oligonucleotide was biotin-labeled on their 3′ end using the DNA 3′ End Biotinylation Kit ( Thermo Scientific ) . Labeled complementary oligonucleotides were annealed by standard protocol to make binding sites for gel shift assays . Labeling efficiency for each binding site was determined using a quantitative Dot Blot assay ( DNA 3′ End Biotinylation Kit , Thermo Scientific ) . All gel shift reactions included 20 fmol of one labeled binding site and GST-DSX DNA Binding Domain ( DBD ) fusion protein [20] in General Footprint Buffer ( 50 mM HEPES pH 7 . 9 , 100 mM KCl , 1 mM DTT , 12 . 5 mM MgCl2 , 0 . 05 mM EDTA , 17% glycerol ) . For each binding site , a reaction was done that included an amount of DSX protein ranging from 500 ng down to 8 ng . For each binding site , a control reaction was done that lacked DSX protein . Binding reactions were carried out for 30 minutes on ice . Reactions were then separated through a 5% non-denaturing polyacrylamide gel for 2 hours at 200 V . Following electrophoresis , reactions were transferred and cross linked to a Hybond-N+ membrane ( GE Healthcare Amersham ) for chemiluminescent detection using the Chemiluminescent Nucleic Acid Detection Module and manufacture's protocol ( Thermo Scientific ) . Chemiluminescent images were taken using a BioChemi gel documentation system ( UVP ) . The results shown in Figure 5 were representative of those obtained in independent replicate experiments ( n = 3 ) . Whole-mount images were taken using an Olympus SZX16 Zoom Stereoscope outfitted with an Olympus DP72 digital camera . Projection images for immunohistochemistry and reporter transgenes where obtained using an Olympus Fluoview FV 1000 confocal microscope and software . All TIFF images used in a specific comparison were processed through the same modification using Photoshop CS3 ( Adobe ) .
Trait development occurs through networks of genes that are connected by interactions between transcription factor proteins and binding site sequences within cis-regulatory element ( CRE ) DNA sequences . These interactions enable CREs to function as switches that control the expression of a gene ( s ) they regulate . Little is known about the molecular paths by which CREs evolve . Here , we identify a CRE that has repeatedly been the target of mutations that generate diverse pigmentation phenotypes on the abdomen of Drosophila melanogaster and its close relatives . By reconstructing and testing the ancestral form of this enhancer in vivo , we demonstrate that individuals from widely distributed Drosophila melanogaster populations possess modified forms of this CRE . Interestingly , the majority of this divergence proceeded without modifying previously identified binding sites for body plan and sex determination transcription factors . This pattern of extreme functional divergence , with contrasting conservation of transcription factor inputs may reflect strong constraint against modifying regulatory sequences that are required for expression in multiple body regions through shared binding sites .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[]
2013
Recurrent Modification of a Conserved Cis-Regulatory Element Underlies Fruit Fly Pigmentation Diversity
For over 130 years , invasive pneumococcal disease has been associated with the presence of extracellular planktonic pneumococci , i . e . diplococci or short chains in affected tissues . Herein , we show that Streptococcus pneumoniae that invade the myocardium instead replicate within cellular vesicles and transition into non-purulent biofilms . Pneumococci within mature cardiac microlesions exhibited salient biofilm features including intrinsic resistance to antibiotic killing and the presence of an extracellular matrix . Dual RNA-seq and subsequent principal component analyses of heart- and blood-isolated pneumococci confirmed the biofilm phenotype in vivo and revealed stark anatomical site-specific differences in virulence gene expression; the latter having major implications on future vaccine antigen selection . Our RNA-seq approach also identified three genomic islands as exclusively expressed in vivo . Deletion of one such island , Region of Diversity 12 , resulted in a biofilm-deficient and highly inflammogenic phenotype within the heart; indicating a possible link between the biofilm phenotype and a dampened host-response . We subsequently determined that biofilm pneumococci released greater amounts of the toxin pneumolysin than did planktonic or RD12 deficient pneumococci . This allowed heart-invaded wildtype pneumococci to kill resident cardiac macrophages and subsequently subvert cytokine/chemokine production and neutrophil infiltration into the myocardium . This is the first report for pneumococcal biofilm formation in an invasive disease setting . We show that biofilm pneumococci actively suppress the host response through pneumolysin-mediated immune cell killing . As such , our findings contradict the emerging notion that biofilm pneumococci are passively immunoquiescent . Hospitalization for community-acquired pneumonia ( CAP ) is an established risk factor for adverse cardiac events that includes heart failure , arrhythmia , and infarction; with as many as one-in-four adults hospitalized for CAP experiencing some form of pneumonia-associated adverse cardiac event ( PACE ) [1 , 2] . Streptococcus pneumoniae ( the pneumococcus ) , the leading cause of CAP [3] , has been directly linked to PACE . In 2007 , Musher et al . reported that one in five individuals hospitalized for pneumococcal pneumonia experienced PACE and these individuals had four-fold greater mortality than those with pneumococcal pneumonia alone [4] . More recently , Eurich et al . reported that pneumococcal pneumonia was specifically associated with greater incidence of heart failure , even during convalescence , and this persisted for a period of up to 10 years [5] . Thus , clinical studies strongly imply that some form of cardiac damage is incurred during invasive pneumococcal disease ( IPD ) . During IPD , circulating S . pneumoniae are capable of binding to the vascular endothelium and translocating into the heart [6] . Within the myocardium , invaded pneumococci form what we have termed “cardiac microlesions” , i . e . non-purulent pockets of pneumococci that are typically adjacent to blood vessels . Cardiac microlesions have been shown to disrupt normal electrophysiology and impair contractile function [6–8] . In antibiotic rescued animals , including non-human primates with experimental pneumococcal pneumonia , tissue damage associated with cardiac microlesions resulted in de novo scar formation [6 , 9] . Thus , the acute and long-lasting damage caused by heart-invaded S . pneumoniae is one explanation for PACE and the adverse cardiac events that occur thereafter . Pertinent to this study , the cholesterol-dependent pore-forming toxin produced by S . pneumoniae , pneumolysin ( Ply ) , has been shown to play an important role in cardiomyocyte and infiltrated macrophage killing [6–8] . Nonetheless and despite all these advances in knowledge , mechanisms by which pneumococci establish themselves within the myocardium without innate immune cell recognition remains unknown . In stark contrast to what occurs during pneumonia and IPD , S . pneumoniae forms biofilms in the nasopharynx during colonization and within the middle ear during otitis media [10 , 11] . Biofilms are surface-attached communities of bacteria encased within an extracellular matrix ( ECM ) [12–15] . In the nasopharynx , biofilm growth of S . pneumoniae confers resistance to desiccation and the host immune response [10] . The slower metabolic rate of bacteria in biofilms also confers intrinsic resistance to antibiotic killing , which helps to explain the recalcitrance of otitis media to treatment [11] . Importantly , a growing body of literature demonstrates that biofilm pneumococci elicit a considerably weaker immune response from host cells when compared to their planktonic or biofilm-dispersed counterparts [15 , 16] . It has been speculated that this promotes long-term colonization by delaying the onset of adaptive immunity [10 , 17] . However , the mechanisms by which biofilms suppress immune cell recognition have not been described . Finally , a role for pneumococcal biofilms during IPD has not been reported . This has instead been attributed to the planktonic phenotype which due to reduced surface area better evade stochastic C3b complement deposition and resultant opsonophagocytic killing [18] . Herein we report that heart-invaded pneumococci present within the myocardium replicate within cellular vesicles and over time transition to a mature biofilm . This is the first report of intracellular replication of S . pneumoniae or pneumococcal biofilm formation in an invasive disease context . Using dual RNA-seq analysis we show that pneumococci within the heart are highly distinct from their circulating counterparts and that virulence gene expression is highly anatomical-site specific; this has key implications on vaccine design . We identify a previously unappreciated genomic island that promotes biofilm formation in vivo and show that heart-invaded biofilm pneumococci establish their non-inflammogenic profile not through passive immunoquiescence as previously speculated , but instead via the rapid killing of cardiac macrophages due to enhanced pneumolysin release . These studies advance our understanding of pneumococcal pathogenesis , shed light on the basis of cardiac damage , and reveal a new role for biofilms and pneumolysin during pneumococcal infection . To determine the morphogenesis of microlesions , heart sections from mice infected with S . pneumoniae serotype 4 , strain TIGR4 were examined by transmission electron microscopy ( TEM ) ( Fig 1A ) . The smallest assemblages of pneumococci that could be detected between 18 and 42 hours post infection ( hpi ) were consistently within clear , spherical , and discrete intracellular vesicles 4–8 μm in diameter each containing 5–10 electron dense diplococci spaced 1–2 μm apart . Pneumococci-filled vesicles were frequently adjacent to swollen mitochondria and alongside or within areas of the cardiomyocytes undergoing hydropic degeneration . All vesicles , even those containing hundreds of pneumococci , had equidistantly spaced diplococci with well-defined separation from the host cytoplasm . The largest microlesions appeared to be the result of bacterial replication along with the expansion , budding , and merging of smaller vesicles . Larger microlesions also had remnants of vesicular membrane present throughout and were consistently associated with cellular debris both within the vesicles and on their periphery . In all instances , larger microlesions were not associated with immune cells . Considerable bacterial heterogeneity was evident within the larger microlesions . We observed an accumulation of dead , i . e . ghost pneumococci ( S1 Fig ) , and differences in regards to the presence of capsular polysaccharide . While pneumococci within the smallest vesicles exhibited uniform capsule distribution ( Fig 1B . 1 ) , those at the periphery of the larger microlesions had capsule only at one pole ( Fig 1B . 2 ) , and those at the center of largest microlesions had little to no detectable capsule present ( Fig 1B . 3 ) . Strikingly , captured TEM images of TIGR4 cardiac microlesions strongly resembled those previously reported for in vitro formed biofilms ( Fig 1C ) [19] . Similarities included the equidistant spacing of pneumococci , the accumulation of ghost cells , and reduced presence of detectable capsule . Of note , serotype 6A , strain 6A-10 did not form cardiac microlesions following mouse challenge , despite the presence of detectable bacteria in the myocardium ( S2A Fig ) . TEM imaging of 6A-10 infected hearts instead showed pneumococci within macrophages adjacent to the vasculature ( S2B Fig ) . Given the morphological similarities of TIGR4 within mature cardiac microlesions to those within in vitro biofilms , we tested the former for salient biofilm properties . Heart-isolated pneumococci ( HIP ) were intrinsically resistant to antimicrobial killing; a phenotype absent in blood-isolated pneumococci ( BIP ) from the same mouse ( Fig 2A ) . This resistance to antimicrobials was lost following >1 hour of HIP outgrowth in THY . We also detected extracellular DNA , an established biofilm ECM component [13] , within cardiac microlesions ( Fig 2B , S3 Fig ) . In the nasopharynx , the absence of glucose and presence of neuraminidase-exposed terminal galactose on mucosal epithelial cells has been shown to promote pneumococcal biofilm formation by de-repressing Streptococcal pyruvate oxidase ( SpxB ) -mediated metabolism . SpxB has also been demonstrated to be required for S . pneumoniae biofilm formation within the nasopharynx [20] . Staining of control and infected heart sections with a fluorescent lectin specific for terminal galactose showed this carbohydrate was exposed only in areas of the heart immediately adjacent to cardiac microlesions ( Fig 2C ) . What is more , a TIGR4 spxB deficient mutant failed to form biofilms in vitro ( Fig 2D ) and cardiac microlesions ( Fig 2E ) in vivo despite causing sustained bacteremia for 42 hours and adhering to rat brain capillary endothelial cells , i . e . RBCEC6 cells , in vitro at normal levels ( S4A and S4B Fig ) . Based upon this collective body of evidence we conclude that cell invaded TIGR4 replicate within small vesicles and transition to a biofilm during mature cardiac microlesion development . Of note , terminal galactose exposure was not evident in mouse hearts having sterile injury due to myocardial infarct ( S5 Fig ) . Thus , the exposure of galactose in areas surrounding microlesions was not due to tissue injury . Pneumococci are phase-variable and stochastically alternate between opaque and transparent colony phenotypes . During nasopharyngeal colonization and in vitro biofilm formation , the transparent phenotype is selected for due to an enhanced capacity to adhere to surfaces . In the lungs during pneumonia and the bloodstream during IPD , the opaque variant instead dominates due to its enhanced resistance to opsonophagocytic killing [19 , 21–23] . Along such lines , the percentage of transparent pneumococci present in blood of TIGR4-infected mice decreased from the inoculum of 84% to 25% during the course of infection ( Fig 3A ) . In contrast and within the same animals , the majority of pneumococci isolated from hearts were transparent over time ( Fig 3A ) . We subsequently hypothesized that translocation into the heart was a selective event and required pneumococci with a tissue-tropic phenotype . If true , then mice infected with HIP should experience greater cardiac invasion than those infected with BIP ( modeled in Fig 3B ) . Strikingly , HIP-infected animals had a 2 . 5-fold greater number of cardiac microlesions than their BIP-infected counterparts despite equivalent levels of bacteremia ( Fig 3C–3E ) . In vitro HIP and BIP exhibited comparable adhesion to and invasion of RBCEC6 cells , yet HIP had enhanced adhesion to and invasion of HL-1 cardiomyocytes ( Fig 3F , S6 Fig ) . Thus , TIGR4 isolated from the myocardium were not primed for translocation across vascular endothelial cells and into the heart , but were instead better suited for cardiomyocyte-related interactions . To elucidate how TIGR4 in the heart differed from those in the bloodstream we performed deep-sequencing of cDNA derived from the infected organs . Total RNA from intact hearts of HIP-infected mice ( ~107−8 CFU per heart ) and pooled blood ( ~107 CFU/mL ) from TIGR4-infected neutropenic mice was used to generate cDNA and ≥300 million RNA-seq reads were performed per biological sample . The normalized number of RNA-seq reads mapping to each TIGR4 gene ( RNA reads per kilobase of transcripts per million mapped reads , RPKM ) is a direct correlate of individual gene expression levels ( S1 Table ) . For pairwise differential expression analyses , RNA-seq reads were first sub-sampled to match the condition with the lowest number of reads; these normalized RPKM values are provided in S2 Table . It is of note that this approach yielded RNA reads that corresponded to ~90% of the TIGR4 genome . This approach also yielded an exhaustive in-depth reading of transcripts corresponding to infected mice . All RNA-seq data generated as part of this study is available through the NCBI Gene Expression Omnibus ( GEO ) database ( see methods ) . While the results provided in S1 and S2 Tables allow for detailed genome-wide comparisons between the HIP and BIP transcriptomes , herein we focused only on established virulence determinants that would inform us on the pathogenic process ( Fig 4 ) . Virulence determinants with the highest levels of gene expression ( i . e . >1000 RPKMs: corresponding to ~10% of encoded genes ) in the heart were: Pneumococcal adhesion and virulence protein B ( PavB; SP_0082 ) , Pneumococcal surface protein A ( PspA , SP_0117 ) , the capsular polysaccharide biosynthesis locus ( SP_0346–0360 ) , Zinc metalloprotease B ( ZmpB , SP_0664 ) , SpxB ( SP_0730 ) , Ply ( SP_1923 ) , Autolysin ( LytA , SP_1937 ) , and Pneumococcal choline binding protein A ( PcpA , SP_2136 ) ( Fig 4A ) . The high levels of gene expression for these virulence determinants suggest that they play an important role within the heart . In stark contrast , the genes encoding PavB and pneumolysin were negligibly expressed within blood . Notably , the genes encoding Pneumococcal pilus-1 ( RlrA pathogenicity islet , SP_0462–0468 ) , Pneumococcal serine-rich repeat protein ( PsrP ) and its accessory proteins ( psrP-secY2A2 , SP_1755–1772 ) , and Choline binding protein A ( CbpA , SP_2190 ) had minimal expression in both heart and blood . Thus , unequivocal anatomical site-specific differences in virulence gene expression occurred in vivo , with some previously identified key determinants having surprisingly low levels of both blood and heart-related transcription ( Fig 4B and 4C ) . qRT-PCR of a 69-gene panel validated these RNA-seq results ( S7 Fig ) . We subsequently performed principal component analysis ( PCA ) using our in vivo RNA-seq data and transcriptome data obtained from in vitro planktonic- and in vitro biofilm-grown pneumococci ( Fig 5A ) . Replicates of the same condition ( same color ) clustered most closely together on the PCA plot as expected . The second principal component , PC2 ( Y-axis ) separated HIP and in vitro biofilm pneumococci ( bottom of the plot ) from the BIP and in vitro planktonic conditions ( top of the plot ) , confirming that the HIP harbored gene expression features more similar to in vitro biofilm pneumococci than to in vitro planktonic pneumococci . Conversely , pneumococci in the blood were more similar to in vitro planktonic pneumococci than in vitro biofilm pneumococci . In addition , pneumococci in vivo also harbored different sets of gene expression features that clearly distinguished them from their in vitro counterparts by virtue of their separation along PC1 ( X-axis , left and right sides of the plot , respectively ) ; circos analyses of the RNA-seq data confirmed these relationships ( Fig 5B ) . The gene expression profiles that governed the separation of biofilm-planktonic ( S8 Fig ) and in vitro-in vivo populations ( S9 Fig ) are provided . Cumulatively , these findings show that in vivo gene expression profile is anatomical site-specific and drastically different from in vitro . Driving the disparity between in vivo and in vitro transcription profiles were genes encoded within Region of Diversity ( RD ) 2 ( SP_0163 to SP_0171 ) , the second half of RD6 ( SP_1057 to SP_1065 ) , and RD12 ( SP_ 1947 to SP_1955 ) , which were only expressed in vivo ( Fig 5C , S9 Fig ) . RDs are horizontally acquired genomic islands not present in all pneumococci , many of which have been shown to contribute to virulence [24 , 25] . We did not focus subsequent attention on RD6 as prior studies have shown that this 27-kb pathogenicity island encodes piaABCD , the pneumococcal iron-acquisition operon , and phgABC , the pneumococcal hyperosmotic growth operon; both of which were required for pneumococcal survival in the blood [26–28] . We explored whether RD2 or RD12 impacted the ability of TIGR4 to form biofilms and cardiac microlesions . While deletion of RD2 had no discernible effect , deletion of RD12 abrogated biofilm formation in a two-day polystyrene plate model ( Fig 6A ) . RD12 encodes a two-component class II pneumococcal lantibiotic bacteriocin called pneumococcin A1/A2 and its accessory proteins ( S10A Fig ) [29] . As such , we speculated that RD12 might promote fratricide which is an important aspect of biofilm ECM formation [30] . Indeed , the RD12 deficient mutant ( T4ΩRD12 ) exhibited a stark absence of propidium iodide stainable DNA , typically accessible in dead pneumococci , following growth on glass coverslips ( Fig 6B ) . This occurred despite the fact that deletion of RD12 did not have any long-term growth defects or changes in autolysis activity as determined by bile solubility assay ( S10B and S10C Fig ) . In vivo , mice infected with the RD2 deficient mutant had equivalent levels of bacteria in the blood compared to wildtype infected controls . T4ΩRD12 was however hyper-virulent with ~10-fold higher bacterial titers in blood of infected mice at 30 hours ( Fig 6C ) . This was not due to changes in capsule levels ( S11 Fig ) . In stark contrast to wildtype TIGR4 , mice infected with T4ΩRD12 demonstrated profuse bacterial dissemination in the heart ( Fig 6D ) accompanied by significantly greater neutrophil infiltration ( Fig 6E ) . T4ΩRD12 did not form biofilms in the heart as evidenced by the loss of intrinsic antibiotic resistance in HIP when compared to paired blood isolates ( Fig 6F ) . Our results suggest that biofilm formation is in some fashion tied to a muted neutrophil response in the heart . In support of this notion , macrophages exposed to wildtype biofilm TIGR4 produced less chemokine-inducing TNFα and CXCL2 than did macrophages challenged with wildtype planktonic TIGR4 or their biofilm-incapable RD12 deficient counterparts ( Fig 6H , S12A Fig ) . HL-1 cardiomyocytes did not produce meaningful TNFα , CXCL1 , or CXCL2 ( Fig 6H , S12A Fig ) following TIGR4 challenge . Ply is highly inflammatory [31–33] . Therefore , the observation that TIGR4 biofilms were immunoquiescent conflicted with our results that showed ply was expressed at higher levels in the heart and within in vitro biofilms than blood or in vitro planktonic TIGR4 , respectively . Subsequent immunoblot analyses confirmed that biofilm TIGR4 released greater amounts of Ply into the supernatant than did planktonic TIGR4 ( Fig 7A ) . Explaining this discrepancy , we observed that biofilm TIGR4 killed macrophages faster than planktonic TIGR4 ( Fig 7B ) ; and that this coincided with a marked reduction in detectable levels of TNFα ( Fig 7C ) and CXCL2 ( S12B Fig ) in supernatants . Planktonic T4ΩRD12 was also less cytotoxic to macrophages than were wildtype pneumococci ( Fig 6G ) , consistent with an observed reduction in pneumolysin release ( Fig 7A ) , and previously described greater inflammogenic profile in the heart ( Fig 6D and 6E ) and in vitro ( Fig 6H ) . Implicating pneumolysin as the principal factor responsible for this phenotype , pneumolysin deficient TIGR4 mutant ( T4Δply ) grown as biofilm had negligible cytotoxicity ( Fig 7B , S12C Fig ) and elicited a robust macrophage response ( Fig 7C , S12B Fig ) . What is more , complementation of the pneumolysin deficient biofilm-TIGR4 with exogenous pneumolysin restored cytotoxicity to wildtype levels ( Fig 7B ) and severely dampened TNFα and CXCL2 production by challenged macrophages ( Fig 7C , S12B Fig ) . Thus in vitro , pneumolysin released by biofilm TIGR4 pre-empted macrophage cytokine and chemokine production by their rapid killing . Of note , 6A-10 which naturally lacks RD12 , did not show enhanced release of pneumolysin during biofilm growth , and had a comparatively modest enhancement in capability to kill macrophages and subvert cytokine production as a biofilm ( S13 Fig ) . This perhaps explains its inability to form cardiac microlesions in vivo . Finally , we examined hearts from TIGR4 and T4Δply infected mice for differences in cardiac macrophage numbers and neutrophil infiltrates . Unfortunately , drastic differences in bacterial burden between cohorts made direct comparisons between these strains and conditions invalid ( S14A and S14B Fig ) . Nonetheless , the rare cardiac microlesion formed within T4Δply infected mice displayed extensive immune cell infiltration when examined by TEM and immunofluorescent microscopy ( Fig 7D , S14C Fig ) . As a work around , we passively immunized mice with neutralizing antibody against Ply and infected mice with wildtype HIP or BIP . Consistent with a prior report [6] , antibodies against Ply had no impact on pneumococcal burden in the bloodstream of TIGR4 challenged mice; nor in this instance , within the heart ( S14D and S14E Fig ) . Cardiac sections from HIP-infected mice that received Ply antibody stained positively for macrophages and neutrophils in the area immediately surrounding microlesions . In contrast , cardiac sections from mice not receiving antibody lacked the same , indicating immune cell death and/or less neutrophil infiltration ( Fig 7E ) . Of note , this robust modulation of immune cells was seen only at the interface of biofilm contact with the host cells in the myocardium . At the gross level and using flow cytometry of whole heart extracts as measure , mice that received Ply antibody had more neutrophils in their hearts than did untreated controls following HIP challenge ( Fig 7F ) , whereas the number of cardiac macrophages remained constant ( Fig 7G ) . What is more , untreated mice infected with BIP had the greatest number of cardiac macrophages and neutrophils detected by immunofluorescent microscopy and flow cytometry ( Fig 7E–7G , S14F Fig ) . Thus , biofilm formation in the myocardium by HIP indeed suppressed subsequent immune cell infiltration and this occurred in a highly focal and Ply-dependent manner . This is the first report to describe intracellular replication of S . pneumoniae and to demonstrate an integral role for biofilms during IPD . Together these are a mechanism by which pneumococci establish themselves within the myocardium and subvert their clearance . Our dual RNA-seq approach , in addition to providing insight in regards to the pathogenic process , revealed anatomical-site specific differences in S . pneumoniae virulence gene expression that may have major implications on antigen selection for future protein-based vaccines . The unexpected observation that biofilm pneumococci in the heart pre-empt immune cell infiltration through rapid resident macrophage killing strongly suggests that the immunoquiescent profile of biofilm attributed to pneumococci at other sites , i . e . nasopharynx , may be through similar means . Thus , this study advances our understanding of pneumococcal pathogenesis and the roles of biofilms and pneumolysin . TEM examination of infected hearts revealed S . pneumoniae is capable of intracellular replication within the myocardium . This observation was unexpected since S . pneumoniae is prototypical for extracellular Gram-positive bacteria [34] . Although observations of intracellular pneumococci within adenoid biopsy specimens from children with otitis media or rhinosinusitis have been reported [35 , 36] , and pneumococci are known to translocate across vascular endothelial cells within intracellular compartments [37] , this is to our knowledge the first report to suggest intracellular replication as an integral step in pneumococcal pathogenesis . Importantly , the exact cell type ( s ) affected within an infected heart remains to be elucidated . Most probable candidates based on their abundance include cardiomyocytes , resident macrophages , and/or fibroblasts . Moreover , the origin of the early bacteria-filled vesicle is also unclear . The most likely possibilities are: 1 ) extracellular uptake in a clathrin-coated vesicle from the cell surface [37] , or 2 ) xenophagy , the process by which a cell directs autophagy against an internalized pathogen [38] . Ongoing studies are focused on identifying the specific cells types involved and molecular basis for cardiomyocyte invasion . We propose that intracellular replication allows heart-invaded pneumococci , initially in a planktonic state , to evade innate and adaptive immune mechanisms as they transition into a biofilm . Bacteria within biofilms are characterized by intrinsic resistance to antibiotic killing , attachment to a surface , and production of an extracellular matrix [12]; all properties observed for pneumococci within infected hearts . S . pneumoniae biofilms have also been associated with an accumulation of non-viable cells within the biofilm , greater frequency of the transparent phenotype , heterogeneous production of capsule , enhanced adhesiveness to cells , and a requirement for pyruvate oxidase [15 , 19 , 21 , 23] . These features were also observed for pneumococci within infected hearts . Given that pneumococci can be detected within other organs , such as the spleen , following bacteremia [39] , it is reasonable to propose that biofilms may be forming in other organs during disseminated infection . Of particular interest are the kidneys , since pneumococcal infection has also been linked to acute kidney injury and long-term dysfunction [40] . Dual RNA-seq is a powerful technique that provided a snapshot of how S . pneumoniae adapts to and interacts with the host in the heart . For sake of brevity , we do not discuss the host response and limit our discussion to the established virulence determinants that provide insight on the pathogenic process . Ply is a pore-forming toxin that kills cells via necroptosis during microlesion formation [6 , 7] . As shown by Shak et al and herein , its release by biofilm pneumococci helps the bacterium to form biofilms on the host cell surface [41] and establish residency by killing host tissue-resident macrophages . PavB is a fibronectin-binding MSCRAMM ( i . e . microbial surface component recognizing adhesive matrix molecules ) [42] . Fibronectin in damaged heart tissue has been conclusively reported and PavB most likely acts as a cardio-adhesin [43] . PspA is the major choline binding protein found on the surface of the pneumococcus and is involved in resistance to complement [44] . Pneumococcal choline binding protein A , PcpA , not to be confused with the adhesin CbpA , has been demonstrated to play a vital role in modulating the host immune response by recruiting myeloid-derived suppressor cells and controlling the inflammatory environment within the lungs during pneumonia [45] . We are currently testing whether this occurs in the heart and is an additional explanation for the lack of immune cells associated with cardiac microlesions . Finally , each of the RDs up-regulated in the heart were associated with a copy of the Tpr/Phr peptide quorum sensing-signaling cassette ( SP_0163–0164 in RD2 , SP_1057–1058 in RD6 , and SP_1946–1947 in RD12 ) [46] . Other investigators have demonstrated a role for Tpr and its orthologs in biofilm formation and virulence of diverse bacteria [47–49] . The reasons for why an RD12 deficient mutant was hyper-virulent are not fully clear . In the heart , and based upon our in vitro results , we propose that RD12 encoded Pneumococcin A contributes towards biofilm formation by inducing fratricide and it is the resultant release of bacterial products including DNA and Ply that helps to establish the ECM [13 , 41] . The latter also contributing to the non-inflammogenic biofilm phenotype that was observed . We and others have previously reported that pneumococci within biofilms elicit a muted host response from nasopharyngeal epithelial cells in vitro and the airways of challenged mice [15 , 16] . As such , we initially hypothesized that the absence of immune cell infiltrates within cardiac microlesions was due to some form of passive immune evasion . Our observation that macrophages responded robustly to pneumococci lacking pneumolysin in biofilms disproved the former . Instead , it became evident that the observed muted cytokine response was due to rapid killing of cardiac macrophages by pneumolysin , thereby pre-empting the host response . Importantly , our previous study i . e . Gilley et al . concluded that pneumolysin produced by pneumococci in the heart killed infiltrating monocytes [7] . Herein , we tie the release of pneumolysin to the biofilm phenotype and demonstrate that heart resident macrophages are depleted in a pneumolysin dependent manner . Rapid macrophage death then precludes immune cell infiltration by restricting cytokine and chemokine production . Thus , this study changes our understanding of how biofilms modulate the host response and corrects our prior report by providing a more detailed molecular explanation for the observed immunoquiescent phenotype . Our cardiac findings are consistent with the known role for Ply in establishing early residency within nasopharynx [50] . They also explain why TEM imaging of nasal septa from colonized mice showed considerably greater mucosal epithelial cell perturbation , yet less cytokine production when compared to mice colonized with a biofilm deficient mutant [15] . Although cardiomyocytes are killed as a result of pneumolysin exposure [6 , 8] , and the same occurs for respiratory epithelial cells [51] , the fact that these cells are not immune cells may explain why their damage or death does not lead to overt cytokine and chemokine production [15] . We therefore propose that biofilm-mediated release of pneumolysin and resident macrophage killing may be an explanation for the low levels of cytokines and chemokines detected during nasopharyngeal colonization . This notion warrants testing . It is important to note that mice infected with strain 6A-10 did not develop cardiac microlesions and instead had pneumococci entrapped within cardiac macrophages . This indicates not all clinical isolates are capable of causing microlesions as seen with TIGR4 . The most likely explanation for this difference is the considerable genetic heterogeneity that occurs between different clinical isolates; ~10% of their genomes [24] . For example , 6A-10 does not encode RD12 . In fact , we identified RD12 in only five of the S . pneumoniae genomes publically available through PubMed . While the association between severe S . pneumoniae disease , cardiac damage , and adverse cardiac events in humans is now unequivocal [1 , 2 , 4–6 , 9] , the exact cardio-pathological hallmarks and how they vary between clinical isolates remains unknown . These are undoubtedly impacted by the genetic content of the invading strain , and based upon our results herein , the resultant biofilm forming ability and strain-specific dynamics of pneumolysin release . Importantly , the role RD12 plays could presumably be compensated for by the other bacteriocin systems known to be present in S . pneumoniae [52] . One striking observation was the anatomical-site specific differences in pneumococcal virulence gene expression . For example , the genes encoding Ply and PavB were expressed in the heart but not the blood . Similarly striking were the major differences between in vivo and in vitro gene expression , which shows that our in vitro condition is not an adequate mimic of the host . As such , a pressing need to expand on our studies and comprehensively determine the transcriptome of S . pneumoniae in the lungs , nasopharynx , and other body sites is now highly evident . Such a comprehensive in vivo gene atlas would not only elucidate the pathogenic process but would also serve to identify pneumococcal proteins that are consistently expressed across body sites . Presumably , these would be among the most suitable vaccine antigens to protect against all stages of pneumococcal disease . For example , that antibody against Ply does not protect against bacteremia following TIGR4 challenge was shown herein following passive immunization and also reported as part of our original manuscript that describes cardiac microlesion formation [6] . Alternatively , PspA , which was highly expressed in both heart and blood and has already been shown to be a protective antigen [53] , may be such a candidate antigen . In summary , we report that cardiac microlesion formation during IPD involves an intracellular stage and requires that the pneumococci transition into a biofilm . Pneumococci within the heart have a unique transcriptional profile that surprisingly does not include several established virulence determinants but included genes within three RDs that were drastically up-regulated in vivo . This highlights the dynamic nature of pneumococcal gene expression in vivo and this should be taken into consideration when considering vaccine antigens . We show that RD12 is important for biofilm formation in vitro and reduced cardio-pathology in vivo . This possibly implicates an important role for bacteriocin systems in human disease . Finally , we show that biofilm pneumococci subvert host response through the rapid killing of cardiac macrophages in a Ply-dependent manner . Moving forward , studies that examine in vivo bacterial gene expression are critical for a fuller understanding of bacterial pathogenesis , host-pathogen interactions and rational vaccine design . S . pneumoniae serotype 4 , strain TIGR4 was the parent wildtype strain used and its annotated finished ( gap-free ) genome is available [54] . An isogenic mutant lacking spxB ( T4 ΔspxB ) was created by allelic exchange using a mutagenic PCR construct consisting of the ermB erythromycin cassette flanked by upstream and downstream fragments of the gene as previously described [25] . Isogenic TIGR4 mutants lacking the Regions of Diversity , RD2 ( T4ΩRD2 ) , and RD12 ( T4ΩRD12 ) have been previously described [25] . S . pneumoniae serotype 6A , strain 6A-10 and its isogenic mutant lacking ply ( 6A-10Δply ) have also been previously described [15] . Pneumococci were grown in Todd Hewitt Broth ( THB ) ( Acumedia , Neogen ) with 0 . 5% yeast extract ( THY ) at 37°C in 5% CO2 for experiments . All mouse experiments were reviewed and approved by the Institutional Animal Care and Use Committees at The University of Alabama at Birmingham , UAB ( Protocol # IACUC-20175 ) and The University of Texas Health San Antonio ( Protocol # 13032-34-01C ) . At both institutes animal care and experimental protocols adhered to Public Law 89–544 ( Animal Welfare Act ) and its amendments , Public Health Services guidelines , and the Guide for the care and use of Laboratory Animals ( U . S . Department of Health & Human Services ) . Female 6-7-week-old BALB/cJ mice were challenged with ~103 CFU of exponential phase pneumococci in 100μL phosphate-buffered saline ( PBS ) by intraperitoneal injection . For studies with Blood Isolated Pneumococci ( BIP ) - and Heart Isolated Pneumococci ( HIP ) -infected mice , pneumococci were obtained as described below . Blood for assessment of bacterial burden was obtained by tail bleeds . At fixed time points or when deemed moribund , mice were euthanized by CO2 asphyxiation and death was confirmed by pneumothorax before heart-collection . For passive immunization , mice were administered 3μg of anti-pneumolysin neutralizing antibody , PLY-4 ( # ab71810 , Abcam ) intraperitoneally in 100μL PBS 1 hour before bacterial challenge and 14 hours post- infection . Excised hearts were washed with ice cold PBS , fixed with phosphate buffered 4% formaldehyde with 1% glutaraldehyde , and then processed as previously described [55] . Once embedded within resin and sectioned at 1μm in thickness , electron microscopy was performed using a JEOL JEM-1230 transmission electron microscope ( Peabody , MA ) . Blood was collected from anesthetized mice retro-orbitally and transferred to heparin-coated collection tubes . Following euthanasia , hearts were surgically excised and washed in PBS to remove blood . Isolated hearts were homogenized in 5mL of PBS followed by filtration of the homogenate through a 40μm cell strainer . Paired blood ( BIP ) and strained heart ( HIP ) samples were flash-frozen at -80°C in working aliquots with 10% glycerol . Freshly collected BIP and HIP samples were serially diluted in PBS and plated on tryptic soy agar plates supplemented with 100μL of catalase ( MP Biomedicals , LLC ) , and incubated at 37°C in 5% CO2 for 16 hours . Colonies were examined under oblique transmitted light to determine the frequency of transparent versus opaque colony variant [56] . Our TIGR4 parent wildtype strain was composed of 84% transparent pneumococci and 16% opaque pneumococci . The effect of antimicrobial agents on BIP and HIP was determined using a modified version of the standard micro-dilution assay [19 , 57] . These assays were conducted using samples immediately frozen after their collection to preserve their BIP or HIP phenotypes . Thawed aliquots of BIP and HIP were diluted in Dulbecco’s Modified Eagle’s Medium , DMEM ( Corning ) containing penicillin ( 0 . 125μg/mL ) or erythromycin ( 0 . 5μg/mL ) to a final concentration of 103 CFU/mL . BIP and HIP in DMEM without antibiotic were used as controls . At regular time intervals of 1 hour , 5μL of each bacterial suspension was spotted on tryptic soy blood agar plates ( Remel , USA ) and incubated at 37°C in 5%CO2 for 16 hours . The percentage fraction of antibiotic tolerant pneumococci in each sample per time point was calculated as: ( # recovered CFU in antibiotic / # CFU in non-antibiotic control ) x 100 . Hearts collected from infected mice were washed thoroughly with PBS then embedded in cassettes with Optimal Cutting Temperature Compound ( Tissue-Tek , 4583 ) . Frozen 7μm thick cardiac sections were fixed with 10% neutral buffered formalin , permeabilized in 0 . 2% Triton X and blocked with PBS containing 5% serum from species to which the secondary antibody belonged ( blocking buffer ) . Sections were then incubated overnight at 4°C with blocking buffer containing a 1:1000 dilution of primary antibody: rabbit anti-serotype 4 capsular polysaccharide antibody ( Statens serum Institut: cat #16747 ) , or rabbit anti-mH2A . 1 antibody ( EMD Millipore , cat#ABE215 ) . The next day sections were vigorously washed with 0 . 2%Triton X and then incubated for 1 hour at room temperature ( RT ) with blocking buffer containing secondary antibody at 1:2000 dilution: FITC labeled goat α-rabbit antibody ( Jackson Immuno Research , cat#111-096-144 ) , or rhodamine labeled donkey α-rabbit antibody ( EMD Millipore , cat#AP182R ) . For neutrophil and macrophage staining , cardiac sections were incubated with rat α-mouse Ly-6G primary antibody ( clone 1A8; BD Biosciences ) and rat α-mouse CD107b primary antibody ( clone M3/84; BD Biosciences ) diluted at 1:500 in the blocking buffer for 1 hour at RT . After incubation , sections were washed and incubated with Alexa 488- conjugated goat α-rat secondary antibody ( Jackson Immuno Research , West Grove , PA ) diluted at 1:500 in blocking buffer for 45 minutes at RT . Exposed galactose was stained for with fluorescein labeled Erythrina crystagalli lectin ( Vector Laboratories , FL-1141 ) after blocking with carbo-free blocking solution for 1 hour ( Vector Laboratories , SP-5040 ) . All slides were stained with DAPI ( Molecular Probes by Life Technologies , R37606 ) mounting the sections with FluorSave ( Calbiochem: 345789 ) and covering with coverslip for visualization . Images of cardiac sections were captured at The University of Texas Health San Antonio using a Zeiss LSM 710 confocal microscope and at UAB using a Leica LMD6 microscope equipped with DFC3000G monochrome camera . Image stitching of whole IFM stained cardiac sections was performed using the Leica LASX software . When indicated , cardiac microlesions were enumerated by counting foci of pneumococci in three capsule stained heart sections , each section at least >50μm apart . The first section was cut 300 μm into the heart from the surface . Static pneumococcal biofilms were grown in 6-well polystyrene plates ( COSTAR ) as previously described [15] . FilmTracer LIVE/DEAD Biofilm viability kit ( Invitrogen , L10316 ) was used to determine viability of 24-hour biofilms grown on 1% BSA coated coverslips as per Manufacturer’s protocols . Biofilms were visualized using a Leica LMD6 microscope with DFC3000G monochrome camera and Z-stacked to construct 3D-images . Adhesion assays on HL-1 mouse atrial cardiomyocytes ( generously provided by Dr . William Claycomb , New Orleans , LA ) and RBCEC6 rat brain capillary endothelial cells were performed as described previously [58] . All experiments were performed in triplicates . For HIP-RNA , hearts were excised from HIP-infected mice ( n = 3 ) when deemed moribund . Hearts were rinsed , diced , fragments washed with ice cold PBS , and the fragments homogenized in RNAprotect bacteria reagent ( Qiagen ) and stored at -80°C . We empirically determined that high titers of pneumococci were necessary to capture the bacterial transcriptome in the bloodstream when using dual RNA-seq . These levels were not routinely seen following conventional challenge and we resorted to neutrophil depletion to achieve the necessary titers ( >107 CFU/mL ) . Ten mice pre-depleted for neutrophils using anti-Ly6G antibody ( BioXCell , clone RB6-8C5 ) were infected with TIGR4 . Blood was collected in RNAprotect bacteria reagent ( Qiagen ) when the mice were deemed moribund such that blood from 5 mice were pooled as one sample ( n = 2 BIP samples ) and stored at -80°C . On the day of total RNA isolation from heart homogenates , the samples were thawed and spun down to discard the supernatant . The pellets were further homogenized in 600 μL RLT with B-ME buffer using a motorized mortar for 30 seconds . The re-homogenized samples were then disaggregated in a Qiashredder followed by RNA extraction with the RNeasy Micro Kit ( Qiagen ) with DNase treatment on column and in solution . The isolated RNA was quantitated using Nanodrop and Bioanalyzer . Samples were then depleted of rRNAs using the Ribo-Zero rRNA Removal Kit for Gram-positive bacteria and human/mouse/rat ( Illumina , San Diego , CA ) . For the in vitro biofilm and planktonic samples: planktonic mid-log phase ( OD620nm = 0 . 5 ) TIGR4 grown in THB were used to seed continuous once-flow through biofilm reactors . Biofilms were allowed to grow for 48 hours prior to collection of bacteria . RNA was isolated from the paired planktonic seed cultures ( n = 3 ) and their respective biofilms ( n = 3 ) . Total RNA was extracted from each replicate separately using enzymatic lysis of pneumococcal cells ( 10μL mutanolysin at 25 units/μL , 20μL proteinase K at 20mg/mL , 15μL lysozyme AT 15mg/mL , and 55μL TE ) followed by RNA extraction using the same protocol mentioned above . Blood samples were isolated in a similar manner as biofilm and planktonic samples , including enzymatic lysis , except that cells were first disaggregated with the Qiashredder like for the heart samples . Illumina strand-specific RNA-seq libraries were constructed with the TruSeq RNA Sample Prep kit ( Illumina , San Diego , CA ) per manufacturer’s protocol . Between 1st and 2nd-strand cDNA synthesis , the primers and nucleotides were removed from the samples with NucAway spin columns ( Ambion , Austin , TX ) . The 2nd strand was synthesized with a dNTP mix containing dUTP . Adapters containing 6 nucleotide indexes were ligated to the double-stranded cDNA . After adapter ligation , the 2nd strand cDNA was digested with 2 units of Uracil-N-Glycosylase ( Applied Biosystems , Carlsbad , CA ) . Size selection of the library was performed with AMPure XT beads ( Beckman Coulter Genomics , Danvers , MA ) . In order to achieve sufficient levels of RNA sequencing for bacterial transcripts in the presence of an abundance of mouse transcripts ( dual RNA-seq ) , libraries were loaded on 150nt paired-end runs of the Illumina HiSeq4000 sequencing platform as follows: half of a channel for each of the BIP and HIP libraries , a quarter of a channel for each of the biofilm and planktonic libraries , and an eighth of a channel for each of the three-control uninfected mouse heart libraries . Raw RNA-seq data and associated in silico analyses for BIP , HIP , biofilm and planktonic TIGR4 are deposited at GEO ( accession number GSE86118 ) . Reads from each of the 3 HIP-infected heart samples , 2 pooled BIP samples , 3 biofilm- and 3 planktonic- pneumococci samples were mapped onto the reference S . pneumoniae TIGR4 genome using Bowtie version 0 . 12 . 9[59] . The alignment BAM files from Bowtie were used to compute gene expression levels and test each gene for differential expression . The number of reads that mapped to each TIGR4 gene was calculated using the python package HTSeq version 0 . 4 . 7 [59] . Differential gene expression analysis was conducted using the DESeq R package version 1 . 5 . 24 ( available from Bioconductor ) [60] . The DESeq analysis resulted in the determination of potential differentially expressed genes when compared between the control planktonic samples and the in vitro biofilm , heart and blood samples , respectively . Read counts for each sample were normalized for sequencing depth ( RPKM ) and distortion caused by highly differentially expressed genes . Then the negative binomial ( NB ) model was used to test the significance of differential expression between three pairs of conditions ( i . e . in vitro biofilm vs . planktonic , heart vs . planktonic , and blood vs . planktonic ) . The differentially expressed genes were deemed significant if the False Discovery Rate ( FDR ) was less than 0 . 05 , the gene expression was above the 10th percentile and showed greater than 2-fold change difference ( up-regulated or down-regulated ) between the paired conditions . We performed PCA analysis ( R statistical software v2 . 15 . 2 ) on log10 ( RPKM ) gene expression values across all growth conditions and all replicates for each condition , i . e . in vitro planktonic ( n = 3 ) , in vitro biofilm ( n = 3 ) , heart ( n = 3 ) and blood ( n = 2 ) samples . This analysis was heavily skewed by genes that showed no expression at all in vivo ( RPKM = 0 ) . In order to circumvent this problem , we used a cutoff of RPKM>1 across all conditions prior to PCA analysis . This analysis revealed tight clustering of all replicates within a given condition . In addition , principal component 1 ( PC1 , Fig 5A X-axis ) separated in vivo from in vitro conditions , while PC2 ( Fig 5A Y-axis ) separated biofilm ( in vitro and heart ) from planktonic ( in vitro and blood ) . It should be noted that the overall depth of coverage of gene expression value interrogation in the in vivo conditions was significantly lower than for in vitro cultures ( due to the overwhelming presence of mouse transcripts ) , explaining the PCA skew when including genes with zero expression . Yet , the distribution of RPKM values across the TIGR4 genome revealed similar average RPKMs of 605 and 520 for in vivo and in vitro samples , respectively . In addition , maximum expression values observed were ~25 , 000 in vitro in contrast to ~125 , 000 in vivo . Finally , many of the very highly in vivo expressed genes were part of operons within the RDs described in the results and discussion sections . Therefore , our interrogation of the in vivo gene expression profiles was robust . We then computed correlation values ( R statistical software v2 . 15 . 2 ) for all genes to PC1 and PC2 separately . Using a correlation cutoff of 85% , we identified 142 and 105 genes that correlated with PC1 and PC2 , respectively ( S8 and S9 Figs ) . A Circos plot for whole transcriptome comparisons of BIP , HIP , in vitro biofilm- and in vitro planktonic-pneumococci samples for expression levels of 2 , 105 TIGR4 genes was generated . Read counts were computed for 2 , 105 genes within each sample using the python package ‘htseq’ . Genes with a count of 0 across all samples were excluded resulting in 2090 genes . The read counts were normalized for library size in each sample and a normalized value of counts per million-mapped-reads ( CPM ) was computed for all genes . Additionally , genes with CPM values < 1 in all samples were excluded resulting in 1969 genes . The mean expression value for each gene was computed within each of the 4 conditions . The average gene expression values were converted to z-scores and were used to rank the genes within each condition . Genes with z-scores ≥ +1 were classified as genes with ‘high’ expression ( red color ) while genes with z-scores ≤ -1 were classified as genes with ‘low’ expression ( green color ) . The remaining genes were classified as genes with ‘intermediate’ expression . These gene expression values , gene ranks and gene stratification were utilized to generate the circular plots ( Fig 5B ) using the ‘Circos’ tool version 0 . 69 [61] . qRT-PCR confirmation of RNA-seq results was conducted using the ABI 7900HT Fast Real-Time PCR System ( Applied Biosystems ) . 69 pneumococcal genes of interest were analyzed by qRT-PCR across all conditions and replicates of the RNA samples used for RNA-seq . Gene expression data was normalized using three genes that were unregulated across all conditions: SP_0378 , SP_1489 , and SP_1667 . The comparative critical threshold ( Ct ) method was used to determine the ΔCt . The ΔCt was then correlated to the log2 ( Ratio ) of expression from RNA-seq results through a linear regression . The primer sequences used for qRT-PCR are provided in S3 Table . Freshly excised hearts were minced on ice and digested in serum free Iscoves DMEM supplemented with 2mg/mL of Collagenase Type 2 ( Worthington , Cat #LS004176 ) and 0 . 02mg/mL of Deoxyribonuclease I from bovine pancreas ( Sigma , Cat #DN25-100MG ) at 37°C for 30 minutes . These cell suspensions were then neutralized with IDMEM containing 10% FBS and filtered through 0 . 45μm strainer before blocking with 2 . 4G2 ( BD Pharmingen , Cat #553141 ) for 30 minutes on ice , and staining with Gr-1-APC ( Clone RB6-8C5 , eBioscience ) , CD11b-APC-Cy7 ( M1/70 , BD Pharmingen ) , Ly 6C-PerCP-Cy5 . 5 ( HK1 . 4 , eBioscience ) , Ly6G-FITC ( 1A8 , eBioscience ) , MHC-II PE-Cy5 ( M5/114 . 15 . 2 , eBioscience ) , MerTK-PE ( DS5MMER , eBIoscience ) , F4/80-PE-Cy5 ( BM8 , eBioscience ) and CD64-Biotin ( X54-5/7 . 1 , Biolegend and used in conjunction with Streptavidin PE-Cy7 , eBioscience ) antibodies for 30 minutes on ice protected from light . Cells were washed with PBS prior to flow cytometry . The samples were then analyzed on BD LSR-II ( UAB Flow Cytometry Core Facility ) . Neutrophils were identified as Gr-1+CD11b+Ly-6G+F4/80-MHC-II- . Cardiac macrophages were first gated on F4/80+CD11b+ cells and further gated for expression of MerTK and CD64 . Flow cytometry data were analyzed using FlowJo software . Percent neutrophils and macrophages were determined as ( Percent live gated cells from the heart ) x ( percent positive ) . Percentage cytotoxicity and TNFα , CXCL1 , and CXCL2 production by mouse J774A . 1 macrophages and HL-1 atrial cardiomyocytes at designated time-points following exposure to an equal biomass of pneumococci ( corresponding to multiplicity of infection of ~10 planktonic bacteria in DMEM ) were measured by Pierce LDH cytotoxicity assay kit ( Thermo Scientific ) and ELISA ( R&D systems ) , respectively . To set equal biomass , biofilm-pneumococci were flushed out of the continuous flow-through systems using THY and the optical density of the biofilm suspension adjusted to that of log-phase planktonic bacteria grown in parallel in THY ( OD620nm = 0 . 5 ) . Samples for western blot quantification for pneumolysin levels were prepared by lysing pellets of planktonic- and flow through biofilm-cultures of S . pneumoniae at equal biomass using pneumococcal lysis buffer ( 0 . 01% SDS , 0 . 1% DOC , and 0 . 015 M Na-citrate ) and concentrating supernatant proteins using acetone precipitation [62] . Samples were frozen with protease inhibitors ( Sigma ) . Equal biomass of pellets and supernatants were loaded after BCA quantification . Isogenic pneumolysin deficient strains were tested as the negative controls . Normalized densitometric quantification of pneumolysin levels in the supernatant was performed using ImageJ processing software . Statistical analysis of in silico data is provided in the Supplemental Experimental Procedures . For wet lab research , multiple group analyses were performed using One-Way ANOVA Kruskall-Wallis Test with Dunn’s Multiple Comparison Post-test . For all non-parametric data sets , we used a Mann-Whitney test while Student’s t-test was used to analyze parametric data sets . These statistical analyses were performed using Prism 5 . 0 ( GraphPad Software: La Jolla , CA ) . Data are represented as mean ± SEM . P-value ≤0 . 05 were deemed significant .
Since its discovery in 1881 , invasive disease caused by Streptococcus pneumoniae , the leading cause of community-acquired pneumonia and a prototypical extracellular pathogen , has been tied exclusively to the planktonic phenotype , i . e . individual diplococci or short chains . Herein , we report that heart-invaded pneumococci can instead replicate intracellularly and transition into an immunoquiescent biofilm . Using dual RNA-seq technology we capture the complete gene expression profile of S . pneumoniae within infected hearts as well as the bloodstream . In doing so , we affirm that pneumococci within the heart are indeed forming biofilms and identify virulence determinants that play important roles in vivo . Accordingly , we identify a novel role for the genomic island Region of Diversity 12 in promotion of biofilm formation , virulence , and dampening of the host response . We subsequently show that biofilm pneumococci prevent immune cell infiltration into the heart not by passive means but instead through enhanced fratricide-mediated release of the toxin pneumolysin that kills resident macrophages , pre-empting their response . Collectively our manuscript describes a novel site , i . e . intracellular; previously unreported growth phenotype during invasive disease , i . e . biofilm formation; and counter-intuitive molecular mechanism , i . e . resident macrophage killing; for how pneumococci establish themselves in the heart without inflammation .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]
[ "biofilms", "blood", "cells", "bacteriology", "medicine", "and", "health", "sciences", "immune", "cells", "body", "fluids", "pathology", "and", "laboratory", "medicine", "pneumococcus", "cardiovascular", "anatomy", "pathogens", "immunology", "microbiology", "organisms", "bacteria", "neutrophils", "bacterial", "pathogens", "white", "blood", "cells", "animal", "cells", "medical", "microbiology", "gene", "expression", "streptococcus", "microbial", "pathogens", "bacterial", "biofilms", "blood", "anatomy", "cell", "biology", "physiology", "genetics", "biology", "and", "life", "sciences", "cellular", "types", "macrophages", "heart" ]
2017
Streptococcus pneumoniae in the heart subvert the host response through biofilm-mediated resident macrophage killing
Once interruption of transmission of lymphatic filariasis is achieved , morbidity prevention and management becomes more important . A study in Brugia malayi filariasis from India has shown sub-clinical lymphatic pathology with potential reversibility . We studied a Wuchereria bancrofti infected population , the major contributor to LF globally . Children aged 5–18 years from Odisha , India were screened for W . bancrofti infection and disease . 102 infected children , 50 with filarial disease and 52 without symptoms were investigated by lymphoscintigraphy and then randomized to receive a supervised single oral dose of DEC and albendazole which was repeated either annually or semi-annually . The lymphatic pathology was evaluated six monthly for two years . Baseline lymphoscintigraphy showed abnormality in lower limb lymphatics in 80% of symptomatic ( 40/50 ) and 63·5% ( 33/52 ) of asymptomatic children . Progressive improvement in baseline pathology was seen in 70·8 , 87·3 , 98·6 , and 98·6% of cases at 6 , 12 , 18 , and 24 months follow up , while in 4·2 , 22·5 , 47·9 and 64·8% , pathology reverted to normal . This was independent of age ( p = 0·27 ) , symptomatic status ( p = 0·57 ) and semi-annual/bi-annual dosing ( p = 0·46 ) . Six of eleven cases showed clinical reduction in lymphedema of legs . A significant proportion of a young W . bancrofti infected population exhibited lymphatic pathology which was reversible with annual dosage of DEC and albendazole . This provides evidence for morbidity prevention & treatment of early lymphedema . It can also be used as a tool to improve community compliance during mass drug administration . ClinicalTrials . gov No CTRI/2013/10/004121 Treatment with albendazole and either ivermectin or diethylcarbamazine citrate ( DEC ) used in the Global Program for Elimination of Lymphatic Filariasis ( GPELF ) works on the basis of elimination of microfilaria that are infective to the mosquito vector . Transmission within communities is thereby reduced . Based on the data from studies with DEC in India and DEC and ivermectin in the Pacific [1] , it has been possible to model the transmission dynamics to estimate the critical levels for parasite survival within populations . The GPELF works on the assumption that parasite prevalence below 1% in any population will lead to failure of effective parasite transmission and eventual extinction within that population . The program targets annual single dose mass drug administration ( MDA ) for at least five years with population compliance above 80% to achieve this [2 , 3] . Providing an effective rationale for individuals , communities , and those countries implementing MDA is a problem , since ‘treating now to prevent in the future’ is a rather tenuous concept , although it is the well-established concept behind all vaccination programmes . It therefore would be helpful to have evidence that intervention is also beneficial to the treated population and particularly to those who are actually infected . It should also be emphasized that management of chronic filarial disease ( lymphedema and hydrocele ) is challenging , since currently available morbidity management tools fail to effectively cure advanced disease [4 , 5] . A preventive approach would hence be a pre-requisite for morbidity control and one strategy would be to show the beneficial effect of MDA on lymphatic morbidity . Moore et al described DEC induced reversal of early lymphatic dysfunction in a patient with bancroftian filariasis [6] . Clinical trials [7 , 8 , 9] with long term doxycycline have also demonstrated macrofilaricidal activity and improvement in lymphatic pathology and morbidity ( lymphedema and hydrocele ) in W . bancrofti infected populations . Shenoy et al . [10 , 11] showed that pre-existing lymphatic pathology caused by Brugia malayi could be reversed with single dose DEC and albendazole . Using lymphoscintigraphy to visualize the lymphatics and the collateral circulation that developed during symptomatic and asymptomatic infection , they demonstrated a significant and sustained improvement in the lymphatic circulation following treatment . Although the available literature gives indication that lymphatic pathology can also be reversible in W . bancrofti filarial disease , long treatment with doxycycline may not have wide acceptance and the limited results from a B . malayi population would not be directly applicable . Furthermore , W . bancrofti and B . malayi are not only different in terms of transmission and disease development , adult worms responsible for lymphatic morbidity are also different in size and localization . Hence , it was felt essential to generate more evidence on clinical or sub clinical lymphatic pathology and its reversibility in the geographically more wide spread parasite , W . bancrofti , causing lymphatic filariasis ( LF ) , using the drugs used in the elimination program . Bal et al . [12] clearly showed that W . bancrofti infection , as demonstrated by circulating filarial antigen ( CFA ) , is mostly acquired at an early age ( 3-5yrs ) in an endemic area; while microfilaraemia develops later and progressively increases over time . These data suggest that intervention needs to occur as early as possible . Even if microfilaria clearance is the proven impact of MDA , there is no current evidence that MDA is of benefit to the young asymptomatic population with respect to bancroftian filarial disease . Thus , the asymptomatic period after initial infection in early childhood until development of lymphedema and hydrocele leaves a window to be explored . The present study was designed to ( a ) determine the prevalence of lymphatic pathology in children with W . bancrofti infection aged between 5 and 18 years with or without symptoms of filarial disease using lymphoscintigraphy and ( b ) assess any improvement or reversal of the existing lymphatic pathology , using repeated lymphoscintigraphy , after treatment with annual or bi-annual doses of DEC and albendazole as used in the current MDA program in India . The study enrolled eligible participants from a W . bancrofti endemic area of Khordha district in Odisha State , India . Twelve villages with microfilaria positivity of ≥ 5% detected by rapid screening were selected for the study . All were located between 20 . 17° to 20 . 27° N latitude and 85 . 67° to 85 . 84° E longitude within 15–50 kilometres of the Regional Medical Research Centre ( Indian Council of Medical Research ) in Bhubaneswar . Assuming a prevalence of lymphatic pathology in 70% of W . bancrofti infected children , based on the findings from Shenoy et al [10 , 11] , the sample size was calculated as 100 subjects , at 90% confidence level with 0 . 15 width of confidence interval . This sample would also be sufficient if 80% of those showing lymphatic abnormality would show improvement after treatment . Hence , it was targeted to include around 100 children in the target age group ( 5 to 18 years ) with W . bancrofti infection . Children between 5 and 18 years of age with evidence of W . bancrofti infection with adult worm ( Og4C3 ) antigenemia with/without microfilaraemia were considered eligible for enrolment into the study . Pregnant females were excluded from the study . Subjects with serum ALT ≥ 30 IU/l , creatinine ≥ 1 . 2 mg/dl , hemoglobin level < 10gm/dl , or evidence of other systemic illnesses were also excluded from enrolment . Baseline screening was done in night camps arranged in the villages , after initial awareness generation activities had been carried out by the study team . Prior to enrolment , written consent was obtained from parents or guardians in case of minors and from individuals who were 18 years of age . History of filarial disease manifestations were recorded in a pre-designed format and included present or past history of adeno-lymphangitis , chyluria , recurrent hematuria , lymphedema or hydrocele , and any antifilarial treatment received by the individual . Clinical examination was undertaken to detect any other existing systemic illness and for signs of filarial disease , particularly adeno-lymphangitis , lymphedema and hydrocele and recording the grade of lymphedema [13] . Enrolment was continued until 100 eligible children ( 50 symptomatic and 50 asymptomatic ) had been identified . Night blood samples ( 4–5 ml ) were collected aseptically between 21:00 and 23:00 hours . The sample was divided into 2 aliquots: first aliquot of 2ml blood preserved in EDTA and the second aliquot stored in plain vial for separation of serum . The serum samples were stored at -20°C and the EDTA collected sample was preserved at 4°C . Microfilaria counts in the individual specimens were determined by microscopic examination of 1ml EDTA preserved blood concentrated by Nuclepore membrane filtration technique . Adult worm antigen ( Og4C3 ) was measured from serum using the TropBio ELISA test kit ( TropBio , Townsville , Australia ) , following the manufacturer’s instructions [12] . Serum ALT and creatinine were estimated by an automated biochemistry analyser ( Cobas Integra 400 , Roche ) . Blood cell counts and hemoglobin estimation were done in a cell counter ( MS-4 , Melet Schloesing Laboratories , France ) . Pregnancy in females between 14–18 years of age was ruled out by a urine β-hCG test . After eligibility screening , all subjects were investigated by Doppler ultrasonography using a 7 to 12 MHz probe ( GE Logique 400 PRO , Wipro ) to detect the filarial dance sign ( FDS ) of adult filarial worms in axillary , inguinal and scrotal areas . To document lymphatic abnormality in the lower limbs , TC99 labelled radio-nucleotide lymphoscintigraphy was used as described below . The study was conducted as a parallel group randomized study . It was planned to enrol 100 subjects with W . bancrofti infection , half being asymptomatic , and half with some evidence of filarial disease . Subjects in both asymptomatic ( n = 52 ) and symptomatic ( n = 50 ) groups were randomized , using a list generated using Prism software ( Graph Pad Prism 6 . 0 ) , into equal subgroups to receive either six monthly ( semi-annual ) or yearly ( annual ) treatment . On completing baseline investigations , all subjects were administered a single oral dose of diethylcarbamazine citrate ( DEC ) tablets ( Banocide forte , GSK , Nasik , India ) and albendazole tablets ( Zentel , GSK , Solan , India ) supervised by the investigators . The albendazole dose was 400mg for all ages while the DEC dose was 200mg for 5 to 14 years and 300mg for 15 to 18 years of age , as recommended by the National Filariasis Elimination Program , NVBDCP , India [17] . Drugs were taken at home , supervised by the study personnel . Follow up household visits were made to record and manage any adverse events , daily for a week or until resolution of any events following drug administration . Study participants were enrolled in batches of four to six , treated according to the predetermined randomization list and followed at six monthly intervals with drug dosing as appropriate and repeat investigations . After baseline evaluation , all children received the first dose of DEC and albendazole in doses appropriate to their age . DEC and albendazole treatment was repeated as follows . The semi-annual group received treatment at 0 , 6 , 12 , 18 and 24 months and the annual group received treatment at 0 , 12 and 24 months . Hematological investigations , urine pregnancy test , and lymphoscintigraphy were repeated in each of the participants at each follow up point , while ultrasonography was repeated only on subjects who had demonstrated FDS at baseline . One hundred children completed successful follow up at the different time points and were available for analysis . One symptomatic child was withdrawn shortly after baseline evaluation with a diagnosis of pulmonary tuberculosis and one asymptomatic subject was lost to follow up from 12 months onwards . Both were in the semi-annual treatment group . Individual information from baseline screening to last follow up visit at 24 months were recorded in a pre-designed form and entered into an Excel database . Data accuracy was ensured by double data entry , matching and subsequent data cleaning . Data analysis was performed using SPSS version 16 with appropriate tools for parametric and non-parametric variables . The results were expressed as percentage frequency and percentage changes . Chi-square test was used to compare the proportions . The study was undertaken following ICH-GCP guidelines with written informed consent from parents , guardians , or individuals as applicable . The protocol and consent information was approved by Human Ethical Committee of Regional Medical Research Centre , Bhubaneswar . The study protocol was further reviewed by the Indian Council of Medical Research ( ICMR ) and Health Ministry Screening Committee , Govt . of India . Patient safety was assured by monitoring and management of adverse events . The study was registered under CTRI , ICMR ( No CTRI/2013/10/004121 ) . The study population was mostly of middle and lower socio- economic status living in an agriculture-based economy . In total , 3055 children between 5 and 18 years of age were screened to identify W . bancrofti infected subjects who were either Og4C3 antigen positive ( n = 480 , 15·5% ) or Mf positive ( n = 154 , 5% ) . From them , 100 individuals were screened for eligibility after obtaining written informed consent . All of them satisfied the inclusion and exclusion criteria and were enrolled into the study . Fifty had signs or symptoms of filarial disease and are referred to as ‘symptomatic’ while the remaining 50 without signs and symptoms of disease are referred to as ‘asymptomatic’ . Due to early drop-out of two subjects , these were replaced , both in the asymptomatic group . Only data from those who completed the 24 months of follow-up were analysed . The age and sex distribution of the children is presented in Table 1 . There were more males than females in all groups . It was originally intended that there would be a greater proportion of younger ( pre-pubertal ) children in the population , although this was not a primary criterion for enrolment . In the event , significantly more symptomatic subjects ( 74% ) were found in the older ( 12–18 years ) age group ( p = 0 . 02 ) , leading to a greater proportion of older children overall . A history of adeno-lymphangitis ( n = 32 ) , early grade ( I or II ) lymphedema ( n = 11 ) , hydrocele ( n = 6 ) and microscopic hematuria ( n = 1 ) were the filarial disease manifestations recorded in the symptomatic individuals . Thirty ( 29·5% ) of the enrolled subjects were microfilaraemic with counts ranging from 30 to 1540/ml of blood . All the subjects ( n = 102 ) had circulating filarial antigen ( Og4C3 ) , the antigen level ranging from 182 to 15107 units . Ultrasonography detected filarial dance sign ( FDS ) of adult worms in the inguino-scrotal area in 9 ( 8 . 8% ) of the enrolled subjects . Lymphoscintigraphy studies at baseline revealed lymphatic abnormality/ pathology in 73 ( 71·6% ) cases ( Table 2 ) . At baseline , LSG demonstrated sluggishness in lymphatic flow in one of the lower limbs in 60 ( 82% ) cases of the 73 children with abnormalities . Collateral lymphatic channels were visualized in 9 ( 12·3% ) and persistent visualization of popliteal lymph nodes was recorded in 25 ( 34·2% ) of the children with lymphatic pathology . The youngest child with lymphatic flow abnormality was an asymptomatic 6 year old male with circulating filarial antigen and no microfilaraemia . Examples of abnormalities of lymphatic flow in children prior to treatment are shown in Figs 3–6 . The post treatment evaluation of LSG images with the baseline lymphatic pathology/abnormality showed improvement in 70·8 , 87·3 , 98·6 and 98·6% of children at 6 , 12 , 18 and 24 months respectively ( Table 3 ) . Moreover a return to a normal LSG image was seen in 4·2 , 22·5 , 47·9 and 64·8% of cases at the same time points post treatment . Examples of improvement/normalization in lymphatic flow , disappearance of collateral channels and popliteal nodes following treatment are presented in Figs 7–10 . There was no statistically significant difference in the frequency of improvement or reversal of the baseline lymphatic abnormality/pathology in comparisons between symptomatic and asymptomatic subjects ( p = 0·57 ) ( Table 3 ) or between annual and semi-annual dose groups ( p = 0·46 ) . At 12 months follow up higher percentage of improvement was observed in mf positive subjects ( p = 0 . 02 ) ( Table 4 ) and males ( p = 0 . 009 ) ( Table 5 ) . There was also an improvement in the grade of lymphedema in the symptomatic subjects ( 6/11 ) as recorded by clinical examination ( Fig 7A ) . Among the above , all three with grade I lymphedema at baseline became normal; while out of eight subjects with grade II edema at baseline , three became grade I . Repeat ultrasonography showed disappearance of FDS in 78% of those with FDS at baseline . Two subjects remained persistently positive . Microfilarial clearance and Og4C3 antigen clearance were also noted to be greater than 90% at 18 and 24 months of follow up ( Table 6 ) . Adverse events ( AEs ) were seen in 9·8% ( 10/102 ) of the children following the first dose of DEC and albendazole . Following drug intake at 6 , 12 and18 months the AE frequency was 8% ( 4/50 ) , 2·9% ( 3/101 ) and 4% ( 2/50 ) respectively with no reports of AEs following the 24 month dose . All the adverse events were mild in nature and could be managed at home . The AEs observed included fever , headache , leg pain , nausea and light-headedness that appeared within a mean of 14 hours and disappeared within a mean of 48 hours . No serious adverse event ( SAE ) was reported during the study . The study demonstrated a high prevalence ( 71·5% ) of lymphatic pathology in children aged between 5 and 18 years infected with W . bancrofti . Furthermore , sub-clinical pathology was demonstrated in 63·5% of the asymptomatic but infected children . Lymphatic abnormalities were significantly more frequent ( 81·1% , p = 0 . 006 ) in symptomatic group subjects aged over eleven years , while there was no difference in the frequency of lymphatic abnormality ( p = 0·82 ) in the two age groups in asymptomatic subjects . This provides the first evidence of the extent of subclinical lymphatic abnormality that develops between initial infection with W . bancrofti in early childhood and the appearance of symptomatic filarial disease in later age . The current observations in W . bancrofti infection as with Brugia malayi infection[10] show that sub clinical lymphatic dysfunction precedes overt disease by some considerable time . Lymphoscintigraphy is also shown to be a valuable tool for assessment of lymphatic damage in both symptomatic and asymptomatic infection in children and adults [18 , 19] . It adds substantially to previously scarce demonstrations of subclinical lymphatic damage in adults demonstrated by lymphoscintigraphy , ultrasonography or nephritic manifestations [18 , 20 , 21 , 22] . An observation of collateral lymphatic channels in lymphoscintigrams of nine ( 12·3% ) subjects with lymphatic abnormality provides evidence for host compensatory mechanisms to prevent lymph stasis . At the same time it gives an opportunity to explore whether drug interventions are additive to the host attempts at compensating for the functional loss . In this study , DEC and albendazole at the standard annual dose used in the Indian filariasis elimination programme progressively reduced the lymphatic abnormality or improved the lymphatic flow in 69·9% to 96·0% of subjects within 6 months to 2 years . Furthermore , the pathology/abnormality reverts to a normal picture in around two third ( 63% ) of cases . Importantly , there was no significant additional benefit of twice annual treatment on lymphatic pathology . No gender difference was observed in the reversibility of lymphatic abnormality , although males were shown to have a higher frequency of improvement at 12 months . Similar observations were also noted for Mf positives with greater improvement at 12 months but no difference at other time points . It is clear that considerable improvement occurs within the first 6 months following initiation of treatment , but there is further progress over the next 18 months . As might be expected , there is a lag before normal function is established in around two thirds of those investigated , and it appears that some may never regain full function . These findings are quite encouraging for the GPELF programme as it explores a new dimension of MDA against the prevailing concern that LF associated lymphedema cannot be cured [4 , 5] . It also substantiates the role of DEC in reversing lymphatic pathology or preventing and treating disease [23 , 24 , 25 , 26 , 27] . Studies in non-human primate models and detection of loss of motility of adult filarial worms by ultrasonography with reversal of early dilatation of lymphatic channels have already provided a suggestion that early treatment could reverse pathology [28] . These findings in W . bancrofti infection and the reversibility previously demonstrated in B . malayi infected children [10] shows the potential impact of combinations of DEC and albendazole in reversing the existing lymphatic pathology and lymphatic dysfunction [11] . Thus , it could be expected that the Global Programme will reduce or prevent overt filarial disease that can occur in 10–30% of those infected [20] . The message that many children with W . bancrofti infection already have hidden pathology , starting as early as five years of age but potentially reversible with MDA , will be useful as an advocacy tool to improve community compliance to the elimination programme in LF endemic countries , which covers an affected population of around 120 million in 83 countries , most of whom ( >90% ) have W . bancrofti infection [20 , 28] . This should encourage funding agencies and managers of the GPELF , ultimately helping programme sustainability with the high coverage that is essential to achieve targeted elimination by 2020 [29] . It remains to be demonstrated whether similar benefits occur in W . bancrofti infections treated with ivermectin/albendazole in Africa . LF morbidity management and disability prevention remains a critical problem in many endemic areas , despite much progress being made in interruption of LF transmission [30 , 31] . Morbidity control remains a special concern since as many as 40 million have symptomatic LF disease [32] . This study has shown improvement in lymphatic abnormality as observed by lymphoscintigraphy in those with established symptoms and signs in a similar proportion ( p>0·05 ) to those without clinical evidence of disease . Visible reduction in lymphedema was also noted in subjects presenting with early edema of the lower limb ( Fig 6 ) . The study in B . malayi infected children with LF disease also demonstrated lymphoscintigraphic as well as clinical improvement with anti-filarial drugs used in the programme [11] . This gives the hope that early treatment of lymphedema will prevent permanent disfigurement . Observations from this study have explored an additional benefit of pharmaceutical intervention in the management of LF related disease . Thus , it can also be promoted as a tool for secondary prevention i . e . , early recognition of lymphatic disease and treatment with annual DEC and albendazole . Together with improvement or reversal of lymphatic pathology , treatment also resulted in clearance of microfilaria ( 93·3% ) and circulating filarial antigen ( 95% ) as well as disappearance of FDS ( 77·7% ) in a paediatric population . In addition to the benefits of single dose DEC and albendazole in the elimination of LF disease from endemic areas , it could also be of significance in LF non-endemic areas [33] . It is well understood that , despite threat of infection diminishing in many areas , increased travel to tropical regions exposes more people to filariasis [34] . The Geo-Sentinel Surveillance Network [35] has noted a shorter interval of presentation of W . bancrofti infection in travellers ( 1–6 months ) after return from endemic countries . These observations highlight the need for early diagnosis and case treatment . Hence , physicians need to be aware of LF disease , its early diagnosis with clinical evaluation and lymphoscintigraphy in patients who have travelled to or are from endemic countries , while expatriates , service personnel and other travellers to endemic countries are encouraged to take personal protective measures . Equally , migrants from endemic areas need to be aware of the possibility of harbouring filariasis [35] . Thus , the present report may help to ensure treatment of early disease and prevent morbidity in those with asymptomatic pathology or early disease among non-endemic populations exposed to the risk of infection using single doses of DEC and albendazole .
Infection with lymphatic filarial parasites usually occurs early in childhood in endemic areas , but clinical signs appear much later . Reversal of lymphatic pathology has been shown with the much less common Brugia malayi infection using DEC and albendazole and there is scarce evidence whether the same occurs with bancroftian filariasis using the above drugs . We designed this study to look for prevalence of lymphatic pathology in children with and without clinical signs of infection and to observe the effect on the pre-treatment pathology using DEC and albendazole given once or twice a year . We have shown , using lymphoscintigraphy , that lymphatic vessel changes occur very early in infection , and treatment can reverse these changes , even when clinical symptoms are already apparent . This has important implications for lymphedema prevention , case management and for advocacy in the Lymphatic Filariasis Elimination Program . It also strengthens the previous evidence of benefit of treatment on early lymphedema management .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "invertebrates", "medicine", "and", "health", "sciences", "legs", "diagnostic", "radiology", "lymphoscintigraphy", "pathology", "and", "laboratory", "medicine", "geographical", "locations", "limbs", "(anatomy)", "india", "animals", "health", "care", "pediatrics", "brugia", "signs", "and", "symptoms", "pediatric", "infections", "edema", "morbidity", "research", "and", "analysis", "methods", "musculoskeletal", "system", "imaging", "techniques", "wuchereria", "bancrofti", "wuchereria", "lymphedema", "people", "and", "places", "radiology", "and", "imaging", "eukaryota", "diagnostic", "medicine", "health", "statistics", "anatomy", "asia", "brugia", "malayi", "nematoda", "biology", "and", "life", "sciences", "organisms" ]
2017
Lymphatic pathology in asymptomatic and symptomatic children with Wuchereria bancrofti infection in children from Odisha, India and its reversal with DEC and albendazole treatment
Genetically identical cells grown in the same culture display striking cell-to-cell heterogeneity in gene expression and other traits . A crucial challenge is to understand how much of this heterogeneity reflects the noise tolerance of a robust system and how much serves a biological function . In bacteria , stochastic gene expression results in cell-to-cell heterogeneity that might serve as a bet-hedging mechanism , allowing a few cells to survive through an antimicrobial treatment while others perish . Despite its clinical importance , the molecular mechanisms underlying bet hedging remain unclear . Here , we investigate the mechanisms of bet hedging in Saccharomyces cerevisiae using a new high-throughput microscopy assay that monitors variable protein expression , morphology , growth rate , and survival outcomes of tens of thousands of yeast microcolonies simultaneously . We find that clonal populations display broad distributions of growth rates and that slow growth predicts resistance to heat killing in a probabalistic manner . We identify several gene products that are likely to play a role in bet hedging and confirm that Tsl1 , a trehalose-synthesis regulator , is an important component of this resistance . Tsl1 abundance correlates with growth rate and replicative age and predicts survival . Our results suggest that yeast bet hedging results from multiple epigenetic growth states determined by a combination of stochastic and deterministic factors . Clonal populations of cells grown in a constant environment display a striking amount of cell-to-cell heterogeneity . For example , in bacteria , yeast , and mammalian cell lines , levels of some gene products vary widely between cells [1]–[5] . A crucial challenge is to understand how much of this heterogeneity serves a biological function [6] , [7] . That is , does variability in gene expression between clonal cells simply reflect the noise tolerance of a robust system , or does the variation itself increase population fitness ? In bacteria , several examples exist in which clonal variation in gene expression correlates with a morphological or physiological state that presumably confers a fitness advantage in some environments . These examples include competence to uptake foreign DNA [8]–[10] , initiation of sporulation [11] , [12] , and expression of cell surface pili [13] , [14] . In each case , a binary fate decision is controlled in part by stochastic expression of a crucial regulatory protein . A population-fitness advantage for heterogeneity is even more obvious for the phenomenon known as bacterial persistence . When a clonal population of Escherichia coli is exposed to a lethal dose of ampicillin , the vast majority of the population dies at a fast exponential-decay rate but rare , slow-growing “persister” cells die at a much slower rate [15] , [16] . These persister cells can subsequently switch to the common , fast-dividing state , thereby restoring the population after removal of the antibiotic . Persistence is therefore considered a canonical example of a bet-hedging mechanism ( Box 1 ) , whereby a population maximizes its long-term fitness in an unpredictably changing environment by distributing risk among individuals [16] , [17] . In a benign environment , most E . coli cells adopt the sensible strategy of fast growth , whereas a small proportion of cells adopt the high-risk strategy of entering the persister state , which could reap large benefits should the environment change . Single-cell observations in a microfluidic chamber suggest that , as with competence and the other examples above , persisters and non-persisters constitute binary states that interconvert through a stochastic mechanism [16] . However , despite the clinical importance of persistence , and despite indications that slow growth might be a general means of surviving stress [18]–[20] , the molecular mechanisms underlying persistence remain unclear [21] , [22] . This is due , in large part , to the experimental difficulty of identifying and characterizing a rare persister subpopulation prior to antimicrobial treatment . Like clonal populations of bacteria , those of the budding yeast S . cerevisiae have also been shown to contain a large amount of cell-to-cell heterogeneity [3] , [4] , [23] , [24] . In both bacteria and yeast , one component of gene-expression variation is so-called “intrinsic” noise , which is operationally defined as fluctuations that are not correlated between identical promoters in the same cell [3] . In yeast , as in other eukaryotes , an important component of intrinsic noise is fluctuation between more or less accessible chromatin states [3] , [5] , [25]–[28] . Mutations in yeast genes associated with chromatin remodeling alter the extent of heterogeneity in both protein expression [3] , [29] and cell morphology [30] . By contrast , “extrinsic” noise is defined as variation that is correlated between different alleles of the same gene , or between different genes [3] . Such variation reflects either fluctuations in the concentrations of upstream regulators ( i . e . , intrinsic noise upstream can produce extrinsic noise downstream ) , or fluctuations in global cell state , such as the abundances of ribosomes [31] or mitochondria [32] . In yeast , evidence suggests that a fraction of what might operationally be defined as extrinsic noise is instead due to deterministic factors . For example , fluctuations of many gene products have been found to correlate with the cell cycle [4] and cell size [3] , [33] , [34] . Additionally , unequal segregation of certain molecular components between mother and daughter cells [35]–[38] or daughter-specific expression [39] could produce meaningful replicative-age-dependent heterogeneity within a yeast population [24] . For example , cells that have undergone ∼eight replicative cycles survive ultraviolet irradiation better than younger or older cells [40] . A combination of stochastic and deterministic influences could provide the basis for more complex bet-hedging mechanisms than the binary switches that appear to be primary in bacteria . Indeed , the pathogenic yeast Candida albicans displays at least seven different metastable colony morphologies when grown on agar [41] . Another opportunistic pathogen , C . glabrata , which despite its name is actually a member of the Saccharomyces clade , shows similar multi-stability [42] , [43] . It should also be kept in mind that bacterial bet-hedging mechanisms might be more complex as well , and that the apparent primacy of binary switches might be a product of the phenotypes chosen for study and of experimental limitations in phenotypic measurement . For example , although E . coli antibiotic persistence is commonly described as a two-state system , recent observations of macroscopic bacterial colonies on agar have found a continuous distribution of growth rates [44] . Additionally , asymmetric cell division has been found to underlie bet hedging to starvation in the bacterium Sinorhizobium meliloti , indicating that deterministic factors may be important in prokaryotes as well [45] . Here , we investigate the mechanisms of bet hedging and persistence in S . cerevisiae using a new high-throughput microscopy assay capable of monitoring variable protein expression , morphology , growth rate , and survival outcomes of tens of thousands of yeast microcolonies simultaneously . We find that clonal populations of yeast grown in a rich , benign environment display a wide and continuous distribution of growth rates that can be modulated by mutations in genes involved in chromosome organization or other core regulatory functions . Using a bioinformatic screen , we identify candidate gene products whose expression correlates with growth rate and establish that Tsl1 , a protein involved in the synthesis of the disaccharide trehalose , is a molecular marker for slow growth in the benign environment . Using quantitative measurements of microcolony growth rates and abundance of fluorescently tagged Tsl1 , we show that both slow growth and Tsl1 abundance predict survival of heat stress in a graded rather than binary fashion and that Tsl1 is an important component of the stress survival . Lastly , we investigate replicative age as a potential source of heterogeneity in this stress-survival system and in protein expression in general . We find that Tsl1-abundant cells tend to be older and , more generally , that replicative age is an underlying component of cell-to-cell variation in the expression of many proteins . Microbial fitness assays have historically been limited to ensemble measurements that calculate the difference in mean growth rate or the competitive fitness advantage of one population over another . Besides suffering severe limitations in the number of replications that are experimentally feasible , these assays do not measure the variance of growth rates , even though this is likely to be an evolutionarily meaningful parameter in both static and fluctuating environments and over the course of population bottlenecks [17] , [24] , [46]–[49] . To overcome these limitations , we developed a high-throughput assay that measures microcolony growth by time-lapse bright-field microscopy ( Figure 1A; Videos S1 and S2 ) . Exponentially growing cells are plated at a low density in rich , liquid medium on glass-bottomed micro-well plates and allowed to grow into isolated microcolonies of up to ∼100 cells ( Materials and Methods ) . During this growth period , 1-h time-lapse images of ∼3 , 000 low-magnification fields are captured in parallel allowing for simultaneous observation of ∼105 microcolonies . Custom-written image analysis software tracks changes in area over time , and these measurements are used to calculate the specific growth rate of each microcolony ( the change in the log of the area per hour ) . Each growing microcolony displays log-linear growth over the period of observation ( Materials and Methods ) , yet different microcolonies grow at vastly different rates ( Figure 1B and 1C ) . The automated measurements of microcolony area correlate extremely well with manual cell counts over a range of growth rates ( R2>0 . 9 ) ( Figures 1C , S1 , and S2 ) , indicating that changes in area are representative of cell-division rates . Growth-rate distributions generated from all individual microcolony growth rates measured within a well of a 96-well plate are highly reproducible between wells on a single plate or between experimental days ( Figure S3 ) . In wild-type populations grown in a benign environment , a large fraction of microcolonies grow at less than half the median population growth rate ( 1 . 3%–10% , depending on the strain ) ( Figure 1D; Table S1 ) . Because growth rate is extremely consistent within a microcolony over the duration of tracking , this wide distribution indicates that substantial differences in growth rate between isogenic cells exist and are heritable over several generations . We hypothesized that , as in bacteria , cell growth rates constitute a phenotypically observable component of epigenetic cell states that together act as a bet-hedging mechanism in yeast . That is , the lowered relative fitness of slow-growing cells in the benign environment would have an increased relative fitness in other , perhaps harsher , environments , allowing a clonal population to maximize the population fitness over multiple environments . We first ruled out several alternative technical and biological explanations of slow growth . One possibility is that local nutrient depletion by neighboring microcolonies causes closely spaced microcolonies to grow slower than distantly spaced microcolonies . With the exception of microcolonies within 35 µm ( 4–8 cell lengths ) of each other , microcolony growth rate distributions showed no observable dependence on the proximity of a microcolony to its nearest neighbor ( Figure S4 ) . A slight difference in growth-rate distribution of microcolonies that fall within 35 µm of each other could be detected and is likely due to a technical bias of the experiment rather than local nutrient depletion ( Materials and Methods ) . Regardless of the cause , removing closely spaced microcolonies had a minimal effect on observed growth rate distributions . Nonetheless , to be conservative , we ignored these microcolonies in all data reported here . A second possible explanation for the frequent occurrence of slow-growing microcolonies is that these cells are petites , having lost mitochondrial function . Such losses can occur frequently in yeast [50] . To test this possibility , we generated growth rate distributions of single-deletion strains of several genes necessary for growth as a petite [50] , [51] and compared these to growth distributions of control strains of the same genetic background but with a deletion of a dubious open reading frame ( Figures 1E and S5 ) . Petite-negative strains generally contain as many or more slow-dividing microcolonies than controls , suggesting petites are not a major component of slow-dividing microcolonies in our assay . Lastly , we considered a high mutation rate as a possible source of slow growth . Based on mutation accumulation experiments , the spontaneous mutation rate in S . cerevisiae is estimated to be ∼0 . 003–0 . 006 per cell per DNA replication when mutations to homopolymeric runs are excluded [52] , [53] , and ∼0 . 3 when mutations to homopolymeric runs are included [52] . Assuming that each mutation results in an observable difference in growth rate in our assay , mutation rates of this magnitude could explain a large fraction of the growth rate variation . However , the deleterious mutation rate is expected to be far lower than the spontaneous mutation rate , and orders of magnitude below the number of slow-growing colonies we observe . Indeed , in S . cerevisiae the rate of fitness-altering mutations has been estimated to be 1 . 37×10−4 per haploid genome per generation [54] . Moreover , we show below that slow growth is reversible for both single cells and cell populations , suggesting that a large component of growth rate heterogeneity is metastable and epigenetic in nature . We have shown that wild-type yeast populations grown in a benign environment contain a wide distribution of growth rates , a property likely to impact population fitness in both static and fluctuating environments . A static environment favors low variance in growth rate , as the long-term population growth rate of a single genotype is its geometric mean , which weighs lower values of a distribution more heavily [55] . In contrast , a fluctuating environment can favor high variance , if growth rate correlates with stress survival [17] . The variance of the growth-rate distribution is therefore an important evolutionary parameter , but one that is invisible to standard , population-level measurements of growth rate . To examine whether mutations can alter the variance in growth rate , we selected candidates from previous studies that had shown that deletions in gene products involved in chromosome organization or those with a large number of genetic and physical interactions increase the cell-to-cell heterogeneity in gene expression or morphology [3] , [29] , [30] , [56] . We find that the variance in growth rate can also be modulated by these deletions ( Figure 1E; Table S1 ) . For example , deletion of histone variant HTZ1 or the protein scaffold BEM1 results in a greater than 4-fold increase in slow-growing microcolonies ( operationally defined as microcolonies growing at less than half of the population median ) when compared to control deletions of dubious open reading frames . Interestingly , some gene deletions decrease the growth rate variance . For example , deletion of SWA2 , a gene that encodes a product involved in clathrin-dependent vesicular transport , and NOT5 , a gene that encodes a global transcriptional regulator , each result in a greater than 2-fold decrease in the number of slow-growing microcolonies . There does not appear to be a trivial relationship between the mean growth rate and variance ( Figure S6 ) . A deletion resulting in a reduced mean growth rate can result in an increased ( DIA2 , RAD50 , HTZ1 , BEM1 ) or decreased ( SWA2 , SNF6 ) variance when compared to a deletion of a dubious open reading frame . To allow for further investigation into the nature of growth heterogeneity , we next sought to identify a molecular marker of slow-dividing cell subpopulations . We reasoned that such a marker would have at least two general characteristics: ( 1 ) a correlation between its expression level and growth rate , and ( 2 ) high cell-to-cell variation in its expression to match the observed variation in growth rate . Genome-wide expression profiling of cells grown at different growth rates in nutrient-limited chemostats has revealed a large number of genes whose transcript levels correlate with growth rate , no matter what the limiting nutrient [18] . However , a correlation between a gene's average expression level and the bulk growth rate might merely indicate that the gene is part of a generalized stress response . Indeed , genes whose transcript levels correlate with growth rate overlap significantly with those that are induced as part of a general environmental stress response [18] . To identify candidates among these genes that might be relevant to growth heterogeneity under constant , benign conditions , we therefore cross-referenced the growth-correlation data with data on cell-to-cell variation in each protein's abundance , as measured by flow cytometry of cells engineered to encode a GFP fusion protein at the corresponding endogenous gene [4] . Plotting these two measures revealed several gene products that anti-correlate with the population growth rate and that also exhibit a large amount of cell-to-cell variation in protein levels under benign conditions ( Figure 2A ) . Using strict cut-offs for the growth-correlation and protein-variation datasets , we identified 78 candidate markers of cell-to-cell variation in growth rate ( Materials and Methods ) ( Table S2 ) . We next investigated the candidate markers of cell-to-cell variation in growth rate for enrichment in gene ontology ( http://www . geneontology . org ) process , function , and component terms ( Table S3 ) . As a group , the candidates appear to be involved in energy storage or mobilization . Specifically , candidates are highly enriched for mitochondrial genes in the proton-transporting ATP synthase complex ( p<9×10−5 ) and genes involved in the metabolism of the disaccharide trehalose ( p<0 . 002 ) . Trehalose is synthesized by a trimeric complex consisting of two enzymatic subunits , Tps1 and Tps2 , and one of two interchangeable cofactors , Tps3 and Tsl1 [57] , [58] . Among these , Tps1 , Tps2 , and Tsl1 were identified as candidates in our screen , with Tsl1 ranking especially high for both the growth correlation and protein noise datasets ( Figure 2A ) . As a class , genes involved in trehalose biosynthesis are highly over-represented among those whose expression levels negatively correlate with growth rate and that are induced by heat shock [20] . Both trehalose and Tsl1 appear to be correlated with a stress-resistant cell state in yeast . Expression levels of Tsl1 and bulk trehalose content remain relatively low during exponential growth but rise rapidly as cells reach saturation and become more stress resistant [58] , [59] . Trehalose is thought to preserve protein folding under stress [60] , and indeed cellular trehalose content correlates with resistance to various forms of stress , including heat , freezing , desiccation , and high ethanol content [60]–[63] . Consistent with a direct role for Tsl1 in stress resistance , deletion of TSL1 results in increased sensitivity to killing by high ethanol concentrations [61] . Taken together , these data suggest that Tsl1 might not only serve as a marker for a slow-growing , stress-resistant cell state , but might also be an important component of heterogeneity-dependent stress resistance . We therefore chose to focus further examinations on the role of Tsl1 in bet hedging . To determine if TSL1 expression correlates with individual slow growth phenotypes in a non-stressful environment , we simultaneously monitored microcolony growth and green fluorescent protein ( GFP ) fluorescence of cells encoding a Tsl1-GFP fusion protein at the endogenous TSL1 locus [64] . As mentioned , Tsl1 abundance increases at saturation [58] . To avoid the possibility that variability in exit from stationary phase could confound our results , we maintained cells in logarithmic growth for a minimum of 24 h prior to any measurements . Consistent with previous flow-cytometry data [4] , the expression of Tsl1 varies between cells ( Figure 2B ) . An examination of individual microcolonies suggests that , as predicted , Tsl1-GFP fluorescence correlates negatively with cell-division rate . Figure 2B shows that cells undergoing few or no cell divisions over the course of 8 h are highly Tsl1-GFP fluorescent . Although GFP expression level and growth status tend to persist within a cell lineage , they can change . Microcolonies founded by a fast-dividing cell occasionally produce a highly fluorescent cell with a low cell-division rate ( Figure 2B ) . Cells can switch in the opposite direction as well: a highly fluorescent cell with a low cell-division rate can produce low-fluorescence fast-growing progeny ( Video S3 ) . In general , slow-dividing cells appear to be larger than fast-dividing cells ( Figure 2B ) , suggesting that these cells might have altered the influence of cell size on the Start transition in late G1 [18] , [65] . The connection between high Tsl1-GFP fluorescence and low cell-division rate , which we observe in individual cases such as that shown in Figure 2B , holds as a general trend across many microcolonies tracked in our assay . To control for alterations in Tsl1 abundance that may be caused by differences in cell size , we measured the Tsl1-GFP intensity per unit area of each microcolony ( Figures 2C and S7 ) . A negative correlation between Tsl1 abundance and microcolony growth rate is observed across the range of growth rates ( Figures 2C and S8 ) , indicating that Tsl1 is a general marker of growth state rather than a marker for only extreme slow growth . Also of note is that the correlation is specific to Tsl1 , not a generic property of any protein with variable expression . Expression of the control protein Tma108-GFP , which has a similar average abundance as Tsl1 [66] and is highly variable from cell to cell [4] , shows no correlation with microcolony growth rate ( Figure S9 ) . Having shown a correlation between Tsl1 abundance and growth rate at the individual microcolony level , we next assayed for differential susceptibility of microcolonies to heat killing . Tsl1-GFP cells were grown normally in our microcolony growth assay for 6 h ( producing microcolonies of 1–20 cells ) , heat shocked under conditions that kill most cells , and placed back under the microscope for an additional 14–20 h of observation ( Videos S4 and S5 ) . Figure 3A shows a typical result: a highly fluorescent cell in a slow-growing microcolony survives heat shock , undergoes one or two cell divisions at a slow rate , and then produces fast-growing progeny . Again , this individual case is representative of a general relationship . Microcolonies with a higher Tsl1 content are significantly more likely to contain a survivor , as shown by a plot of the survival frequency of microcolonies binned by the Tsl1-GFP fluorescence prior to heat shock ( Materials and Methods ) ( Figure 3B ) . We next asked if Tsl1 is directly involved in heterogeneity-dependent heat resistance or instead acts only as a marker of resistant cells . We generated a genotypically similar TSL1 knockout strain , by replacing the coding sequence of the Tsl1-GFP fusion protein with that of the fluorophore mCherry , to compare the heat killing susceptibility of the TSL1Δ-mCherry strain to the TSL1-GFP strain ( Figure 3C ) . Multiple logistic regression was used to isolate the effects of growth rate and TSL1 genotype on survival ( Materials and Methods ) . Independent of genotype , growth rate before heat shock is a major determinant of survival , with slower growing microcolonies being more likely to contain a survivor ( multiple logistic regression , p<10−28 ) . Because , prior to the heat shock , slow-growing microcolonies produce far fewer cells than do fast-growing microcolonies , the difference in survival per cell is necessarily greater than the differences reported in our microcolony survival assay . In support of a direct role of Tsl1 in heterogeneity-dependent stress resistance , functional Tsl1 improves survival when controlling for growth rate ( multiple logistic regression , p<0 . 01 ) ( Figure 3C ) . The median growth rate of TSL1Δ-mCherry populations is slightly reduced compared with TSL1-GFP populations ( Figure S10 ) and thus TSL1Δ-mCherry populations would be expected to have more survivors if survivorship is independent of TSL1 content . However , TSL1-containing cells are slightly more likely to survive heat killing even without controlling for the effects of growth rate on survival ( Figure 3C ) . One possibility to explain differential survival between the TSL1-GFP strain and the TSL1 deletion is that TSL1 is an important component of an induced heat shock response rather than a component of a bet-hedging mechanism that renders a proportion of cells heat resistant prior to any environmental shift . To test this possibility , we compared the survival upon extremely acute stress between a TSL1 knockout from the yeast deletion collection and a second strain from that collection with a deletion of a dubious open reading frame ( YFR054C ) . Performing a 2-min heat shock at 60°C in a small volume of liquid medium followed by plating on agar to count survivors ( Materials and Methods ) , we find that TSL1 directly contributes to heat resistance under these conditions in which an induced response is unlikely to be relevant ( Figure 3D ) . Having established TSL1 as both a predictor of susceptibility to heat killing and an important component of the survival machinery , we next sought to characterize the distribution of TSL1 expression in yeast populations and how this distribution relates to survival . As discussed previously , in bacteria , bistable gene expression patterns underlie several binary phenotypic states thought to act as bet-hedging mechanisms [8]–[14] , [24] , [67] , [68] . Thus , levels of certain proteins show a bimodal distribution across cells . Using flow cytometry to measure cellular Tsl1-GFP fluorescence , we observe a continuous distribution in Tsl1 abundance rather than the bimodal distributions characteristic of bistable bacterial systems ( Figure 4A ) . Sorting cells into discrete bins at the high end of the Tsl1-GFP fluorescence distribution , then subjecting these groups of cells to heat shock reveals that Tsl1 abundance predicts survival in a graded or probabilistic manner rather than a binary manner: the higher the level of Tsl1-GFP , the higher the chance of survival ( Figure 4B ) . Taken together , observations of continuous or graded distributions in Tsl1 abundance ( Figure 4A ) , growth rate ( Figure 2C ) , and stress survival ( Figure 4B ) suggest that populations of yeast might contain a continuum of metastable epigenetic cell states that each confer a different fitness in a given environment . This hypothesis is supported by growth-rate distributions derived from cells sorted by Tsl1 abundance . Cells sorted for higher Tsl1-GFP content yield growth-rate distributions containing more slow-growing microcolonies ( Figure 4C ) . If the altered growth-rate distribution of the cells sorted for high Tsl1-GFP fluorescence reflects selection of a subset of metastable cell states , then prolonged culturing of a population founded by the sorted cells should result in a distribution similar to the initial unsorted population , which is presumably a steady-state distribution . The altered growth-rate distribution is indeed transient . After 42 generations of growth , a population founded by sorted cells has a growth-rate distribution that is indistinguishable from that of one founded by unsorted cells ( Figure 4C , right ) . As discussed previously , a combination of stochastic and deterministic influences is likely to underlie the continuous and graded distributions we observe here . Several characteristics of stress-resistant cells led us to hypothesize that replicative age ( the number of cell divisions an individual cell has undergone ) could be a deterministic factor underlying yeast bet hedging . For example , both old cells and stress-resistant cells have an increased cell size , altered cellular morphologies , and a slowed cell cycle ( Figure 2B ) [69]–[71] . To test this hypothesis , we stained TSL1-GFP cells with wheat-germ agglutinin ( WGA ) -tetramethyl rhodamine isothiocyanate ( TRITC ) , a fluorescent marker that specifically stains bud scars , and measured single cell correlations in GFP and TRITC fluorescence by flow cytometry . Older cells show higher levels of TRITC fluorescence because each cell division leaves an additional bud scar [72] . As predicted , cells that abundantly express Tsl1 also show high levels of WGA-TRITC fluorescence ( Figure 5A ) . An alternative explanation is that cell states with high Tsl1 abundance more efficiently take up the WGA-TRITC stain , and the observed correlation is due to differences in staining rather than replicative age . To test this possibility , we sorted cells for high Tsl1-GFP content and compared the number of bud scars in this subpopulation to an unsorted population by performing manual bud scar counts . In further support of an age dependence of Tsl1 expression , we find that cells with abundant Tsl1 tend to have more bud scars ( p<10−7 , Wilcoxon-Mann-Whitney test ) ( Figure 5B ) . The finding that variation in replicative age partially underlies heterogeneity in TSL1 expression ( and presumably heterogeneity in growth rate and stress resistance ) led us to hypothesize that population demography might underlie a significant fraction of protein-expression variation generally thought to be a consequence of extrinsic noise . To test this hypothesis , we used data from an existing microarray study that measured differences in expression between young ( one to three generations ) and old ( 16–18 generations ) cells [73] . We then compared these expression differences to data on cell-to-cell variation in each protein's abundance [4] . These abundances were measured by flow cytometry of cells engineered to encode a GFP fusion protein at the corresponding endogenous gene and therefore capture both intrinsic and extrinsic noise , although a major source of extrinsic noise ( the cell cycle ) was minimized by gating the cells by size and complexity of shape [4] . Plotting the cell-to-cell variation in expression of genes binned by their age expression ratio ( AER , the mean expression in young cells divided by the mean expression in old cells ) reveals that cell age does indeed contribute significantly to protein-expression variation ( Figure 5C , Wilcoxon-Mann-Whitney test ) . Transcripts that become over- or under-expressed in old cells tend to result in protein levels that are more variable across cells in exponential growth . The absolute log AER explains approximately 1% of the variation in protein-expression variation , which is on par with other significant contributors to protein-expression variation , including mRNA half-life , ribosomal density of mRNA , and translation rate per mRNA [4] . We show that: ( 1 ) clonal yeast populations contain a wide and continuous distribution of growth rates when cultured in a benign environment; ( 2 ) growth differences are transient and reversible over the course of a few generations; ( 3 ) mutations can alter the mean and variance of the steady-state growth rate distribution; ( 4 ) Tsl1 is a marker for the slow-growing cells within an exponentially growing population; ( 5 ) Tsl1 abundance and slow growth predict resistance to heat killing; ( 6 ) TSL1 is an important component of heterogeneity-dependent heat shock survival; ( 7 ) Tsl1-abundant cells tend to be of higher replicative age; and ( 8 ) replicative age is likely to underlie a fraction of gene expression heterogeneity for many gene products besides Tsl1 . These results describe a bet-hedging phenomenon in yeast that might be an adaptation to life in an unpredictably varying environment ( Box 1 ) . As is true in descriptions of bacterial bet hedging and persistence [16] , slow growth is a crucial predictor of stress survival in yeast . Both bacteria and yeast appear to be maximizing population fitness by balancing fast growth in good conditions with bet hedging against bad ones [17] . Yet , some crucial differences between bacterial and yeast bet hedging appear prominent . One difference appears to be in the nature of the heterogeneity underlying bet hedging . Single-cell observations in bacteria suggest that persisters and non-persisters constitute binary growth states that predict survival in an all-or-none fashion [16] . That is , bacterial persisters generally survive and non-persisters generally perish in stress . We find that yeast populations contain a continuous rather than bimodal distribution of growth states and that these states predict survival in a probabilistic manner . That is , the slower a yeast cell grows , the greater its probability of surviving stress . Although the mechanism of bacterial persistence has yet to be elucidated , persisters and non-persisters are thought to interconvert through a stochastic mechanism [16] , as is true for the vast majority of characterized bacterial two-state systems [8]–[14] . In yeast , differences in growth and survival appear to be due to a more complex combination of stochastic and deterministic factors . Taken together , these results suggest that bet hedging in yeast is a consequence of a spectrum of metastable inheritable epigenetic states that confer differential fitnesses across environments . The processes underlying interconversion between epigenetic states , and the different phenotypes associated with these states , are of great importance not just for yeast but also in metazoan development and disease . Interconverting epigenetic states have been shown to underlie phenomena as diverse as antibiotic resistance [16] , stem cell reprogramming [74] , and cancer progression [75]–[77] . For example , recent work has shown that rare cells within a melanoma tumor divide slowly but give rise to highly proliferative daughter cells , and vice versa [78] . This behavior can be thought of as a bet-hedging mechanism , and likely contributes to the poor long-term performance of chemotherapies that target fast-dividing melanoma cells [78] . Current theoretical models of bet hedging focus on the dynamics of two-state systems [17] , [16] . Our results and recent work in human cancer cell lines [77] suggest that future models must account for a distribution of multiple cell states and the transitions between them [79] , [80] . Interconversion between multiple Tsl1-abundance and growth states presents an experimentally tractable system that can be exploited to test and parameterize such models . For example , sorting cells by Tsl1 abundance and following changes in growth rate distributions over time might allow for theoretical estimates of the number cell states and the transition rates between them [77] . Additionally , because the microcolony growth and survival assay presented here relies on simple microscopy and image analysis routines , these methods could be relatively easily exported to the above-mentioned cell culture systems to provide additional quantitative measures of metazoan multi-stability and bet hedging . A correlation between growth and the deterministic factor of replicative age has been previously noted , with increasing age resulting in slower progression through the cell cycle until no more cell cycles can be completed [71] . Here we show that replicative age also correlates with Tsl1-abundant , presumably stress-resistant cell states . We note , however , that replicative age does not appear to be the sole determinant of slow growth , Ts1-abundance , or stress resistance . Both slowed growth and high fluorescence of TSL1-GFP cells persist in newborn cells and their daughters ( Figure 1B , 1C; Video S3 ) . Additionally , we observe newly born cells surviving heat shock ( Videos S4 and S5 ) . A more likely possibility is that both the deterministic factor of replicative age and stochastic mechanisms contribute to stress resistance , although more research is required to establish causal links . The possibility that old age contributes to stress resistance provides a particularly compelling bet-hedging mechanism: an old cell with few remaining cell cycles maximizes its contribution to a clonal population if a cell cycle is completed after a stressful event that results in a mass killing of the younger , fast-growing cells . Thus , a slowed cell cycle in old cells—and , with high probability , their few remaining progeny , as implied by inheritance of TSL1 abundance and slow growth from mother to daughter ( Video S3 ) —might be selected to maximize bet hedging . In this scenario , the influence of age , although independent of the environment , could nonetheless be probabilistic , if the age signal or its transduction is noisy . An alternative possibility is that older cells have accumulated minor stresses throughout their lifetimes , so that induction of TSL1 and other genes represents a genuine stress response despite the benign environment . If true , this possibility would still represent a bet-hedging mechanism , because the induced response protects against a subsequent , unpredictable , and acute stress . We have previously shown that a large number of gene deletions result in a decreased phenotypic robustness , increasing the cell-to-cell heterogeneity in morphology [30] . Here we show that some of these mutations also alter the growth rate distribution in a benign environment , often resulting in a greater variance in growth rates with proportionally more slow-dividing cells . It is unclear whether the large number of slow-dividing cells in the distributions of these single-deletion strains represent , as they do in wild-type populations , meaningful bets that are more fit in harsh environments , or instead represent unfit cell states in any environment . Yet , the possibility that a large number of mutations could result in increased fitness in harsh environments presents a dynamic picture of the tension between bet hedging and robustness in yeast . That is , selection for a robust phenotype in a given environment ( i . e . , the fittest phenotypic state ) is countered by selection for distributed phenotypes ( i . e . , multiple phenotypic states that constitute a series of bets on changing environments ) [81] . When the environment is not constant and when slow growth in a benign environment confers resistance to an acute stress , then the growth rate distribution of a mutant will be more informative about fitness than the mean growth rate . The high-throughput microcolony assay of growth and stress survival offers a way to explore these distributions systematically using yeast gene-deletion strains or strains segregating natural variation . Another way to explore the tension between robustness and bet hedging would be to test the expectation that organisms evolved in fluctuating environments should exhibit a wider distribution of growth rates than those evolved in static environments . We have shown here that Tsl1 , a protein involved in the synthesis of trehalose , is both a marker and an important component of a stress-resistant cell state . Trehalose appears to function as a general stress protectant across biological kingdoms , approaching 20% of the dry weight of stress-protected organisms such as yeast and nematodes , which regularly encounter harsh conditions [59] . Thus , it is quite plausible that the bet-hedging mechanism described here will provide mechanistic insights into ecological adaptation in a wide range of organisms , as well as into how pathogenic eukaryotes , such as C . albicans [82] or indeed strains of S . cerevisiae [83] , colonize humans and evade therapeutic agents . Identification of Tsl1 as marker for stress-resistant cell states in yeast will be of great value to elucidating the molecular mechanisms underlying persistence , an endeavour that has been elusive in bacterial models [21] . For example , comparisons of the gene expression profiles between cells sorted for abundant Tsl1-GFP and unsorted populations will provide a list of candidate gene products involved in heterogeneity-dependent stress resistance . These candidates can subsequently be tested for correlation to or necessity within a stress-resistant cell state using methods similar to those described here for Tsl1 . Haploid deletion strains were converted from the diploid BY4743 YKO magic marker strains ( Open Biosystems , MATa/α ura3Δ0/ ura3Δ0 leu2Δ0/leu2Δ0 his3Δ1/ his3Δ1 lys2Δ0/LYS+ met15Δ0/MET15+ can1Δ::LEU2+-MFA1pr-HIS3/CAN1+ xxx::kanMX/XXX+ ) as described [84] . Tsl1-GFP yeast ( MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ) are part of the yeast-GFP collection [64] and were purchased from Invitrogen . The TSL1Δ-mCherry strain was constructed directly from the TSL1-GFP strain by replacing the coding region of the genomic TSL1-GFP chimera with that of mCherry and the selectable marker , NatMX , through homologous recombination [85] . The mCherry-NatMX insert was amplified by PCR from the pCZ-Nat plasmid ( GenBank accession number JN580989 ) using the primers ( 5′→3′ ) CAAACAAAGCAAAGAATACAATAGCAACGCAAGATCAACACAATGGTGAGCAAGGGCGAGGAGGA and AAGTTCATACCCAAGAAAATTAAAATTTTAAAATGGTAAAATTTATGAGCTCCAGCTTTTGTTCC . The PCR product was extended for homologous recombination using the primers CCGTGTCATTGCACATCCACCCACCCGTCGATTAAAAAACCAAACAAAGCAAAGAATACAATAGCAACG and TAGAATTGATATATAATAAGCAGTTGAAAATAAAAGTTCATACCCAAGAAAATTAAAATTTTAAAATGG . Transformation of the PCR construct was performed with lithium acetate , as described [86] , and homologous recombinants were selected for incorporation of NatMX with nourseothricin . Proper integration was confirmed by sequencing . For all strains , a single colony was selected and grown overnight in YPD to generate a frozen stock . Frozen stocks were struck onto YPD plates at a high density and populations from the streak were used to initiate experiments . Growth rate and survival assays were preformed in synthetic complete liquid medium or on synthetic complete plates . Deletion strains were allowed to reach saturation in liquid culture . A day prior to plating , saturated cell cultures were diluted 1∶60 and grown overnight to saturation . On the day of plating , cultures were again diluted 1∶60 and allowed to reach early logarithmic phase by growing for 3–4 h with shaking at 30°C . Because TSL1 expression increases for all cells during late log phase and saturation [58] , the TSL1-GFP and TSL1Δ-mCherry strains were instead maintained in early- to mid-log phase for at least 24 h prior to any experiments . We estimate ∼50 generations of growth between a single cell bottleneck and growth rate measurements for single gene deletions strains and ∼60 generations for TSL1-GFP and TSL1Δ-mCherry strains . Cells were sonicated for 90 s on high in a Diagenode Bioruptor water bath , counted using a hemocytometer , and diluted to a final concentration of 5–20×103 cells/ml . Glass-bottomed 96-well plates ( Matrical MGB096-1-2-LG ) were coated with 200 µl of 200 µg/ml Concanavalin A ( Type V , Sigma ) for 2–6 h . Wells were washed once with 200 µl of water and 400 µl of cells were plated per well . Plates were sealed with an optically clear film ( Axygen PCR-SP ) and spun at 360 g for 2 min . Before placing the samples on the microscope , the bottom surface of the 96-well plate was dusted using compressed air to remove any particles that may interfere with the Nikon Perfect Focus System ( PFS ) . Micrographs were captured on a Nikon TE2000e microscope equipped with PFS for infrared high-speed focusing and a fully automated stage equipped with a full-stage environmental chamber . All images were collected with a Nikon Plan Apo 10× ( 0 . 45 numerical aperture ) air objective using Nikon NIS Elements software to drive stage movement and acquisition . Because NIS Elements readily accepts externally written XML files for position and PFS control , we created homemade R- and C-based scripts to assign plate position and focus coordinates that minimize stage travel time and optimize PFS offsets over the plate surface . The environmental chamber was set to 30°C at least 2 h before observation to prevent heat gradients . Prior to image acquisition , a 45-min focusing routine was performed to determine the optimal PFS offset for each well . This focusing routine was necessary because the PFS maintains focus on the plane between the bottom of the cover glass and the air . Thus , alterations in thickness of the cover glass surface result in images that are slightly out of focus , which we found can have mild effects on measured growth rates ( unpublished data ) . Microcolonies begin growing during the focusing routine , thus some microcolonies might contain two cells by the time an initial image is taken . Microcolony growth was monitored by capturing a micrograph of each field every hour . For routines that require only bright field images , we were able to monitor ∼3 , 000 fields in parallel ( ∼100 , 000 microcolonies at 2×104 cells/ml ) because each image requires ∼1 s to capture including stage travel time . Tsl1-GFP fluorescence was captured for 4 s at 2× gain . The long exposure time required for fluorescence measurements considerably slowed our assay , allowing us to monitor ∼360 fields ( ∼3 , 000 microcolonies at 0 . 5×104 cells/ml ) . Image processing and microscope control routines were written in Matlab , R , C , and shell scripts . Cell counts were performed on a haploid segregant of a dubious open reading frame knockout ( YFR054C ) from the BY4743 YKO collection . Microcolonies on which to perform manual cell counts were selected at random from the following growth rate bins: below two standard deviations ( 2 SD ) from the mean population growth rate , between 2 SD and 1 SD below the mean , between 1 SD below the mean and 1 SD above the mean , between 1 SD and 2 SD above the mean , and above 2 SD above the mean . Counts did not try to distinguish budding cells that have not undergone cytokinesis from two separate cells ( i . e . , a cell with a small bud was counted as two cells ) . Microcolonies under ∼100 cells generally grew as a monolayer on the glass surface and cell counts correlated extremely well with automated colony area measurements ( Figures 1C , S1 , and S2 ) . We did notice that automated measurements slightly overestimated the cell number of the slowest growing microcolonies when they became large ( Figure S2A ) and slightly underestimated the cell number of the fastest growing microcolonies when they became large ( Figure S2E ) . For both slow and fast growing microcolonies , the automated measurements provide conservative estimates for the deviation from the mean growth rate ( i . e . , slow-growing colonies are measured as growing faster than their true growth rate ) , and thus the automated growth rates were not adjusted . For microcolonies over ∼100 cells , we did notice some piling of cells on top of each other resulting in automated colony area measurements underestimating the total number of cells ( unpublished data ) . We therefore limited all of our quantitative assays to colony sizes below 100 cells . In each well of a 96-well plate , approximately equal cell numbers of a single-gene deletion strain and an easily distinguishable GFP fluorescent control strain , FBA1-GFP [64] , were grown together for 9 h . The mean growth rate of the FBA1-GFP microcolonies was used to normalize deletion strain growth rates across different experimental wells . All reported single-gene deletion distributions are the combined microcolony growth rates from at least three replicate wells of a 96-well plate . Relative growth rates reported in Figures 1E and S3 and Table S1 were calculated by setting the mean growth rate of the control dubious open reading frame deletions ( YHR095W and YFR054C ) equal to one . Alternatively , growth rate distributions of deletions resulting in petite-negative and dubious open reading frame control strains ( Figure S5 ) are reported as raw specific growth rates without normalization . Using microarray analysis of cells grown in nutrient-limited chemostats , the regression slope for the transcriptional response to changes in growth rate has been determined for all transcripts [18] . The cell-to-cell variation , or noise , in protein level has been quantified for a large number of genes using flow cytometry of endogenously expressed GFP fusions and is summarized in a measure called DM [4] . To identify gene products that might mark cell-to-cell variation in growth rate , we set the following thresholds: a growth rate slope of less than −2 and noise ( DM in synthetic dextrose medium ) of greater than 5 . Gene ontology enrichment was calculated using the GO Term Finder in the Saccharomyces genome database website ( http://www . yeastgenome . org/cgi-bin/GO/goTermFinder . pl ) on September 7 , 2011 using the default settings . Genes with a reported value for both growth rate slope and noise in synthetic dextrose medium were used as the set of background genes for statistical comparisons . Reported p-values are corrected for multiple hypothesis testing . As is true for the single deletion studies , we observed only nominal differences between replicate wells or days ( unpublished data ) . Thus , growth rate distributions and fluorescence correlation studies for the TSL1-GFP strain and associated controls were generated by pooling microcolony growth rates across a minimum of 12 replicate wells and two experimental days . Growth rate distributions associated with microscopy-based survival assays were performed by pooling microcolonies from a minimum of 80 replicate wells over a minimum of two experimental days . Heat shock of film-covered glass-bottomed micro-well plates was performed by removing plates from the microscope and sandwiching them between two pre-heated standard aluminum heat blocks in a hydrated oven for 70 min at 60°C . Heat shock of TSL1 and control knockout strains was performed in liquid suspension for 2 min at 60°C . Heat shock of sorted and unsorted TSL1-GFP strains was performed in liquid suspension for 6 min at 52°C . A TSL1Δ-mCherry control strain was constructed as a direct descendant of the TSL1-GFP strain ( see “Yeast Strains and Cloning” ) . This genetic manipulation resulted in a mild but detectable decrease in mean population specific growth rate ( 0 . 366 h−1 for TSL1Δ-mCherry , 0 . 377 h−1 for TSL1-GFP , p<10−10 , Wilcoxon-Mann-Whitney test ) . Because TSL1 expression is generally low , requiring long exposure times and thus high fluorescence background , TSL1Δ-mCherry and TSL1-GFP were grown in a checkerboard pattern on separate wells on the same plate , rather than in the same well , to avoid genotype miscalls . We observed no obvious biases in growth rate or survival frequency over a plate's surface for either genotype and observed similar survival patterns over four similar heat-shock experiments . Heat-shock survival is a binary dependent variable , so multiple logistic regression was used to test the effects on it of growth rate ( prior to heat shock ) and genotype ( TSL1-GFP vs . TSL1Δ-mCherry ) . A full linear model including main-effect terms for growth rate and genotype as well as an interaction between the two was compared to reduced models using AIC , as implemented in the glm and anova functions of R . The full model was not significantly better than a reduced model with the two main effects but without the interaction . Removing either main effect from this no-interaction model made the model significantly worse . Therefore reported p-values come from the model with both main effects but no interaction . Heat shock of liquid suspensions was performed in triplicate . Survival frequency was determined by counting the number of cells in liquid before heat shock and comparing this to the number of colonies that grew on an agar plate following heat shock . p-Values were determined by performing a Student's t test on the arcsin of the square root of the proportion surviving . Cells were sonicated for 90 s in a Diagenode Bioruptor water bath prior to sorting . Cell sorting was performed on a FACSaria ( BD ) sorter . The pulse width was used to separate individual cells from cell clumps . FITC gates used to isolate cells with high levels of Tsl1-GFP are shown in Figure 4A . Because most cells contain low levels of Tsl1 ( ∼2 , 000 molecules per cell on average [66] ) , the sorter was not sensitive enough to sort cells in the bottom 85% of the distribution . Sorted cells were immediately resuspended in synthetic complete medium following sorting . A fraction of cells were immediately plated for growth rate distribution and heat shock survival analysis . A second fraction was grown in early- to mid-log phase for 48 h , allowed to reach saturation , grown again in early- to mid-log phase for 24 h , and plated for growth rate distribution analysis . Assuming an average specific growth rate of 0 . 4 h−1 for each sorted fraction , 76 h of log growth represents ∼42 generations . TSL1-GFP cells were kept in logarithmic growth for a minimum of 24 h , sonicated for 90 s in a Diagenode Bioruptor water bath , washed once in PBS , fixed for 90 min in 3 . 7% formaldehyde , and washed twice in PBS . Bud scar staining was performed for 15 min in 1 mg ml−1 TRITC-labeled WGA . All sorting was done using a tight pulse-width gate to remove cell clumps from the analysis . For co-fluorescence measurements , ∼8×105 cells were measured for WGA-TRITC and Tsl1-GFP fluorescence . Data shown are from one experiment . Replicate experiments yielded similar results . For bud scar counts , cells were sorted until 104 cells were recovered . Cells were pelleted , resuspended in 5 µl Vectashield ( Vector Laboratories ) , and mounted on a glass slide . Bud scars were counted manually using a Nikon TE2000e epifluorescent microscope and a 100× plan apochromat objective with a narrow focal plane . Three sorts for each category were performed and bud scars from ∼100 cells were counted per sort . Similar bud scar distributions were observed in all three sorts . Data shown are the pooled counts from all sorts .
Genetically identical cells grown in the same environment can display heterogeneity in their morphology , behavior , and composition of their cellular components . In some microorganisms , such cellular heterogeneity can underlie a phenomenon known as bet hedging because it enables some cells to survive in harsh environments , hence increasing the overall population fitness when environmental shifts are unpredictable . Bet hedging is likely to be an important strategy by which microbes infect humans and evade antimicrobial treatments , yet little is known of how cellular heterogeneity contributes to microbial survival . Here , we study the mechanisms underlying bet hedging in yeast . We find that populations of genetically identical yeast contain a broad distribution of growth rates and that slow growth predicts resistance to heat killing in a graded fashion . We identify several gene products that are likely to play a role in this bet-hedging strategy and confirm that Tsl1 , a regulator of the production of the disaccharide trehalose , is an important component of acute stress resistance . Finally , we find that old age in cells correlates with a Tsl1-abundant , stress-resistant cell state . Our results suggest that trehalose synthesis is part of a complex and multifactorial mechanism that underlies bet hedging in yeast .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "systems", "biology", "theoretical", "biology", "genetics", "biology", "genomics", "evolutionary", "biology", "microbiology", "population", "biology", "genetics", "and", "genomics" ]
2012
Bet Hedging in Yeast by Heterogeneous, Age-Correlated Expression of a Stress Protectant
Central place foragers , such as pollinating bees , typically develop circuits ( traplines ) to visit multiple foraging sites in a manner that minimizes overall travel distance . Despite being taxonomically widespread , these routing behaviours remain poorly understood due to the difficulty of tracking the foraging history of animals in the wild . Here we examine how bumblebees ( Bombus terrestris ) develop and optimise traplines over large spatial scales by setting up an array of five artificial flowers arranged in a regular pentagon ( 50 m side length ) and fitted with motion-sensitive video cameras to determine the sequence of visitation . Stable traplines that linked together all the flowers in an optimal sequence were typically established after a bee made 26 foraging bouts , during which time only about 20 of the 120 possible routes were tried . Radar tracking of selected flights revealed a dramatic decrease by 80% ( ca . 1500 m ) of the total travel distance between the first and the last foraging bout . When a flower was removed and replaced by a more distant one , bees engaged in localised search flights , a strategy that can facilitate the discovery of a new flower and its integration into a novel optimal trapline . Based on these observations , we developed and tested an iterative improvement heuristic to capture how bees could learn and refine their routes each time a shorter route is found . Our findings suggest that complex dynamic routing problems can be solved by small-brained animals using simple learning heuristics , without the need for a cognitive map . Animals moving in familiar environments often follow habitual routes to navigate between important locations , such as the nest and feeding sites . Most knowledge on route following behaviours has been deduced from the stereotyped paths insects [1]–[4] and birds [5] develop when travelling between home and a single other site . In contrast , very little is known about the routing decisions made by animals that must visit multiple sites before returning home . These routing challenges are common in central place foraging nectarivores and frugivores , which typically exploit familiar food resources that replenish over time . Many of these animals develop stable foraging circuits ( traplines ) between distant food patches [6]–[10] and must sometimes cover several kilometres to fill their crop [11] . Developing an efficient route to reduce the travelling costs between multiple foraging locations is an optimisation task analogous to the well-known travelling salesman problem ( finding the shortest route to visit a set of locations once and return to the origin ) [12] . The most direct approach to solve this mathematical problem is to compare all the possible routes , which often requires extensive computational power as the number of routes increases factorially with the number of locations to be visited ( e . g . , 5 ! = 120 possible routes in a problem with only 5 locations ) . For animals , this problem is of a different nature as they cannot plan a route in advance , using a geographic map , but must gradually acquire information about the locations and the paths linking them . Therefore many animals [13]–[16] , including humans [17] , [18] , navigating between multiple locations are thought to find efficient routes using heuristic strategies , such as linking nearest unvisited sites or planning a few steps ahead . Recent laboratory studies have shown that bumblebees foraging in simple arrangements of artificial flowers in indoor flight cages develop near optimal traplines after extensive exploration , based on learning and spatial memories [15] , [19]–[21] . However , whether similar strategies are observed at larger spatial scales , when animals must search to localise distant feeding sites and when the costs of travelling suboptimal routes are magnified , remains largely unexplored . In addition , over the smaller spatial scales at which bees were previously tested , nearby flowers were typically visible from other flowers , which is often not the case over natural foraging scales in the field . Obtaining data about the ontogeny of traplines in the wild is challenging , since it requires the observer to have information about the spatial location of all available food patches , the complete foraging history of the animals , and their movements with sufficient accuracy to retrace their routes . Here , taking advantage of the possibility to train bumblebees ( Bombus terrestris ) to forage on artificial flowers in the field [22] , to track their complete flight paths with harmonic radar [23] , [24] , and to record all their flower visits with motion-sensitive cameras , we investigate the acquisition of long-distance traplines by animals with known foraging experience . We describe how bees develop stable routes between five feeding locations by combining exploration , learning , and sequential optimization . We then compare bees' optimization performances to those of simple heuristic algorithms and develop a novel iterative improvement heuristic replicating the observed dynamics of route acquisition . Our first aim was to establish whether bees develop repeatable foraging circuits between stable feeding locations . We pre-trained naïve bees to collect sucrose solution rewards from a patch of five artificial flowers ( Figure S1 ) in the middle of the experimental field ( Figure 1 ) . After a day of pre-training , bees of known foraging experience were tested individually with the five flowers arranged in regular pentagon ( 50 m side length ) . Each flower provided a sucrose reward equivalent to one-fifth of the bee's crop capacity and was refilled after each foraging bout . We tested seven bees for seven consecutive hours each on a different day . All visits to the flowers were video recorded with motion-activated webcams at each feeding station ( Video S1 ) . The first flight of an inexperienced forager and the final flight paths of five experienced foragers were recorded with harmonic radar ( Videos S2 , S3 , S4 , S5 , S6 , S7 , S8 , S9 , S10 , S11 , S12 , S13 ) . Bees discovered flowers sequentially and had visited all five flowers at least once after an average of eight foraging bouts ( here , and throughout the text , means are reported ± s . e . m . ; 8 . 14±2 . 43 bouts , n = 7 bees; Figure 2A ) . The two flowers closest to the nest ( F1 and F5 ) were located first , by all individuals . The flower furthest from the nest ( F3 ) was found last by four bees , whereas it was the penultimate flower discovered by the other three . Individual bees consistently approached flowers from the same quadrant of the landing platform ( Video S1 ) , irrespective of the flower visited and of their experience ( Generalized Linear Mixed Model ( GLMM ) , effect of quadrant on the frequency of visits , F3 , 1328 = 90 . 23 , p<0 . 001; effect of flower identity , F4 , 1328 = 1 . 82 , p = 0 . 08; effect of the number of bouts completed , F1 , 1328 = 0 . 07 , p = 0 . 791; all interactions , p>0 . 05 ) . Frequency distributions of approaches in each quadrant were significantly different among bees ( χ218 = 996 , p<0 . 05; Figure 2B ) , indicating that each bee approached and landed on flowers from a different preferred angle . Furthermore , bees departed from the same quadrant as they arrived ( and thus in opposite directions ) in 71 . 41%±1 . 72% ( n = 7 bees ) of visits . The frequency of visits when arrivals and departures occurred in the same quadrant did not vary significantly in relation to flower location or to the foraging experience of bees ( GLMM , effect of flower identity on the frequency of visits where arrival and departure occurred in the same quadrant , F4 , 1328 = 2 . 27 , p = 0 . 065; effect of the number of bouts completed , F1 , 1328 = 0 . 46 , p = 0 . 499; interaction , F4 , 1328 = 4 . 87 , p = 0 . 222 ) . We also found no significant difference in the frequency of these visits among bees ( χ26 = 10 . 29 , p = 0 . 113 ) . Therefore , our data suggest that each bee acquired a directional preference in arrivals to and departure from flowers before the observations began , possibly during the pre-training phase when the bees became familiar with the flower design , and used their directional preference consistently for visiting flowers in all novel locations discovered . As they gained experience , bees increased the number of different flowers visited per foraging bout ( first five bouts , 2 . 29±0 . 35 flowers; last five bouts , 4 . 97±0 . 06 flowers; n = 7 bees; GLMM , effect of the number of bouts completed on the number of flowers visited , F1 , 194 = 149 . 62 , p<0 . 001 ) and reduced the frequency of revisits to empty flowers ( first five bouts , 2 . 83±0 . 58 revisits; last five bouts , 1 . 31±0 . 55 revisits; n = 7 bees; GLMM , effect of the number of bouts completed on the frequency of revisits , F1 , 194 = 6 . 50 , p = 0 . 012 ) . In every bout , a bee's probability to link the nest and a flower or to link two flowers together was determined by its experience . Thus , transition vectors between any two locations used in previous bouts were used more often in subsequent bouts than transitions vectors never previously experienced ( GLMM , effect of the cumulative frequency of all possible transition vectors in previous bouts on their frequency of usage at each bout , F1 , 5848 = 1 , 209 . 5 , p<0 . 001 ) . Among the paths already used , the probability of repeating a transition vector in two successive foraging bouts increased significantly with the optimality ratio ( straight line length of the observed visitation sequence divided by straight line length of the shortest possible sequence to visit the same number of flowers ) of the first bout ( GLMM , effect of optimality ratio of the first bout on the frequency of transition vectors repeated in the second bout , F1 , 1069 = 82 . 64 , p<0 . 001; Figure 2C ) . In other words , transition vectors that generated short routes were likely to be used again in subsequent bouts , while transition vectors producing long routes were gradually abandoned , thus limiting the number of novel transitions over time ( Figure 2D ) . With increasing experience , the sequence in which flowers were visited became more similar over successive foraging bouts ( similarity index—see Materials and Methods—between the first two bouts , 0 . 2±0 . 05; similarity index between the last two bouts , 0 . 89±0 . 07 , n = 7 bees; GLMM , effect of the number of bouts completed on similarity index , F1 , 187 = 78 . 14 , p<0 . 001 ) , leading to a regular repeatable sequence , or “trapline”: the most common five-flower visitation sequence excluding revisits used by each individual bee ( Figure 2E; Table S1 ) . On average , the trapline was used in 27 . 13%±3 . 46% ( n = 7 bees ) of each bee's foraging bouts . It first appeared after 17 . 57±1 . 79 bouts ( n = 7 bees ) and was stabilized ( repeated in at least three consecutive bouts at the end of training ) in six bees after 30±0 . 8 bouts . Among the 120 possible sequences to visit all five flowers once and return to the nest , each bee selected one of the two shortest possible sequences as its trapline , either by visiting the flowers in a clockwise ( sequence , 12345; n = 4 bees ) or an anti-clockwise order ( sequence , 54321; n = 3 bees ) . Radar tracks obtained from five experienced bees , near the end of the training phase , confirmed that the routes followed were highly repeatable and close to minimizing the overall travel distance ( Figure 3B–F; Videos S3 , S4 , S5 , S6 , S7 , S8 , S9 , S10 , S11 , S12 , S13 ) . Flight paths were composed of relatively straight segments linking either the nest and a flower or two flowers together . During each bee's final foraging bout , these flight segments were on average 26 . 09%±0 . 10% ( n = 30 segments ) longer than a straight line . Overall , the bees travelled 458 . 10±29 . 14 m ( n = 5 bees ) , which is 146 . 92±29 . 14 m longer than the shortest possible path to visit the five flowers ( 311 . 8 m ) . This value contrasts sharply with the 1 , 953 . 01 m travelled by a naïve bee during its first foraging bout in the pentagonal array ( Figure 3A; Video S2; for further tracks see Figure S2 ) . Thus , over multiple bouts , bees effectively minimized their travel distances using a relatively direct path to visit all flowers once in an optimal order . Our second aim was to investigate how experienced bees modify their trapline in response to changes in the spatial configuration of flowers . Immediately after radar-tracking the bees in the regular pentagonal array , we removed the flower located in the top corner ( location 3 ) and established a new flower east of the initial pentagon ( location 6 ) . This new location was chosen to maximise the probability that search flights would be performed in the catchment area of the radar ( Figure 1 ) . We recorded the flight paths of three of the seven trained bees for eight consecutive foraging bouts ( Videos S14 , S15 , S16 , S17 , S18 , S19 , S20 , S21 , S22 , S23 , S24 , S25 , S26 , S27 , S28 , S29 , S30 , S31 , S32 , S33 , S34 , S35 , S36 , S37 ) . After the removal of the familiar flower , the bees increased their flight duration by around five times ( last bout in initial array , 245 . 00±32 . 87 s; first bout in modified array , 1221 . 67±894 . 81 s; n = 3 bees ) , their travel distance more than doubled ( last bout in initial array , 455 . 75±33 . 91 m; first bout in modified array , 970 . 93±284 . 24 m; n = 3 bees ) , and they once again started to revisit empty flowers ( last bout in initial array , 0 revisits; first bout in modified array , 4 . 33±2 . 33 revisits; n = 3 bees ) . Bees continued to follow their trapline , visiting all four familiar flowers and the empty feeding location ( location 3 ) in the same sequence as before the spatial arrangement was modified ( Figure 4 , Table S1 ) . However , as bees could not fill their crop to capacity by visiting only four flowers , they repeated the entire circuit once , sometimes twice before returning to the nest , a stereotyped pattern observed in 33 . 33%±15 . 02% of all their foraging bouts ( n = 8 bouts per bee ) . At the same time , bees engaged in local searching manoeuvres , exploring new areas of the experimental field ( Figure 4 ) . Azimuthal directions of the mean flight vectors ( sum of all vectors of the radar track , see Materials and Methods ) indicate that bees did not investigate the entire field ( Watson's test for circular uniformity , p<0 . 01 for every bee ) , but each one restricted their searching activity to a different sector ( average angle for individual 1 , 75 . 09±2 . 91°; individual 2 , −32 . 17±11 . 40°; individual 3 , 32 . 38±13 . 14°; n = 8 tracks per bee; ANOVA for circular data , F2 , 23 = 30 . 31 , p<0 . 001 ) . Sixteen out of 24 flight paths included loops of varying length ( range , 5 . 10–509 . 26 m ) between immediate revisits to the same flower ( Figure 4 ) . During these loops , the bees' ground speed was significantly slower than during other nest-flower or flower-flower flight segments ( speed during loop , 1 . 90±0 . 28 m . s−1 , n = 25 loops; speed during segment , 3 . 72±0 . 07 m . s−1 , n = 173 segments; GLMM , effect of flight type on speed , F1 , 197 = 41 . 16 , p<0 . 001 ) . Slow flight loops were also frequent in the paths of the naïve bee ( loop length , 171 . 60±95 . 47 m , n = 12 loops; speed during loop , 1 . 49±0 . 61 m . s−1; bouts 1–4 in Figure S2 ) , and were observed only once in the paths of experienced bees in the initial spatial arrangement ( bout 36 of individual 1 in Figure 3B ) . This difference in flight speed suggests that bees alternated between phases of exploitation characterized by relatively fast and straight flight segments and phases of exploration characterized by slow and localised flight loops . A similar pattern has been observed in displaced honeybees , which typically exhibit fast vector flights in the expected direction of a familiar location followed by slow search curves after finding that the target is not in its expected location [25] . One bee ( individual 1 ) found the new flower location during its first foraging bout following the rearrangement of flowers ( Figure 4A ) , integrated it into a new optimal sequence ( sequence , 12465 ) during the third bout , and gradually stabilized this new sequence into a trapline . The other two bees ( individuals 2 and 3 ) confined their searching activity in different azimuthal directions and never found the new flower during the eight foraging bouts ( Figure 4B and 4C ) . Wind direction had no significant influence on the bees' searching direction ( correlation coefficient for angular variables , r = −0 . 21 p = 0 . 307 ) . Thus , after the removal of a familiar flower , bees increased their frequency of immediate revisits to flowers exhibiting slow loops . These localised search flights might facilitate the discovery of new flowers by allowing bees to learn the spatial characteristics of new sectors of their environment , while still exploiting familiar flowers along their established trapline . Having established that bees develop optimal traplines without trying all possible solutions and start exploring again if some flowers are removed from and/or introduced to the array , we further examined bees' optimisation strategy by comparing the observed visitation sequences to sequences generated by simple optimisation heuristics . First , we tested the “nearest neighbour” heuristic , in which a model bee chooses the nearest unvisited flower as its next move until all flowers have been visited . This heuristic has been suggested to explain the routing behaviour of some animals [13] , [14] , [17] , [19] , including bees [19] , at small spatial scales . When applied to our experimental situation ( five flowers arranged in a regular pentagon ) the nearest neighbour heuristic predicts that bees should always move between neighbouring flowers along the edges of the pentagon . Although a large proportion of the bees' movements involved linking nearest neighbour flowers , especially in the early bouts when all flowers were not yet discovered ( 77% of all transitions between flowers , n = 50 bouts ) and after the stabilization of an optimal trapline ( 100% of all transitions between flowers , n = 19 ) , this unique rule of thumb is not sufficient to fully explain our data since bees were observed moving between non-nearest neighbour flowers in 52% of the bouts in which all five flowers were visited ( n = 42 bouts; Table S1 ) . Second , we tested the “discovery order” heuristic in which a model bee visits flowers in the order it discovered them . This heuristic has been previously proposed for the establishment of long-distance traplines by bees [16] . However , we found it incompatible with our observations as none of the bees used the discovery order of the flowers as their trapline sequence ( Table S1 ) . There was no significant relationship between the discovery order of the flowers and the directionality ( clockwise or anti-clockwise ) of final traplines ( GLMM , effect of discovery order of flowers on their order in the trapline , F1 , 29 = 0 . 04 , p = 0 . 844 ) . For each bee , the similarity index between the discovery order sequence and the trapline sequence was not different than expected by chance ( similarity index range , 0 . 29–0 . 67 , n = 7 indices; p>0 . 05 for all bees , see Materials and Methods ) . Third , we tested random optimization and implemented a simple random “k-opt” iterative improvement heuristic [12] assuming that ( 1 ) a model bee tries to improve the route between known flowers by randomly shuffling the order in which a number ( k ) of randomly selected flowers are visited and ( 2 ) the route change is kept if the new route is shorter than the previous one ( otherwise it is rejected ) . This heuristic predicts the appearance of an optimal visitation sequence only after completion of around 100 foraging bouts , which is far higher than the 17 . 57±1 . 79 bouts ( n = 7 bees ) observed in our experiments . In general , random optimization processes do not produce stable repeatable sequences and are therefore not compatible with our data We therefore developed an iterative improvement heuristic based on our analysis of bees' movement patterns . In this heuristic , the probability of model bees visiting a particular flower or flying back to the nest is determined by its experience , allowing them to explore , learn , and sequentially optimise their routes ( Figure 5 ) . We assume that ( 1 ) bees can uniquely identify the flower locations using information from path integration and/or the visual context ( landmarks and/or panoramas ) [26]; ( 2 ) bees have a finite transition probability between the nest and each flower and between any two flowers during the very first bout; ( 3 ) this initial probability is higher between nearest neighbour locations than between any other locations; ( 4 ) at each bout bees compute the net length of the route travelled ( rather than the actual distance flown ) by measuring the vector distance between successive flower visits and sum the lengths of all vectors comprising the route using path integration [27]; ( 5 ) if bees have visited all flowers at least once ( and thus filled their crop ) , they compare the length of the current route to the memorised length of the shortest route travelled so far; and ( 6 ) if the new route is shorter , the probability of using the vectors composing this route are enhanced by a common factor . According to our observations , a model bee during its first foraging bout between five flowers arranged in a regular pentagon is most likely to visit flowers 1 and 5 first because the other flowers are farther from the nest . Having found flower 1 , the bee is most likely to find flower 2 next because flowers 3 , 4 , and 5 are more distant , and so on . The order in which flowers are discovered determines the probable order in which they will be visited during the next few foraging bouts; for example , from flower 1 a bee with aforementioned experience is most likely to visit flower 2 next ( and more rarely move to flowers 3 , 4 , and 5 ) . Nonetheless , as shown by our analyses on the flower visitation sequences of real bees ( Figure 2C ) , these transition probabilities are not fixed and change whenever a shorter route is discovered . If a newly travelled route ( e . g . , sequence , 12453 ) is shorter than the shortest route experienced so far by that bee , then the probabilities linking movements between pairs of flowers within this circuit ( 1-2 , 2-4 , 4-5 , and 5-3 ) are enhanced by a common factor . Gradual strengthening of the transition vectors forming the shortest route experienced so far allows the bee to sequentially optimise its visitation sequence and select an optimal route as a trapline ( for more details about the model , see Text S1 ) . Simulation data from this novel heuristic predict that model bees ( 1 ) occasionally visit fewer than all flowers especially during early bouts , ( 2 ) regularly revisit empty flowers during the same bout , ( 3 ) decrease their frequency of revisits with experience , ( 4 ) establish stable optimal routes after about 20–25 bouts , and ( 5 ) can sequentially adjust their routes to incorporate newly discovered flowers in an optimal way ( Figure S3 ) . Quantitative evaluation of the simulated data with the optimisation performance of real bees in the experimental field showed full agreement for the number of bouts to the first appearance of an optimal sequence , the number of bouts to the stabilization of an optimal sequence into a trapline , the number of different routes experienced , the net route length travelled per bout , the number of revisits per bout , and the similarity indices between successive bouts ( Table 1 ) . Therefore , bees' optimization strategy can be captured in a simple iterative improvement routine in which an individual compares the net length of their current route to the net length of the shortest route experienced so far , and increases its probability of reusing the flight vectors comprising this new route if it is shorter . We have recorded complete flower visitation sequences and successive flight paths of bumblebees foraging in field-scale conditions , allowing us to examine the learning processes underpinning multi-destination routing strategies of animals with known foraging history . Over multiple bouts , bees minimized their overall travel distance by flying relatively straight vectors between learnt feeding locations and visiting all flowers once in a stable optimal sequence . When the spatial configuration of flowers was modified , the bees engaged in localised search flights to find new flowers . The observed dynamic of trapline acquisition in our large-scale setup is incompatible with random movements or with an extensive exploration of all possible routes . We also ruled out the hypothesis that bees rely on a single rule of thumb such as visiting all locations in the initial discovery order or moving between nearest neighbour locations . Although a large proportion of the bees' movements involved linking nearest neighbour flowers , especially in the first few foraging bouts , this strategy alone cannot explain our data . Rather , bees developed their traplines through trial and error by combining exploration , learning , and sequential optimisation , thus confirming hypotheses derived from previous observations in smaller enclosed environments [15] , [19]–[21] . Interestingly , however , the optimisation performance of bees under field-scale conditions was much higher as all the bees tested selected an optimal route as their trapline , compared to a maximum of 75% in laboratory studies using comparable numbers of feeding locations ( range , 4–10 flowers ) and training durations ( range , 20–80 foraging bouts per bee ) [15] , [19]–[21] . Presumably , bees' motivation to optimise their routes increases with spatial scale because the costs of travelling long ( suboptimal ) distances are greatly magnified . It is also possible that celestial cues , such as the position of the sun or polarized light patterns that are not typically available in laboratory settings but are known to be involved in navigation [28] , [29] , allow bees to orientate more accurately and develop routes faster in natural environments . How , then , did the bees optimise their routes ? Based on our detailed analysis of bee movement patterns , we implemented a simple iterative improvement heuristic , which , when applied to our experimental situation , matched the behaviour of real bees exceptionally well . The proposed heuristic demonstrates that stable efficient routing solutions can emerge relatively rapidly ( in fewer than 20 bouts in our study ) with only little computational demand . Our hypothetical model implies that a bee keeps in memory the net length of the shortest route experienced so far and compares it to that of the current route travelled . If the novel route is found to be shorter , the bee is more likely to repeat the flight vectors comprising this route . Hence , through a positive feedback loop certain flight vectors are reinforced in memory , while others are “forgotten” , allowing the bee to select and stabilize a short ( if not optimal ) route into a trapline . These assumptions are compatible with well-established observations that bees compute and memorise vector distances between locations using path integration [30] . For instance , bees visiting the same feeders over several bouts learn flight vectors encoding both direction and travel distance to each site , by associating specific visual scenes ( such as salient landmarks or panoramas ) with a motor command [26] , [31] . The optimisation process we describe is analogous to the iterative improvement approach developed in “ant colony optimisation” heuristics , which has been increasingly used to explore solutions to combinatorial problems in computer sciences [32] . The rationale of these swarm intelligence heuristics is based on a model describing how ants collectively find short paths between a feeding location and their nest using chemical signals [33] . “Memory” in ant colony optimisation algorithms has no neurobiological basis but instead takes the form of pheromone trails marking established routes . The shortest route becomes more attractive due to increases in pheromone concentration as multiple ants forage simultaneously along it and continue to lay pheromone , while longer routes are abandoned because of pheromone evaporation . Of course , identification of a similar iterative optimisation principle in bees , although based on very different mechanisms ( bumblebees forage individually and do not recruit using pheromone trails ) , does not imply that bees would equal the performance of swarm algorithms in finding solutions to complex combinatorial problems . However , iterative improvement heuristics are flexible , suggesting that bees can develop functional traplines in their natural environments , where the numbers of flowers , their spatial configuration , and reward values vary over time . The question of how spatial information is encoded and processed in an insect brain is a matter of long-standing debate [25] , [34]–[37] . Recent observations of honeybees using shortcuts between separately learnt foraging locations have been interpreted as evidence for “map-like” memory [25] , [35] , suggesting that bees acquire a coherent representation of the spatial connectivity between important locations in their environment ( such as the nest , flowers , and prominent landmarks ) , allowing them to compute new vectors . Although our study was not conceived to test this hypothesis , our results indicate that the routing behaviour of bumblebees can be replicated without assuming such a map-like representation of space . The proposed heuristic suggests that bees can develop optimal routes by following multi-segment journeys composed of learnt flight routines ( local vectors ) , each pointing towards target locations ( flowers ) and coupled to a visual context ( landmarks and/or panoramas ) . Such a decentralized representation of space is akin to the “route-based” navigation of desert ants , where spatial information is thought to be processed by separate , potentially modular , guidance systems [4] , [34] , [37] , [38] . The fact that trained bees continued to visit the familiar location from which a flower had been removed ( location 3 ) further supports the hypothesis that foragers in our experimental situation relied heavily on learnt sensory motor routines as route-based navigation constrains the ability of individuals to rapidly adjust their routes , in contrast to map-like navigation that should allow for fast computation of entirely novel solutions [36] . Future studies should clarify whether similar learning heuristics apply to insect pollinators foraging at different spatial scales and configurations , and to other animals faced with similar routing problems ( e . g . , hummingbirds [9] , bats [7] , and primates [6] , [10] , [13] , [14] ) . Ultimately , characterizing the neural-computational implementation of functional multi-destination routing solutions in small-brained animals holds considerable promise for identifying simple solutions to dynamic combinatorial problems in situations lacking central control . Experiments were carried out in a flat , open area of mown pasture ( approximately 700×300 m ) on the Rothamsted estate ( Hertfordshire , UK; Figure 1 ) . Global landmarks ( edges between different types of cut grass , tree lines ) and local features ( isolated trees ) were available . The observation period ( October 2010 ) was chosen because there were very few natural sources of pollen and nectar present during this time . The radar equipment was located on the south-east corner of the experimental field to allow maximum catchment area . The Bombus terrestris colony was housed in a wooden nest-box located south of the experimental field . A transparent tube with shutters was fitted at the entrance to control bee traffic . Bees were individually marked with numbered plastic tags within a day of emergence from pupae in order to monitor their complete foraging history . Artificial flowers ( Figure S1 ) were made of a plastic cylinder ( height 8 cm ) covered with a blue horizontal landing platform ( diameter 6 cm ) . Bees could access the flowers equally well from all angles and collect a drop of 40% ( w/w ) sucrose solution from a yellow plastic square ( 2 . 4 cm side ) in the middle of the landing platform . Each flower was positioned on top of a truncated cone-shaped support ( base diameter 30 cm , top diameter 20 cm , height 18 cm ) placed on the ground . A webcam ( Logitech c250 , Fremont , CA ) was mounted directly above the centre of each flower on an independent vertical support ( height 50 cm ) to capture footage of bees when they visited . Webcams were fitted with light filters ( neutral density 0 . 6 ) to attenuate sunlight illumination and connected to a laptop running video motion detection software ( Zone Trigger 2 , Omega Unfold , Quebec , Canada ) . A video clip ( minimum duration 5 s ) was recorded each time a bee moved into the camera field of view ( Video S1 ) . Recording continued until movement stopped , thus capturing complete flower visits from when the bee landed to its departure . Feeding stations were arranged sufficiently far apart ( minimum distance 50 m ) such that each station would be undetectable by the bee visual system from any other . The maximum dimension of a feeding station ( including the flower , webcam , and laptop ) was 70 cm . Given bumblebee's failure to detect targets that subtend less than ca . 3° [39] , a bee should visually detect a feeding station this size from no more than 13 . 4 m away . Each laptop was powered by a small petrol generator ( 850W , length 38 cm , width 33 cm , height 32 cm ) placed 10 m from the feeding station , located outside the pentagonal flower array ( Figure 1 ) . Generators provided potential local landmarks , although due to their small size they should only have been visually detectable for bumblebees at a range of 7 . 3 m and were therefore less prominent to bees than feeding stations . In addition , there is solid experimental evidence that bees could not visually detect feeding stations over the distances tested , since two out of three bees failed to find the new location after a displacement ( see Results ) . The harmonic radar and transponders have been previously described [23] . The radar equipment provided coverage over a range of 700 m and an altitude of about 3 m above the ground . Transponders consisted of a 16 mm vertical dipole ( mass 0 . 8 mg ) that does not affect bees' flight behaviour [24] . Individual bees were caught on departure from the colony , the transponder was attached using double-sided foam tape over the plastic number tag and released at the nest-box entrance tube . Coordinates of the transponder-tagged bee in the experimental field were recorded every 3 s by the radar with a spatial resolution of approximately ±2–3 m [40] . When the bee returned to the nest entrance , the transponder was removed before it re-entered the nest . Wind speed and direction were measured every 10 s by a recording anemometer fixed 2 m above the ground , located 10 m west of the nest-box ( Figure 1 ) . Experiments were performed between 09:00 and 18:00 on days when the sun or blue sky was visible . Bees were individually pre-trained to collect sucrose solution from the five artificial flowers arranged in a linear array ( 150 cm length ) , located 50 m north-west of the nest entrance ( Figure 1 ) . Flower rewards were refilled ad libitum with 10 µL . The mean volume of sucrose solution ingested by a given bee during three successive foraging bouts was used to estimate its crop capacity ( range = 75–100 µl ) [20] . We tested seven bees , each on a different day . In the first phase of the experiment , bees were observed foraging on the five flowers arranged in a regular pentagon ( 50 m side , Figure 1 ) , until they visited all flowers in at least five consecutive foraging bouts . This required about 7 h of observation and 28 . 86±2 . 22 foraging bouts per bee ( n = 7 bees ) . Each flower contained a sucrose reward equivalent to one-fifth of the test bee's crop capacity ( volume range = 15–20 µl ) and was refilled after each foraging bout . All departure and arrival times at the nest-box were recorded by an experimenter . Flower visits were automatically recorded using motion-activated webcams ( Video S1 ) . Flight paths of five bees were tracked with harmonic radar towards the end of training ( up to four foraging bouts per bee , including the final bout ) . In the second phase of the experiment , one flower was removed from location 3 and a new one was established at location 6 ( Figure 1 ) . Three bees were observed for eight additional foraging bouts in this new spatial arrangement ( all these bouts were monitored by both webcam recordings and radar tracking ) . The five remaining bees were not tested because of insufficient daylight to pursue the observations on the day they were trained . In total , 230 foraging bouts , 1 , 354 video clips , and 36 radar tracks of flight paths were analysed .
Many food resources , such as flowers refilling with nectar or fruits ripening on a tree , replenish over time , so animals that depend on them need to develop strategies to reduce the energy they use during foraging . Here we placed five artificial flowers in a field and set out to examine how bumblebees optimize their foraging routes between distant locations . We tracked the flight paths of individual bees with harmonic radar and recorded all their visits to flowers with motion-sensitive video cameras . This dataset allowed us to study how bees gradually discover flowers , learn their exact position in the landscape , and then find the shortest route to collect nectar from each flower in turn . Using computer simulations , we show that the level of optimisation performance shown by bees can be replicated by a simple learning algorithm that could be implemented in a bee brain . We postulate that this mechanism allows bumblebees to optimise their foraging routes in more complex natural conditions , where the number and productivity of flowers vary .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "psychology", "social", "and", "behavioral", "sciences", "animal", "behavior", "ecology", "evolutionary", "biology", "behavior", "biology", "zoology", "neuroscience", "behavioral", "ecology" ]
2012
Radar Tracking and Motion-Sensitive Cameras on Flowers Reveal the Development of Pollinator Multi-Destination Routes over Large Spatial Scales
Double-strand break ( DSB ) repair through homologous recombination ( HR ) is an evolutionarily conserved process that is generally error-free . The risk to genome stability posed by nonallelic recombination or loss-of-heterozygosity could be reduced by confining HR to sister chromatids , thereby preventing recombination between homologous chromosomes . Here we show that the sister chromatid cohesion complex ( cohesin ) is a limiting factor in the control of DSB repair and genome stability and that it suppresses DNA damage–induced interactions between homologues . We developed a gene dosage system in tetraploid yeast to address limitations on various essential components in DSB repair and HR . Unlike RAD50 and RAD51 , which play a direct role in HR , a 4-fold reduction in the number of essential MCD1 sister chromatid cohesion subunit genes affected survival of gamma-irradiated G2/M cells . The decreased survival reflected a reduction in DSB repair . Importantly , HR between homologous chromosomes was strongly increased by ionizing radiation in G2/M cells with a single copy of MCD1 or SMC3 even at radiation doses where survival was high and DSB repair was efficient . The increased recombination also extended to nonlethal doses of UV , which did not induce DSBs . The DNA damage–induced recombinants in G2/M cells included crossovers . Thus , the cohesin complex has a dual role in protecting chromosome integrity: it promotes DSB repair and recombination between sister chromatids , and it suppresses damage-induced recombination between homologues . The effects of limited amounts of Mcd1and Smc3 indicate that small changes in cohesin levels may increase the risk of genome instability , which may lead to genetic diseases and cancer . Genome stability is maintained by a network of proteins that ensure faithful DNA replication and efficient response to DNA damage . Variation in levels of proteins across the cell cycle , between tissues and even through natural fluctuations are common [1] , [2] , [3] and could influence genome stability especially for proteins that are present in limiting amounts . Proteins with limited expression are likely to be weak links in genome maintenance and , therefore , could be risk factors in disease , especially cancer predisposition , when combined with environmental stress . This could be particularly important for the cases where small , environmentally relevant amounts of genotoxins inhibit a mutation avoidance repair system [4] . Even a cell with WT genotype may be at risk for genome instability due to fluctuation in expression of limiting proteins . Many genes are involved in spontaneous and damage-induced homologous recombination ( HR ) ensuring efficiency and accuracy . The repair of double-strand breaks ( DSBs ) by HR is an evolutionarily conserved process ( for review , see [5] ) and is generally considered error free since it uses information from an undamaged DNA template . However , since HR can also occur between related as well as identical sequences it can lead to genomic instability through loss-of-heterozygosity ( LOH ) and nonallelic recombination between repeats across the genome , which can result in chromosome rearrangements [6] , [7] . These changes are often detected in genetic disorders , cancer and during evolution ( discussed in , [8] , [9] , [10] ) . Mutations in HR components can lead to genome instability and cancer predisposition [11] . Increased genome instability can also result from changes in the amounts of wild type gene products functioning in HR . In yeast , a genome wide analysis identified 178 genes with haplo-insufficiency causing increased chromosome loss in the heterozygote state [12] . Included was RAD55 , which is directly related to HR; it showed both chromosomal instability and sensitivity to DNA damage when heterozygous . Haplo-insufficiency for several human genes leads to DNA damage sensitivity , genome instability and/or cancer susceptibility , suggesting they are present in amounts that are limiting for HR [13] , [14] . We sought to identify more proteins that are present in limiting amounts for HR-mediated DSB repair and to assess the consequences of reduced levels . The identification of proteins that when limiting affect genome stability can be accomplished through manipulation of gene dosage in polyploid cells . Small variations in the amount of a protein can be accomplished with tetraploid strains of the budding yeast Saccharomyces cerevisiae where gene dosage can be varied over a factor of 4 from one ( simplex ) to four copies ( tetraplex; referred to as WT ) by deleting copies of the gene from homologous chromosomes . This scheme provides the opportunity to address the relationship between gene dosage and biological consequences for many genes . It also enables studies reduced amounts of essential gene products . Importantly , unlike other systems for down-regulating proteins , the amount of a protein can be reduced without affecting the coding sequence or other transcription/translation controls of the remaining alleles . This approach was used for the yeast photolyase DNA repair gene PHR1 [15] which can reverse UV-induced pyrimidine dimers and the RAD52 gene [16] which is essential for recombinational repair of DSBs [17] . We applied the reduced gene dosage approach to three genes that impact HR: RAD50 , RAD51 , and MCD1 . The MRX complex in yeast , which includes Rad50 , is responsible for DSB recognition and DNA resection , the first step in HR and in DNA damage signaling at site-specific and in damage-induced genome wide DSBs ( [18] , [19] and references therein ) . The Rad51 protein which is directly involved in recombination including homology search and formation of joint molecule ( for review see , [20] ) was previously suggested to be present in limiting amounts [21] . We found that changes in levels of Rad50 and Rad51 did not affect the response to ionizing radiation . We also investigated the consequences to genome stability of reducing the dosage of genes affecting sister chromatid cohesion . While not directly involved enzymatically in HR [22] , the sister chromatid cohesion complex ( cohesin ) that includes Mcd1 , Smc3 , Smc1 and Irr1 is important in DSB repair in haploid yeast cells ( [23] and for review , see [24] ) . Following induction of DSBs , cohesin is recruited to DSBs via the DNA damage response pathway [22] , [25] . The cohesin becomes cohesive even at undamaged sites of the genome [26] , [27] . Although cohesin facilitates DSB repair between sister chromatids , its impact when homologous chromosomes are present is unknown . Recombination between sister chromatids is generally acknowledged to be more efficient than between homologous chromosomes [28] suggesting that cohesin inhibits recombination between homologous chromosomes . In this sense , cohesin might suppress opportunities for LOH as well as nonallelic recombination and chromosome rearrangements involving repeated DNAs . Previously it was shown that cohesin can influence the pattern of recombination induced by a single DSB in a plasmid based assay [29] . However , since cohesin is an essential gene and viable mutants are likely to be sensitive to ionizing radiation it is not known what role it might play in maintaining recombination fidelity when survival is high . Here we show that even a modest reduction in the level of cohesin dramatically increases the ability of γ–radiation to induce recombination between homologous chromosomes in the G2 but not the G1 phase of the cell cycle even at low radiation doses when survival is high . This finding , which also extends to UV-induced recombination , suggests that cohesin confines recombinational repair to sister chromatids even in the absence of DSBs , thereby reducing the risk of genome instability . In order to identify factors that are limiting for DSB repair , pairs of tetraploid strains were produced that were simplex or WT for genes of interest and examined for IR sensitivity . To develop the simplex strains , diploids of opposite mating types were created and transformed with gene inactivation cassettes containing different antibiotic resistance markers as described in Figure 1 . The diploids were crossed to yield tetraploid strains with only two functional copies ( duplex ) . Tetraploids were confirmed by i ) loss of mating ability , ii ) presence of resistance to G418 and hygromicin antibiotics and iii ) methionine prototrophy due to complementation of met2 and met6 mutants ( see Materials and Methodss ) . Finally , a simplex strain was created by inactivating one of the two remaining functional genes in the duplex strain . Genotypes were confirmed by PCR at all steps in construction . Changes in gene dosage of either RAD51 or RAD50 did not affect gamma sensitivity ( Figure 2 ) after exposure to 80 krad . However , there was a marked increase in sensitivity for the MCD1 simplex compared to WT strains , which was not attributable to growth effects ( Figure 2 and Figure S1 ) . In contrast , a temperature sensitive mcd1-1 diploid cell shows high sensitivity to IR and slower growth based on the appearance of small colonies after 2 days of growth . As expected , reduction in the expression of each of the respective proteins in the logarithmically growing simplex strains was close to 4-fold ( considering variability of Western blot measurements ) in comparison to WT as shown in Figure 2B . Thus , it appears that unlike Rad50 and Rad51 , the Mcd1 protein is limiting for cellular responses to gamma radiation . To address more precisely the importance of cohesin in cells containing sister chromatids and to determine if there are subtle effects in RAD50 and RAD51 simplex strains , cells were gamma irradiated after nocadazole induced G2/M arrest . As shown in Figure 3A , the MCD1 simplex strain was clearly more susceptible to IR than WT . There was a 2-fold increase in the dose-modifying factor ( e . g . , the same killing was achieved with half the dose ) which corresponds to a large difference in survival over the range of 20 to 80 krad ( Figure 3A; 52% for the MCD1 simplex vs 87% for the WT at 20 krad; p = 0 . 009 , n = 13 ) . While survival of the MCD1 simplex strain was lower than WT , it could still tolerate many DSBs ( over 100 , based on estimates from [18] ) . Since the genomes of tetraploid yeast cells are somewhat unstable , we considered the possibility that a portion of the MCD1 simplex population had gained an extra copy of MCD1 . To rule this out , we determined , the survival of 10 MCD1 duplex and 19 MCD1 simplex isolates after 80 krad exposure of cultures arrested by nocadazole . There was no overlap between the duplex and the simplex cells . The survivals of all the MCD1 simplex cultures were 3–300 fold less than the median survival of the duplex MCD1 strains ( data not shown ) . Thus , if there are some cells in a simplex population that have an additional copy of MCD1 , their frequency is small and would only be expected to result in an underestimation of the induced recombination frequencies ( see results and discussion below ) . The role of Mcd1 in resistance to IR was also confirmed with homozygous diploids carrying the mcd1-1 temperature-sensitive allele when cells were plated at the semi-permissive temperature 32°C after irradiation ( Figure S2 ) . Neither RAD50 nor RAD51 simplex strain showed any change from WT strain in the dose modifying factor ( Figure 3A ) , although RAD51 simplex strain were somewhat more sensitive to IR at high doses . Based on these results , we chose to focus the rest of this study on cohesin . Since sister chromatid cohesion is established during S phase and disrupted during anaphase we asked whether cohesin affects the response to IR during the G1 stage of the cell cycle , when cells lack sister chromatids . Previous studies with yeast have been restricted to survival or DSB repair measurements with haploid cells , which would lack any opportunity for repair between homologous chromosomes . The absence of repair of radiation-induced DSBs by nonhomologous end-joining [18] , unlike mammalian cells , render yeast a good model for addressing defects in homologous recombination . The MCD1 simplex and WT cells were grown for 3 days to stationary phase ( >90% G1 cells , based on cell morphology ) and exposed to IR . No significant difference was observed between WT and MCD1 simplex cells at this stage . Importantly , the response of MCD1 simplex cells irradiated at G2/M or as stationary cells ( primarily G1 ) , was comparable to that of WT cells in stationary cells ( Figure 3 ) . These results suggest that the cohesin function associated with sister chromatids has little role in DSB repair that might occur between homologous chromosomes in G1 cells . We examined directly the impact of decreased levels of Mcd1 on DSB repair in the G2/M cells using pulsed field gel electrophoresis ( PFGE ) [6] , [18] . PFGE separates individual chromosomes on the basis of size so that gamma induced DSBs and repair can be readily assessed ( Figure 4 ) . The efficiency of DSB repair is determined by an analysis of restitution of full size chromosomes during post-irradiation incubation ( see Materials and Methods ) . While repair was detected , the MCD1 simplex strain clearly exhibited reduced repair capacity in comparison to WT cells as shown for cells irradiated with 80 krad , corresponding to ∼600 DSBs/cell ( Figure 4 ) , [6] , [18] . The reduced levels of Mcd1 significantly affected the rate of repair at 1 to 4 hr post-irradiation incubation ( see Figure 4 ) . For example , within 1 hr after IR the WT cells repaired ∼70% of the DSBs induced by 80 krad while half as many were repaired in the MCD1 simplex strain ( Figure 4B ) . Increasing post-irradiation incubation time to 4 hr led to more repair in the WT and MCD1 simplex cells; however , there were still about 4 times more unrepaired breaks in the MCD1 simplex than the WT cells ( 23% vs 6% ) . At a lower dose ( 40 krad; Figure 4 ) , reduced levels of Mcd1 had less of an impact consistent with the smaller differences in killing ( Figure 3A ) . We note the limited ability of the PFGE repair assay to detect small differences in DSBR capacity . This is relevant to considerations of IR induced lethality since unrepaired DSBs appear to have a dominant effect on cell killing [30] . Thus , we establish that the level of Mcd1 is critical both for efficient repair of DSBs and maintaining resistance to radiation in G2/M cells . The decreased DSB repair in MCD1 simplex cells arrested at G2/M suggests that there might be a change in interactions between homologous chromosomes . To address directly recombination between homologous chromosomes , we developed the genetic reporter described in Figure 5 . The tetraploid cells carry two versions of chromosome II where two of the four chromosomes contain the 5′ portion of the TYR1 and the other two carry the 3′ portion . The 3′ and 5′ truncations have a 400 bp overlap; such that homologous recombination can lead to Tyr+ cells ( see Figure 5 and Materials and Methods ) . The TYR1 recombinants are likely to arise through a gene conversion process that covers only one of the alleles either associated or not associated with cross-over ( Figure 5A ) . They could also occur by a DSB in the homologous region between the two heteroalleles generating a tract that ends between the alleles , which could result in a reciprocal exchange ( Figure 5 ) . We assume that changes in the frequency of Tyr+ recombinants are directly correlated with changes in number of interactions between homologous chromosomes . We found that the spontaneous rates of Tyr+ recombination were not affected by the level of Mcd1 . The median rates for the simplex and the WT MCD1 strains were 2 . 5×10−6 ( 1 . 1–2 . 9×10−6; 95% confidence interval ) and 1 . 5×10−6 ( 1–2×10−6; 95% confidence interval ) . Exposure to IR increased the frequencies of Tyr+ recombinants in G2/M arrested cells in all strains examined . The efficiency of induction in MCD1 simplex cells ( ∼5–10×10−6 recombinants/survivor/krad ) was approximately 10-fold greater than in WT cells over a range extending from sublethal doses to ∼50% survival at 20 krad ( Figure 6A; see Figure 3A for survival ) , even though DSBR is efficient ( Figure 4 ) . ( Based on an induction efficiency of 0 . 07 DSB/mb/krad [18] there are sufficient DSBs to account for the observed recombinants even if all events are generated by DSBs in the 400 nt overlap region . ) Since the recombination assay scores infrequent events ( <0 . 1% of the population ) , it is possible that some of the Tyr+ colonies were not MCD1 simplex . In order to estimate a change in mcd1 deletion alleles , 160 presumptive MCD1 simplex Tyr+ colonies arising after nocodazole arrest and 20 krad treatment , were replica-plated to the appropriate media to verify the presence of the simplex markers ( G418 and Hygromycin resistance and Ura+ phenotype ) . Only 4 colonies lost one of these markers ( 2 . 5% ) , suggesting that most of the colonies were actually MCD1 simplex . The MCD1 duplex cells ( two functional gene copies out of 4 , see Figure 1 ) also showed a significant elevation in IR-induced recombination frequency ( Figure 6B ) . For example , for WT and MCD1 duplex cells irradiated with 10 krad the induced frequencies were 10±3×10−6 and 28±3×10−6 , respectively ( p = 0 . 013 , n = 9 ) . Responses were very different with G1 stationary cells where HR interactions are restricted to homologous chromosomes . The induction of recombination in the stationary cells was marginally influenced by MCD1 gene dosage ( <2 fold; Figure 6A ) . Interestingly , the recombination frequencies in the WT cells irradiated at stationary stage matched the HR response of the simplex strain irradiated at G2/M ( Figure 6A ) . The cohesin complex itself appears to be limiting since simplex strains of SMC3 , another member of the complex , also showed elevated IR-induced HR between homologues in G2/M cells ( Figure 6A ) . At 10 and 20 krad , corresponding to 80% and 70% survival , respectively , the Tyr+ recombinant frequency in the SMC3 simplex strain was about 5-fold higher than the WT . The impact that a reduced level of Mcd1 has on recombination between homologues was not specific to tetraploid cells . We also addressed the consequences of lowering Mcd1 function in a diploid strain . As shown in Figure S3 , the levels of IR-induced recombination in G2/M arrested WT tetraploid is not higher than in diploid cells , suggesting that the additional chromosomes alone do not increase opportunities for recombination . Since the temperature sensitive mcd1-1 mutation has frequently been employed to address the role of cohesin in haploid cells [31] , we also investigated IR-induced recombination in a homozygous mcd1-1 diploid strain at the semi-permissive temperature of 32°C . The level of induced TYR1 recombination was 3-fold higher ( 18±3×10−6 vs 60±10×10−6; p-value 0 . 002 , n = 6 ) than in the WT diploid following exposure to 20 krad ( there was a 5-fold difference in survival; see Figure S2 ) . However , the impact on recombination was less than for the MCD1 simplex strain ( discussed below ) . Since the MCD1 duplex showed higher recombination frequencies than the WT tetraploid ( Figure 6B ) , it was expected that an MCD1 hetrozygous diploid would have an elevated frequency of induced recombination between homologous chromosomes . Indeed , the induced frequency for the MCD1 hetrozygote was slightly higher than for the WT diploid following exposure to 20 krad: 28±3×10−6 and 18±3×10−6 , respectively . A similar difference was observed at 40 krad ( 43 vs 28 recombinants/10−6 survivors , respectively ) . For both doses the differences were statistically significant based on a one-tailed t test ( p = 0 . 0194 and 0 . 0273 for 20 and 40 krad , respectively; n = 10 ) . The differences between homozygous and heterozygous MCD1 diploids are smaller than the differences between WT tetraploid and MCD1 duplex , suggesting that there is an additional component ( s ) that further sensitizes tetraploid cells to cohesion defects . In support of this view , mcd1-1 tetraploid cells are inviable even at a temperature that enabled growth of the corresponding diploid [32] . In summary , the MCD1 simplex strain shows lower global DSB repair capacity but higher radiation-induced recombination frequencies between homologous chromosomes than the WT cells . We conclude that the limiting levels of cohesin are sufficient to direct repair of gamma induced DSBs towards sister chromatids and that reductions in cohesin open opportunities for recombination between homologous chromosomes . UV radiation can induce recombination between sister chromatids and homologous chromosomes [33] . It does not generate DSBs directly , although they might arise through repair of closely spaced lesions on complementary strands or during replication . Surprisingly , the reduction in MCD1 gene dosage resulted in UV-induced increases in HR frequencies in G2/M cells comparable to those for IR ( Figure 6A and Figure 6C ) : 20±3/106 survivors for the simplex strain vs 2±0 . 7/106 survivors for WT at 10 J/m2 ( p = 0 . 0001 ) . The difference was increased to 20-fold , reaching 132±38 recombinants/106 survivors at 40 J/m2 vs 8±2 recombinants/106 for WT cells ( Figure 6C ) . Based on experiments with rad52 haploid cells that are unable to repair DSBs , the differences between the MCD1 simplex and WT is not attributable to UV being able to generate DSBs directly or indirectly in the G2/M cells . While survival after 40 J/m2 UV irradiation of rad52 cells was 15% , survival after 20 krad was less than 0 . 1% indicating that many more DSBs occur when cells are irradiated with 20 krad than 40 J/m2 UV . The recombination is likely to arise in the G2/M cells rather than in the subsequent S phase . UV- induced recombination in WT stationary cells was over 10-fold greater than in G2/M ( Figure 6C ) suggesting that UV lesions generated at G1 or entering the next S phase are still highly recombinogenic even in WT cells . Surprisingly , the recombination frequency for UV-irradiated stationary phase MCD1 simplex cells was only 2-fold higher than for WT cells ( Figure 6C ) , far less of an effect than for cells irradiated at G2/M . The low UV-induced recombination rates for WT cells irradiated at G2/M could stem from very efficient nucleotide excision repair that removes UV lesions at G2 , possibly suggesting that the high recombination rates of MCD1 simplex might be due to reduced efficiency in removal UV lesions . However , the MCD1 simplex strain was not sensitive to UV . Survival following exposure to 10 and 20 J/m2 at G2/M was 100% for both the WT and MCD1 simplex strains; even at 40 J/m2 the survival was similar for the MCD1 simplex and WT strains ( 68±20% and 89±13% , respectively ) . Also , irradiation of unsynchronized cells showed no difference between WT and MCD1 simplex cells even at high UV doses ( Figure S4 ) . The DNA synthesis inhibitor hydroxyurea ( HU ) can generate stalled replication forks leading to DSBs that can be rescued by recombination [34] . We asked whether differences in MCD1 levels could influence HU-induced recombination between homologous chromosomes . Logarithmically growing cells were treated with HU overnight and recombination between homologous chromosomes was determined . Growth inhibition ranged between 25% and 90% ( 50 to 150 mM ) and was somewhat higher in the simplex as compared to the WT strain ( Figure S5 ) . At these doses there was induction of recombinants in both the MCD1 simplex and WT strains ( Figure 6D ) ; however , the frequencies ( <20 recombinants/106 survivors ) were much lower than for IR- and UV-induced recombination ( Figure 6 ) . Reduction in the level of Mcd1 resulted in only a small ( ∼2-fold ) increase in HU-induced recombination over the WT strain . Since gene conversion may be associated with crossing-over at the chromosome level , increases in Tyr+ prototrophs - are likely to reflect increases in cross-overs and , therefore , LOH . If reciprocal crossing-over occurs in the G2 stage of the cell cycle , half the events would result in chromosomes with long stretches of LOH , depending on segregation of the sister chromatids while crossing-over in G1 would not yield LOH ( see Figure S6 ) . Reciprocal exchange ( RE ) products containing the “y” allele result from cross-overs that fall between the two tyr1 heteroalleles ( Figure 5A ) . These can be identified by PCR genotyping ( Figure 5B ) as short fragments distinct from the wild type recombinant allele ( TYR1 ) and the alleles that were unaffected by the recombination event ( “tyr” and “yr1” ) . If a crossing-over event leading to Tyr+ occurs in G1 or in S phase cells prior to replication of the TYR1 region , the “y” allele would appear in all progeny cells . However , for cross-overs in G2/M only half the “y” alleles would be recovered because of sister chromatid segregation ( see Figure S7 ) ; therefore , the observed frequency of “y” alleles among the Tyr+ recombinants is a minimal estimate of the actual RE frequency . We note that the above PCR based assay only detects cross-overs with a short conversion tract , while events with a long tract will not be discernable ( Figure 5 ) ( see also [35] for conversion tract length in mitotic crossing over ) . We determined the cross-overs among the Tyr+ recombinants after exposure of G2/M cells to 20 krad IR or 40 J/m2 . The “y” allele was observed in a small fraction of the Tyr+ recombinants appearing after IR and UV exposure of the WT and simplex strains ( Figure 7A ) . The minimum frequency of REs among Tyr+ recombinants did not differ significantly between the MCD1 simplex and WT Tyr+ strains . Based on the overall recombination frequencies presented in Figure 6A and Figure 6C and assuming recombination occurred in the G2/M cells , the expected induced RE frequency in MCD1simplex is much higher than in WT cells , as described in Figure 7B . Thus , while a reduction in the amount of Mcd1 does not change the recombination fate in terms of cross-overs vs no cross-overs , we suggest that the a reduced level of Mcd1 places the genome at considerable risk for both IR and UV induced gene conversion and crossing-over . In order to address the consequences of moderate changes in key proteins responsible for DSB repair we developed tetraploid strains with changes in dosage of the corresponding genes . Using survival response to DNA damage as a screening tool , Mcd1 was identified as a limiting factor in DSBR unlike Rad50 and Rad51 whose complete elimination confers extreme sensitivity to IR . This approach by itself may provide tools for identifying targets that could be used for radiotherapy sensitization ( see below ) . More importantly , this approach allowed us to focus on cohesin as a limiting factor in maintaining genome stability . We note that other proteins are also limiting for genome stability maintenance in yeast and mammalian cells as demonstrated for several genes that exhibit haplo-insufficiency [12] , [13] . We were able to show that recombination between homologous chromosome is highly increased in a cohesin simplex strain ( Figure 6 ) suggesting that cohesin channels DSB repair to sister chromatids and suppresses recombination between homologous chromosomes ( Figure 8 ) . As illustrated in Figure S6 restricting recombinational repair to sister chromatids reduces the likelihood of LOH as well as nonallelic recombination , thereby decreasing opportunities for damage-induced variations in genomic structure [6] . The combination of LOH and nonallelic recombination can be a powerful source of carcinogenesis . While it is well established that cohesin facilitates DSB recombinational repair through stabilization of sister chromatid interactions , suggestions that there is a corresponding decrease in opportunities for DSB repair through homologous recombination have lacked experimental support , especially since experiments were done in haploid yeast . Furthermore , there has been no discussion of a potential impact on recombination induced by other agents , particularly those that do not generate DSBs . We have demonstrated a dramatic ( nearly 10-fold ) IR-induced increase in recombination between homologues even at low , sublethal doses ( 5–10 krad , Figure 6A and Figure 3A ) under conditions of moderately reduced levels of cohesin and normal mechanisms of cellular expression . Previous experiments have utilized temperature sensitive cohesin mutants of the essential MCD1 gene , which grow poorly and are radiation sensitive at semi-permissive temperatures ( Figure 2 , Figure S2 ) . The recombination frequencies of the mcd1-1 strain are also greater than for WT . However , the recombination frequency for mcd1-1 at 20 krad ( 20% survival ) is comparable ( 60/106 survivors ) to that estimated for the simplex irradiated with one third the dose ( Figure 6A ) , corresponding to 100% survival ( Figure 3A ) . Also , the MCD1 simplex strain had a growth rate comparable to WT ( Figure S1 ) . Taken together , we suggest that the amount of cohesin is limiting for suppression of recombination between homologous chromosomes . We speculate that there is enough cohesin to hold the sister chromatids after DNA replication , but the non-cohesive reservoir of cohesin in the simplex strain is limiting such that it cannot suppress DNA damage-induced events that occur at G2 [27] . The biological importance of a 3-fold reduction in the amount of a protein ( Figure 2B ) leading to a 10-fold increase in recombination frequency may lie in the stochastic pattern of protein expression where 2–3 fold changes in the amount of proteins appear to be relatively common [2] . For most proteins which are not limiting , such changes are not expected to affect the biological outcome but here we show that a small perturbation in cohesin may place a cell at risk for genome instability . The reduction in gene dosage and protein levels in RAD50 or RAD51 simplexes strains did not lead to IR sensitivity . The lack of difference in sensitivity for RAD51 may be related to the much larger number of molecules per cell: 7000 molecules of Rad51 vs 1000 Mcd1 in logarithmically growing haploid cells [1] . However , expectations based strictly on number of molecules present must be balanced against number of molecules needed for function . For example , the Rad51 repair unit is a multiprotein , single-stranded DNA filament that is likely restricted to regions of DNA undergoing repair . Similarly , even though the number of Rad50 molecules is comparable to that for Mcd1 ( ∼800 per cell , [1] ) restricting these molecules to sites of damage may limit the need for Rad50 . Interestingly , deletion of the RAD50 gene results in hyper-recombination between homologous chromosomes [36] which can be explained by reduced recruitment of cohesin to DSBs [22] . The RAD50 simplex did not exhibit hyper-recombination frequencies ( data not shown ) , suggesting there is enough Rad50 also for cohesin recruitment . The cellular requirements for cohesin are likely much larger given that these molecules are utilized in sister chromatids across the genome . The mean distance between cohesin binding sites is 11 kb [37] , [38] corresponding to around 1000 binding sites in the genome . Also , large amounts of cohesin are recruited directly to DSBs following DNA damage [22] and “noncohesive” cohesin complexes become “cohesive” at sites distal to DSBs [26] , [27] . Limiting amounts of cohesin raises the question of why not more . Possibly , too much cohesin may increase the risk of nondisjunction at mitosis , a view that is supported by the antiestablishment activity of the Rad61-Pds5-Scc3 complex towards cohesin in G2 [39] , [40] . While there have been suggestions that tetraploid and diploid yeast may differ in ability to maintain their genomes [32] , the survival responses to IR are comparable on a per lesion basis from diploids to tetraploid cells ( summarized in [41] ) . Furthermore , we found that IR-induced recombination between homologous chromosomes of G2/M arrested diploid and tetraploid cells did not differ significantly ( Figure S3 ) . We found that Mcd1 suppresses UV- as well as IR-induced recombination between homologous chromosomes . Surprisingly , the UV-induced frequencies in G2/M cells were increased nearly 20-fold in simplex MCD1 cells as compared to WT cells ( Figure 6C ) . For UV , the increased recombination is unlikely to be related to DSBs . First , the recombination frequency is similar for 40 J/m2 UV and 20 krad IR . At this dose of ionizing radiation , DSBs are readily detected , while UV-induced DSBs would be rare . Second , Lettier et al . [42] observed that most UV-induced recombination events are independent of DSB repair since they occur in a rad52 mutant that is completely lacking in DSB repair . Until now , the link between cohesin and homologous recombination was strictly based on the relationship between DSB induction and recruitment of cohesin to the break site . From our results we conclude that cohesin restricts potential recombinational interactions induced by ionizing and UV recombination to sister chromatids ( Figure 6 ) . We consider it likely that recombination induced by other agents would be similarly affected . Cohesin may accomplish sister chromatid preference simply by holding chromatids in close vicinity at normal cohesion attachment sites [37] , [38] such that the undamaged sister becomes the preferred recombination partner . In addition , cohesin may channel recombination to sister chromatids because it is recruited directly to DSBs . Exposure of single strand DNA at a DSB was shown to be important for recruiting cohesin; however , single strand DNA intermediates are found in other DNA repair pathways including repair of UV lesions . In addition , rare DSBs associated with UV damage might lead to greater amounts of cohesion between sister chromatids across the genome as demonstrated for a site-specific single DSB [26] , [27] . The effects of HU and the role of MCD1 gene dosage on recombination differed considerably from IR and UV . HU-induced recombination was only marginally elevated in the MCD1 simplex compared to WT ( Figure 6D ) strain . While HU can cause fork collapse , it might be counteracted by a back-up mechanism ( s ) that would also be anti-recombinogenic , for example , by Srs2 helicase recruitment via PCNA sumoylation [43] . The cohesin complex and its functions are evolutionarily conserved across eukaryotes ( for review see [24] , [44] and references within ) . Mammalian cohesin is recruited to DSBs and is part of the ATM signal transduction and important for survival after IR . [45] , [46] . In addition , the Smc3 cohesin subunit is acetylated to establish cohesin , both in yeast and human cells [47] , [48] . Therefore , cohesin function in DSBR is probably conserved . It will be interesting to determine if cohesin is limiting for responses to DSB inducing agents in mammalian cells . If this is the case , then cohesin might be a useful target during cancer treatment for sensitizing cells to radiation and other drugs that break DNA . Unlike fully differentiated cells , cancer cells spend more time in G2 and S phase , when recombination is highly efficient . Targeting cohesin might be especially efficient when combined with cell cycle inhibitors that cause G2 arrest . It is interesting that mutations in cohesin and related genes were found in many cancer cells that show chromosome instability ( CIN ) . In addition , reduction of the amount of cohesin using RNAi leads to a CIN phenotype in cells with a near diploid genome . Included among the CIN events were the development of tetraploid genomes [49]; hence , a primary defect in cohesin may generate tetraploid cells with further defects in cohesion and genome stability . While the present results indicate a general role of cohesin in control of HR , our overall approach can provide useful insights into genome dynamics as well as genetic processes associated with tetraploidy . Tetraploid cells are common among eukaryotes and during evolution [50] and show unique characteristics regarding chromosome dynamics . In yeast , polyploid cells exhibit increased genome instability in comparison to diploids [32] which makes them an interesting model for a complex genome . Importantly , mammalian hepatocytes , frequently give rise to polyploids [51] . It is worth noting that hepatocytes are continuously exposed to genotoxic insults and polyploidy is often associated with the carcinogenesis process [52] . Therefore , the damage-inducible increase in recombination observed in MCD1 simplex cells might result in further genome instability in natural or transformed tetraploid cells . Finally , we describe here an experimental design that can be used to search for subtle changes in essential and nonessential factors that are limiting for genome stability . Reduction in these factors can synergize with modest ( i . e . , high survival ) levels of genotoxic stress to dramatically increase genetic change . Importantly , our approach utilizes normal , wild type proteins and native gene expression regulation thereby eliminating the uncertainty associated with mutations and variations in gene expression . Tetraploids provide a wider opportunity to vary gene dosage as compared to the simple homozygote-heterozygote approach in diploids and may be more suited for addressing implications of copy number variation , as found in the human genome [53] . For a list of strains , see Table 1 below . Each simplex strain described in the table represents the genotype of at least 2 more independent isolates that originated from 2 independent duplex parents . Haploid strains were derivatives of E134 [54] and its met2 and met6 derivatives DAG 647 and DAG 645 respectively . ura 3–52 has been replaced by a complete deletion that gave rise to strains CS1004 and CS1006 ( see below ) . CS1004 ( relevant genotype MATα met 6-DEL ) was transformed with CORE cassette [55] targeted to the 3′ end of TYR1 , starting at nucleotide 700 of the ORF . Briefly , the following primers were used to amplify G418R in tandem with the URA3 cassette from pCORE [55] in order to create a 5′ tyr1 allele . 5′ATCTATTCGAACAAGTGGCATGTTTACGCAGGATTAGCCATAACAAACCCAAGTGCACAT-GAGCTCGTTTTCGACACTGG and 5′TTATGTATTTCTTTTTTCAGCGGCCGAACGGTCACTAGAATGACTCAGAATGGTTTTTAT-TCCTTACCATTAAGTTGATC . In parallel the CS1006 ( relevant genotype MATa met 2-DEL ) was transformed with a CORE cassette targeted to the 5′ part of the TYR1 ( nucleotide #1 at ORF ) in order to create a 3′ tyr1 allele . This was done by amplifying CORE cassette with primers 5′ATGGTATCAGAGGATAAGATTGAGCAATGGAAAGCCACAAAAGTCATTGGTATAATTGGT-GAGCTCGTTTTCGACACTGG and 5′TGTGTATCAGCGGAATCTTGTCATGCTCTTCATATGTCAAATAAACTTGCTTGCTTACTTTTC-TCCTTACCATTAAGTTGATC . After selection of the CORE integrants , the CORE was removed [55] from CS1004 ( met6-DEL ) using oligonucleotides 5′TGGCATGTTTACGCAGGATTAGCCATAACAAACCCAAGTGCACATATAAAAACCATTCTGAGTCATTCTAGTGACCGTTCGGCCGCTGAAA and 5′TTTCAGCGGCCGAACGGTCACTAGAATGACTCAGAATGGTTTTTATATGTGCACTTGGGTTTGTTATGGCTAATCCTGCGTAAACATGCCA . The CORE was removed from CS1006 derivative ( met2-DEL ) by introducing a portion of the TYR1 gene PCR fragment defined by primers 5′TGAAGGAAAGAGGACAGCATATCCACTTGATAAACAAAGTATTTACCCAAGGACTGCGACATCATTACCGTGCATTCCCTTCATG and 5′GCCACTTGTTCGAATAGATTCTTAGTGATATATTAACTTTCACATTTTCT . Loss of CORE was identified by conversion of strain to G418 sensitive and 5FOA resistant . At the end of this stage , two sets of strains were created; CS1004 derivative with met6 DEL and with tyr1 allele of 1–700 bp and CS1006 derivative with tyr1 allele 300–1359 bp . Diploid cells were made from derivatives of the above ( Strains CS 1061 and CS 1064 see Table 1 ) by introducing plasmid ( YEp-HO ) encoding HO endonuclease under its native promoter to the haploid strains . Non-mating cells were identified as potential diploids and confirmed by ability to sporulate . Each a/α diploid was then transformed with a pGal-HOT plasmid were HO is inducible by galactose [56] . Cells were grown 6 hr on galactose containing media to induce mating type switching . MATaa and αα cells were identified by mating test . The aa and αα strains were isolated from each set of haploid strains ( met 6-DEL 5′ tyr1 allele and met 2-DEL 3′ tyr1 allele ) . The MATaa and αa cells served as Diploid 1and 2 as shown in Figure 1 . WT tetraploid cells were obtained by crossing several diploid isolates with opposite mating strains and complementing met mutations . Cells were selected on media lacking methionine and confirmed to be non-mating . They also exhibited spontaneous and UV induced Tyr+ recombination . The following oligonucleotides were used to create G418R and HygromicinR cassettes that could be targeted to MCD1 open reading frame using plasmids pFA6 and pAG32 respectively: 5′TCCATAACAAAAAAGGACTGGTCAAAGAAAAGACAACTCAATTGCACAATTACTTTACAAGAAACACGACA-CGTACGCTGCAGGTCGACGGATCCCC and 5′TTAAAGTCTTTGATCTATATATGCATCAGCTTATTGGGTCCACCAAGAAATCCCCTCGGCGTAACTAGGTT-ATCGATGAATTCGAGCTCGTTTTCGG . The MCD1 heterozygote diploid was created by transforming Diploid 1 and 2 cells with the targeted G418R and HygromicinR cassettes , respectively . Independent MCD1 heterozygote isolates derived from Diploids 1 and 2 were crossed to create MCD1 duplexes ( two WT alleles with one G418R and one HygromicinR replacement alleles; see Figure 1 ) . Duplexes were transformed with a URA3 cassette that was targeted to an internal ( 23 aa in frame ) portion of the open reading frame by amplifying URA3 gene from pRS306 using primers 5′TGGTTACAGAAAATCCTCAACGTCTTACTGTTTTAAGACTTGCCACCAATAAGGGTCCATTAGCACAAATACAGAG-CAGATTGTACTGAGAGTGCACC and 5′TAAGCATTGATAAACCTTTCAAATAGTGCAGGTTTGGCGTCTATTTTAATATTTCCGAATGCTTCTGTTTG-CGCATCTGTGCGGTATTTCACACCGC . Ura+ transformants were confirmed to be MCD1 simplex if they maintained G418R , HygromicinR , were non-mating and Tyr+ recombinants could be induced . In addition genomic DNA was purified from the putative simplex and MCD1 locus was PCR using the flanking primers 5′GTCGAGAAAATCGCGTCTTTC and 5′AGAAAATTTCGGCTTCACCG . Since the MCD1 ORF size is almost identical to the G418R cassette , another set of PCR derived constructs was developed using a primer within the cassette ( 5′CGTACGCTGCAGGTCGAC ) and a primer outside the ORF ( 5′AGAAAATTTCGGCTTCACCG ) . This analysis revealed two PCR products corresponding to G418R and HygromicinR cassettes . Immediately after PCR verification of the simplex genotype , patches were stored at −70°C . At the beginning of each experiment , cells were streaked from the frozen stock for single colonies which were tested for the presence of the simplex markers . SMC3 simplex strains were created similarly using primers: 5′CATCTTTAAACAGTTTCACCATTTTTTTACAAGACGACCTGCTGGAGTAACGGTAATAGTTCACGTCTGCA-CGTACGCTGCAGGTCGACGGATCCCC and 5′CAGTACCTCTGGGAACTAATCTTTCAAAAACAGCTTCAAAGTTTTCAGAAACCTTTTGGAAAGTAGAATCA–ATCGATGAATTCGAGCTCGTTTTCGA to create G418 and Hygromycin resistant cassettes directed to the ORF as well as primers 5′ATGTATATCAAAAGGGTGATAATTAAGGGTTTTAAAACCTACAGGAACGAAACCATTATTGATAATTTCT-CAGAGCAGATTGTACTGAGAGTGCACC and 5′ACCGCGTTAACCTTTTGTTGTTTTAACTTAACAATTAGATCTTGAATTGAGTCTTTAGATTCATCCAGCTC-CGCATCTGTGCGGTATTTCACACCGC to amplify pRS306 to create a URA3 cassette targeted to the ORF . The simplex was verified by primers flanking the locus: 5′ CATCGAAGTGTACACCTGTCACAT and 5′ GAAAAGTAATCTTTTTTGTACGTCG . RAD50 simplexes were made in a manner similarly to above . Oligonucleotides 5′TTTCACGGCTTTGCCTTGT and 5′TCAAAGGTGCTTACGTGCTTG were used to amplify the flanking region around the RAD50 locus in a null strain from the Saccharomyces cerevisiae deletion library ( G418 cassette replaced ORF ) . Both Diploids 1 and 2 were transfected with a G418 cassette and after obtaining heterozygote diploid isolated , the G418 cassette of one of the diploids was switched to Hygromicin resistance . The two heterozygote diploids were crossed and the tetraploid duplex was transformed with a URA3 cassette targeted to an internal portion of the ORF by amplifying the URA3 gene from pRS306 using the following primers 5′TCTATTCAGGGCATACGGTCTTTTGACTCCAATGATAGGGAAACTATTGAATTTGGCAAGCCTCTGACTTC-AGAGCAGATTGTACTGAGAGTGCACC and 5′TATCGACCCACTCAATTTGTGATTTTTGCCTATCATCTCTCTTGACTTTGAAGAAGTGATCAGTAAATGCCG-CGCATCTGTGCGGTATTTCACACCGC . Simplex strains were selected as described above . Construction of the RAD51 simplex was done by sequential transfection of Diploid1 with G418 and URA3 cassettes , thereby replacing two copies of the gene . Diploid 2 was transfected with a Hygromicin cassette targeted into the ORF of the gene . G418 and Hygromicin cassettes were made based on strains from a Saccharomyces cerevisiae deletion library using primers 5′ TTGAGCATTCCCTGAGCATT and 5′TCCCCTAAAAGGATAAAGCCG . URA3 cassette was created by amplifying pRS306 using primers 5′CATATATCAGAGTCACAGCTTCAGTACGGGAACGGTTCGTTGATGTCCACTGTACCAGCAGACCTTTCACCAGAGCAGATTGTACTGAGAGTGCACC and 5′GGTCACCAACACCATCTTCATAGATCGCGAACACACATTCAGCCTCTGGTAAGCAAGGTGAGTCAACAAC-CGCATCTGTGCGGTATTTCACACCGC . The RAD51 simplex strain was created by crossing the transformed Diploids 1 and 2 . CS1061 and CS1064 ( a and α haploids ) that contain the same types of tyr1 truncation alleles described above crossed to yield WT diploid . Same haploid strains were transformed with an Age1 cut pVG257 [31] to yield the mcd1-1 strain . The mcd1-1 cells were verified by sequencing and later crossed to the opposite mating counterpart to create a diploid mcd1-1 strain . The WT , MCD1 simplex , RAD50 simplex or RAD51 simplex cells were grown overnight and diluted to fresh media and grown to for 3 hr in 30°C and then harvested . Before Cells were washed with double-distilled water and 2×107 cells were re-suspended in 0 . 3ml SDS-running buffer , boiled for 10 min and centrifuge 5 minutes 13 , 000 rpm . Typically 40 µl supernatant was loaded per lane ( corresponding to ∼2 . 5×106 cells ) . Following electrophoresis , the gel was transferred to a membrane using a semi-dry transfer apparatus for 105 min . , according to manufacturer's instructions using a PVDF membrane ( Invitrogen Carlsbad , CA ) . All antibodies were diluted 1∶2000 except anti Rad51 that was diluted 1∶5000 . Anti Mcd1 antibody was kindly provided by Dr . Alexander Strunnikov [31] . Anti yRad50 antibody sc32862 and anti yRad51 antibody sc33626 were from Santa Cruz Biotechnology ( Santa Cruz , CA ) . The anti yHistone 3 antibody was ab1791 from Abcam ( Cambridge , MA ) . The details of nocodazole arrest , and gamma irradiation have been described [6] , [18] . Briefly , nocodazole ( 20 µg/ml , final concentration ) was added to cells that were growing logarithmically at 30°C in YPDA media ( 1% yeast extract , 2% Bacto-Peptone , 2% dextrose , 60 µg/ml adenine sulfate ) . G2 arrest was monitored by cell morphology . Cells were collected by centrifugation , washed and re-suspended in ice-cold sterile water . The cell suspensions were kept on ice while being irradiated in a 137Cs irradiator ( J . L . Shepherd Model 431 ) at a dose rate of 2 . 3 krads per minute . Irradiated cells were harvested by centrifugation and resuspended in YPDA at 30°C with nocodazole for post-irradiation incubation . PFGE procedures were done as previously described [18] . Briefly , Contour-clamped Homogeneous Electric Field ( CHEF ) systems were used for electrophoresis of yeast chromosomes in this study . Using a CHEF Mapper XA system ( Bio-Rad , Hercules , CA ) . These plugs were prepared in 0 . 5% LE agarose ( Seakem , Rockland , ME ) using 1–2×107 G2-arrested cells per 100 ul plug . They were cut to a thickness of ∼2 mm and loaded in the bottom of a preparative well so that the entire DNA migrated very close to the bottom surface of the CHEF gel . PFGE running conditions were according to the CHEF auto-algorithem separates DNA's in the 250–1600 kb range . To quantify DSBs in irradiated samples , pulsed-field gels were stained with SybrGold ( Invitrogen , San Diego , CA ) and photographed using a GelLogic200 imaging system ( Eastman Kodak , Rochester , NY ) . Bands were measured using Kodak MI software ( version 4 . 0 ) and the data were exported into Microsoft Excel ( version 11 . 5 . 3 ) for further manipulations to determine DSBs . More details on the analysis are found in Figure S1A of [18] . Briefly , for each band corresponding to a complete unbroken Chromosome Y ( any chromosome ) , the fraction of chromosomes remaining unbroken ( FChrY ) after a given dose is simply the net intensity of the band divided by the net intensity of the corresponding band in the 0 krad control lane . From the Poisson distribution , the average number of DSBs ( ) is given by the formula: Plotting the experimentally determined values of N ( number of breaks per chromosome ) vs Molecular Weight for each chromosome band from a given dose results in an approximate straight line whose slope is in units of DSBs/mb and is independent of the total amount of DNA loaded in each lane as long as enough DNA is loaded for accurate detection of the bands . For details see reference [18] . The experimentally determined values of the slope for a given dose are highly reproducible . Tyr+ cells were grown over-night in 30°C in YPDA in deep well 96 plates with shaking . Genomic DNA was purified using DNeasy of Quiagen ( Valencia , CA ) and amplified using primers 5′ GAATACCGTAGCACTTGAAGGAAAGAGGACAGCATATCCA and 5′ CACAAAAGAAGGCCTAATATTATAGGAAATCAGCATTAAAAAC .
The cellular concentrations of individual proteins are expected to be kept within an optimal range , but protein expression is often stochastic . Some proteins are known to be in limiting amounts , so that even modest reduction can lead to malfunction . Within the network of genes that determine genome stability , proteins that are limiting impose a risk for the cell , because fluctuation in their amounts may start a cascade of genomic alternations that will influence many biochemical pathways either under normal growth conditions or in response to chromosome damage . We sought to identify genes that are limiting for DSB repair by lowering the dosage of key genes from 4 to 1 in tetraploid Saccharomyces cerevisiae strains . We found that the complex that holds sister chromatid cohesion together ( cohesin ) is limiting in DSB repair . In addition , when it is reduced modestly , recombination between homologous chromosomes is highly increased , suggesting that the risk for loss of hetrozygosity ( LOH ) is increased too . These results should also be considered in light of increasing evidence that copy number variation can impact cellular function .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "genetics", "and", "genomics/chromosome", "biology" ]
2010
Cohesin Is Limiting for the Suppression of DNA Damage–Induced Recombination between Homologous Chromosomes
Most infections induce anorexia but its function , if any , remains unclear . Because this response is common among animals , we hypothesized that infection-induced diet restriction might be an adaptive trait that modulates the host's ability to fight infection . Two defense strategies protect hosts against infections: resistance , which is the ability to control pathogen levels , and tolerance , which helps the host endure infection-induced pathology . Here we show that infected fruit flies become anorexic and that diet restriction alters defenses , increasing the fly's tolerance to Salmonella typhimurium infections while decreasing resistance to Listeria monocytogenes . This suggests that attempts to extend lifespan through diet restriction or the manipulation of pathways mimicking this process will have complicated effects on a host's ability to fight infections . Infectious diseases are predicted to drive the natural selection of behaviors that increase fitness . Loss of appetite ( anorexia ) is a common behavior that sick animals exhibit when faced with an immune challenge [1]–[5] . Traditionally , anorexia was thought of as an adverse secondary response to infection that served no function to the host; immune responses are energetically expensive and thus an infection-induced reduction in food intake seems paradoxical [1]–[3] . Since this phenomenon occurs in so many animals , including both vertebrates and invertebrates , an alternative explanation is that this response is a conserved adaptive strategy to increase the chance of surviving an infection [1]–[3] . Experimental evidence from anorexia and acute starvation studies are consistent with this notion and suggest that this behavioral change is actively induced by the host during infection and is advantageous [3] , [6]–[9] . For example , mice infected with L . monocytogenes ( a firmicute and facultative intracellular pathogen ) that were fed ad libitum had increased survival when compared to similarly infected but force fed mice [6] . The mechanism behind these changes in survival is unknown . Diet restriction is a common method used for increasing an animal's lifespan . One explanation for this is that diet restriction increases responses required to survive stress [10] . Much progress has been made in determining the signaling pathways that trigger this process but the effector mechanisms remain elusive [10] . Most diet restriction experiments are done in the lab and a side effect of this is that the tested organisms are not exposed to a normal range of pathogens; thus , we do not have a deep understanding about how diet restriction can affect immune defenses . Individual immunological indicators of a potential immune response often improve upon diet restriction [1] , [4] , [11] . However , in the few cases where diet restricted animals have been given an infectious challenge , the diet restricted host often fared poorly , in spite of molecular indicators that its immune system would prevail [12]–[16] . Thus there seems to be a disconnect between the potential and realized immune response in diet restricted animals , suggesting that we are not measuring the relevant parts of the immune response . Hosts can evolve two ways of defending themselves against infections [17]–[19] . The first , resistance , is the ability of the host to reduce pathogen levels . The second , tolerance , is the ability to limit the impact of infections . The theoretical basis for this distinction is grounded on work in plants , but recent work , from a number of groups , demonstrated that animals can also vary in their tolerance . In animals , tolerance traits appear relatively common and simple to identify genetically [17] , [20]–[22] . For example , when screening for mutant flies with altered sensitivity to L . monocytogenes , we found that one-third of the mutants we recovered had no apparent defects in resistance and succumbed to infection because of defects in tolerance . At least when studying Drosophila , it seems clear that much has been missed in our studies of immunity by focusing on resistance mechanisms and ignoring tolerance [22] . Our work here was provoked by our identification of a mutation in a fly gustatory receptor , gr28b , that altered immune defenses [22] . We found that gr28b mutant flies had reduced appetites . This led us to the hypothesis that the feeding changes induced by anorexia might alter the immune response in an adaptive manner . We found that L . monocytogenes and S . typhimurium ( a gamma proteobacterium and intracellular pathogen ) , both induce anorexia in infected flies , suggesting that diet restriction can be a normal part of the fly's response to infection . Mimicking this diet restriction by testing the gr28b mutant or by feeding the flies diluted food , we found that diet restriction reduced resistance against L . monocytogenes but increased tolerance against S . typhimurium . We propose that the degree of anorexia that is exhibited by an infected fly and the changes this decrease in food consumption imposes on the immune response will be continuously shaped by the pathogens a fly encounters in the wild . The innate defenses important for resistance can be divided in three: the humoral , cellular , and melanization responses [23] , [24] . The humoral response is the most deeply characterized and involves the secretion of antimicrobial peptides into the hemolymph ( circulating “blood” ) of the fly . Antimicrobial peptide transcript levels are regulated by the Toll and Imd pattern recognition pathways and these peptides are secreted predominantly by the fat body into the circulation of the fly and kill invading microbes [23] . Flies with mutations blocking the activation of these pathways quickly succumb to infections and have higher bacterial loads than do wild-type flies , suggesting that the principal defect in these mutant flies is in resistance [24]–[30] . The humoral response is induced over the course of several hours following a systemic infection . The cellular response is an immediate acting response and involves hemocytes ( fly “blood” cells ) , which phagocytose small particles , encapsulate large particles , and secrete antimicrobial compounds [23] , [24] . Melanization is a second immediate immune response in the fly occurring at sites of tissue damage and infection . Melanin deposits are visible as dark brown patches at these sites and its synthesis requires the proteolytic activation of the enzyme phenoloxidase . Reactive oxygen species are produced as a byproduct of this response that can cause damage to the fly thus affecting tolerance in addition to resistance [23] , [24] . We examined the three arms of the fly immune response for changes caused by anorexia and diet restriction and found that melanization drops drastically upon diet restriction; the pattern of antimicrobial peptides induced during infection changes; but there were no apparent change in phagocytosis . The changes in melanization alone can explain the loss of resistance to L . monocytogenes . The explanation for the increase in tolerance to S . typhimurium is more complicated because the loss of melanization is expected to decrease resistance . We suggest that the fly compensates with another resistance mechanism , possibly antimicrobial peptides , while at the same time increasing tolerance . This work suggests that diet restriction will have complicated effects on immune defenses as it can alter both resistance and tolerance and its effects are microbe specific . This work supports the idea that the environment does not just affect a fly's immune response but rather is an integral part of immunity . We measured infection-induced feeding changes in adult Drosophila challenged with three different bacterial pathogens of humans and Drosophila , L . monocytogenes , S . typhimurium , and Enterococcus faecalis ( a firmicute and extracellular fly pathogen ) ( Figure 1 ) [18] , [23] , [31]–[33] . We chose these microbes because they represent very different types of bacteria and cause well characterized lethal infections in the fly; lethal microbes let us measure both increases and decreases in survival rates whereas nonpathogens only allow us to measure decreases . Feeding rates were determined by measuring how quickly flies took a meal when presented with new food and by recording how much food they consumed during this meal . The feeding rate assays were used primarily to determine the appropriate time window to perform the less subjective consumption assays . L . monocytogenes and S . typhimurium infections reduced food intake in both assays compared to unmanipulated and media-injected controls . By contrast , we detected no effect of E . faecalis infection on either feeding assay , demonstrating that illness-induced anorexia occurs in the fly in a microbe dependent manner . Dead L . monocytogenes also induced anorexia , suggesting that a simple immune response and not an active infection is sufficient to reduce the fly's appetite ( Figure 1; Tables S1 , S2 , S3 ) . Together , these results demonstrate that flies may enter a state of diet restriction when infected . We sought to determine how immune-induced diet restriction might alter the resistance and tolerance of the fly to a variety of pathogens . We previously identified a mutation in a taste receptor ( gr28b ) that reduced flies' resistance to L . monocytogenes infection while increasing defenses for S . typhimurium [22] and show here that these mutant flies eat less than wild-type controls ( Figure 2; Table S4 ) . We measured the feeding rates and ingestion volume of gr28b mutants and found that they ate at a significantly reduced rate compared to wild-type flies and that their ingestion volume was also reduced ( Figure 2 ) . These mutant flies also lived longer than parental controls when left unmanipulated ( Figure S1 ) as would be expected for diet restricted flies [34] . Thus the gr28b mutant pointed to a potential functional link between anorexia and an altered immune response and provided a simple method of creating a constitutively anorexic fly . To determine how anorexia affects the realized immune response of flies , we measured the survivorship of infected gr28b mutants and compared these rates to those of infected wild-type flies ( Figure 3 ) . Consistent with what we had observed previously [22] , we found that when infected with L . monocytogenes , gr28b mutants died faster than wild-type flies; mutant flies died with a median time to death ( MTD ) of 4 d compared to 6–7 d for wild-type flies . In contrast , when infected with S . typhimurium , gr28b mutants lived longer than wild-type flies with a MTD of 15 d compared to 8 d . When infected with E . faecalis , gr28b and wild-type flies died at the same rate . Thus , gr28b flies have altered interactions with microbes but this could be due to the mutant's reduced appetite or to pleiotropic effects of the mutation . Anorexia is a symptom and there are potentially many different ways of becoming anorexic; thus we wanted to test another method that would simply restrict food intake . To test the hypothesis that a reduction in food intake is responsible for the array of survival phenotypes we observe in gr28b mutant flies , we measured the effects of diet restriction on wild-type flies that we raised on standard diet that was diluted with 1% agar so that each stage of their lifecycle was completed on the diluted food . Typically in diet restriction studies , the introduction of restricted food occurs at the adult stage . We chose to utilize adult flies that had been raised their entire life on restricted food for two reasons: First , we reasoned that because gr28b mutants are inherently anorexic , they experience reduced food consumption at all stages of their life and we wanted to better emulate the reduced food intake of gr28b mutants . Second , for our initial experiments we used adult flies that had been diet restricted at 24 h prior to infection and we found that the phenotypes were enhanced as the amount of time on diet restricted food increased and we chose to maximize the effect . We infected food-restricted wild-type flies and compared survivorship to wild-type flies raised on standard food ( Figure 3 ) . Flies fed a 0 . 5× diet had phenotypes similar to the gr28b mutation in every way tested: diet restricted flies were more sensitive to L . monocytogenes; less sensitive to S . typhimurium; and showed no change in sensitivity to E . faecalis . Our results are in agreement with past observations that diet restriction has no effect on the survival rate of E . faecalis infected wild-type flies [15] . Previous studies examining the effects of diet restriction in the fly have reported neutral or weak positive effects on fly survival for Pseudomonas aeruginosa ( gamma-proteobacterium and extracellular pathogen ) . Libert et al . reported that diet restriction has no effect on survival when challenged with P . aeruginosa . The pathogen load was not measured in this study and thus it cannot be determined whether there were compensatory changes in resistance and tolerance [14] . Diet restriction was reported to have positive effects on survival of P . aeruginosa-infected flies in an age dependent manner , where an increase in survival was seen in flies 30 d old or older but not 20 d or younger . This result demonstrated that the life history of a fly is another important factor to consider when measuring the interactions between diet restriction and immunity [14] . Pathogen load was not determined in this study and thus it cannot be determined whether the changes in old flies were due to changes in resistance or tolerance . The lifespan of unmanipulated flies raised on a 0 . 5× diet was extended , which is in agreement with what has been previously observed in diet restricted flies and similar to what is seen in gr28b mutants . As diet restriction produced a complete phenocopy of the mutant phenotypes , we concluded that gr28b influences fly immunity by regulating food intake . In Drosophila , we can determine whether a fly succumbs to an infection because of defects in resistance or tolerance mechanisms by monitoring both fly survival and pathogen growth over the course of the infection [12] , [35] . We found that both resistance and tolerance mechanisms are affected by anorexia and the effect depended on the type of infection . Both gr28b mutant flies and diet-restricted wild-type flies exhibited increased growth of L . monocytogenes during infection ( Figure 4 ) . This growth , combined with the increased death rate we observed in both models , suggested that these flies died because of defects in resistance to L . monocytogenes; that is , reduced food intake blocked the ability of a fly to limit L . monocytogenes growth and thus the flies died faster . Because of the way we measure resistance and tolerance in the fly we cannot always measure changes in tolerance as microbe levels are changing; therefore , it is possible tolerance also changes under food restricted conditions in L . monocytogenes–infected flies . By contrast , during S . typhimurium infections , food restricted and gr28b mutants exhibited similar levels of bacteria to what we observed in wild-type flies ( Figure 4 ) yet they lived longer . This suggested that resistance was unchanged but tolerance was increased . A drawback of our diet restriction protocol is that it raises the caveat that lifelong food limitation has effects on immunity because of developmental changes . To determine whether short-term diet restriction could produce symptoms similar to those seen in gr28b flies or flies diet restricted since hatching , we placed flies on diet restriction food 24 h before challenging them with microbes ( Figure 5 ) . L . monocytogenes–infected flies showed significantly decreased survival , whereas S . typhimurium-infected flies showed increased survival comparable to that seen in gr28b flies . These experiments support the idea that diet restriction in adults affects defenses by altering the fly's physiology without causing developmental changes . In summary , diet restriction has varied effects on tolerance and resistance in the fly: diet restriction causes no change during E . faecalis infections , reduces resistance to L . monocytogenes , and increases tolerance to S . typhimurium . To determine the mechanism behind the changes in resistance and tolerance we observe under diet restriction , we examined the three resistance mechanisms of the Drosophila innate immune response that are important for limiting microbial growth: phagocytosis , antimicrobial peptide production , and melanization [23] , [24] . We saw no change in phagocytosis rates in anorexic flies ( Figure S2 ) but found significant differences in the other two immune responses . We measured the levels of antimicrobial peptide transcript levels in L . monocytogenes–infected gr28b mutants and food-restricted flies and found that they elicit similar effects ( Figure 6; unpublished data ) . In gr28b mutants we found that postchallenge transcript levels for drosomycin and drosocin were significantly reduced compared to wild-type flies ( gr28b , 20× ) . However , anorexia did not affect all antimicrobial peptide transcripts in the same way; attacin transcripts were found at higher levels in gr28b flies ( Figure 6 ) , whereas diptericin transcripts showed no consistent change ( unpublished data ) . The antimicrobial peptide response has been well-characterized for newly infected but otherwise healthy flies; We found that three antimicrobial peptides typically described as being coordinately regulated ( attacin , diptericin , and drosocin ) are regulated independently during diet restricted conditions . We show that the rules governing AMP expression are variable and depend not only upon the specific immune challenge but also upon environmental conditions . To determine if diet restriction affected the melanization response , we infected flies with L . monocytogenes or S . typhimurium , both of which elicit a robust disseminated melanization response in the fly , and examined flies for evidence of melanization ( Figure 6 ) [35] . We found that approximately 90% of our wild-type flies fed a standard diet exhibited melanization , whereas less than 10% of gr28b mutants melanized when infected with either S . typhimurium or L . monocytogenes . We also observed a significant reduction in melanization in diet restricted wild-type flies . Nutrient deprivation studies in the mosquito and the darkling beetle have also demonstrated that melanization is reduced under food restricted conditions [36] , [37] . These results demonstrate that diet restriction/anorexia causes down-regulation of infection-induced melanization . In wild-type flies , L . monocytogenes establishes an intracellular infection; in CG3066 fly mutants defective in melanization , we find an extracellular population of bacteria in addition to the typical intracellular population [35] . Because diet restriction causes a reduction in the melanization response , we hypothesized that flies will also produce an extracellular population of L . monocytogenes when diet restricted . To test this idea we performed a gentamicin chase experiment ( Figures 4 and 5 ) [32] . Infected flies were injected with the antibiotic gentamicin or with water at 0 , 24 , and 48 h postinfection and surviving bacteria were counted; gentamicin kills extracellular bacteria while the intracellular bacteria are protected . Indeed , gr28b flies and diet restricted flies had a large extracellular population of L . monocytogenes , in contrast to wild-type and normally fed flies , which did not . We also observe this effect in flies that were diet restricted only 24 h prior to infection but the phenotype is dramatically enhanced in flies that were raised on a restricted diet ( Figures 4 and 5 ) . The effects of diet restriction on melanization seem easily interpretable with respect to L . monocytogenes infections but reveal an exciting complexity with S . typhimurium . Inhibition of melanization in a CG3066 mutant has the same effect on L . monocytogenes and S . typhimurium infections— a loss of resistance [35] . Therefore , the loss of melanization in diet restricted flies can explain the entire L . monocytogenes infection phenotype because the phenotype is the same as that seen in CG3066 mutants . This is not the case with S . typhimurium; loss of melanization was anticipated to reduce resistance to S . typhimurium; instead , we found an increase in tolerance and no change in resistance . If a resistance mechanism is lost when melanization is removed because of diet restriction , some resistance mechanism must replace it to prevent S . typhimurium growth . In addition , the increase in tolerance in these flies needs to be explained . One possible explanation is that , in diet restricted flies , the loss of melanization increases the tolerance to S . typhimurium infections and the rebalancing of antimicrobial peptide levels replaces the resistance that would have been lost through the loss of melanization . More complex explanations require proposing the induction of unknown resistance and tolerance mechanisms . Regardless of the effects diet restriction has on individual resistance mechanisms , the practical outcome of this work is its demonstration that sensitivity to infections changes in diet restricted flies . This can benefit the host , as is seen in S . typhimurium infected flies or harm the host , as seen in L . monocytogenes infections . In the field , the effect of an anorexic response to infection on evolution should depend upon the pathogens to which a population was exposed . For example , Salmonella-like organisms should drive an increase in anorexia responses while Listeria-like pathogens would have the opposite effect . By highlighting the contribution of feeding to defense , this work has practical implications for fly immunity experiments . Changes in nutrition due to food variation could explain week-to-week alterations in survival curves or plating experiments within a lab . Similarly , differences in food recipes could explain lab-to-lab variability . The finding that diet affects both specific antimicrobial peptide transcript levels and melanization means that experiments using these responses as outputs must be interpreted carefully; for example , when pathogens are fed to flies , test subjects fed a high dose of bacteria receive a different diet than flies fed food lacking these microbes and might be expected to have a different immune response just because the food differs . Microbe dose may be difficult to regulate when feeding sick flies if anorexia is induced by the infection; this could lead to confounding results where mutant flies that do not become anorexic take larger doses of the infecting microbes than do wild-type flies . In cases like this , nonanorexic flies could pay for their dietary indiscretion with their lives . Recent work in a variety of animals demonstrates that our native microbiota affect our immune system [38] , [39] . Certainly some of this comes from the direct interaction between the microbes and pattern recognition pathways but native microbiota often play a role in contributing to host nutrition and metabolism . Therefore , care should be taken when comparing immune responses between axenic and normally raised flies; not all immune changes will be due to the mere physical exposure of the fly to microbes . This work adds to a growing literature on regulatory loops linking the fly's immune responses to the environment [40]–[46] . A fly's susceptibility to infection is altered by temperature and several insects have behavioral fevers induced by infection; such fevers can affect the outcome of infections [47]–[52] . Immune challenges alter circadian rhythms in flies and this can feedback to change immunity in ways that can be either helpful or destructive [43] , [45] , [46] . It perhaps came as no surprise that nutrition affects fly immunity but what we demonstrated here was that during an immune response , the fly actively alters its nutrition and , again , this leads to feedback loops that can aid or collapse the immune response . Recent work on the African armyworm , Spodoptera exempta , demonstrates that this insect may not only change its appetite , but also changes its preference for protein or carbohydrate rich foods during infection [53] . This raises the possibility that the anorexia response we measure in flies could be complicated as it is difficult to distinguish an “I am not that hungry” response from “yuck , I do not want to eat this junk , ” if the flies are presented with just one food choice . All of this suggests that the fly's immune response isn't merely sensitive to ambient environmental conditions , rather the fly uses the environment as an integral part of its immune response . Diet restriction can increase the lifespan of animals allowed to come to a generic “natural death” in the lab [10] . Though the ultimate mechanisms regulating aging remain unknown , signaling pathways linking diet restriction and aging are emerging as potential drug targets . Our model provided an opportunity to measure the effects of a naturally induced diet restriction on deaths induced by different pathogens . The work reported here should raise a cautionary flag as it demonstrates that diet restriction can have complex effects on the realized immune response of a diet-restricted animal . We must determine how diet restriction affects realized immune responses in addition to basic immune effectors and anticipate that this will differ in a pathogen-specific manner . The wild-type parental strain used in all experiments is white1118 ( Bloomington stock center , stock 6326 ) . The gr28bc01884 allele was obtained from Bloomington stock center ( stock 10743 ) . The piggy bac line was generated on the white1118 background and backcrossed further onto the white1118 background for four generations . Flies were kept in standard fly bottles containing dextrose medium and raised under a 12-h light-dark cycle at 25°C prior to experiments . L . monocytogenes strain 10403s [54] was stored at −80°C in brain-heart infusion ( BHI ) broth containing 15% glycerol . S . typhimurium strain SL1344 and E . faecalis strain V583 were stored at −80°C in Luria Bertani ( LB ) medium containing 15% glycerol . E . faecalis and S . typhimurium cultures were grown overnight at 37°C in LB medium . E . faecalis cultures were shaken , while S . typhimurium cultures were grown standing . S . typhimurium cultures were diluted to OD600 of 0 . 1 with fresh LB medium prior to injection . E . faecalis cultures were diluted to an OD600 of 0 . 05 with medium . L . monocytogenes was grown overnight in BHI medium . L . monocytogenes was grown standing and injected at an OD600 of 0 . 01 . 5- to 7-d-old males were used for injection . Flies were anesthetized with CO2 and injected with 50 nl of culture or medium using a picospritzer ( Parker Hannifin ) and pulled glass needle . Flies were injected in the anterior abdomen on the ventrolateral surface . Flies were then placed in vials containing dextrose medium in groups of 20 ( or ten for feeding assays ) and incubated at 29°C 65% CO2 under a 12-h light-dark cycle . Flies were injected with 1 , 000 CFUs of live or dead L . monocytogenes , 10 , 000 CFUs of S . typhimurium , or 5 , 000 CFUs of E . faecalis For each microbe tested , w1118 and gr28b mutants were injected with the microbe or medium as a control . Flies were placed in dextrose vials in groups of 20 after injection and a total of 60 flies were assayed for each condition . The number of dead flies was counted daily . Using Prism software , Kaplan-Meier survival curves were generated and statistical analysis was done using log-rank analysis . Survival was tested for each microbe at least three times and gave similar results for each trial . All survival experiments were done at 29°C . Infected flies were homogenized in media supplemented with 1% Triton X-100 and serially diluted . Dilutions were plated on LB agar plates and incubated over night . The data were plotted as box and whiskers plots using Graphpad Prism software for three independent experiments . Using an unpaired two-tailed t-test , the p-value was determined . For the gentamicin chase experiments , flies were injected with 50 nl of 1 mg/ml gentamicin or water 3 h prior to homogenizing and plating . Flies were incubated at 29°C post infection Total RNA was extracted from infected or control flies that were incubated at 29°C in groups of five flies using the Qiagen RNeasy kit ( Qiagen ) at 0 and 6 h postinjection . The samples were treated with DNase ( Promega ) . Quantitative real-time RT-PCR was performed with rTth polymerase ( Applied Biosystems ) using a Bio-Rad icycler ( Bio-Rad ) and the following primer sets: drosomycin 5′ 5′-gacttgttcgccctcttcg-3′ , drosomycin 3′ 5′-cttgcacacacgacgacag-3′ , drosomycin Taqman probe 5′-tccggaagatacaagggtccctgtg-3′ , diptericin 5′ 5′-accgcagtacccactcaatc-3′ , diptericin 3′ 5′-cccaagtgctgtccatatcc-3′ , diptericin taqman 5′-cagtccagggtcaccagaaggtgtg-3′ , attacin 5′ 5′-caatggcagacacaatctgg-3′ , attacin 3′ 5′-attcctgggaagttgctgtg-3′ , attacin Taqman probe 5′-aatggtttcgagttccagcggaatg-3′ , drosocin 5′ 5′-ttcaccatcgttttcctgct-3′ , drosocin 3′ 5′-agcttgagccaggtgatcct-3′ , drosocin Taqman probe 5′-gtttttgccatggctgtggccact-3′ . Concentrations of AMP transcripts were normalized to the expression of the Drosophila ribosomal protein 15a transcript for each sample [55] . All experiments were performed with three biological replicates and each experiment was performed at least three times . Flies were infected as described above with L . monocytogenes or S . typhimurium and incubated at 29°C for 4 d . Flies were then visualized by light microscopy and examined for a disseminated melanization response . Flies that exhibited melanization beyond what is observed at the injection site are scored as positive for a melanization response . Flies that observe no melanization or melanization only at the site of injection are scored as negative for a melanization response . All experiments were performed as described above using flies that were raised on restricted diets . Restricted food was generated by diluting the standard 1× diet 1∶2 or 1∶4 in 1% agar water to generate the 0 . 5× or 0 . 25× diet . Vials were placed on a rocker while food solidified to prevent settling of the food . Feeding rate and ingestion amount were done using standard fly dextrose diet supplemented with 0 . 1% bromophenol blue and 0 . 5% xylene cyanol [56] , [57] . Our standard fly food recipe contains the following chemicals in 1 l of cooked food: 129 . 4 g dextrose , 7 . 4 g agar , 61 . 2 g corn meal , 32 . 4 g yeast , 2 . 7 g tegosept . Flies were injected as described above or were left unmanipulated and were incubated at 29°C under 12-h light-dark cycle for at least 24 h to allow flies adequate time to recover from CO2 treatment on their standard diet without tracking dye . To measure feeding rate , flies were transferred to vials containing food with tracking dye and incubated at room temperature for time point collections . We chose to keep the flies at room temperature because we found that the opening and closing of the incubator door at each time point disturbed feeding activity . Experiments were performed at the same time of day ( 2 pm , ZT5 ) . At each time point flies are then transferred to empty vials that contain no food . At the end of the time course all flies are examined for the presence of blue dye inside their bodies and the percentage of flies that ingested a meal was recorded . For each experimental condition three groups of at least ten flies were tested for each time point . The average percentage of flies that ingested a meal was plotted and a Fisher's exact test was done for statistical analysis . To measure ingestion amount at the desired time points at least three groups of ten flies that have been feeding on the tracking food were collected and homogenized in 100 µl of 1×TE buffer with 0 . 1% Triton X-100 . 1 ml of 1×TE was added and then homogenates were centrifuged at 14 , 000 rpm for 3 min . Supernatants were collected and the absorbance at 614 nm was measured . The average absorbance for each experimental condition was recorded and ANOVA and a Tukey test was done for statistical analysis .
Two routes to decreasing susceptibility to infection are resistance ( the ability to clear pathogens ) and tolerance ( the ability to limit damage in response to pathogens ) . Anorexia induced by sickness puts animals into a diet-restricted state , a state that is generally believed to extend lifespan . We asked whether anorexia induced by sickness would alter the immune response . We measured the effects of diet restriction on both resistance and tolerance to two different infections in the fruit fly , Drosophila melanogaster . In one case we found that infection induced anorexia and the resulting diet restriction increased tolerance to this infection , thereby increasing survival of flies infected with this pathogen; however , this is not a universal effect . In a second case we found another pathogen that induced anorexia but here diet restriction lead to a reduction in resistance that collapsed the immune response and caused the fly to die faster . The relationship between diet restriction and immunity is complicated and must be evaluated on a pathogen-by-pathogen basis .
[ "Abstract", "Introduction", "Results/Discussion", "Methods" ]
[ "immunology/cellular", "microbiology", "and", "pathogenesis", "nutrition", "microbiology/innate", "immunity" ]
2009
The Role of Anorexia in Resistance and Tolerance to Infections in Drosophila
The Polyomaviridae constitute a family of small DNA viruses infecting a variety of hosts . In humans , polyomaviruses can cause infections of the central nervous system , urinary tract , skin , and possibly the respiratory tract . Here we report the identification of a new human polyomavirus in plucked facial spines of a heart transplant patient with trichodysplasia spinulosa , a rare skin disease exclusively seen in immunocompromized patients . The trichodysplasia spinulosa-associated polyomavirus ( TSV ) genome was amplified through rolling-circle amplification and consists of a 5232-nucleotide circular DNA organized similarly to known polyomaviruses . Two putative “early” ( small and large T antigen ) and three putative “late” ( VP1 , VP2 , VP3 ) genes were identified . The TSV large T antigen contains several domains ( e . g . J-domain ) and motifs ( e . g . HPDKGG , pRb family-binding , zinc finger ) described for other polyomaviruses and potentially involved in cellular transformation . Phylogenetic analysis revealed a close relationship of TSV with the Bornean orangutan polyomavirus and , more distantly , the Merkel cell polyomavirus that is found integrated in Merkel cell carcinomas of the skin . The presence of TSV in the affected patient's skin was confirmed by newly designed quantitative TSV-specific PCR , indicative of a viral load of 105 copies per cell . After topical cidofovir treatment , the lesions largely resolved coinciding with a reduction in TSV load . PCR screening demonstrated a 4% prevalence of TSV in an unrelated group of immunosuppressed transplant recipients without apparent disease . In conclusion , a new human polyomavirus was discovered and identified as the possible cause of trichodysplasia spinulosa in immunocompromized patients . The presence of TSV also in clinically unaffected individuals suggests frequent virus transmission causing subclinical , probably latent infections . Further studies have to reveal the impact of TSV infection in relation to other populations and diseases . Members of the polyomavirus family ( Polyomaviridae ) infect mammals ( rodents , bovines , primates ) and birds ( fowl , psittacines ) , and can affect various organs . So far five human polyomaviruses have been described . Two of these , JC-polyomavirus ( JCPyV or JCV ) and BK-polyomavirus ( BKPyV or BKV ) , are established pathogens in immunocompromized hosts causing progressive multifocal leukoencephalopathy in AIDS patients and nephropathy in renal transplant recipients , respectively . In 2007 , two additional human polyomaviruses were described , KI-polyomavirus ( KIPyV or KIV ) and WU-polyomavirus ( WUPyV or WUV ) [1] , [2] , which were isolated from the respiratory tract and whose pathogenicity is still unclear . The most recently discovered human species concerns the Merkel cell polyomavirus ( MCPyV or MCV ) found to be integrated in a large proportion of Merkel cell carcinomas of the skin [3] , but detected in apparently healthy skin , plucked eyebrow hairs and other cutaneous carcinomas as well [4] . The transforming , oncogenic potential of polyomaviruses was recognized long ago in rodents following natural infection , and after experimental infections with JCV or BKV causing tumors in newborn hamsters [5] , [6] . Here we describe the identification of a new human polyomavirus that combines specific properties of other human polyomaviruses , as it infects the skin and seems to cause disease only in immunocompromized patients probably as the result of unrestricted virus and host cell proliferation , possibly the inner root sheath cells of hair follicles . Trichodysplasia spinulosa ( TS ) , also known as pilomatrix dysplasia , cyclosporine-induced folliculodystrofy or virus-associated trichodysplasia , is a rare skin disease characterized by the development of follicular papules and keratin spines known as spicules [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] . The lesions are most striking in the face , especially on the nose , eyebrows and auricles , but other parts of the body can be affected as well . The disease is accompanied by thickening of the skin and alopecia of eyebrows , sometimes also of lashes and scalp hairs , in some cases leading to distortion of facial features and a leonine appearance [8] . Histologically , TS is characterized by distended and abnormally maturated hair follicles with high numbers of inner root sheath cells containing excessive amounts of trichohyalin [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] . From these aberrant follicles the keratin spicules originate that become 1–3 mm in length . TS is exclusively found in immunocompromized patients , such as solid organ transplant recipients and acute lymphocytic leukemia patients [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] . Initially , the condition was described as a side-effect of cyclosporine treatment , but later it was also observed in patients treated with other immunosuppressive or chemotherapeutic drugs . In analogy with other diseases exclusively occurring in immunocompromized patients , an infectious etiology was suspected . In 1999 , Haycox and coworkers for the first time by transmission electron microscopy ( TEM ) demonstrated the intracellular presence of virus particles probably belonging to the papovavirus family [8] . Since 2000 the papovaviruses are classified in separate families , the Papillomaviridae and Polyomaviridae . In subsequent TS case reports , the presence of crystalloid arranged clusters of 40-nm virus particles preferentially in the nuclei of inner root sheath cells was confirmed [11] , [13] , [14] . Shape , size and localization suggested the presence of a small , non-enveloped DNA virus , likely a polyomavirus , but attempts to culture or detect the virus using PCR methods based on polyomavirus or papillomavirus-specific primer sets failed [8] , [13] , [14] . Here we describe a new case of TS and report the amplification , cloning and identification of a new human polyomavirus isolated from TS spicules . This virus was provisionally called TS-associated polyomavirus ( TSPyV or TSV ) and phylogenetic analysis was performed to determine its evolutionary position among the other known polyomaviruses . To investigate the putative causal relationship between TSV infection and TS , the clinical response after treatment with the anti-viral drug cidofovir was monitored , and TSV-specific quantitative PCR was developed to measure the TSV load in clinical samples before and after antiviral treatment . With this new PCR we also estimated the TSV prevalence in a group of unaffected immunosuppressed patients and provide evidence for TSV circulation outside TS patients as well . In the late spring of 2009 , a 15 years old Caucasian male heart transplant patient was seen in the Dermatology outpatient clinic of the Jeroen Bosch Hospital because of spots and spines in the face . One and a half year prior to presentation , a year after transplantation and start of immune suppressive treatment , his skin condition had started to develop with desquamation of the eyebrows , gradually followed by the development of follicular , skin-colored , indurated papules on the eyebrows , nose , ears , malar-region and forehead ( Figure 1A ) . Subsequent symptoms were loss of eyebrow hairs and partially of the eyelashes . From the enlarged follicular orifices , small hyperkeratotic white-yellowish spicules started to protrude on the eyebrows , nose and ears ( Figure 1B ) . Comparable solitary hyperkeratotic papules and spicules also developed on the legs . Over a period of one year , as skin of his ears , eyebrows and nose had thickened , his overall facial appearance had changed dramatically . In the end of 2006 he had been transplanted elsewhere for dilated cardiomyopathy of unknown cause and was placed on immunosuppressive treatment . Transplantation was complicated by a cerebrovascular event and epilepsy due to embolization from the left ventricle . A year after transplantation he was treated for an EBV-positive large B-cell lymphoma with rituximab and lowering of the immunosuppressive treatment . At presentation in 2009 , apart from the immunosuppressive regimen ( tacrolimus 2 . 0 and 1 . 5 mg daily; mycophenolate mofetil 750 mg 2 dd; methylprednisolone 10 mg 1 dd ) , he was also using amplodipine ( calcium-antagonist ) , pravastatine ( statin ) and levetiracetam ( anti-epilepticum ) . As the patient refused the taking of biopsies , an hematoxylin-eosin ( HE ) -stained section was retrieved , prepared from a biopsy taken previously of a hyperkeratotic papule from the eyebrow region , as well as a snap-frozen fragment thereof . The HE section showed substantially distended and enlarged hair follicles ( Figure 1C ) . Some hair bulbs were hyperplastic and bulbous , encroaching on hair papillae that were diminished in size . Some hair follicles showed presence of poorly formed hairs . Attempts to demonstrate the presence of TSV particles by TEM in the biopsy fragment failed because of poor sample quality . The constellation of findings was diagnostic of viral-associated TS in an immunosuppressed patient . Based on this diagnosis and the assumption that a polyomavirus was causing the disease , the patient was started on topical cidofovir 1% cream treatment twice a day . Gradually over the first three months the patient's condition improved considerably with diminution of the follicular spines , regrowth of eyebrow hair and reduction of the thickened skin of the ears and nose ( Figure 1D ) , supporting the original diagnosis . Combined with what has been described in the literature , the above observations indicated that the patient could carry a polyomavirus causing TS . To identify this virus , spicules collected from the nose were dissolved in lysis buffer and subjected to nucleic acid extraction . The extracted material was used as a template for rolling-circle amplification ( RCA ) [15] . Instead of taq-polymerase used in conventional PCR , the RCA-method employs the DNA-dependent φ29-polymerase , a proofreading enzyme that preferentially amplifies circular DNA while using random primers . The RCA product was cut with restriction enzymes and analyzed on gel revealing a number of bands ( Figure 2 ) . Based on the size of these fragments , the amplicon was estimated about 5000 base pairs ( bp ) in length . This size is typical for polyomavirus genomes; papillomavirus commonly have larger genomes of around 8000 bp . The presence and location of the specific enzyme restriction sites was later confirmed when the viral sequence was elucidated . The 3600 and 1600-bp RCA fragments , obtained after EcoRI digestion ( Figure 2 ) , were ligated and cloned into plasmid pUC19 . Sequencing of each fragment was started from primers located up and downstream of the pUC19 multiple cloning site . Sequential sequence reactions were performed on the cloned RCA fragments , each time using newly designed primers based on the previously obtained sequence . A list of primers used for this “primer walking” is shown in Table S1 . Finally , all obtained sequences were assembled into one continuous ( circular ) DNA contig of 5232 nucleotides . With the use of the newly designed primers , the resolved sequence was verified and confirmed in the original RCA product by direct sequencing . Blast-mediated GenBank searches using the obtained 5232-bp circular DNA sequence as query consistently identified polyomaviruses as having most similar sequences . Analysis of this putative new viral genome revealed the presence of several open reading frames ( ORFs ) located on both strands . The orientation and relative size of these ORFs , as well as the presence of a non-coding control region ( NCCR ) in between , were similar to those of known polyomaviruses ( Figure 3 ) . Downstream of the NCCR that contains the origin of replication ( ori ) , three putative late genes , VP1 , VP2 and VP3 , could be identified . Upstream of the NCCR , on the opposite strand , reside the candidate genes encoding the viral T antigens . In the NCCR , a total of ten putative large T-binding sites could be identified , seven and three respectively on each strand ( Figure 4 ) . An A/T-rich domain , probably harboring the TATA box , is located downstream of the last large T-binding site . By analogy with most other human polyomaviruses and SV40 , nucleotide position 1 of the genome was chosen within the NCCR , in large T-binding region . Because of obvious similarities in genome length , organization and sequence , we propose to group this newly identified TS-associated virus among the polyomaviruses . Nucleotide positions , length and estimated mass of all putative viral genes and proteins , respectively , are listed in Table 1 . The level of protein sequence similarity with other known ( human ) polyomaviruses in shown in Table 2 . The TSV genome and putative gene sequences , as well as the putative protein amino acid sequences have been submitted to GenBank ( accession number GU989205 ) . Like in all other polyomaviruses , the putative TSV small and large T antigens are expressed from a common primary transcript subject to alternative splicing [16] , [17] ( Figure 3 ) , and share their N-terminal part of 80 amino acids in length . The most likely large T splice products were compared to the large T amino acid sequences of other known polyomaviruses producing two possible versions of the TSV large T antigen that differ only 6 amino acids in size . Version 1 was used for further analyses ( Tables 1 and 2 ) . The putative TSV large T antigen contains characteristic sequence motifs in different domains , such as the J-domain , the ori DNA-binding domain and an ATPase/helicase domain ( Figure 5 ) . Within the N-terminal J-domain the highly conserved HPDKGG amino acid sequence is located , important for efficient polyomavirus DNA replication , transformation and virion assembly [18] , [19] . Other characteristic polyomavirus large T sequence signatures are the pRb family-binding motif and a nuclear localization signal downstream of the J-domain [18] . In the ATPase/helicase domain , a zinc finger motif is recognized , two NTPase/helicase “Walker” motifs and an SF3 motif [20] . A sequence putatively involved in p53 complex formation that matches the p53-binding motif described by Pipas and coworkers ( GPX1X2X3GKT [18] ) , overlaps with the “Walker” A motif located within the ATPase/helicase region shown in Figure 5 [19] , [21] . In addition to the N-terminal J-domain shared with the large T antigen and similar to other known polyomaviruses , the putative TSV small T antigen contains protein phosphatase 2A subunit-binding motifs ( not shown ) [19] , [21] . Complete sequences of 20 polyomavirus genomes were selected to represent the Polyomaviridae family in the RefSeq database [22] . They include 5 human , 4 avian and 11 non-human mammalian polyomavirus species . Except for the hamster polyomavirus ( sequence incomplete ) and Simian agent 12 ( almost identical to simian virus 12 ) , all of these plus the genome of the recently sequenced Sumatran orangutan polyomavirus [23] species were included in our analysis . We produced multiple sequence alignments and conducted phylogenetic analyses to determine the evolutionary position of TSV with respect to other members of the family ( Figure 6 ) . The phylogenetic analysis involving VP1 , VP2 and large T antigen , as well as a merged set of these proteins , recognized seven clades among polyomaviruses ( colored triangles in Figure 6 ) . In all trees analyzed , TSV was found to form a tight monophyletic cluster with the Bornean orangutan polyomavirus ( OraPyV1 ) ( violet clade in Figure 6 ) . The distances separating these two closely related viruses resemble those found between JCV , BKV , SV40 and SV12 that form another compact monophyletic cluster ( dark green clade in Figure 6 ) . These findings support the classification of TSV as a new polyomavirus species rather than a strain of any of the known species . In trees based on the VP2 , large T antigen or the combination of VP1 , VP2 and large T proteins , TSV and OraPyV1 are found within a monophyletic group formed by MCV , Sumatran orangutan polyomavirus , murine polyomavirus and African green monkey polyomavirus ( yellow clade in Figure 6 ) . The position of the TSV/OraPyV1 branch within this clade somewhat varies between trees , but consistently splits off the cluster trunk after the basal African green monkey polyomavirus lineage . In the VP1 tree , TSV and OraPyV1 form a separate clade , although this separation should be viewed with some caution since VP1 is the least conserved protein and the lineage branching in the yellow cluster is poorly resolved ( Figure 6 ) . The rare occurrence of TS suggests the ( sub-clinical ) circulation of TSV in larger populations outside this patient cohort . To investigate this possibility we developed three TSV-targeted quantitative PCR assays . Primers and probes were chosen in the VP1 and Large T genes , and in the NCCR , respectively , and listed in Table S2 . As expected on the basis of chosen primer and probe sequences , none of the TSV PCRs recognized any of the other human polyomaviruses when performed on JCV-positive cerebrospinal fluids ( n = 5 ) , BKV-positive blood plasmas ( n = 20 ) , KIV or WUV-positive respiratory samples ( n = 20 ) and MCV-positive plucked eyebrow hairs ( n = 30 ) ( data not shown ) . In contrast , each TSV PCR detected the presence of TSV DNA in the patient's biopsy fragment and in the plucked spicules from the nose . Calculation of the viral load in both samples revealed a mean TSV copy number of 2×105 per cell . To estimate the prevalence of TSV in immunosuppressed hosts , we analyzed a set of plucked eyebrows from long-term immunosuppressed renal transplant patients . Three out of 69 transplant patients ( 4% ) were TSV-positive . The viral copy numbers detected in the plucked hairs of three of these patients were below one TSV copy per cell and , therefore , much lower than those detected in the patient's biopsy and spicules . Analysis of the patient's plucked eyebrow hairs collected six months after cidofovir treatment revealed a TSV load of 104 copies per cell in all three PCRs . Spicules collected from untreated solitary lesions located on the legs contained amounts of TSV comparable to those detected in the spicules from the patient's nose before treatment , 3×105 copies/cell . With the discovery of TSV the total number of described human polyomaviruses is brought to six . Pathogenicity profiles have been established for JCV and BKV , to some extent for MCV , but not yet for KIV and WUV [24] , [25] . Although not definitive , the evidence presented for TSV with regard to the clinical entity of TS can be considered reasonably strong . Previously , electron micrographs have shown the presence of a polyoma-like virus in nuclei of inner root sheath cells [8] , [11] , [13] , [14] . These cells lay at the base of the distended and enlarged hair follicles that give rise to papule and spicule formation , the clinical hallmarks of the disease . From these spicules we could isolate the TSV genome and detect large amounts of the virus . The rapid response of clinical signs to cidofovir treatment , also suggested a causal relationship between TSV and disease . Concomitantly a reduction in viral load was observed , but not as pronounced as expected based on the clinical response . To what extent TSV loads measured in plucked hairs and in spicules can be compared , and whether the difference in load measured between the two reflect the actual reduction in TSV load is unclear at the moment . Whether a threshold exists in TSV load , above which the clinical signs of TSV infection start to develop , is not known . Detection of TSV in high copy numbers in samples from earlier reported TS cases should provide further evidence for the pathogenicity of TSV . Over the last years , different and often highly sophisticated methods for the detection of previously unknown viruses have been developed . RCA is a rather simple technique that takes advantage of the property of φ29-polymerase to preferentially amplify circular DNA while using random primers [15] . By strand displacement synthesis , a high molecular-weight DNA is produced containing multiple linear copies of the circular genome . The validity of this approach was demonstrated by the discovery of a number of newly identified papilloma and polyomaviruses [26] , [27] , [28] , [29] , [30] . Limitations of this method with respect to the minimal excess amount of viral over genomic DNA or maximum length of the viral genome are not exactly known . The absence of background smears or bands in our preparation shown in Figure 2 suggested a relative excess of circular ( viral ) DNA within the RCA product preparation , which was confirmed by qPCR on the spicules . Analysis of the TSV genome revealed five putative genes probably encoding the VP1 , VP2 and VP3 capsid antigens and the small and large T antigens . For the latter we have identified two putative splice variants , large T antigen 1 and 2 . We found no ORF upstream of the TSV VP2 gene , indicating the absence of an LP1/Agno protein . Also the presence of alternative T proteins , such as middle T , seems unlikely . TSV lacks a third ORF within the T-antigen coding region as found in rodent polyomaviruses that encodes a middle T antigen , and no corresponding splice junctions were found . Further experimental investigation of TSV transcription is required to elucidate which ( additional ) TSV genes are expressed and how this is regulated . Within the TSV large T protein several characteristic motifs could be located , including those required for binding the tumor-suppressor proteins pRb and p53 . As shown for other well characterized polyomaviruses and also for papillomaviruses , binding and inactivation of these proteins promote cell transformation [19] , [21] , [31] , a property that to some extent is shared by these small DNA ( tumor ) viruses . For high-risk human papillomaviruses oncogenicity has been established in the development of anogenital carcinomas . Integration of MCV probably plays a role in Merkel-cell carcinoma development [3] . If TSV possesses transformational properties and may play a part in carcinogenesis remains to be studied . In that respect , it is necessary to sort out whether TSV is potentially involved in other ( hyper ) proliferative ( skin ) diseases as well . For MCV for instance , observations have been made supporting a role also in development of cutaneous squamous-cell carcinomas [32] . Phylogenetic analysis of 20 fully sequenced polyomaviruses , including TSV , suggests the existence of seven polyomavirus clades . In all trees in Figure 6 , substantial protein and virus-specific differences in branch lengths representing evolutionary distances were observed , e . g . between KI and WU polyomavirus in the VP1 and VP2 tree , and goose hemorrhagic , crow , finch and budgerigar fledgling polyomavirus in the large T antigen tree . In combination with some topological incongruence of the three protein-specific trees , these observations are indicative of a complex evolutionary history for most polyomaviruses . OraPyV1 was isolated from blood of wild-caught and housed Bornean orangutans . Potentially , the properties of this virus , which remains poorly characterized beyond the genome sequence [23] , could be insightful for understanding TSV . The murine and African green monkey polyomaviruses were isolated from leukemic extracts [33] and lymphoblastoid cells [34] , respectively , also indicative of systemic infection . MCV was isolated from a Merkel-cell carcinoma and has been detected in other samples as well , including healthy skin biopsies , squamous-cell carcinomas , plucked hairs , and recently in respiratory samples as well [4] , [32] , [35] , [36] , [37] , [38] . Initial attempts to detect TSV in other than skin-derived materials , such as blood plasma or serum , urine , cerebrospinal fluid failed . So far , we could detect TSV only in trichodysplasia tissue , spicules and plucked eyebrow hairs suggestive of a tropism specific for squamous epithelium , but larger studies are needed to confirm this finding . Although the occurrence of TS is limited to severely immunocompromized patients , this implies that TSV would be present in larger , probably immunocompetent populations as well . By analogy with most human polyomaviruses , one could anticipate that TSV is highly immunogenic and infects many people , probably early in life without apparent disease [39] , [40] . Further ( sero ) epidemiological studies have to reveal if this indeed is the case and whether TSV causes low-level persistent ( latent ) infection , as described for other polyomaviruses . Although we have tested thus far only a limited number of individuals , TSV was detected in another three unrelated immunosuppressed patients without signs of trichodysplasia . This would be compatible with occasional infections from an unknown reservoir . In view of the lack of any epidemiological marker of the disease , however , it seems more likely the virus is common among humans but generally without causing disease , comparable to for instance JCV and BKV . In that case , at least in adults and in plucked eyebrow hairs , ( latent ) TSV loads may often be too low to be detected by our current assays . Although the eyebrow region is particularly affected in TS and eyebrow hairs were shown suitable material to detect ( polyoma ) viruses [4] , [41] , with 50% MCV-positivity in this study ( data not shown ) comparable to what Wieland and coworkers have found [4] , it is not known at the moment whether eyebrow hairs represent a suitable clinical sample to detect low level TSV infections . In conclusion , a new human polyomavirus was discovered and identified as the possible cause of TS in an immunocompromized patient . We provided evidence for the presence of TSV also among unaffected patients suggestive of subclinical , possibly latent infection . Additional studies in different populations and age groups using different clinical materials are needed to establish the ( sero ) prevalence and epidemiology of TSV infections , and its possible relation to the occurrence of other ( skin ) diseases , including cancer . For a general picture , these epidemiological studies should be complemented with experimental studies on TSV replication , transcription and transformation . The TS patient and his mother gave oral consent to collect spicules and eyebrow hairs for viral diagnosis and treatment monitoring . The Medical Ethics Committee of the LUMC declared in writing that no formal ethical approval was needed to analyze these clinically obtained materials . Written consent from the patient ( minor ) and his legal guardian ( mother ) was obtained for publication of his case and for showing his pictures . Plucked eyebrow hair samples from a population of renal transplant patients visiting the Dermatology outpatient clinic of the LUMC were obtained after informed oral consent from the subjects , which was documented in the patient files . Subject's written approval was not collected for this purpose ( plucking of eyebrow hairs ) , as oral consent was considered appropriate in this case by the Medical Ethics Committee of the LUMC who approved of the study ( Protocol P07 . 024: Risk factors for non-melanoma skin cancer/Genetic and environmental risk factors for the development of skin cancer in organ-transplant recipients ) . Ten spicules from the nose collected with sterile tweezers in a sterile vial were shipped to the Leiden University Medical Center ( LUMC ) at room temperature . Upon arrival the plugs were dissolved in a proteinase K-containing lysis buffer for overnight incubation at 56°C . Total DNA was isolated with the QIAamp DNA Mini Kit ( Qiagen ) according to the QIAmp tissue protocol , with some minor alterations [42] . In parallel , approximately 100 ng commercially available human genomic DNA ( Promega ) was extracted as a negative isolation control . The TempliPhi 100 RCA Kit ( GE Healthcare , UK Limited ) was used following manufacturer's instructions with some slight modifications . In brief , 1 µl , 1∶100 of the isolated total DNA , was diluted in 5 µl sample buffer , denatured at 95°C for 3 minutes and cooled down slowly to 4°C to allow primer annealing . Meanwhile a premix of 5 µl reaction buffer , 0 . 2 µl TempliPhi enzyme ( bacteriophage φ29 DNA polymerase ) and an extra 450 µM of each dNTP was prepared and added to the denatured DNA in sample buffer . The RCA reaction was preformed at 30°C for 16 hours followed by inactivation of the enzyme at 65°C for 10 minutes . The RCA product was stored at −20°C . The RCA product was diluted 1∶1 in miliQ H2O and 2 µl was digested with EcoRV , HindIII , EcoRI or XbaI . The two fragments of the EcoRI digestion were isolated from gel , ligated and cloned into pUC19 , and subsequently sequenced using M13 forward and reverse primers . The resulting sequences were used as a template to design new primers located at the end of the newly identified sequences and listed in Table S1 . Sequence reactions were carried out with the BigDye Terminator kit ( Applied Biosystems ) and analyzed on an ABI Prism 3130 Genetic Analyzer ( Applied Biosystems ) . Contig sequence assembly was performed with ContigExpress , included in the vector NTI software package program , that uses CAP3 computations to drive the assembly process [43] . Putative splice donor and acceptor sites were identified based on consensus splice donor and acceptor sequences as published [44] , [45] , and automated splice-site predictions ( http://zeus2 . itb . cnr . it/~webgene/wwwspliceview . html ) . Putative large T binding sites within the NCCR were identified according to described motifs [46] . Domain searches within the TSV large and small T antigen sequences were performed against the domain profile database SCOP [47] using the HHsearch software [48] . Hits against all 3 domains were strongly significant ( E-values <E-12 ) . Amino acid sequence similarities between the putative TSV gene products and those of other polyomaviruses , as shown in Table 2 , were calculated with the AlignX program in vector NTI version 11 , which uses the ClustalW algorithm with default alignment parameters . For the phylogenic analyses all available polyomavirus genome sequences present in the RefSeq database in December 2009 were downloaded [22] . The Sumatran orangutan polyomavirus [23] and the identified TSV genome sequences were added to this set . The following genome sequences were included in the analysis: Simian virus 40 ( NC_001669 ) , Goose hemorrhagic polyomavirus ( NC_004800 ) , Simian virus 12 ( NC_012122 ) , Squirrel monkey polyomavirus ( NC_009951 ) , Finch polyomavirus ( NC_007923 ) , Crow polyomavirus ( NC_007922 ) , Bovine polyomavirus ( NC_001442 ) , Merkel cell polyomavirus ( NC_010277 ) , WU Polyomavirus ( NC_009539 ) , KI polyomavirus Stockholm 60 ( NC_009238 ) , Budgerigar fledgling polyomavirus ( NC_004764 ) , African green monkey polyomavirus ( NC_004763 ) , JC polyomavirus ( NC_001699 ) , BK polyomavirus ( NC_001538 ) , Murine polyomavirus ( NC_001515 ) , Murine pneumotropic virus ( NC_001505 ) , Myotis polyomavirus VM-2008 ( NC_011310 ) , Bornean orangutan polyomavirus isolate Bo ( FN356900 ) , Sumatran orangutan polyomavirus isolate Pi ( FN356901 ) and Trichodysplasia spinulosa-associated polyomavirus ( GU989205 ) . Multiple amino acid alignments were compiled for VP1 , VP2 and large T antigen using the Muscle program [49] , followed by manual inspection assisted by the Viralis software platform [50] . For VP2 and large T antigen , only partial alignments were used covering , respectively , the part not overlapping with VP1 ( positions 407 to 1307 in the TSV genome sequence ) and the large exon including the helicase domain ( positions 4130 to 2600 in the TSV genome sequence ) . The three protein-specific alignments and their concatenation were submitted to phylogenetic analyses . Bayesian posterior probability trees were compiled utilizing the BEAST software [51] . MCMC chains ( two per dataset ) were run for 2 million steps ( 10% burn-in , sampled every 50 generations ) under the WAG amino acid substitution model [52] , and rate heterogeneity among sites ( gamma distribution with 4 categories ) . For each analysis three molecular clock models ( strict , relaxed with lognormal distribution , relaxed with exponential distribution ) were tested [53] . The more complex model , e . g . relaxed molecular clock , was favored over the simpler model , e . g . strict molecular clock , if the Bayes factor ( ratio of tree likelihoods ) was bigger than five [54] . Convergence of runs was verified and Bayes factors were estimated using Tracer software ( http://beast . bio . ed . ac . uk/Tracer ) . For the detection of TSV DNA , three real-time quantitative PCRs were developed with primers and Taqman probes located in the NCCR , and the VP1 and Large T ORFs , respectively ( Table S2 ) . Primers and probes were chosen with the help of Beacon Designer software ( Premier Biosoft ) . The VP1 3′ primer had a 84% match with BKV , but none of the chosen TSV probes had similarities with any of the other known polyomaviruses . The 50 µl PCR reactions consisted of 1× GeneAmp PCR buffer ( 15 mM Tris-HCl [pH 8 , 0] , 50 mM KCl , 3 , 6 mM MgCl , 0 , 3 mM of each dNTP , 15 pmol of each primer , 7 , 5 pmol probe and 2 U of AmpliTaq Gold polymerase ( Applied Biosystems ) . Real-time PCR was performed in the iCycler ( Biorad ) and cycle conditions are 9′ at 95°C , followed by 50 cycles of amplification ( 94°C for 1 min . and 65°C for 1 min . ) . TSV copy number was calculated against a plasmid titration series of pUC19-TSV included in each PCR assay that contains the full length TSV genome cloned in XbaI . Cell number was calculated with a PCR specific for the beta-actin gene , which was run in parallel on a dilution series of human genomic DNA ( Promega ) . The sensitivity of each TSV PCR was found to be between 1–10 TSV genome copies . The cerebrospinal fluids , blood plasmas and respiratory samples with proven JCV- , BKV- , KIV- or WUV-positivity used for validation of the developed TSV-specific PCRs were selected from clinical samples routinely send for viral diagnosis to the LUMC , Dept of Medical Microbiology . The plucked eyebrow hair samples were obtained with written permission from renal transplant patients visiting the Dermatology outpatient clinic of the LUMC .
Diseases that occur exclusively in immunocompromized patients are often of an infectious nature . Trichodysplasia spinulosa ( TS ) is such a disease characterized by development of papules , spines and alopecia in the face . Fortunately this disease is rare , because facial features can change dramatically , as in the case of an adolescent TS patient who was on immunosuppressive drugs because of heart-transplantation . A viral cause of TS was suspected already for some time because virus particles had been seen in TS lesions . In pursuit of this unknown virus , we isolated DNA from collected TS spines and could detect a unique small circular DNA suggestive of a polyomavirus genome . Additional experiments confirmed the presence in these samples of a new polyomavirus that we tentatively called TS-associated polyomavirus ( TSPyV or TSV ) . TSV shares several properties with other polyomaviruses , such as genome organization and proteome composition , association with disease in immunosuppressed patients and occurence in individuals without overt disease . The latter indicates that TSV circulates in the human population . Future studies have to show how this newly identified polyomavirus spreads , how it causes disease and if it is related to other ( skin ) conditions as well .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "virology/persistence", "and", "latency", "evolutionary", "biology/microbial", "evolution", "and", "genomics", "virology/virus", "evolution", "and", "symbiosis", "dermatology/skin", "cancers,", "including", "melanoma", "and", "lymphoma", "virology", "virology/emerging", "viral", "diseases", "dermatology/skin", "infections", "virology/viruses", "and", "cancer" ]
2010
Discovery of a New Human Polyomavirus Associated with Trichodysplasia Spinulosa in an Immunocompromized Patient
Studies of synthetic , well-defined biomolecular systems can elucidate inherent capabilities that may be difficult to uncover in a native biological context . Here , we used a minimal , reconstituted translation system from Escherichia coli to identify efficient ribosome binding sites ( RBSs ) in an unbiased , high-throughput manner . We applied ribosome display , a powerful in vitro selection method , to enrich only those mRNA sequences which could direct rapid protein translation . In addition to canonical Shine-Dalgarno ( SD ) motifs , we unexpectedly recovered highly efficient cytosine-rich ( C-rich ) sequences that exhibit unmistakable complementarity to the 16S rRNA of the small subunit of the ribosome , indicating that broad-specificity base-pairing may be an inherent , general mechanism for efficient translation . Furthermore , given the conservation of ribosomal structure and function across species , the broader relevance of C-rich RBS sequences identified through our in vitro evolution approach is supported by multiple , diverse examples in nature , including C-rich RBSs in several bacteriophage and plants , a poly-C consensus before the start codon in a lower eukaryote , and Kozak-like sequences in vertebrates . The ribosome is widely recognized as a broad-specificity ribozyme that is able to translate mRNA at different rates to maintain appropriate relative protein levels and thereby fulfill the dynamic needs of the cell [1]–[3] . Problems with increased or decreased translation of certain messages are known to lead to cancer and various other hereditary diseases in humans [4] . One of the major determinants of translational efficiency is the 5′ untranslated region ( 5′ UTR ) , which may contain a canonical RBS such as the Shine-Dalgarno ( SD ) sequence [5] in prokaryotes or the Kozak sequence [6] in vertebrates . Recently , it has been noted that , while the SD consensus sequence ( 5′-GGAGGU-3′ ) is generally an important cue for ribosome binding in prokaryotes , there are actually more non-SD-led genes than SD-led genes in some microbial genomes [7] . Additionally , the Kozak sequence is a relatively weak consensus , as only a very small fraction of vertebrate genes ( ∼0 . 2% ) have the exact GCCGCC ( A/G ) CCAUGG sequence [8] . These observations do not immediately suggest a universal answer to the following fundamental question: what 5′ UTR sequences inherently enable a ribosome to bind mRNA , initiate translation , and proceed to elongation as quickly as possible ? Although efficient RBSs have been previously identified by library approaches both in vivo [9] , [10] and in cell extracts in vitro [11] , [12] , the mechanisms of efficient translation are confounded by the multitude of uncharacterized biomolecular interactions in these environments . Furthermore , both the library size and the sequencing throughput in earlier studies have been limited , hindering identification of statistically significant motifs . To more directly answer the question posed above , we performed selections on a large RBS library ( ∼3 . 7×1013 mRNA molecules; ∼6 . 9×1010 unique sequences ) in a minimal , well-defined , E . coli-based translation system [13]–[15] using ribosome display [16] . By using a minimal translation system , we removed unnecessary confounding variables and took a “bottom-up” approach to address the question of what sequences inherently promote the fastest translation . One of the major goals of synthetic biology is to reveal new fundamental biological insights through the use of well-defined systems . The present study complements previous advances in the field that utilized or focused on differential RBS function , including work on riboregulators [17]–[19] and the RBS Calculator [20] , as well as early work on synthetic gene networks that used RBSs of various strengths to adjust the gene expression dynamics of synthetic constructs [21] . Here , we were able to attribute the selected RBSs directly to the contents of the translation system because of its fully defined nature; additionally , we were able to consider general aspects of RBSs , which are not necessarily E . coli-specific , as the basic translational machinery is highly conserved across species . High-throughput sequencing of the library after stringent selection for translational efficiency surprisingly revealed mostly non-SD motifs . These library members , some of which were nearly as efficient as the SD-containing 5′ UTR sequence derived from enterobacteriophage T7 gene 10 , were generally highly C-rich . While it is well appreciated that SD sequences help to form the preinitiation complex by binding to the anti-SD sequence in the unpaired 3′ end of the 16S rRNA in the 30S ribosomal subunit , we further hypothesized that our efficient non-SD RBSs also achieve fast translation by optimizing binding to the 16S rRNA . ( “Fast translation” in our study should be considered rapid in the context of the minimal system; the potential speed of translation may be much higher in vivo . ) Based on statistical analyses and competition studies , we conclude that base-pairing between the short , C-rich motifs of the non-SD RBSs and the G-rich rRNA of the small ribosomal subunit allows for fast translation , most likely through fast repositioning of the mRNA on the small ribosomal subunit to form a productive preinitiation complex that is then able to join the large ribosomal subunit and proceed quickly to elongation . We have demonstrated that pure poly-cytosine ( poly-C ) is a poor RBS , and we have used rational mutagenesis to show that the specific positioning of non-C nucleotides in a C-rich context is a critical determinant of translational efficiency . We also show that the activity of C-rich RBSs , but not SD RBSs , can be strongly decreased in vitro by the addition of random oligonucleotide competitor sequences , which can explain their differential activities in vivo . Furthermore , we report similarities between the most common motifs in our selected RBSs and those in human RBSs , suggesting that structurally and functionally conserved ribosomes from diverse organisms are inherently capable of utilizing C-rich sequences directly upstream of AUG start sites . The broader relevance of C-rich RBSs is further supported by several other examples in nature , including C-rich RBSs in non-E . coli bacteriophage , C-rich RBSs that base-pair to a G-rich rRNA element in plants [22] , [23] , and a poly-C consensus before the start codon in a lower eukaryote [24] . The overall goal of this study was to determine inherent requirements for fast translation , and our experimental and computational results together provide evidence of a general , broad-specificity mechanism for efficient protein synthesis . To investigate what upstream sequences promote fast translation , we chose a minimal , reconstituted , E . coli-based in vitro translation system: PURExpress ( New England Biolabs ) developed from PURE technology [13] , [25] , [26] . Ribosome display has previously been used to evolve peptides and proteins with desirable properties , including enhanced affinity and stability [16] , [27]–[29] . Briefly , the standard method involves multiple cycles of generating a DNA library , in vitro transcription , in vitro translation , selection through binding , and recovery . The mRNA contains , at minimum , an RBS followed by a region encoding the gene of interest and an unstructured protein spacer with no stop codon , so that the ribosome stalls at the end of the mRNA , forming an mRNA-ribosome-polypeptide complex ( hereafter called a ribosomal complex ) . In our adaptation ( Figure 1A ) , we used a randomized 5′ UTR ( Figure 1B ) and progressively shortened the translation time in each round to impart an increasing selection pressure . The 5′ UTR from the ribosome display vector pRDV [30] was considered the wild-type ( WT ) sequence . It includes a 5′ stem-loop to prevent degradation and a translational enhancer and SD RBS derived from enterobacteriophage T7 . In the library version , the 18 nucleotides just prior to the start codon ( 5′-TAAGAAGGAGATATATCC-3′ in WT; SD sequence underlined ) were fully randomized , creating a theoretical diversity of 418 = 6 . 9×1010 different sequences , which can be nearly exhaustively sampled in our in vitro system . The SD sequence , when present , generally has a context-dependent optimal position within this region [31] . Additionally , previous studies investigating the position of mRNA on the 30S ribosomal subunit have suggested that approximately 15 bases prior to the start codon are protected by the ribosome during initiation [32] , making this a region of particular interest . The invariant coding region was chosen to be a fusion protein containing ( from N- to C-terminus ) an initiating Met , Ala , FLAG-tag , Gly-Ser ( BamHI site ) , off7 [30] , Lys-Leu ( HindIII site ) , and a modified version of the pRDV tolA spacer that contains out-of-frame stop codons . Off7 is a designed ankyrin repeat protein ( DARPin ) that was evolved to bind maltose-binding protein of E . coli with nanomolar affinity ( ∼4 . 4 nM ) [30] . We chose this model protein because it translates and folds well in vitro . Additionally , its high affinity for maltose-binding protein enables easy affinity purification of only those ribosomal complexes with fully translated protein . We performed three rounds of selection ( 30 min , 5 min , and 3 min translation at 37°C; the “30-5-3 selection” ) and , despite increasingly stringent translation times , the number of recovered mRNA molecules climbed from ∼4 . 4×109 in the first round to ∼1 . 5×1010 in the second round to ∼2 . 2×1010 in the third round . Quantitative reverse transcription-PCR ( qRT-PCR ) data and accompanying experimental details are presented in Figure S1 . mRNA recovery from the third round was comparable to that produced from the WT pRDV RBS , which is highly efficient both in vitro and in vivo . This third round pool was subjected to in-depth analysis . We sequenced the enriched pools from each round in the 30-5-3 selection using the Roche 454 platform . Approximately 7 , 000 raw sequences were obtained from each round: 7 , 268 from round 1; 6 , 825 from round 2; and 7 , 525 from round 3 . Sequences were excluded from analysis if they did not have 18 bases in the randomized region , if they included an in-frame AUG within the randomized region that could serve as an alternate start site , or if there were errors in the 10 bases on either side of the randomized region . Approximately 5 , 000 sequences were analyzed from each round: 5 , 202 ( 4 , 933 unique ) from round 1; 4 , 880 ( 4 , 586 unique ) from round 2; and 4 , 863 ( 4 , 551 unique ) from round 3 . SD sequences were broadly defined as any sequence containing one of the following four-base motifs which could base-pair to the 3′ tail of the 16S: AAGG , AGGA , GGAG , GAGG , and AGGU . The overall incidence of SD motifs in each round is shown in Figure 1C . The positional and overall frequencies of each individual SD motif at the end of each round are presented in Figure S2 . In our data , the first G of GGAG is enriched most prevalently around position −12 , while the same nucleotide is favored around position −10 in E . coli [31] . Certainly , mRNA context may affect the optimal position of SD motifs , as may different in vitro or in vivo conditions . Position-dependent enrichment of SD motifs validated our selection method . Remarkably , of the sequences analyzed from the third round , 3 , 696 ( 76% ) were considered non-SD candidates ( 3 , 491 unique ) . While we expected that perhaps a significant portion of these non-SD candidates could still be acting by slightly mismatched SD-anti-SD interactions , this did not appear to be the case . In fact , we observed that these sequences were highly C-rich . Of the non-SD candidates , 2 , 244 ( 61% ) contained nine or more cytosines out of 18 . This cytosine richness did not appear to be position-dependent . Base frequency versus position and cytosine content histograms are shown in Figure 2A and 2B , respectively , for non-SD , SD , and combined populations from the third round of selection . We hypothesized that these C-rich sequences might be operating by base-pairing with the 16S rRNA in the 30S ribosomal subunit , which is generally G-rich . Indeed , this idea has been suggested in both prokaryotic [33] and eukaryotic [34] systems , although consensus on the issue is lacking [35] , [36] . We looked at four- , five- , six- , seven- , and eight-base potential complementarities . Overlapping windows of these lengths from the 18-base randomized region of third-round products were compared to all identically-sized windows of E . coli 16S rRNA . We considered all 4 , 863 18-base regions in this analysis , including both SD and non-SD sequences . The frequency of motifs in our data set that were Watson-Crick ( A/U or C/G ) reverse complements of each window on the 16S rRNA was determined . We assigned a p-value to each window on the 16S rRNA based on the probability distribution obtained from analyzing ∼100 , 000 randomly generated libraries equal in size to the dataset ( probability of each base = 0 . 25 ) . The 30S ribosomal subunit of E . coli ( PDB 3DF1; [37] ) is shown in Figure 3 with potential mRNA-rRNA base-pairing sites shown in red . To be highly stringent , only significant ( p<0 . 01; Bonferroni-corrected ) seven-base windows that shared six bases with at least one neighboring significant window were highlighted . Potential mRNA-rRNA base-pairing sites primarily fell on the body of the 30S subunit on the face that becomes buried after assembly with the 50S ( Figure 3 , first panel ) . The mRNA tunnel lies between the body and head on this face . Full results from the 16S rRNA comparison are presented in Table S1 . We also found that the overall propensity of the enriched library to form secondary structure resembled that of the starting library ( Figure S3 ) , underscoring the importance of primary structure ( i . e . , nucleotide sequence ) in ribosome binding . The lack of a strong pressure for low secondary structure in the RBS region may have resulted from compensatory low secondary structure in the first ∼40 nucleotides of the coding region . Based on the observed C-rich trend and the complementarity to the G-rich 16S rRNA , we decided to perform a naïve motif search to reveal any interesting local patterns . We determined the frequency of all possible four- , five- , six- , seven- , and eight-base motifs within the 18 bases , independent of the 16S rRNA , and asked whether specific motifs were significantly overrepresented compared to what would be expected in the naïve library ( i . e . , N18 ) . We considered all 4 , 863 18-base regions from the third-round products in this analysis , including both SD and non-SD sequences . As expected based on overall base frequencies , nearly all of the top sequences were highly C-rich . More striking was that the most frequent motifs from the motif search exhibited unexpected similarities to the Kozak consensus sequence found in vertebrates . To investigate these observed similarities in more detail , the most frequent motifs found in the 18 nucleotides prior to the start codon in human ( NCBI TaxID 9606 ) from the Transterm database [38] were considered . Four of the top nine five-base motifs in our selected sequences were also present within the top 17 motifs in human: CCACC , CCGCC , CCCGC , and GCCCC ( Table 1 ) . The full results from this motif search are provided in Table S2 . Previous studies involving prokaryotic RBSs have not recognized the inherent ability of 70S ribosomes to efficiently translate from C-rich start sequences , including those resembling the Kozak consensus sequence , probably because those studies were not conducted in a minimal translation system . The Kozak sequence has been previously investigated for its complementarity to the rRNA of the small subunit in eukaryotes [39] , much as we have done with our selected RBS sequences . The Discussion provides further insight into the parallels between our study and this previous analysis performed in a eukaryotic system , suggesting universal features of the ribosome . All motifs found to be significant in the motif search ( FDR<0 . 01 ) were given further consideration for their co-occurrence with other significant motifs within the same 18-base randomized RBS region . A co-occurrence metric was defined as the number of RBS regions that contained both motif 1 and motif 2 divided by the number of RBS regions that contained motif 2 only . Through this measure , we identified “enhancers” of canonical SD motifs . Variations of an AC dinucleotide repeat were found to correlate strongly with GGAGG . Interestingly , AC dinucleotide repeats downstream of the start codon have previously been reported to enhance translation [40] . Results from the co-occurrence analysis are provided in Table S3 for all pairs of significant motifs that had a non-zero co-occurrence metric . Co-occurrence of C-rich motifs with other C-rich motifs is also evident in Table S3 . We tested the poly-C consensus RBS against the WT pRDV RBS and one of our C-rich RBS clones in single-clone ribosome display . mRNA recovery was quantified by qRT-PCR ( Figure 4A , top three sequences ) . Surprisingly , the poly-C consensus was not efficient . To determine which non-C nucleotides in a C-rich context enabled efficient translation , we performed single-clone ribosome display on a panel of our most C-rich clones ( with cytosines at 15 of 18 positions ) . We considered clones from the basic selection scheme ( three rounds: 30 min , 5 min , 3 min translation; “30-5-3” ) as well as two alternate selection schemes ( four rounds: 30 min , 30 min , 1 min , 1 min translation with or without an additional 1-min round; “30-30-1-1-1” and “30-30-1-1 , ” respectively ) . mRNA recovery from the alternate selection schemes , quantified by qRT-PCR , is presented in Figure S1 . Most clones exhibited activity well above background ( Figure 4A ) ; however , highly similar clones exhibited greatly different activities , suggesting that the placement of non-C nucleotides in a C-rich context is crucial . We investigated two clones , 30-30-1-1 high C 1 ( GCCCCCCCCGCCCCCUCC; ∼80% WT activity ) and 30-5-3 high C 7 ( CCGCCCCCCCGCCCCUCC; ∼10% WT activity ) more closely . These two clones differ only in the position of two guanines: one near the 5′ end of the random region and one near the middle . To investigate the nucleotides responsible for the differential activity of these two clones , we performed single-clone ribosome display on an extended panel of mutant RBSs ( Figure 4B ) . Mutation of the first G to A , C , or U in 30-30-1-1 high C 1 had no major effect , while mutation of the second G to A , C , or U greatly decreased activity . Mutation of the U to A , C , or G also decreased activity . Finally , shifting the first G from −18 to −17 or −16 or shifting the second G from −9 to −8 greatly decreased activity . To investigate our base-pairing hypothesis experimentally , we performed single-clone ribosome display of WT and a C-rich clone ( 30-30-1-1 high C 1 ) in the presence of various ssDNA oligonucleotide competitors . We used five different 18-base competitors: random ( N ) , clone 30-30-1-1 high C 1 , a similar C-rich clone ( 30-5-3 high C 7 ) , WT , and poly-C . This panel of competitors was designed to interrogate specificity of translational inhibition ( if any ) . The activity of the WT clone was only moderately inhibited by a large excess of any oligonucleotide , while the activity of the C-rich clone was effectively eliminated by random or C-rich competitors . Even WT competitor strongly inhibited the C-rich clone , though to a lesser extent than the other competitors ( Figure 5A ) . Finally , we tested a panel of clones in vivo by fusing off7 to emGFP through a short linker ( Figure 5B ) and then monitoring green fluorescence in E . coli ( Figure 5C ) . This panel of clones included five C-rich pre-AUG 18-base regions from E . coli ( derived from the 5′ UTRs of thiI , bisC , gsk , nrdB , and uxuR ) , 15 clones from the 30-5-3 selection with maximal redundancy ( two with four instances , 13 with three instances ) , three representative clones with high C content from the 30-5-3 selection , three of the most C-rich 18-base upstream regions present in phage annotated on EMBL-EBI , and the WT pRDV sequence . The average median fluorescence of these 31 clones from at least three independent experiments is provided in Figure 5C . The induced WT signal was over 580 times above that of 30-5-3 high C 7 , while 5′ UTR mRNA levels were only about 14-fold different , which only accounts for a small fraction of the discrepancy in protein levels . This suggests that observed differences in the in vivo responses for WT and the C-rich clones can be primarily attributed to their differential translational efficiencies . The poor performance of C-rich upstream regions from phage was not unexpected , because the phage from which those 5′ UTRs were derived do not naturally infect E . coli . In support of a base-pairing mechanism , native hosts of phage having C-rich 5′ UTRs ( e . g . , Burkholderia cenocepacia , Mycobacterium tuberculosis H37Rv , and Synechococcus sp . WH 8109 ) clearly have more C-rich 5′ UTR profiles than E . coli ( Figure S4 ) . Although most of our selected clones performed poorly in vivo , at least two synthetic sequences ( 30-5-3 clones 11 and 12 ) exhibited activity >2-fold over background , on par with that of the native 18-base sequence immediately upstream of E . coli gsk . In light of our competition experiments in vitro , we conclude that the in vivo environment of E . coli contains a large quantity of endogenous RNA species that out-competes mRNA containing a C-rich RBS . However , given the two examples of synthetic sequences that retain some activity in vivo , the magnitude of this competition effect is likely to be sequence-specific . Ribosome display , employed as a tool for investigating the non-coding regions of mRNA , particularly in a minimal translation system , has the potential to generate insights not available through previous studies . The large library sizes of ribosome display ( easily up to ∼1014 with reasonable scale-up ) allow much more exhaustive sampling than any technique requiring a transformation step . Coupling these selections with high-throughput sequencing enables the discovery of statistically relevant motifs in the selected sequences . Furthermore , a synthetic biology approach , in which a well-defined translation system is used , can elucidate inherent capabilities of the translational machinery and new insights into the function of natural biomolecules that may be difficult to uncover in a native biological context . In the present study , ribosome display and high-throughput sequencing were used to demonstrate that efficient translation in a minimal , well-defined , E . coli-based in vitro translation system can be mediated by C-rich RBSs which are postulated to base-pair to G-rich 16S rRNA motifs . The identification of highly C-rich RBSs using ribosome display in the PURExpress system underscores the high structural and functional conservation of the ribosome and shows that , if given optimal conditions , ribosomes from one species can bind to mRNAs which are more frequent in other species in nature . Highly C-rich RBSs have been found in multiple diverse organisms , including non-E . coli phage , lower eukaryotes , plants , and vertebrates . A discussion of such natural examples as well as the notable lack of C-rich RBSs in E . coli genes is presented further below . Interestingly , our selected sequences had an overall consensus of poly-C , although the poly-C sequence by itself was not efficient . The inability of this global consensus sequence to promote efficient translation in the PURExpress system provided an important insight for this study: the overall 18-base consensus does not describe the selected library well . Instead , shorter , significant ( FDR<0 . 01 ) motifs that were analyzed independently of the 16S rRNA comprise many local consensus sequences . There was no striking position-dependence of individual local consensus sequences when viewed over the entire population; this contrasted starkly with the SD motifs , which were much more position-dependent . Additionally , our consensus did not contain a “purine peak” at position -3 , which is frequently found in humans and other vertebrates [6] . This purine peak may not be present in lower eukaryotes such as Encephalitozoon cuniculi , an intracellular eukaryotic parasite that frequently infects immunodeficient patients . This organism has short leaders but also contains a poly-C consensus prior to the start codon [24] , much as we observed in our selections . The mechanism by which this parasite initiates translation is currently unknown , although the present study may provide some insight by demonstrating non-native functions of E . coli ribosomes that reflect the RBS preferences of other organisms . The presence of C-rich sequences in phage 5′ UTRs suggests that some aspect of the host environment enables their fast translation . Based on our observations of the effect of competitor oligonucleotides , we propose that phage with C-rich 5′ UTRs best utilize these genes in an environment low in nucleic acids . Interestingly , the Burkholderia phage KS14 contains its most C-rich 5′ UTR prior to its gene for tail completion protein R . Therefore , at least one of the most C-rich motifs in phage precedes a highly-produced late protein ( i . e . , structural protein ) , although the general lack of annotation of phage genes limits our analysis . In late-stage infection , host mRNAs are often repressed , globally or locally [41]–[43] , so highly efficient C-rich RBSs may also serve to temporally control the production of certain proteins ( e . g . , structural proteins should be abundantly synthesized , but only towards the end of phage assembly ) . Phage with C-rich 5′ UTRs may infect slow-growing organisms , such as M . tuberculosis [44] , which may have lower basal mRNA content than other species , such as E . coli . The co-occurrence of multiple short C-rich motifs within the 18-base RBS region suggests that multiple segments of the RBS may interact either sequentially or concurrently with the 16S rRNA , which has multiple binding sites itself . Fast binding and unbinding of these short mRNA motifs to various positions on the ribosome may help maintain a high concentration of ribosomes near the start codon while still permitting necessary mRNA repositioning for initiation and transition to elongation . The concept of multiple mRNA-rRNA interactions has been described as clustering for eukaryotic ribosomes [45] , and we suggest that a similar mechanism may be at work here . In theory , the entire length of an mRNA molecule may be able to interact with the rRNA , but it is the initiation region that determines the accessibility of the start codon and the efficiency of forming the preinitiation complex [46] . mRNA-rRNA complementarity has also been found to enhance translation in plants . For example , the ARC-1 element ( 18S rRNA positions 1115–1124 , GGGGGAGUAU ) was shown to enhance translation when present in the leader or intercistronic region of model mRNAs [22] . This study also showed that linking three or more copies of this enhancer element augmented translation to levels directed by natural enhancers in tobacco mosaic virus and potato virus Y mRNAs . A subsequent investigation by the same group showed that enhancer activity was inhibited in the presence of competitor oligonucleotide and that the same oligonucleotide , when modified at the 5′ end with an alkylating group , hybridized to the ARC-1 element [23] . Intriguingly , part of the homologous E . coli 16S rRNA region was found to be a potential mRNA hybridization site in our study . While it has been recognized for some time that the ribosome is , in fact , a broad-specificity ribozyme , there has not been much discussion of universally efficient RBSs in the literature . Recently , species-independent translational sequences have been reported [47] . These utilize a poly-A or UUUUA repeat to create a long , unstructured region prior to the start codon . The impressive efficiency of poly-A and ( to a lesser extent ) poly-U RBS constructs in vitro and in vivo is consistent with this report ( Figure S5 ) . An analysis of all eukaryotic start sequences has identified two distinct patterns , AAAAAA and GCCGCC , which supposedly work by distinct mechanisms [48] . S . cerevisiae , for example , prefers the former consensus , while human and other vertebrates generally use a sequence closer to the latter . Interestingly , the S . cerevisiae rRNA is rich in poly-U tracts , while vertebrate rRNAs are generally rich in poly-G tracts , further supporting the notion that transient rRNA-mRNA base-pairing may be a broad-specificity mechanism for translational regulation . Additionally , the base-pairing of Kozak sequences to the 18S rRNA has been proposed [39] . In this study , Sarge and Maxwell presented a competitive-displacement model for the initiation of translation involving the intermolecular base-pairing of 5S rRNA , 18S rRNA , and mRNA . They proposed that a particular segment of the 18S rRNA complementary to the Kozak sequence was able to lock the mRNA in place so that a 48S preinitiation complex could form . The 60S subunit would then join , and the 5S rRNA would displace the mRNA . Although the details of this model may not apply directly to the present study , there is indeed precedence in the literature for C-rich , Kozak-like sequences to show evidence of binding to the rRNA of the small subunit prior to initiation of translation [39] . More generally , the fact that ribosomes from distantly related organisms ( i . e . , E . coli and human ) can use both poly-A and Kozak-like patterns to initiate translation provides interesting material for further research on the universality of the ribosome . Because E . coli grows quickly and has large amounts of RNA compared to slower-growing bacteria , it is quite possible that competition for potential pairing sites on the ribosome from other nucleic acids or other molecules prevents translation of mRNAs containing C-rich RBSs . We make this assertion based on the fact that C-rich sequences are inhibited from facilitating translation in vitro when competitor oligonucleotides are added . Most E . coli genes are not C-rich , which highlights the fact that our results using E . coli ribosomes must be considered in the context in which they were selected . Our objective was to gain insight into the inherent capabilities of the ribosome , so we used a minimal in vitro translation system; by contrast , if the ultimate goal of a study is to simply increase in vivo expression , the selections should be performed in vivo . It is theoretically possible that C-rich mRNA sequences may have been selected in part because of their ability to outcompete other sequences for binding to ribosomes , not necessarily because they are the most efficient at promoting fast translation , which requires speed in forming the initiation complex and also in transitioning to elongation . However , the enriched libraries performed translation very well overall , suggesting that this should not be a major concern . The computational analysis was performed without knowledge-based bias of where base-pairing occurs in available ribosomal crystal structures . Many of the potential pairing sites are at least partially base-paired in the crystal structure , but a large number of these sites may be vulnerable to displacement at the translation temperature . The ribosome is a highly dynamic macromolecule and surface-proximal potential pairing sites could easily be involved in transient complementary interactions . Additionally , it is possible that the 23S and/or 5S rRNAs of the large ribosomal subunit may be involved in some of the interactions . The ribosomes in the PURExpress system are 70S complexes , although IF3 is able to separate them [49] . When an analysis identical to that shown in Figure 3 was performed with the 23S rRNA and 5S rRNA , we found 56 and 2 potential pairing sites , respectively . Based on what is known about the translation of leadered mRNAs , we would expect the 16S rRNA to play the major role; however , we cannot exclude the possibility of the large subunit rRNAs mediating mRNA-ribosome interactions , which , for example , could serve to increase the local mRNA concentration until a binding event resulting in translation initiation occurred . Finally , based on the traditional model of prokaryotic translation , we assume that the 18-base randomized region before the coding region functions primarily in translation initiation , although it is possible that this region could exert some effects on elongation , perhaps if the C-rich sequences could interact with the ribosome in or near the exit tunnel to facilitate mRNA movement through the 70S ribosome . Differences in mRNA recovery could theoretically result from effects of the randomized RBS region on elongation , but current dogma suggests that this is less likely . In the present study , we uncovered both expected SD sequences and unexpected C-rich non-SD sequences as efficient RBSs in a minimal , reconstituted E . coli system . All of these sequences appear to operate by base-pairing to the rRNA of the small subunit of the ribosome . This general design principle represents an inherent , broad-specificity mechanism for efficient translation in vitro that is further refined in vivo ( Figure 6 ) . Notably , the specific subset of RBSs that are utilized in vivo can be different for different hosts: E . coli does not appear to utilize C-rich RBSs in translating its native genes , likely due to the fact that SD sequences perform more robustly in its intracellular environment; bacteria such as Mycobacterium tuberculosis have more C-rich 5′ UTRs than E . coli , suggesting that both SD and C-rich RBSs play functional roles in these hosts; and human and other vertebrates widely use C-rich sequences ( including Kozak-like motifs ) , but not SD-like sequences , for translation . Our results suggest the intriguing possibility that RBSs in different organisms that may appear unrelated by sequence may actually share a common mechanism for translation initiation based on broad-specificity mRNA-rRNA base-pairing . Procedures for construction of the naïve RBS library , the single-clone constructs used for single-clone ribosome display , and the single-clone constructs used for the in vivo expression studies are provided in Text S1 . All oligonucleotides specific to these procedures are listed in Table S4 . Ribosome display selection particles were generated using the well-defined PURExpress in vitro protein synthesis kit ( New England Biolabs ) . Since the concentration of ribosomes in the standard PURExpress reaction is specified by the manufacturer ( 2 . 4 µM ) , we could accurately control the RNA∶ribosome ratio ( ∼10∶1 in the first round , ∼4∶1 in subsequent rounds ) by using RNA , and not DNA , as the template . Kit components ( Solution A and Solution B ) , RNA , RNasin ribonuclease inhibitor ( Promega , Madison , WI ) and water ( if necessary for dilution ) were mixed according to the manufacturer's instructions , except in cases where fewer ribosomes ( found in Solution B ) were required to achieve high RNA∶ribosome ratios . In the first round of selection , 18 µg mRNA ( corresponding to ∼3 . 7×1013 molecules ) was used in a total volume of 16 µL . The translation reaction was incubated at 37°C for 30 min in order to allow full translation of any mRNAs that contained an RBS . The translation was stopped using 400 µL cold WB buffer ( 50 mM Tris-acetate , pH 7 . 5 at 4°C , 150 mM NaCl , 50 mM magnesium acetate; [28] ) . Then , the stopped translation was subjected to ultrafiltration using a 100 kDa cut-off Microcon centrifugal filter unit ( Millipore , Billerica , MA ) . The ultrafiltered translation was diluted up to 100 µL with WBT ( WB plus 0 . 05% Tween-20 ) containing RNasin , mixed thoroughly , and used for binding in one well . Binding was performed using NUNC Maxisorp plates ( Thermo Fisher Scientific , Rochester , NY ) prepared as follows: plates were coated with 100 µL 66 nM NeutrAvidin ( Thermo Fisher Scientific ) for at least 16 h at 4°C , washed with TBS ( 50 mM Tris-HCl , pH 7 . 4 at 4°C , 150 mM NaCl ) , blocked with 25 mg/mL casein ( Sigma-Aldrich , St . Louis , MO ) or 10 mg/mL BlockAce ( AbD Serotec , Raleigh , NC ) in TBS at room temperature for at least 1 h with shaking , incubated with biotinylated maltose-binding protein of E . coli in blocking solution for at least 1 h at 4°C with shaking , and washed with TBS and WBT . Binding was performed for 1 h at 4°C with shaking . The plate was washed with WBT and then once with WB prior to reverse transcription . Reverse transcription was performed using AffinityScript reverse transcriptase ( Agilent Technologies , Santa Clara , CA ) and reverse primer tolA_stops_HindIII_rev ( 5′-GGC CAC CAG ATC CAA GCT T-3′ ) that anneals just downstream of off7 . An in situ reverse transcription protocol [50] was adapted as follows: 12 µL Solution 1 ( 11 . 375 µL water and 0 . 125 µL reverse primer tolA_stops_HindIII_rev ) was pipetted into the well , incubated at 70°C for 10 min , and removed from heat for 5 min . 8 µL Solution 2 ( 3 µL dNTPs [5 mM each] , 2 µL 10× AffinityScript buffer , 2 µL 0 . 1 M DTT , and 1 µL AffinityScript reverse transcriptase ) was added and the reaction was incubated at 45°C for 1 h , then heat-inactivated at 70°C for 15 min . Half of the 20 µL reaction was taken as template for a 100 µL PCR with primers T7_ext_fwd ( 5′-ATA CGA AAT TAA TAC GAC TCA CTA TAG GGA CAC CAC AAC GGT TTC CCT AAT TGT GAG CGG ATA ACA ATA GAA ATA ATT TTG TTT AAC TT-3′ ) and tolA_stops_HindIII_rev . T7_ext_fwd anneals just before the 18-base randomized region to maximize recovery; additionally , by only recovering those sequences which contain enough bases upstream of the RBS region to facilitate primer annealing , we can be assured that potential nuclease processing near or within the RBS is not significantly influencing our results . The PCR product ( 624 bp ) was gel-purified and digested with HindIII . The tolA spacer was made by amplifying pRDVstops-off7 with HindIII_tolA_stops_fwd ( 5′-TAC TGC AAC AAG CTT GGA TCT GGT GGC CAG AA-3′ ) and tolAk ( 5′-CCG CAC ACC AGT AAG GTG TGC GGT TTC AGT TGC CGC TTT CTT TCT-3′ ) [30] to form a 303 bp product . Both pieces were digested with HindIII , ligated , and gel-purified to generate the full-length construct ( 899 bp ) . This product was amplified with T7_no_BsaI ( 5′-ATA CGA AAT TAA TAC GAC TCA CTA TAG GGA CAC CAC AAC GG-3′ ) and tolAk to obtain enough product for transcription for the second round . Different selection schemes were performed based on this first round with 30 min translation . In one scheme , two additional rounds ( 5 min and 3 min , respectively ) were performed with no ultrafiltration ( “30-5-3” selection ) . In an alternate scheme , three additional rounds ( 30 min , 1 min , and 1 min ) were performed with ultrafiltration ( “30-30-1-1” selection ) followed by a final 1-min round without ultrafiltration ( “30-30-1-1-1” selection ) . The volume in round 1 ( 16 µL ) was chosen to be higher than in subsequent rounds because we expected few mRNAs in the original library to contain a functional RBS . After the initial round , the pool was highly enriched , so much smaller volumes could be used effectively . Pipetting errors were kept to a minimum by preparing translation reactions of at least 5 µL . After translation , the reactions were diluted , divided into four parts ( each containing at least 1 . 25 µL translation ) , and used for binding in duplicate positive wells and duplicate negative wells . Thin-walled PCR tubes were used for incubation , so all volumes quickly reached the translation temperature ( 37°C ) . The products of all rounds were quantified by qRT-PCR on the Applied Biosystems 7300 Real-Time PCR System using TaqMan Universal PCR Master Mix ( Applied Biosystems ) , off7_fwd ( 5′-TCC ATC GAC AAC GGT AAC GA-3′ ) , tolA_stops_HindIII_rev , and off7_probe ( 6-FAM-5′-TGG CTG AAA TCC TG-3′ ) . Products from all selection schemes were sequenced on a Roche/454 GS FLX sequencer at the University of Pennsylvania DNA Sequencing Facility . Sanger sequencing was also performed on the 30-30-1-1 selection . Sequences from the 30-5-3 selection were chosen for extensive sequence analysis . Highly C-rich clones from the 30-30-1-1 and 30-30-1-1-1 selections were also investigated . Prior to some rounds ( 5 min and 3 min rounds from 30-5-3 selection and final 1 min round from 30-30-1-1-1 selection ) , off7-tolA amplified with BsaI_FLAG_fwd2 ( 5′-ACT GAT TAG GTC TCA GAT GAC GAT GAC AAA GGA TC-3′ ) and tolAk was digested with BsaI and ligated onto the BsaI-digested library , made by PCR on the reverse transcription product using BsaI_FLAG_rev ( 5′-ACT GAT TAG GTC TCT CAT CTT TGT AGT CCG CCA T-3′ ) and T7_no_BsaI . Sequence-verified minipreps were amplified with T7_no_BsaI and tolAk for in vitro transcription . Generally , ∼1 µL translation was used per well to make sure that the signal stayed in the linear range . The RNA∶ribosome ratio was 4∶1 in all experiments . Translation was performed for 10 min , which is optimal for WT . If applicable , DNA oligonucleotide at a concentration of 2 . 5 mM was added to the translation to a final concentration of ∼400 µM , which provided ∼40-fold molar excess compared to mRNA ( ∼9 . 6 µM ) . Five different DNA oligonucleotides were used: 18b_N , 5′-NNN NNN NNN NNN NNN NNN-3′; 18b_ ( 30-30-1-1_high_C_clone_1 ) , 5′-GCC CCC CCC GCC CCC TCC-3′; 18b_ ( 30-5-3_high_C_clone_7 ) , 5′-CCG CCC CCC CGC CCC TCC-3′; 18b_WT , 5′-TAA GAA GGA GAT ATA TCC-3′; and 18b_C , 5′-CCC CCC CCC CCC CCC CCC-3′ . Oligonucleotides were added to the translation just prior to the mRNA . Selected sequences were cloned into pET-3a ( Novagen , Madison , WI ) and sequence-verified minipreps were transformed into E . coli BL21 ( DE3 ) pLysS ( Agilent , Santa Clara , CA ) for expression . Individual colonies were inoculated into LB containing 100 µg/mL ampicillin ( to maintain pET-3a ) and 50 µg/mL chloramphenicol ( to maintain pLysS ) and grown for ∼16 h overnight at 37°C . Ampicillin was omitted from the negative control ( background strain ) . The next morning , cultures were diluted 1∶50 in 1 mL LB without antibiotic and allowed to grow for 3 h at 37°C . Half of each culture was then induced with 1 mM isopropyl β-D-1-thiogalactopyranoside ( IPTG ) . Cultures were grown for another 4 h at 37°C and analyzed on a Guava flow cytometer ( Millipore ) . The average median fluorescence of three separate experiments was used to determine whether or not induction was appreciable ( i . e . , greater than two-fold over background fluorescence of the strain ) . The 5′ UTRs of WT and 30-5-3 high C 7 were quantified using qRT-PCR with 5′_UTR_qPCR_fwd ( 5′-CCA CAA CGG TTT CCC TAA TTG T-3′ ) , FLAG_qPCR_rev ( 5′-GTC ATC TTT GTA GTC CGC CAT-3′ ) , and 5′_UTR_probe ( 6-FAM-5′-AGC GGA TAA CAA TAG AAA T-3′ ) . Raw sequences were filtered to make sure the randomized region was of the expected length ( 18 bases ) and in the expected context ( TGTTTAACTT upstream and ATGGCGGACT downstream ) . Sequences with an in-frame ATG present in the randomized region were excluded from analysis . For the rRNA comparison , a virtual library of 4 , 863 random 18-base sequences was generated ( equal in size to the actual sequence pool analyzed ) . From each 18-base sequence , 19−k windows of length k were considered for k = 4–8 . These 4 , 863× ( 19−k ) windows were compared to E . coli 16S rRNA , and the number of reverse complements present in the virtual library for each window of length k on the 16S rRNA was recorded . Approximately 100 , 000 virtual libraries of this sort were generated to develop a probability distribution at each index of the 16S rRNA starting a k-base window . Bonferroni-corrected p-values are presented as P . rand in Table S1 . The significance threshold was set at 0 . 01 . For k = 7 , significant windows neighboring at least one other significant window were considered to be part of a group of significant windows . PyMOL [51] was used to visualize these groups on the crystal structure . There appeared to be no correlation between the position of these groups on the crystal structure and the position of the complementary motif within the randomized region . Permuted ( scrambled ) 5′ UTRs were also used to calculate p-values ( Bonferroni-corrected; P . perm in Table S1 ) . P . rand allows us to recognize sequences that deviate from randomness in terms of their base composition and order of bases , while P . perm allows us to recognize the importance of the order of bases only . For the naïve motif search , all possible k-base motifs , k = 4–8 , were generated . The virtual libraries ( with random or scrambled 5′ UTRs ) were again generated and the incidence of each k-base motif was assessed; to correct for multiple tests , FDR was applied , and the resulting q-values for the motif search are presented as Q . rand and Q . perm in Table S2 . To analyze dependencies between motifs , each significant k-base motif ( FDR<0 . 01 ) was assessed to determine if it was more likely to occur in a 5′ UTR context containing another particular motif . This dependency was quantified by a co-occurrence metric: [# 5′ UTRs having non-overlapping motifs 1 and 2]/[# 5′ UTRs having motif 2] . These values ( when non-zero ) are reported in Table S3 . mRNA secondary structure analysis was performed using the following procedure , which was adapted from previously published work [52] . Sequencing reads of selected library sequences were computationally trimmed to yield mRNA molecules consisting of a 26-base region immediately prior to the randomized region , the 18-base randomized region immediately prior to the start codon , and another 26-base region starting from the start codon . Each 70-base mRNA molecule was further processed to yield five overlapping 30-base windows using an offset of 10 bases . Finally , each 30-base window was assessed for secondary structure using the UNAFold suite ( program melt . pl ) , and the corresponding ΔG values were recorded . For comparison , a library of 350 , 000 simulated mRNA molecules having random 18-base regions ( probability of each base = 0 . 25 ) was assessed for secondary structure using the procedure described above .
In order to maintain an appropriate balance of proteins in the cell , the protein factories ( ribosomes ) translate different messages ( mRNAs ) into protein at different rates . Many human diseases , including cancer and certain hereditary diseases , are caused by making too much or too little protein . Additionally , infections caused by bacteria and viruses are enabled by the ability of these organisms to produce protein very quickly while situated in their host . For these reasons , it is important to understand the ways in which ribosomes may recognize mRNAs and initiate translation into protein . We developed an experimental system that allowed us to uncover the inherent mRNA–binding ability of the ribosomes in a common bacterium , Escherichia coli . We found evidence that , when removed from the native cellular environment , these ribosomes are able to make protein very efficiently using previously unidentified ribosome binding sites on the mRNA that closely resemble known ribosome binding sites in diverse organisms , including plants and humans . Our results suggest a general , ubiquitous mechanism of mRNA–ribosome association during translation initiation .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "biotechnology", "bioengineering", "gene", "expression", "synthetic", "biology", "biology", "molecular", "cell", "biology", "molecular", "biology", "engineering" ]
2012
Broad-Specificity mRNA–rRNA Complementarity in Efficient Protein Translation
Sleep is homeostatically regulated , such that sleep drive reflects the duration of prior wakefulness . However , despite the discovery of genes important for sleep , a coherent molecular model for sleep homeostasis has yet to emerge . To better understand the function and regulation of sleep , we employed a reverse-genetics approach in Drosophila . An insertion in the BTB domain protein CG32810/insomniac ( inc ) exhibited one of the strongest baseline sleep phenotypes thus far observed , a ∼10 h sleep reduction . Importantly , this is coupled to a reduced homeostatic response to sleep deprivation , consistent with a disrupted sleep homeostat . Knockdown of the INC-interacting protein , the E3 ubiquitin ligase Cul3 , results in reduced sleep duration , consolidation , and homeostasis , suggesting an important role for protein turnover in mediating INC effects . Interestingly , inc and Cul3 expression in post-mitotic neurons during development contributes to their adult sleep functions . Similar to flies with increased dopaminergic signaling , loss of inc and Cul3 result in hyper-arousability to a mechanical stimulus in adult flies . Furthermore , the inc sleep duration phenotype can be rescued by pharmacological inhibition of tyrosine hydroxylase , the rate-limiting enzyme for dopamine biosynthesis . Taken together , these results establish inc and Cul3 as important new players in setting the sleep homeostat and a dopaminergic arousal pathway in Drosophila . Sleep is a homeostatically regulated process , consuming roughly one-third of our lives , yet its function remains a mystery . To identify novel pathways governing sleep , we and others have employed a genetic approach in Drosophila . The fruit fly shares several core features of sleep with its mammalian counterparts , including behavioral quiescence , reduced responsiveness to sensory stimuli , and homeostatic responses to sleep deprivation [1] , [2] . To date , several forward-genetics screens have been performed , successfully identifying mutants that increase or decrease sleep duration to varying degrees , highlighting the roles of ( 1 ) membrane excitability via the Shaker potassium channel [3]–[6] , ( 2 ) neurotransmitters such as dopamine [7]–[10] , ( 3 ) growth factors such as epidermal growth factor [11] , and ( 4 ) signal transduction pathways among others [12]–[14] . Of these mutations , those affecting Shaker or dopamine yield the most robust phenotypes [4]–[6] , [9] , [15] . Yet how these key pathways regulate sleep homeostasis remains unclear . Here we report the result of a reverse-genetics approach aimed at identifying regulators of sleep and arousal in Drosophila . We focused on the gene with the most robust phenotype , insomniac ( inc ) , a target identifier for the E3 ubiquitin ligase Cullin-3 ( Cul3 ) [16] . We find that flies lacking inc or Cul3 exhibit strikingly reduced and poorly consolidated sleep . Developmental expression of inc and Cul3 in post-mitotic neurons contributes to these adult sleep phenotypes . In addition to their baseline sleep phenotypes , both inc and Cul3 also exhibit reduced homeostatic responses to sleep deprivation as well as hyper-arousability to mechanical stimuli . Baseline sleep in flies deficient for inc or Cul3 can be rescued by pharmacological inhibition of dopamine synthesis , but are behaviorally resistant to pharmacologically increased dopamine synthesis , consistent with the hypothesis that these genes operate in a dopamine arousal pathway . Taken together , our data indicate a central role for inc and Cul3 in sleep homeostasis and dopamine-mediated arousal . To identify novel sleep genes , we performed a reverse-genetics screen , focusing on genes previously reported to have sleep/wake-dependent expression [3] , [17] , circadian expression [3] , [18] , Clk-target genes [19] , kinases/phosphatases , GTPase-activating proteins , guanine nucleotide exchange factors , G-protein coupled receptors , ion channels , and synaptic components ( Flybase ) . Of the initial 2203 genes , we were able to analyze 1015 with potential loss-of-function alleles ( Figure 1A ) . To compensate for potential differences in genetic background , previously existing alleles were tested over a deficiency ( Df ) from the isogenic DrosDel collection [20] , and allele/Df combinations shifted at least 2 standard deviations from the population mean for sleep duration and/or average sleep bout length ( ABL ) in males in 3 separate behavior experiments were considered hits . In the case of X-linked genes , allele virgins were crossed to X-linked deficiency males and the F1 allele/Y males were tested . At the conclusion of the primary screen we identified 45 alleles with reproducible sleep duration or bout length phenotypes ( Figure 1A–1C ) . To determine the influence of genetic background on these phenotypes we backcrossed the 45 allele hits for 5 generations into the isogenic iso31 background developed by DrosDel [20] . Surprisingly , despite outcrossing the alleles to isogenic Df lines in the primary screen , only 6 of the hits retained their sleep phenotypes after backcrossing ( Figure 1A ) . For example , in the primary screen we identified the following insertion alleles as having a striking effect on sleep behavior: ( 1 ) mXrDG17503 exhibited increased sleep duration , ( 2 ) CG9135f03307 had increased ABL , and ( 3 ) RhoGDIEY02738 resulted in reduced sleep ( Figure S1A–S1C ) . However , after backcrossing into the iso31 background the sleep phenotypes are no longer observable ( Figure S1A–S1C ) . To distinguish between a potential suppressor in the iso31 background and a flanking sleep mutant in the original RhoGDIEY02738 background , we analyzed sleep in precise excisions of the EY02738 transposon . Importantly , we found that the RhoGDIEY02738 short-sleep phenotype persists after precise excision of the P-element , suggesting that a distinct mutation in this background is responsible for the phenotype . Taken together , these observations highlight the important modulatory effect genetic background has on sleep . Furthermore , these results make clear that simply outcrossing an allele to a deficiency line is insufficient to rule out genetic background as a primary cause of phenotype . Importantly , these results do not exclude a role for sleep regulation for the 39 primary screen hits that do not retain a sleep phenotype after backcrossing , as either the iso31 or the original background may have a modifier that enhances or suppresses the sleep phenotype . Future work will be required to confirm a sleep regulatory role for these alleles . Despite the influence of genetic background , we were able to identify one allele with a robust and reproducible sleep reduction even after backcrossing: f00285 , a piggyBac insertion in the 5′ untranslated region of insomniac ( inc , CG32810 ) ( Figure 1B–1C , Figure S2A ) , a gene selected for its BTB protein-protein interaction domain and recently linked to sleep regulation [21] . To complement this allele we created a second allele by knocking in a miniwhite gene just upstream of the inc stop codon ( Figure S2A , incmw ) . After backcrossing , expression of inc transcript in incf00285 flies was nearly undetectable ( Figure 2A ) ; furthermore , although we do not expect incmw to affect transcript levels given the insertion location , we observed that INC protein was undetectable in both incf00285 and incmw ( Figure 2B ) indicating that the insertions strongly disrupt inc function . We found that these backcrossed inc alleles sleep greater than 600 minutes less than their isogenic control flies ( Figure 2C–2D ) . To further show the sleep relevant phenotypes were due to a disruption of inc , we demonstrated that sleep duration and bout length phenotypes could be rescued with Y-linked genomic duplications that included the inc genomic region , but not with a Y-linked duplication from the same collection that did not contain inc ( Figure S2A–S2D ) . Furthermore , we found that a deletion that removes inc , as well as inc transheterozygotes , failed to complement the recessive inc phenotype ( Figure S3 ) . inc mutants displayed reduced sleep during both light and dark periods with increases in locomotor activity; nonetheless , their sleep levels dropped in anticipation of light-dark and dark-light transitions , consistent with intact circadian clock function ( Figure 2D ) . These effects on sleep duration are comparable to those observed for the Shmns allele [15] in the iso31 background under our conditions ( data not shown ) and represent one of the strongest sleep phenotypes thus far observed in Drosophila or any animal model . The sleep reduction is associated with specific changes in sleep architecture . First , inc flies displayed a decrease in sleep bout length ( Figure 3A ) that was accompanied by an increase in sleep bout number ( Figure 3B ) , suggesting that flies were repeatedly attempting to initiate sleep but were unable to maintain it . Flies , like humans , typically fall asleep rapidly after the lights turn-off . However , inc files exhibited an increased latency to sleep after lights-off ( Figure 3C ) . Despite the dramatic reduction in sleep levels , inc flies were not hyperactive , instead displaying a modest reduction in activity during wakefulness , suggesting a primary effect on sleep rather than activity ( Figure 3D ) . Although sleep duration and consolidation are important behavioral characteristics for determining the role a gene plays in sleep homeostasis , the gold standard is to analyze the homeostatic response to sleep loss , thus determining the role a gene plays in the sleep homeostat . Interestingly , whereas isogenic control flies had a significant increase in sleep after 12 hours of sleep deprivation by mechanical stimulation , we found that inc flies did not exhibit a detectable sleep rebound ( Figure 4 ) . Importantly , increasing the length of deprivation to 24 hours , which equalized the magnitude sleep loss in inc mutant flies to iso31 12 h sleep deprivation , still did not reveal a significant rebound in inc mutants ( Figure S4 ) . To test whether inc is required in neurons for proper sleep/wake regulation , we employed the Gal4/UAS system to knock down inc in all post-mitotic neurons with elav-Gal4 . Using two distinct UAS-inc-RNAi transgenes targeting different parts of the inc transcript ( Figure S2A ) in concert with UAS-dcr2 to enhance RNAi effects [22] we found that RNAi phenocopies the inc mutant sleep duration and consolidation phenotypes ( Figure 5A–5B ) . We were unable to identify more-refined Gal4 drivers that phenocopy the inc mutant sleep phenotype in combination with UAS-inc-RNAi alone . Next we took advantage of the UAS element present in the piggyBac element in incf00285 ( Figure S2A ) to rescue inc phenotypes . We screened Gal4s with expression patterns in known sleep-regulatory regions of the brain , including the mushroom bodies ( MB ) ( 247 , 30Y , c309 , G0451 , c305a ) , the pars intercerebralis ( PI; 50Y , c767 , dilp2 ) , the ellipsoid bodies ( EB; c547 , c305a ) , circadian cells ( pdf , tim ) , glia ( repo ) , and a number of functional neuronal groups , such as dopaminergic neurons ( Figure S5 ) . In addition , we generated and tested flies in which the putative inc promoter ( −2550…+340 bp relative to the transcription start site ) drives Gal4 expression . We find that both the cholinergic driver Cha-Gal4 and one of the inc-Gal4 lines provided rescue of the sleep duration phenotypes ( Figure 5C–5D , Figure S5 ) . Notably , the SH regulator sleepless ( sss ) also functions in Cha-Gal4 neurons to regulate sleep [5] . Furthermore , the rescuing inc-Gal4 line drove expression in known sleep regulatory loci including the MBs [23] , [24] , PI [11] , [25] , and fan-shaped body [26] as well as in the larval ventral nerve cord ( Figure 5E–5F ) . Consistent with this pattern , we observed that 3 Gal4 drivers that overlap in the MB gave partial rescue ( Figure 5C–5D , Figures S5 and S6; 30Y , c309 , G0451 ) ; however , the more highly restricted MB driver 247-Gal4 did not rescue suggesting that additional neural loci and/or broader MB expression may be required . We next sought to determine if Gal4s that rescue baseline sleep also rescue sleep homeostasis in inc mutants . We focused on Cha-Gal4 and 30Y-Gal4 given their robust rescue and functional or regional expression specificity ( Figure S6 ) . We found that expression of inc with either Cha-Gal4 or 30Y-Gal4 was sufficient to rescue sleep homeostasis defects ( Figure 5G ) . The highly conserved mammalian homolog of INC , KCTD5 , physically interacts with the E3 ubiquitin ligase CULLIN-3 ( CUL3 ) and ubiquitin , consistent with a role as a substrate recognition adaptor for targeting ubiquitin-dependent degradation [16] , [27] . In agreement with similar studies in Drosophila [21] , we verified these interactions by co-immunoprecipitation of epitope-tagged CUL3 and INC proteins in S2 cells ( Figure 6A ) . Furthermore , we found that INC could physically interact with itself , similar to findings reported for the mammalian homologue [16] ( Figure 6A ) . To test if inc regulates sleep through Cul3 , we analyzed pan-neuronal RNAi knockdown of Cul3 ( Cul3 is an essential gene , and therefore there were no viable loss-of-function alleles to analyze ) . We found that 2 distinct insertions of an RNAi construct against Cul3 , when driven in post-mitotic neurons , phenocopied the inc mutant phenotype , exhibiting reduced , poorly consolidated sleep , and increased latency to sleep . Furthermore , we found that the baseline sleep phenotypes could be partially rescued by co-expression of wild-type Cul3 using UAS-Cul3 ( Figure 6B–6D , Figure S7A–S7C ) . Importantly , we observed reduced Cul3 transcript levels with pan-neuronal RNAi knockdown ( Figure 6E , Figure S7D ) . To examine genetic interactions between Cul3 and inc , we employed the 30Y-Gal4 driver in combination with RNAi . We found that whereas 30Y-Gal4 driven RNAi knockdown of either inc or Cul3 alone was insufficient to affect sleep , knockdown of both simultaneously results in a significant synthetic decrease in sleep duration and consolidation ( Figure 6F–6G ) , consistent with the hypothesis that each partially impairs the same pathway . To determine if Cul3-RNAi displays a similar sleep homeostasis phenotype as inc , we examined the behavioral response of Cul3-RNAi flies to mechanically induced sleep deprivation . We did not detect any significant rebound in these Cul3-RNAi knockdown flies ( Figure 6H ) . These results further support a role for the INC/CUL3 complex in sleep homeostasis . With few exceptions , and largely limited to overexpression [11] , [14] , prior discoveries of sleep mutants have not typically been accompanied by a direct assay to establish if effects are due to their function in development or in adulthood . To determine if inc and Cul3 expression must be initiated developmentally or acutely in the adult to regulate sleep we employed the RU486-inducible pan-neuronal Gal4 driver elavGeneSwitch [28] . To drive adult expression only , adult flies were placed on RU486-laced behavior food starting 48 h prior to monitoring sleep behavior . To initiate developmental expression , parent flies were mated on RU486-laced food , after which eclosed F1 progeny were removed to drug-free food for 5 days prior to monitoring behavior ( Figure 7A ) . We find that incf00285;elavGeneSwitch flies fed RU486- or vehicle-laced food after eclosion are indistinguishable for sleep ( Figure 7B ) , whereas flies exposed to RU486 , but not vehicle alone , during development exhibit rescue of sleep behavior ( Figure 7C ) . Likewise , the short-sleep phenotypes observed with inc- and Cul3-RNAi knockdown were only present when driven during development , but not in adult flies ( Figure 7D–7G ) . We next tested the effectiveness of elavGeneSwitch-driven rescue of inc by asking ( 1 ) is INC protein detectable in adult heads after adult only rescue and ( 2 ) is cessation of RU486 exposure for 5 d sufficient to remove residual INC protein ? We found that incf00285;elavGeneSwitch flies fed RU486-laced food as adults exhibit wild-type INC protein levels in their heads ( Figure 7H ) . However , incf00285;elavGeneSwitch flies exposed to RU486-laced food prior to eclosion retained some INC even after 5 d on RU486-free food ( Figure 7H ) . These results suggest that INC has a long half-life , and raise the possibility that developmental transcription may contribute to adult protein levels . Taken together , these data demonstrate that inc and Cul3 expression during development in post-mitotic neurons may contribute to adult sleep . If inc and Cul3 have developmental functions , we would predict morphological phenotypes , especially in sleep-regulatory regions . Whereas we observed no gross defects in the PI , clock neurons , or dopaminergic neurons ( Figure S8 ) , we did observe a low penetrant stochastic branching defect in the MB ( Figure S9A–S9C ) . We labeled the MB with α-FASII immunofluorescence and 247dsRed to visualize the α/β , α′/β′ , and γ lobes . Whereas all MB lobes were observable in 15 of 15 wild-type brains , a subset of inc mutant flies lacked either an α- or β-lobe ( incf00285: 10/50; incmw: 12/38 brains; Figure S9B–S9C ) . These observed defects were non-symmetrical , such that only a single lobe was missing from a brain ( i . e . , the same lobe was present in the other hemisphere ) . Notably , Cul3 has also been reported to play a role in MB branching [29] . We found that Shmns and DATfmn flies had normal MB morphology , arguing that reduced sleep or increased dopaminergic signaling alone is not the underlying cause of the morphological phenotype ( Figure S9D–S9E ) . To determine if it was possible for these defects to be the underlying cause of the inc sleep phenotype we compared the morphological penetrance to the sleep behavior penetrance . Whereas 20–32% of inc mutants exhibited the MB morphological defect , >90% of inc mutant flies slept less than the shortest sleeping iso31 fly ( Figure S9F ) , and almost 75% of inc mutant flies exhibited less consolidated sleep than the most extreme iso31 example ( Figure S9G ) . Based on these findings the MB branching defect cannot be the sole cause of the sleep phenotypes . To further elucidate the underlying mechanism of the inc/Cul3 phenotype , we next asked whether inc flies had difficulty maintaining and initiating sleep because they were hyper-arousable . To test arousability , we used a mechanical apparatus to rotate the DAM monitors , and therefore the glass capillary tubes that housed the flies , off horizontal at ZT16 and examined the waking response of flies that were asleep prior to the stimulus ( Figure 8A , see methods ) . Controlling for flies that spontaneously awoke in the absence of a stimulus , we found that whereas roughly 25% of sleeping wild-type flies woke up in response to this rotational stimulus , >85% of incf00285 and incmw flies woke up , arguing that inc mutants indeed are hyper-arousable ( Figure 8B ) . In addition , we also examined arousability in flies expressing Cul3-RNAi pan-neuronally and observed similar results with >80% of flies responding to the stimulus ( Figure 8C ) . This makes inc mutants and Cul3-RNAi flies distinct from Shmns and CnA knockdown flies , which exhibit short-sleep phenotypes in the absence of hyper-arousability [15] , [30] . Dopaminergic signaling is a key regulator of arousal in both flies and mammals [7]–[10] , [31] , [32] . To determine if inc functions in a dopaminergic arousal pathway we first took a genetic approach and asked if the sleep duration phenotype in inc mutants was additive with the dopamine transporter mutant DATfmn . We found that incf00285;DATfmn and incmw;DATfmn flies did not sleep significantly less than single mutants ( Figure 9A ) , in support of our hypothesis that inc and DAT operate in the same arousal pathway . To test the specificity of inc function we took a pharmacological approach to modulate two known arousal pathways . Flies were fed ( 1 ) L-DOPA to increase dopaminergic arousal or ( 2 ) carbamazepine ( CBZ ) , an antagonist of the GABA receptor Rdl previously demonstrated to increase arousal by modulation of the PDF cells [33]–[35] . We found that , whereas wild-type iso31 and inc mutants exhibit robust reductions in sleep with CBZ ( Figure S10A ) , inc mutants were resistant to the sleep effects of L-DOPA ( Figure S10B ) arguing that the inc effects are specific to a dopaminergic , but not the Rdl , arousal pathway . The CBZ data further indicate that there is not a “floor” effect preventing further reductions in inc sleep . We next pharmacologically examined the dopamine-dependence of inc and Cul3 sleep phenotypes . Flies were fed one of two inhibitors of the rate limiting step in dopamine synthesis , tyrosine hydroxylase ( TH ) : ( 1 ) 3IY ( 3-iodo-tyrosine ) [36] , or ( 2 ) AMPT ( α-methyl-p-tyrosine methyl ester , Figure 9B ) [37] . Inhibition of dopamine synthesis with either drug suppressed the inc mutant and Cul3-RNAi short-sleep phenotypes , as well as that of DATfmn , which is thought to increase arousal through increased dopaminergic signaling [9] ( Figure 9C–9D ) . One possibility is that 3IY and AMPT act non-specifically to increase sleep; therefore , we next sought to determine the specificity of these drugs by restoring L-DOPA , the enzymatic product of TH ( Figure 9B ) . We found that wild-type iso31 flies fed L-DOPA alone exhibited a dose-dependent decrease in sleep; furthermore , 3IY and AMPT did not suppress the L-DOPA effect , as expected if they operate upstream of L-DOPA ( Figure 9E–9F ) . Importantly , we also observed decreased head dopamine levels after 3IY consumption , and increased levels after L-DOPA consumption , biochemically verifying drug mechanism/efficacy ( Figure S11 ) . Interestingly , inc phenotypes were not rescued by expression in dopaminergic neurons using a tyrosine hydroxylase-Gal4 ( TH-Gal4 ) or a Dopa decarboxylase-Gal4 ( Ddc-Gal4 ) ( Figure S5 ) ; furthermore , TH protein levels were not altered in inc mutant brains ( Figure S11C ) , suggesting that inc does not control sleep via its function in dopaminergic neurons . Given that Cul3 and inc affect sleep homeostasis and dopaminergic arousal pathways , we next sought to determine if these phenotypes are linked . We found that wild-type flies fed 3IY displayed intact sleep homeostasis ( Figure 10 ) . On the other hand , 3IY consumption restored sleep homeostasis in Cul3-RNAi , indicating the dopamine dependence of the homeostatic defect in these flies . We also found that 3IY could restore rebound sleep in incf00285 but not in incmw ( Figure 10 ) . While the molecular lesion differs between the two inc alleles , we are not aware of how this might explain the different drug responses between these two mutants . Although we did test a 5-fold higher concentration of 3IY than that sufficient to rescue Cul3-RNAi and incf00285 and found that it was still insufficient to rescue incmw ( Figure 10 ) , we cannot rule out a trivial explanation for the negative results in incmw such as insufficient drug uptake/dopamine suppression . Nonetheless , the positive results with Cul3 and incf00285 support a model in which the homeostatic defect in these flies depends on dopamine . We are not aware of another example of pharmacological rescue of sleep rebound , at least in Drosophila . Taken as a whole , these results suggest that inc functions in a group of cholinergic neurons , as defined by Cha-Gal4 and 30Y-Gal4 , and that in its absence excess dopaminergic signaling underlies the resulting sleep phenotype . Using genetic backcrossing to an isogenic ( iso31 ) strain , we made the striking observation that the vast majority of the mutants ( 39/45 , nearly 90% ) identified in our primary genetic screen did not have a significant phenotype after backcrossing , indicating a remarkably pervasive role for genetic background in mediating sleep phenotypes in a variety of mutant strains . There are two main possibilities for how genetic background influences sleep phenotypes: ( 1 ) the tested allele indeed affects sleep; however , there are suppressors of this phenotype present in the iso31 background but absent from the original background . ( 2 ) The sleep phenotype is not due to the transposon insertion but instead is caused by one or more flanking mutations present in the original mutant background but absent from the iso31 background . Isolated examples of ( 1 ) have been observed in the case of Sh and mutants of the Sh regulatory subunit Hyperkinetic [6] , [15] as well as Crc and Sema-5c effects on olfactory , startle , and sleep behavior [38] and of ( 2 ) in the discovery of DATfmn mutants in the background of a timeless mutant strain [9] . These are consistent with observations in C . elegans indicating the limitations of backcrossing for removing flanking mutations [39] and in Drosophila indicating the widespread presence of background mutations that can suppress mutant-induced behavioral phenotypes [38] . Our experience with RhoGDIEY02738 suggests that scenario 2 may be more common than previously thought ( Figure S1C ) . Nonetheless , the sheer number of examples observed here indicates that the presence of genetic variation at sleep regulatory loci among laboratory stocks is both prevalent and perhaps even sufficiently important to mask or induce significant sleep phenotypes . Moreover , in the case of Sh a single outcross was sufficient to unmask the short sleep phenotype . Indeed , we assumed that if we outcrossed mutant alleles to a deficiency strain this would effectively remove the influence of accumulated recessive mutations that flank the allele . However , our backcrossing data indicates that this strategy did not remove those concerns , suggesting that background variants may exert dominant effects ( see also [38] ) . Practically , our experience suggests that outcrossing to deletion stocks alone may not be sufficient to verify the function of a genetic locus in sleep . Overall , this observation has important implications for the role of genetic modifiers in sleep , the conduct and design of sleep genetic screens , and for the interpretation of sleep and other behavioral mutant phenotypes in general . While backcrossing can remove flanking genetic variants that may contribute to an observed phenotype , alone it is not sufficient to definitively establish genotype-phenotype causation . Despite the large modulatory effect of genetic background , we were able to observe persistent phenotypes with inc , which showed the most robust and reproducible sleep phenotypes , in particular demonstrating an important role in the homeostatic regulation of sleep . Several independent lines of evidence support the role of inc in sleep homeostasis . First , 2 inc alleles ( incf00285 and incmw ) were backcrossed for 5 generations into an isogenic background and retained their short sleep and suppressed sleep homeostasis phenotypes , each among the strongest observed , as compared to isogenic control lines . Second , we rescued incf00285 in 2 distinct ways: ( 1 ) with genomic duplications encompassing the gene but not those that do not include the gene , and ( 2 ) using the GAL4/UAS system , the latter rescuing both baseline and homeostatic phenotypes . Third , we demonstrated failure to complement with a deletion removing the inc genomic locus , or inc transheterozygotes . Fourth , we demonstrated that two independent RNAi lines that target two different regions of inc phenocopy the inc mutant phenotype . In addition , we provided evidence that the INC-interacting protein , the E3 ubiquitin ligase CUL3 , functions to regulate sleep levels suggesting that inc links protein turnover to sleep homeostasis . Two independent inserts of a Cul3-RNAi line that effectively suppress Cul3 mRNA levels resulted in reduced sleep , and induction of a wild-type Cul3 transgene could rescue these phenotypes . We verified the CUL3/INC interaction in S2 cells and observed synthetic genetic interactions between Cul3 and inc using RNAi , consistent with the model that they operate together to affect sleep . A core concept in understanding sleep behavior is its homeostatic regulation , i . e . , the observation that the drive to sleep reflects the duration of prior wakefulness . Sleep homeostasis typically is measured by enforcing wakefulness/depriving sleep for a defined period and assaying the increase in subsequent rebound sleep . Importantly , we demonstrated that both inc and Cul3 have robust effects on sleep homeostasis where reduced inc or Cul3 was accompanied by suppressed or absent sleep rebound under our conditions . These results suggest that inc and Cul3 , and by extension , protein degradation , are important for the accumulation of sleep need during wake and/or dissipation of sleep need after deprivation . For the large majority of sleep mutants that have been described , assessment of developmental and adult contributions has not formally been addressed , raising questions regarding their precise function in sleep . Here we provided evidence that inc induction or Cul3-RNAi knockdown during development , but not exclusively during adulthood , could rescue ( in the case of induction ) or phenocopy ( in the case of knockdown ) their respective mutant/RNAi phenotypes . The Cul3 results are consistent with an established role for Cul3 in dendritic and axonal arborization , in which dendritic and axonal arborization are reduced in Cul3 mutants [29] , [40] . Our data also revealed a stochastic branching defect in MB neurons in 26% of inc mutants , in which they lack a single α- or β-lobe . Based on the incomplete penetrance of this morphological defect , it cannot explain the sleep behavior phenotype; however , it may be reflective of other morphological phenotypes that are causative for behavior . Alternatively , the necessity for developmental expression of Cul3 and inc may be for the appropriate processing , maturation and/or localization of these proteins in the adult . The apparent long half-life/persistence of this protein after induction only during development is consistent with the possibility that developmentally expressed transcription is important for adult protein expression and function . Regardless , it will be of interest to examine the relative adult and developmental requirements of other sleep mutants . We found that the reduced sleep phenotype depends largely on a single neurotransmitter , dopamine , establishing a transmitter basis to inc/Cul3 function . Dopaminergic signaling is a key regulator of sleep/wake behavior . In humans , sleep deprivation has been associated with increased brain levels of dopamine [41] . Treatment of Parkinson's disease with L-DOPA can alleviate daytime sleepiness , or in the extreme result in insomnia [42]–[45] . In Drosophila , genetic loss or pharmacological inhibition of tyrosine hydroxylase increases sleep [46]–[48] . Furthermore , flies that lack a functional copy of DopR exhibit increased sleep and general arousal defects , including reduced arousing effects of caffeine [8] , [49] . Conversely , in DATfmn flies , or flies fed dopamine-enhancing methamphetamine , sleep levels are severely reduced [9] , [48] . Dopamine arousal effects are modulated by light [50] . Moreover , sleep deprivation induced reductions in learning can be suppressed by enhancing dopaminergic signaling [51] . Other than dopamine receptors and DAT , members of the dopaminergic arousal pathway remain largely unknown . We report here that inc and Cul3 function in the dopaminergic arousal pathway . First , inc mutants , Cul3-RNAi , and DATfmn all showed robust sleep duration and consolidation phenotypes . Second , all three groups were hyper-arousable to mechanical stimuli . Third , disruption of inc , Cul3 , and DAT all exhibited suppressed or absent homeostatic responses to sleep deprivation . Fourth , the short-sleep phenotypes of inc and DATfmn were non-additive in double mutants . Fifth , while wild-type flies exhibited reduced sleep when fed the dopamine precursor L-DOPA , inc mutants were resistant to these effects , but not the arousing effects of the Rdl antagonist CBZ . Finally , the sleep duration phenotypes in flies with disrupted inc , Cul3 , and DAT could be suppressed by pharmacologically inhibiting dopamine synthesis with 3IY or AMPT , linking short sleep to excess dopamine function . Importantly , we demonstrated that inhibition of dopamine synthesis via tyrosine hydroxylase inhibition does not affect L-DOPA-induced sleep reductions . We also observed that 3IY could restore sleep homeostasis to Cul3-RNAi . Similar 3IY effects on homeostasis were only observed in one of the two inc alleles . Nonetheless , these studies do further link dopamine signaling to sleep homeostasis . To our knowledge inc and Cul3 are the first genes that are not known dopamine receptors reported to function in the dopaminergic arousal pathway , further reinforcing the pivotal role of dopamine in sleep homeostasis . Our data suggests Cul3/inc function to regulate dopaminergic signaling downstream of dopamine . inc phenotypes did not map to dopaminergic neurons nor were we able to identify consistent changes in global dopamine levels among Cul3-RNAi and inc mutants ( data not shown ) . Thus , Cul3/inc may be involved in active turnover of dopamine receptors or their effectors in neurons defined by Cha-GAL4 and 30Y-GAL4 . We examined double mutants of inc and a major dopamine receptor involved in arousal in Drosophila , DopR , and failed to observe suppression of inc baseline phenotypes; moreover , we found that DopR mutant flies were responsive to 3IY consumption ( i . e . exhibit increased sleep; data not shown ) , suggesting that additional dopamine receptors function in Cul3/inc-based dopamine arousal . Drosophila has 2 other dopamine receptors and we have observed partial suppression of inc with DopR and DopR2 RNAi ( data not shown ) , suggesting that multiple dopamine receptors may contribute to these effects . Alternatively , Cul3/inc may be important for protein turnover of other homeostatically regulated components . For example , extensive and dose-dependent changes in synaptic protein expression throughout the brain with sleep deprivation and recovery [52]–[54] may depend on Cul3/inc-dependent turnover of these proteins during sleep . Interestingly , Cul3 has also been linked to sleep behavior via a candidate gene for Restless Leg Syndrome ( RLS ) and BTB gene , BTBD9 [55] . Unlike our studies , disruption of the Drosophila BTBD9 is not associated with reduced sleep , a reduced level of waking activity , nor elevated dopaminergic signaling . In addition , the phenotypes map in part to dopaminergic neurons in the case of BTBD9 rather than cholinergic neurons for inc . Thus , Cul3/inc likely represents a distinct pathway regulating sleep . Nonetheless , these studies further highlight the importance of Cul3/BTB adaptor pathways in sleep regulation in both Drosophila and humans . Future work will be required to identify the dopamine and sleep-relevant ubiquitination target ( s ) of inc and Cul3 . Flies were raised on cornmeal-yeast-agar food at 25°C , 12 h∶12 h Light∶Dark . Alleles and deficiencies for the reverse-genetics screen were acquired from the Drosophila Stock Centers based in Bloomington , Kyoto , Harvard , and Szeged . The deficiencies were from the isogenic collection created by DrosDel . Stocks of particular interest: inc-spanning duplications ( Bloomington #6021 [Dp ( 1;Y ) Sz280] , 33872 [Dp ( 1;Y ) BSC308] , 33871 [Dp ( 1;Y ) BSC307] ) , non-inc-spanning ( #33875 [Dp ( 1;Y ) BSC311] ) , inc-spanning deficiency ( Bloomington #934 [Df ( 1 ) S39] ) , inc-RNAi ( Vienna Drosophila RNAi Center #18226 , #108816 ) , Cul3-RNAi ( National Institute of Genetics – Kyoto #11861R-1 , #11861R-2 ) , UAS-Cul3 ( Bloomington #9936 ) , Cdk4-spanning deficiency ( Bloomington #9213 [Df ( 2R ) ED3181] ) , mXr-spanning deficiency ( Bloomington #9276 [Df ( 2R ) ED1742] ) , CG9135-spanning deficiency ( Bloomington #9186 [Df ( 2L ) ED353] ) . The following Gal4s were used: 50Y , c929 , 5HT7 , 5HT1a , ple , Ddc , DopR , DopR2 , Hdc , Tdc , vGlut , repo , Cha , elav ( Bloomington #30820 , 25373 , 23066 , 27807 , 27820 , 8848 , 7010 , 24743 , 19491 , 25260 , 9313 , 26160 , 7415 , 6793 , 8765 ) , 247 , 30Y , c309 , c767 , c547 , c305a [24] , pdf [34] , tim [56] , Trh [57] , G0451 [58] , dilp2 [59] , Gad [60] , elavGeneSwitch [28] . inc-Gal4 was created using 3 Kb upstream of the inc ATG start site ( −2550–+340 bp relative to the transcription start site ) from an inc genomic BAC ( CHORI: CH223-4018 ) . The promoter region was inserted into pPTG4 , and the inc-Gal4 vector was injected into embryos by BestGene Inc . incmw was created by cloning 4197 bp upstream of the inc stop codon ( left arm ) and 4052 bp downstream and including the stop codon ( right arm ) into pw25 ( DGRC 1166 ) , flanking a miniwhite gene . Constructs were injected into embryos by BestGene Inc . To knock in the miniwhite , the fragment with inc flanking regions was mobilized by hsFLP , digested in vivo with Sce-I ( Bloomington #6934 ) , and candidate knock-in flies were screened behaviorally and molecularly by PCR . For adult-specific expression with elavGeneSwitch , flies within 3 d after eclosion were placed on behavior food laced with 500 µM RU486 ( Sigma ) or vehicle alone ( 4% ethanol final concentration ) for 48 h prior to behavior monitoring , and then monitored for 3 d in behavior . For developmental expression , parental flies were crossed on normal cornmeal-based food laced with 50 µM RU486 or vehicle alone ( 0 . 4% ethanol final concentration ) . Within 3 d after eclosion , F1 progeny were moved to drug-free food for 5 d prior to behavior monitoring , and then monitored for 3 d in behavior . To identify new genetic regulators of sleep we screened genes with sleep/wake-regulated expression [3] , [17] , genes with circadian expression patterns [3] , [17]–[19] , enriched in the MB [61] , and genes involved in neuronal and intracellular signaling ( flybase . com ) . We screened 1297 alleles covering 1015 genes . In each case a previously existing allele was tested over a deficiency ( Df ) from the isogenic DrosDel collection [20] , and allele/Df combinations shifted ≥2SD from the population mean for sleep duration and/or average sleep bout length in males were considered hits . In the case of X-linked genes , allele virgins were crossed to X-linked deficiency males and the F1 allele/Y males were tested . Whenever possible we tested proven loss-of-function alleles , followed by mutations that affect in descending order of preference: exonic regions , 5′untranslated region , 3′untranslated region , intronic regions , promoter regions . Flies 4–8d post-eclosion were frozen on dry ice , heads were removed by dry ice cold vortexing and isolated on frozen sieves . RNA was isolated from 20 heads/sample with Trizol ( Invitrogen ) . qPCR was performed using a QuantiTect SYBR Green PCR Kit ( Qiagen ) . Sleep behavior was analyzed using the Drosophila Activity Monitor system ( Trikinetics ) , and processed with a custom written Excel macro [62] . Flies 2–5d post-eclosion were individually loaded into 5×65 mm glass capillary tubes with a 5% sucrose 2% agar food source and analyzed for 5 d 25°C 12 h∶12 h Light∶Dark . Sleep was defined as ≥5 min inactivity ( zero infrared beam crossings ) . To determine lifespan , flies were maintained in DAM monitors as described above and transferred to fresh behavior tubes every 7 d . Sleep deprivation was performed as described previously [24] . Briefly , activity monitors were placed in an apparatus that rotates and jostles the flies at varying intervals . Flies age-matched within 24 h were loaded into behavior 2 d after eclosion and allowed at least 36 h to acclimate followed by 24 h without sleep deprivation to determine 24 h baseline sleep . For behavioral analyses flies were deprived ZT11–ZT23 for 12 h deprivation and ZT0-ZT0 for 24 h deprivation . Non-deprived controls were handled similarly to deprived flies , in a separate incubator from the sleep deprivation apparatus . We confirmed behaviorally that the flies lost ≥90% of their sleep with this protocol . To determine Δsleep , baseline sleep was subtracted from sleep obtained during the recovery period for individual flies from both sleep-deprived and non-deprived populations then non-deprived Δsleep was subtracted from sleep-deprived Δsleep . Flies were loaded into the same apparatus as for sleep deprivation ( see above Methods section ) , and given 1 day to acclimate . On the second night flies were stimulated by mechanically rotating the DAM monitors 36° off horizontal and back 10 times for inc experiments and 18° 2 times for Cul3 experiments at ZT16 , and the number of sleeping flies to wake up within 5 min of the stimulus was determined . We took into account the number of flies that would wake spontaneously by determining the number of flies to wake up at ZT15:50 and normalizing the percent awoken with the following formula: Flies were raised on cornmeal-yeast-agar food and presented with drug-labeled food throughout the behavior experiment . 3IY , AMPT , and L-DOPA were dissolved in 5% sucrose 2% agar behavior food as the sole food-source during the behavior experiment . For 3IY- and AMPT-only experiments , flies were presented with drug-laced food for 12–16 h before monitoring behavior for 2 d . For L-DOPA experiments , flies were presented with drug-laced food for 12–16 h before monitoring behavior for 1 d ( after 48 h on L-DOPA the flies become unhealthy ) . The following antibodies were used in this study: ms-α-PDF ( DSHB; 1∶100 ) , rab-α-tyrosine hydroxylase [63] ( 1∶1000 ) , ms-α-FASII ( DSHB: 1∶50 ) , gt-α-ms-Alexa488 , gt-α-rab-Alexa488 ( Invitrogen; 1∶500 ) . Flies were dissected in 1% TritonX-100 , 3 . 7% formaldehyde in PBS , fixed for 1 h post-dissection in 3 . 7% formaldehyde in PBS , and permeablized in 0 . 3% TritonX-100 in PBS overnight . Brains were incubated with all antibodies in 0 . 3% TritonX-100 , 7% goat normal serum in PBS overnight . Wash steps were with 0 . 3% TritonX-100 in PBS . Expression constructs were made using inc cDNA ( DGRC #GM03763 ) and Cul3 cDNA ( DGRC #LD10516 ) . V5-tagged constructs were made by cloning the coding region into pAc5 . 1-V5/His ( Invitrogen ) . HA-tagged constructs were made using a modified version of pAc5 . 1-V5/His , in which the coding region for V5/His was replaced with HA . Transfections were done on Drosophila S2 cells using Effectene reagent ( Qiagen ) . 24 h after transfection , cells were lysed with T150 lysis buffer ( 25 mM Tris-Cl pH 7 . 5 , 150 mM NaCl , 10% glycerol , 1 mM EDTA , 1 mM DTT , 0 . 5% NP-40 , 1 mM PMSF ) , supernatant was mixed with α-V5 agarose beads ( Sigma ) for 1 . 5 h at 4°C , after washing beads were boiled with SDS loading buffer , and eluted sample was run on 10% acrylamide gels . Hybond membranes ( GE Lifesciences ) were blotted with α-HA ( 1∶2500 , Roche ) , and developed with ECL Plus ( GE Lifesciences ) . For INC head western blots , 30 flies/sample were flash frozen on dry ice at ZT6 , and heads were homogenized in 30 µL lysis buffer ( 20 mM HEPES [pH 7 . 5] , 100 mM KCl , 10 mM EDTA , 50 mM NaCl , 0 . 1% Triton X-100 , 10% glycerol ) . Samples were run on 15% acrylamide gels . Hybond membranes ( GE Lifesciences ) were blotted with α-INC ( 1∶2000 [21] ) , and developed with ECL Prime ( GE Lifesciences ) . 20 age-matched male flies were presented with drug-free behavior food , or food laced with 2 mg/mL 3IY , 2 mg/mL L-DOPA , or 5 mg/mL L-DOPA for 2 d under 12 h light∶12 h dark conditions . They were then frozen on dry ice at ZT6 , heads were removed by dry ice cold vortexing and isolated on frozen sieves . Dopamine levels were determined by HPLC by Dr . Raymond F Johnson at the Vanderbilt University Neurochemistry Core Lab . To compare quantifiable groups with normal distributions ( as determined by the Shapiro-Wilk Test ) we used the two-tailed Student's t test . To compare sleep bout lengths , which are not normally distributed , we used the Mann-Whitney U Test . p<0 . 05 was considered statistically significant .
Sleep is an essential behavior that encompasses roughly a third of our lives; however , the underlying function remains a mystery . The fruit fly has emerged as an important model system for understanding sleep behavior , exhibiting several behavioral and genetic similarities with mammalian sleep , including consolidated immobility , an elevation of arousal threshold to a range of stimuli , homeostatic drive , and manipulation by proven stimulants and sedatives . We tested disruptions of candidate sleep genes and identified a gene called insomniac that exhibits one of the strongest and most robust sleep phenotypes to date , including a suppressed homeostatic response to sleep deprivation . We find similar phenotypes for a gene previously shown to interact with inc and a known regulator of protein degradation , Cul3 , linking sleep homeostasis to protein turnover . Importantly , we find that insomniac functions in a known arousal system in the brain , as defined by the neurotransmitter dopamine . This work provides an important insight into the genetic basis of sleep homeostasis with the discovery of a new molecular component of a dopaminergic arousal pathway . Given the conservation of fly and mammalian systems , these studies may lead to new insights into the molecules that mediate sleep homeostasis and arousal in humans .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "animal", "models", "drosophila", "melanogaster", "animal", "genetics", "model", "organisms", "behavioral", "neuroscience", "genetic", "screens", "genetics", "neurotransmitters", "biology", "neuroscience", "genetics", "and", "genomics" ]
2012
Cul3 and the BTB Adaptor Insomniac Are Key Regulators of Sleep Homeostasis and a Dopamine Arousal Pathway in Drosophila
Elucidating how appropriate neurite patterns are generated in neurons of the olfactory system is crucial for comprehending the construction of the olfactory map . In the Drosophila olfactory system , projection neurons ( PNs ) , primarily derived from four neural stem cells ( called neuroblasts ) , populate their cell bodies surrounding to and distribute their dendrites in distinct but overlapping patterns within the primary olfactory center of the brain , the antennal lobe ( AL ) . However , it remains unclear whether the same molecular mechanisms are employed to generate the appropriate dendritic patterns in discrete AL glomeruli among PNs produced from different neuroblasts . Here , by examining a previously explored transmembrane protein Semaphorin-1a ( Sema-1a ) which was proposed to globally control initial PN dendritic targeting along the dorsolateral-to-ventromedial axis of the AL , we discover a new role for Sema-1a in preventing dendrites of both uni-glomerular and poly-glomerular PNs from aberrant invasion into select AL regions and , intriguingly , this Sema-1a-deficient dendritic mis-targeting phenotype seems to associate with the origins of PNs from which they are derived . Further , ectopic expression of Sema-1a resulted in PN dendritic mis-projection from a select AL region into adjacent glomeruli , strengthening the idea that Sema-1a plays an essential role in preventing abnormal dendritic accumulation in select AL regions . Taken together , these results demonstrate that Sema-1a repulsion keeps dendrites of different types of PNs away from each other , enabling the same types of PN dendrites to be sorted into destined AL glomeruli and permitting for functional assembly of olfactory circuitry . In the olfactory system , odorant inputs are detected by olfactory sensory neurons ( OSNs ) in the periphery and converged into individual glomeruli of the primary olfactory center , termed the antennal lobe ( AL ) in Drosophila and the olfactory bulb in mice , where projection neurons ( PNs in Drosophila and mitral/tufted cells in mice ) relay these inputs to other brain regions for decoding [1] . In Drosophila , most PNs are generated from four neural stem cells ( called neuroblasts ) and therefore can be assigned into four neural lineages: anterodorsal PNs ( adPNs ) in the ALad1 lineage , ventral PNs ( vPNs ) in the ALv1 lineage , lateroventral PNs ( lvPNs ) in the ALlv1 lineage , and lateral PNs ( lPNs ) from a lateral group of mixed PNs and local interneurons ( LNs ) in the ALl1 lineage [2 , 3] ( also see S1 Fig ) . Among these PNs , dendrites of most types of adPNs and lPNs innervate individual glomeruli ( as uni-glomerular PNs ) within the AL [4–9] , whereas many types of vPNs and lvPNs establish poly-glomerular dendritic arborization patterns in the AL [10 , 11] . Intriguingly , various types of adPNs , lPNs , vPNs and lvPNs distribute their dendrites in distinct but overlapping patterns within the AL [11] . Elucidating the molecular mechanisms underlying how types of PNs within different neural lineages generate the complex patterns of dendritic arborizations in discrete AL glomeruli is crucial for comprehending the formation of the functional olfactory circuitry . Since it is unclear whether the same molecular mechanisms are utilized to generate the complex dendritic patterns of adPNs , lPNs , vPNs and lvPNs , it is important to examine the roles of the same organizing cues in the formation of appropriate dendritic patterns for different types of PNs . For example , it has been previously reported that initial PN dendritic targeting in the developing AL is mediated through opposing gradients of repulsive semaphorin cues , Sema-2a/-2b , and a receptor for these cues , the transmembrane protein semaphorin-1a ( Sema-1a ) . The ventromedial ( VM ) expression of secreted Sema-2a/-2b from degenerating larval OSN axons is proposed to influence PN dendritic elaboration that is dependent upon dorsolateral ( DL ) expression of membrane-tethered Sema-1a in PNs [12 , 13] . Dendrites of DL1 adPNs and DA1 lPNs underwent a DL-to-VM shift when the whole animal was deficient for Sema-2a/-2b , or when Sema-1a was selectively removed from PNs , suggesting a crucial role for a repulsive Sema-2a/-2b gradient that is read by the receptor Sema-1a in setting up appropriate dorsal dendritic patterns for adPNs and lPNs [12 , 13] . In contrast , RNAi knock-down of Sema-1a caused the dendrites of DA1 vPNs to no longer be constrained within the DA1 glomerulus , with a substantial fraction of these dendrites invading the DA3 glomerulus [14] , implicating Sema-1a as a regulator of vPN dendritic morphogenesis . However , it is rather puzzling why the shifted DA1 vPN dendrites in the absence of Sema-1a , which are perpendicular to those of Sema-1a-deficient DL1 adPNs , do not exhibit a DL-to-VM shift , a prediction of the current model [12 , 13] . Therefore , it is possible that the Sema-1a signal is transmitted differently in adPNs and lPNs compared to vPNs , or even that an alternative model accounts for these Sema-1a loss-of-function ( LOF ) dendritic phenotypes . Here , using genetic LOF and rescue studies we identify a previously unknown role for Sema-1a in preventing aberrant dendritic invasion of both uni-glomerular and poly-glomerular PNs into select AL regions , including the DA3 glomerulus and the region close around the VC1 glomerulus; this role is distinct from previously explored functions of Sema-1a in global control of initial PN dendritic targeting along the DL-to-VM axis of the AL [12] . Intriguingly , the prevention of dendritic mis-targeting to the DA3 glomerulus mediated by Sema-1a seems to be PN-origin dependent , i . e . , the occurrence of the Sema-1a-deficient dendritic mis-targeting phenotype only in the types of PNs derived from adPN and vPN neuroblasts but not from the lPN neuroblast . Further , ectopic expression of Sema-1a caused DA3 adPNs that normally send their dendrites to the DA3 glomerulus to mis-project their dendrites into adjacent glomeruli . Taken together , our results suggest that repulsive Sema-1a signals in adPNs , lPNs and vPNs keep different types of PN dendrites away from each other , ensuring that they instead navigate to their destined glomeruli to establish appropriate dendritic patterns for assembling the functional olfactory circuitry to decode odorant information from the external world . We sought to label adPNs ( or lPNs ) and vPNs in distinct colors that permits the simultaneous visualization of how the dendrites of different PN populations distribute within the AL during development . Applying the twin-spot MARCM ( mosaic analysis with repressible cell markers ) system [15] , we induced independent fluorescent labeling of adPN ( or lPN ) and vPN neuroblasts of newly hatched larvae ( NHL ) to visualize larval-born-adPNs ( or -lPNs ) and -vPNs in two different colors ( labeled by GAL4-GH146 and GAL4-MZ699 ) [6 , 10] . We found that dendrites of adPNs ( or lPNs ) and vPNs were initially segregated at the early pupal stage , became apparently mixed at 48 hours APF and turned into fully intermingled in the adult AL ( S2 Fig ) . The observation of dendritic mixing among adPNs ( or lPNs ) and vPNs in the developing AL raises an interesting question as to whether or not vPNs employ similar or different molecular mechanisms from those used by adPNs and lPNs to generate appropriate dendritic patterns during their morphogenesis . Previous work demonstrates that dendrites of DL1 adPNs and DA1 lPNs , as opposed to those of DA1 vPNs , have qualitatively distinct phenotypes in LOF studies of Sema-1a ( see S3A Fig for the illustrative drawing of Sema-1a-deficient dendritic phenotypes observed in DL1 adPNs and DA1 vPNs ) [12 , 14] . To verify that mis-targeting of dendrites to the DA3 glomerulus we observed in the Sema-1a RNAi knock-down DA1 vPN ( also see S3B–S3G Fig ) actually resulted from the absence of Sema-1a rather than from an off-target effect of Sema-1a RNAi [16] , we conducted MARCM experiments on a severe Sema-1a LOF mutation ( Sema-1aP1 ) using GAL4-GH146 , which labels four types of vPNs: DA1 , diffuse , VA1lm and VL1 ( Fig 1 ) [4 , 5 , 17] . In contrast to the wild-type DA1 vPN dendrites , which predominantly innervated the DA1 glomerulus ( Fig 1A; Table 1 ) , the Sema-1aP1 mutant DA1 vPN dendrites robustly mis-target into the DA3 glomerulus ( Fig 1B; 95% , n = 21; Table 1 ) . Notably , this DA3-glomerular dendritic mis-targeting defect was completely rescued by restoring the expression of Sema-1a in DA1 vPNs such that dendritic innervation was almost exclusively within the DA1 glomerulus ( Fig 1C; Table 1 ) . These results show that DA1 vPN dendrites aberrantly invade into the DA3 glomerulus when the expression of Sema-1a is disrupted . It is unclear whether this dendritic mis-targeting phenotype is unique to DA1 vPNs or occurs in other types of vPNs ( e . g . , diffuse- , VA1lm- and VL1-vPNs ) as well in the absence of Sema-1a . Interestingly , diffuse vPNs also exhibited a similar dendritic mis-targeting to the DA3 glomerulus in the Sema-1aP1 mutant: they accumulated in the DA3 glomerulus and aberrantly projected to the subesophageal zone ( SEZ ) , in contrast to wild type , where the dendrites of diffuse vPNs were loosely distributed to nearly all of the AL glomeruli ( Fig 1D–1E and their insets; 100%; Table 1 ) . Both the DA3-glomerular dendritic accumulation and SEZ mis-projection phenotypes disappeared when Sema-1a was over-expressed in Sema-1aP1 diffuse vPNs ( Fig 1F and its inset; Table 1 ) . On the other hand , we did not observe the DA3-glomerular dendritic mis-targeting phenotype in the other two types of GAL4-GH146-positive vPNs , VA1lm- and VL1-vPNs , when the expression of Sema-1a was altered ( S4 Fig; S1 Table ) . Taken together , the DA3-glomerular dendritic mis-targeting defect observed in the Sema-1a-deficient DA1- and diffuse-vPNs demonstrates that Sema-1a plays a crucial role in establishing appropriate dendritic patterns of both uni-glomerular and poly-glomerular vPNs , supporting a model that Sema-1a counteracts putative attractive force of the DA3 glomerulus , and this specific dendritic mis-targeting defect seems deviated from the prediction of the current model in which PN dendrites shift along the DL-to-VM axis of the AL in the absence of Sema-1a [12] . Since dendrites of wild-type DA1- and diffuse-vPNs are distributed close to the DA3 glomerulus , and since dendrites of Sema-1a-deficient DA1- and diffuse-vPNs mis-target into the DA3 glomerulus ( Fig 1 and S3 Fig ) , we wondered whether Sema-1a signaling serves to prevent aberrant dendritic invasion into the DA3 glomerulus by surrounding PNs . To test this hypothesis , we examined Sema-1a LOF effects on the adPNs and lPNs which normally project their dendrites to surround the DA3 glomerulus ( Fig 2 ) : these adPNs and lPNs include DL3- and DA1-lPNs and VA1d- , DA4l- , DA4m- and D-adPNs , which project their dendrites clockwise to surround the DA3 glomerulus; they also include DL4- , DC3- and DC1-adPNs , which send their dendrites posteriorly covering the DA3 glomerulus ( Fig 2A ) . Notably , all the PNs we examined except DA1- and DL3-lPNs mis-targeted their dendrites into the DA3 glomerulus in the absence of Sema-1a ( Fig 2B–2S; S5 and S6 Figs; Table 1 ) , similar to the dendritic mis-targeting phenotype we observed in the Sema-1aP1 DA1- and diffuse-vPNs ( Fig 1 ) . Among the adPNs we examined , DC3 adPNs displayed the most severe defect , with full dendritic invasion into the DA3 glomerulus in all Sema-1a-deficient animals ( Fig 2E , 2F and 2S; Table 1 ) . The rest of the adPN types exhibited differing degrees of penetrance and expressivity of the DA3-glomerular dendritic mis-targeting phenotype in the Sema-1aP1 mutant and Sema-1a RNAi knock-down samples ( Fig 2B , 2C , 2H , 2I and 2K–2S; S5 Fig; Table 1 ) . The DA3-glomerular dendritic mis-targeting phenotype in the Sema-1aP1 D- , DC3- and VA1d-adPNs was no longer observed when wild-type Sema-1a was over-expressed in these same PNs ( Fig 2D , 2G and 2J; Table 1 ) . Notably , in the rescue experiments the dendrites of D- and VA1d-adPNs remained situated on the edge and outside of the D glomerulus ( insets of Fig 2D; 100% , n = 13; Table 1 ) and at the ventral portion of the VA1d glomerulus ( Fig 2J; 100% , n = 14; Table 1 ) , implicating that dendrites of the rescued and remaining wild-type adPNs may repel with each other . Taken together , the dendritic mis-targeting defect we observed in Sema-1a-deficient adPNs and vPNs suggests that the DA3 glomerulus serves as a select AL region for extending dendrites in the absence of Sema-1a . Intriguingly , in this DA3-glomerular dendritic mis-targeting phenotype , the PNs with dendrites that surround the DA3 glomerulus , including seven types of adPNs ( D , DA4l , DA4m , DC1 , DC3 , DL4 and VA1d ) and two types of vPNs ( DA1 and diffuse ) , but not DA1- and DL3-lPNs , tend to aberrantly extend their dendrites into the DA3 glomerulus when Sema-1a is absent . The disappearance of dendrites from the DA3 glomerulus in diffuse vPNs observed in the Sema-1aP1 mutant with Sema-1a over-expression ( Fig 1F and its inset ) prompted us to ask how PN dendrites that normally project into the DA3 glomerulus ( e . g . , DA3 adPNs ) would behave when Sema-1a expression is altered ( Fig 3 ) . Since dendrites of wild-type DA3 adPNs already distribute themselves into the DA3 glomerulus that attracts Sema-1a-deficient dendrites ( Fig 2 ) , we predicted that DA3 adPN dendritic projections to the DA3 glomerulus should remain unaffected when Sema-1a is mutated . Indeed , we found that DA3 adPNs rarely displayed abnormal dendritic phenotypes in the Sema-1aP1 mutant ( Figs 2S , 3A and 3B; Table 1; see S2 Table for information on the birth-order of Sema-1aP1 DL1- , DA3- and DC2-adPNs in our synchronized MARCM experiments ) , implicating that the endogenous Sema-1a expression may be low ( if there is any expression ) and may not play a crucial role in the dendritic targeting of the DA3 adPNs . Furthermore , if Sema-1a counteracts the attraction in the DA3 glomerulus , DA3 adPN dendrites should be sensitive to an excessive level and ectopic time window of the Sema-1a gain-of-function paradigm . Since we did not systematically conduct the synchronized MARCM experiments to over-express Sema-1a in the wild-type PNs , we , instead , altered Sema-1a expression in DA3 adPNs by over-expressing Sema-1a in the Sema-1aP1 mutant PNs . Using this approach , we did not find any adPNs with dendritic projections into the DA3 glomerulus . Instead , we found many adPNs whose dendrites projected into the DL3 glomerulus in the majority of the cases ( 63% , n = 16; Table 1; Fig 3C ) and in a few cases into the DA4l glomerulus ( 12% , n = 16; Table 1; Fig 3D ) or both the DA4l and DL3 glomeruli ( 25% , n = 16; Table 1; Fig 3E and 3F ) during the developmental time window for the generation of DA3 adPNs . We noted that our determination of the identity of these DL3/DA4l PNs as DA3 adPNs was based on their anterodorsal soma position , ruling out their being lPNs ( Fig 3C–3F; the only wild-type DL3 PNs labeled by GAL4-GH146 are DL3 lPNs [6] ) . Further , the birth of these DL3/DA4l PNs occurred prior to the birth of DC2 adPNs but after the birth of DL1 adPNs in our synchronized MARCM experiments , establishing their identity as DA3 adPNs ( S3 Table; the birth order of the embryonic-born DA4l adPN and larval-born DL1- , DA3-and DC2-adPNs has been reported previously [6] ) . Taken together , these results using manipulation of Sema-1a expression in DA3 adPNs reinforces our hypothesis that the DA3 glomerulus acts as a select AL region to attract nearby PN dendrites when counteracting Sema-1a signaling is absent . When we analyzed the phenotypes of those PNs that mis-targeted their dendrites into the DA3 glomerulus , we observed that Sema-1aP1 DC1 adPN dendrites were also mis-projected to a region close to the VC1 glomerulus ( 100% , n = 3; Fig 2P and its inset; Table 2 ) . This observation led us to look for additional select AL regions ( besides the DA3 glomerulus ) that could attract dendrites from different sets of PNs when Sema-1a is absent . In our MARCM experiments additional seven types of lPNs and nine types of adPNs were also labeled using GAL4-GH146 , allowing us to examine their dendritic patterns ( Fig 4 ) . Similar to the DC1 adPN , DC2- , DP1m- and VL2p-adPNs and VA5- and VA7m-lPNs also displayed the phenotype of dendritic mis-targeting to the region around the VC1 glomerulus , with variable mis-projection positions , penetrance and expressivity ( 31%~87%; Fig 4A–4J; Table 2 ) . Again , over-expressing wild type Sema-1a in Sema-1aP1 DC2 adPNs and VA5- and VA7m-lPNs rescued this mis-targeting phenotype: dendrites remained in relatively wild type locations , with dendritic occupancy at the edge and outside of the DC2 , VA5 and VA7m glomeruli ( S7 Fig; Table 2; similar findings were seen in the DL1 adPNs and DA1- and DL3-lPNs , see S6F , S6I and S8C Figs ) . The possibility of there being multiple select AL regions that attract dendrites in the absence of Sema-1a was further strengthened by our observation of another striking dendritic mis-targeting phenotype , in which dendrites mis-projected into the DA3 glomerulus and into regions in close proximity to the VC1 glomerulus when Sema-1a was mutated in two embryonic-born adPNs ( Fig 4K and 4L; Tables 1 and 2; the only wild-type DA3 adPNs labeled by GAL4-GH146 are larval-born [6] ) . Moreover , the VM7v and VA4 glomeruli and the region ventral to the DP1m glomerulus were also prone to aberrant dendritic invasion by DM1- , DM2- and VA7m-lPNs and DL1- , DL5- and DM3-adPNs ( S8B and S9 Figs; S4 Table; VM7v adPNs are the only PNs labeled by GAL4-GH146 that innervate the VM7v glomerulus [6] ) . However , whether the VM7v and VA4 glomeruli and the region ventral to the DP1m glomerulus behave as the select AL regions to attract other PN dendrites remains unclear and awaits the characterization of dendritic patterns for the rest of the PNs that we did not examine here . In contrast , VA2- , VA3- and VM3-adPNs and VA4- , VC1- and VC2-lPNs did not exhibit specific dendritic mis-targeting phenotypes when Sema-1a was absent ( S1 Table ) . We also noted that many embryonic-born , and a few larval-born , Sema-1aP1 adPNs/lPNs mis-projected their dendrites into the SEZ without AL innervation , which we never observed in the wild-type samples ( S10 Fig; S5 Table ) . Taken together , these results suggest that one additional select AL region ( i . e . , the region close to the VC1 glomerulus ) may co-exist along with the DA3 glomerulus and that they serve to attract PN dendrites when Sema-1a is absent . Secreted ligands and cell surface molecules act in concert to regulate the cell-morphogenetic and neurite-sorting processes that generate appropriate patterns of axonal branches and dendritic arbors in neurons of the olfactory system , resulting in constructing the complex , functional olfactory circuitry [18 , 19] . In the present study , we discover a new role of Sema-1a to prevent dendrites of adPNs , lPNs and vPNs from aberrantly invading into the select AL regions , which is crucial for generating appropriate discrete PN dendritic patterns to construct the olfactory map within the AL . Construction of the Drosophila adult AL is a complex process to integrate neurites of multiple populations of PNs , LNs and OSNs during the pupal stage [8 , 20] . What are the roles of specific molecules in this complex process of neurite sorting and integration among PNs , LNs and OSNs ? Sema-1a was proposed to control initial dendritic targeting of PNs along the DL-to-VM axis of the AL based on observations of dorsolateral-enriched expression of Sema-1a in the developing AL and mis-targeting of Sema-1aP1 DL1 adPN dendrites into the region ventromedial to the developing AL ( Fig 4C of the Komiyama study ) [12] . Interestingly , we observed similar phenotypes in Sema-1aP1 adPNs and lPNs , in which their dendrites mis-projected into the SEZ , a neuropile ventromedial to the adult AL , with or without entering the AL ( Figs 1E and 4F; S5B–S5D Fig , S6E and S10 Figs ) . Therefore , the repulsive Sema-1a signal does play an essential role in the step of initial dendritic targeting by preventing PN dendrites from mistakenly invading the region ventromedial to the AL ( e . g . , the SEZ; also see the schematic drawing in Fig 5 ) . The above described step of initial dendritic targeting directed by Sema-1a was further proposed to link with the later refinement and sharpening of boundaries among glomeruli through intercellular interactions , for example dendrite–dendrite interactions among PNs mediated by N-cadherin [12 , 21] . A correlated range of severity of the DL-to-VM dendritic shift phenotypes found in Sema-1aP1 PNs—in which DL1 adPNs ( having the farthest dorsolateral dendrites ) displayed the most severe DL-to-VM dendritic shift compared to the moderate and mild phenotypes of the dendrites of DA1 lPNs and DC3 adPNs—supported the idea that the distribution of PN dendrites in the AL is determined by the Sema-1a expression gradient [12] . Although we observed similar DL-to-VM dendritic shift defects in Sema-1aP1 DL1 adPNs and DA1 lPNs ( S6D and S8B Figs ) , these phenotypes may be also interpreted as mis-targeting of dendrites of DL1 adPNs and DA1 lPNs into unidentified select AL regions ( e . g . , the region ventral to the DP1m glomerulus for DL1 adPNs shown in the inset of S8B Fig ) in the absence of Sema-1a . In contrast , Sema-1aP1 DC3 adPNs exhibited the DA3-glomerular dendritic mis-targeting defect and did not show the DL-to-VM dendritic shift phenotype in our study ( Fig 2G ) . Upon close examination of two Sema-1aP1 DC3 adPN images from Komiyama et al . ( their Fig 3D ) [12] , we noted that the bottom right image displays a mild dendritic mis-targeting defect , occupying the ventral tip of the DA3 glomerulus , and the bottom left image was horizontally flipped , so it is not likely to be a DC3 adPN . Both DC3 adPN examples shown in the Komiyama study [12] , taken together with the DA3-glomerular dendritic mis-targeting defect we observed in DC3 adPNs ( Fig 2G ) and also in other PN samples ( D- , DA4l- , DA4m- , DC1- , DC3- , DL4- and VA1d-adPNs and DA1- and diffuse-vPNs in Figs 1 and 2 ) , complicates the straightforward DL-to-VM dendritic shift model that was proposed in order to account for the function of Sema-1a in the establishment of appropriate dendritic patterns once PN dendrites have projected into the developing AL . How then does Sema-1a regulate the formation of dendritic patterns once multiple populations of PNs ( e . g . , adPNs , lPNs and vPNs ) have sent their dendrites into the developing AL ? We found here that different sets of uni-glomerular- and poly-glomerular PNs among adPNs , lPNs and vPNs mis-targeted their dendrites into select AL regions , including the DA3 glomerulus and a region close around the VC1 glomerulus , when Sema-1a was mutated ( Figs 1 , 2 and 4 ) . Intriguingly , the PNs that displayed the Sema-1a-deficient DA3-glomerular dendritic mis-targeting defect seem to associate with their deriving origins , i . e . , the occurrence of the dendritic mis-targeting phenotype only in adPNs and vPNs but not lPNs ( Figs 1 and 2 and S6 Fig ) . In the light of these results , it may be reasonable to speculate the presence of sorting centers in the developing AL for controlling dendrites of uni-glomerular- and poly-glomerular-PNs among adPNs , lPNs and vPNs toward their destined glomeruli . Interestingly , dendrites of adPNs and vPNs seem to accumulate in the anterior dorsolateral portion of the developing AL at 24h APF ( double-arrowhead in S2C Fig ) , which maybe correlate with PN dendritic innervation in the DA3 glomerulus and anterodorsal glomeruli of the AL . When the Sema-1a repulsion ( as a driving force ) is gone , PN dendrites will be trapped in the sorting centers , which results in to dendritic mis-targeting into select AL regions . However , without figuring out the identity of those PN dendrites in the anterior dorsolateral portion of the developing AL ( e . g . , whether the green adPN dendrites are destined toward the DA3 glomerulus and its surrounding glomeruli , and whether the magenta PN dendrites are derived from vPNs but not from the DL1 adPN; double-arrowhead in S2C Fig ) , the presence of dendritic sorting centers in the developing AL remains elusive . No matter whether the dendritic sorting centers exist in the developing AL or not , how does Sema-1a transmit the repulsion signal in PN dendrites to prevent inappropriately mixing together ? Previously , the expression of Sema-2a/-2b in the ventromedial corner of the developing AL has been reported as ligands of Sema-1a to regulate the PN dendritic targeting [13] . However , it may take a slightly complicate mechanism to only involve the graded expression of Sema-2a/-2b to elicit the repulsive Sema-1a signal in PNs to avoid dendritic accumulation in select AL regions . Therefore , it is reasonable to speculate the presence of not-yet identified factors which may work in concert with the repulsive Sema-2a/-2b-Sema-1a signal in PNs . Dependent on the distribution of those not-yet identified factors in the developing AL , various types of uni-glomerular- and poly-glomerular-PNs among adPNs , lPNs and vPNs may interpret the repulsive Sema-1a signal instructively or permissively to generate distinct but overlapping dendritic patterns in discrete glomeruli of the AL . Based on the differing degrees of penetrance and expressivity of the Sema-1a-deficient DA3-glomerular dendritic mis-targeting phenotype , we speculate that the repulsive Sema-1a signal may be differentially transmitted in various types of PNs with high ( e . g . , DC3 adPNs and DA1- and diffuse-vPNs ) , moderate ( e . g . , D- , DA4l- , DA4m- , DC1- , DL4- and VA1d-adPNs ) to low ( e . g . , DA3 adPNs ) degrees ( see Fig 5 for the schematic drawing ) . Removing Sema-1a from PNs ( e . g . , DC3- and DA4l-adPNs and DA1 vPNs ) that are normally affected by the repulsive Sema-1a signal may turn these Sema-1a-deficient PN dendrites to behave more like dendrites of PNs that normally do not respond to the repulsive Sema-1a signal ( e . g . , DA3 adPNs ) , which results in dendritic mis-targeting in a wrong AL region ( e . g . , DC3 and DA4l Sema-1aP1 adPNs and DA1 Sema-1aP1 vPNs mis-target their dendrites into the DA3 glomerulus ) . On the other hand , excessive and ectopic expression of Sema-1a in the PNs ( e . g . , DA3 adPNs ) that presumably expresses low or no endogenous level of Sema-1a may convert these Sema-1a-manipulated PNs into sensitive to the repulsive Sema-1a signal , which results in steering their dendrites away from their destined region ( e . g . , the DA3 glomerulus ) into adjacent glomeruli ( e . g . , DA4l and DL3 glomeruli; see Fig 5 for the schematic drawing ) . While our proposed model is able to account for the dendritic mis-targeting phenotypes observed among Sema-1a-deficient adPNs , lPNs and vPNs , questions remain concerning how and what not-yet identified factors work together with Sema-1a in various types of PNs to set up correct dendritic patterns . Future identification and investigation of these factors may reveal how extracellular signals that elicit the Sema-1a repulsion in PNs and how Sema-1a works in concert with ensembles of molecules among various types of PNs to generate a precise and functional olfactory map that decodes external olfactory inputs into essential internal information for the survival of animals . Standard molecular biological techniques were used to generate UAS-Sema-1asy , in which the Sema-1a open reading frame from a UAS-Sema-1a cDNA construct [17] was cut with XhoI and XbaI and re-cloned into pJFRC7-20XUAS-IVS-mCD8::GFP [22] . The UAS-Sema-1asy construct was injected into a fly stock carrying an attP docking site ( VK00033; e . g . , Bloomington stock number ( BL ) 9750 ) to generate the transgenic fly stock via the service provided by Rainbow Transgenic Flies , Inc . The fly strains used in this study were as follows: ( 1 ) hs-FLP122 [8]; ( 2 ) FRT40A , UAS-rCD2::RFP , UAS-GFP-miRNA [15]; ( 3 ) FRT40A , UAS-mCD8::GFP , UAS-rCD2-miRNA , GAL4-GH146 [6]; ( 4 ) GAL4-MZ699 [23]; ( 5 ) GH146-FLP [24]; ( 6 ) UAS-FRT<stop<FRT-myrGFP [22]; ( 7 ) R95B09-GAL4 ( BL47267 ) ; ( 8 ) UAS-Sema-1a RNAiTRiP ( BL34320 ) ; ( 9 ) FRT40A , UAS-mCD8::GFP , GAL4-GH146 [25]; ( 10 ) FRT40A , UAS-mCD8::GFP , Sema-1ak13702 , GAL4-GH146 ( Sema-1ak13702 is Sema-1aP1 [17] and the original source of Sema-1ak13702 was from Kyoto Stock Center with the stock number 111328 ) ; ( 11 ) FRT40A , tubP-GAL80 [26]; ( 12 ) UAS-Sema-1asy ( generated in this study ) ; ( 13 ) R38B04-GAL4 ( BL49984 ) ; ( 14 ) GAL4-MZ19 [27]; ( 15 ) actin-FRT<stop<FRT-GAL4 [2] . Flippase-out mediated intersection samples and mosaic clones for the MARCM , flip-out MARCM and twin-spot MARCM studies were generated as previously described [2 , 14 , 15 , 26] . MARCM samples were obtained by collecting embryos in vials and inducing mosaic clones at various developmental periods with heat-shock for 10 to 15 minutes . To pin-point birth-order of PNs in Sema-1a LOF and Sema-1a rescued MARCM experiments ( S2 and S3 Tables ) , larvae were picked up as NHL to synchronize samples , and mosaic clones were induced at four-hour intervals from 26h ALH to 56h ALH with heat-shock for 10 to 15 minutes . For the flip-out MARCM and twin-spot MARCM experiments , mosaic clones of larval-born adPNs , lPNs , vPNs and lvPNs were generated by heat-shock for 10 to 25 minutes in NHL . Dissection , immunostaining and mounting of more than 5 , 000 fly brains were performed as described in a standard protocol [26] . Primary antibodies used in this study included rabbit antibody against GFP ( 1:800 , Invitrogen ) , rabbit antibody against RFP ( 1:800 , Clontech ) , rat antibody against DN-cadherin ( DN-Ex #8 , 1:50 , DSHB ) , rat antibody against mCD8 ( 1:100 , Invitrogen ) , and mouse antibody against Bruchpilot ( nc82 , 1:50 , DSHB ) . Secondary antibodies conjugated to different fluorophores ( Alexa 488 , 546 , and 647 ( Invitrogen ) ) were used at a 1:800 dilution . Immunofluorescent images of single neurons from single-cell MARCM clones and intersection experiments ( around 900 images ) and groups of neurons from multi-cellular neuroblast clones of flip-out MARCM and twin-spot MARCM experiments ( around 100 images ) were collected by Zeiss LSM 700 or 780 confocal microscopy , processed using the Zeiss LSM image browser , and the image intensity adjusted using Photoshop . For the purpose of presentation , wild-type mCD8::GFP- and rCD2::RFP-positive multi-cellular neuroblast clones in the twin-spot MARCM experiments are shown in green for adPNs and lPNs and in magenta for vPNs in S2 Fig . The original LSM files used in the present study are available upon request . Scoring of the dendritic patterns of DL1 adPNs was modified from a previous report [12] . Images of interest were projected from confocal stacks containing DL1 adPN dendrites using the Zeiss LSM image browser . Dendritic regions of DL1 adPNs were manually selected and the DL-to-VM axis of the AL was rotated to make it vertical by using Image J . Fluorescent signals were converted into binary numbers using Huang's fuzzy thresholding method provided by Image J [28] . The scoring region was selected and divided into ten bins along the DL-to-VM axis based on Brp-positive staining . The value of the dendritic pattern of DL1 adPNs within the scoring region was summed by Image J . Dendritic intensity and mean position within the scoring bins for dendrites of DL1 adPNs were then calculated . Student’s t-test was used for statistical analysis .
In the Drosophila olfactory system , olfactory projection neurons ( PNs ) are derived from four neural stem cells ( called neuroblasts ) during the development . Intriguingly , these PNs generate complex dendritic patterns within the primary olfactory center of the brain , the antennal lobe ( AL ) , to relay odorant information from olfactory sensory neurons in the periphery to neurons in higher olfactory centers . In this study , we investigate how various types of PNs use a repulsive transmembrane protein Semaphorin-1a ( Sema-1a ) to establish appropriate dendritic patterns within the AL . Previously , Sema-1a was proposed to globally control initial PN dendritic targeting along the dorsolateral-to-ventromedial axis of the AL . In contrast , we disclose an unknown role of Sema-1a , in which this neuronal protein acts to keep dendrites of various types of PNs produced from different neuroblasts away from select AL regions , thereby enabling the dendrites of the same types of PNs to sort correctly into destined glomeruli within the developing AL for assembly of the functional olfactory neural circuitry .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]
[ "invertebrates", "rna", "interference", "cloning", "neuroscience", "animals", "neurites", "animal", "models", "drosophila", "melanogaster", "model", "organisms", "experimental", "organism", "systems", "molecular", "biology", "techniques", "epigenetics", "neuroblasts", "neuronal", "dendrites", "drosophila", "research", "and", "analysis", "methods", "animal", "cells", "genetic", "interference", "gene", "expression", "molecular", "biology", "olfactory", "receptor", "neurons", "insects", "arthropoda", "biochemistry", "rna", "cellular", "neuroscience", "cell", "biology", "nucleic", "acids", "phenotypes", "neurons", "genetics", "biology", "and", "life", "sciences", "cellular", "types", "afferent", "neurons", "organisms" ]
2017
Semaphorin-1a prevents Drosophila olfactory projection neuron dendrites from mis-targeting into select antennal lobe regions
The Pseudomonas syringae type III effector protein avirulence protein B ( AvrB ) is delivered into plant cells , where it targets the Arabidopsis RIN4 protein ( resistance to Pseudomonas maculicula protein 1 [RPM1]–interacting protein ) . RIN4 is a regulator of basal host defense responses . Targeting of RIN4 by AvrB is recognized by the host RPM1 nucleotide-binding leucine-rich repeat disease resistance protein , leading to accelerated defense responses , cessation of pathogen growth , and hypersensitive host cell death at the infection site . We determined the structure of AvrB complexed with an AvrB-binding fragment of RIN4 at 2 . 3 Å resolution . We also determined the structure of AvrB in complex with adenosine diphosphate bound in a binding pocket adjacent to the RIN4 binding domain . AvrB residues important for RIN4 interaction are required for full RPM1 activation . AvrB residues that contact adenosine diphosphate are also required for initiation of RPM1 function . Nucleotide-binding residues of AvrB are also required for its phosphorylation by an unknown Arabidopsis protein ( s ) . We conclude that AvrB is activated inside the host cell by nucleotide binding and subsequent phosphorylation and , independently , interacts with RIN4 . Our data suggest that activated AvrB , bound to RIN4 , is indirectly recognized by RPM1 to initiate plant immune system function . Many Gram-negative bacterial pathogens of plants or animals employ type III secretion systems ( TTSSs ) to translocate type III effector proteins into host cells [1] . Type III effector proteins manipulate host cellular targets and signaling pathways to promote the infection process in genetically susceptible hosts [2 , 3] . In the plant immune system , specific nucleotide-binding leucine-rich repeat ( NB-LRR ) disease resistance proteins can monitor the homeostasis of type III effector targets [4–6] . In several cases , when a type III effector perturbs its target , the corresponding NB-LRR protein is activated . NB-LRR activation leads to a complex output including hypersensitive cell death ( HR ) and a suite of cellular responses that render the plant resistant to infection by pathogen strains expressing that type III effector . The Arabidopsis NB-LRR protein RPM1 ( resistance to Pseudomonas maculicula protein 1 ) recognizes the action of two distinct type III effector proteins , avirulence protein Rpm1 ( AvrRpm1 ) and avirulence protein B ( AvrB ) , which are found in various strains of the plant pathogen Pseudomonas syringae [7 , 8] . The RPM1-interacting protein ( RIN4 ) is required for RPM1 function triggered by either AvrRpm1 or AvrB [9] . RIN4 physically associates in vivo with both AvrB and AvrRpm1 , and with RPM1 [9] . The presence of either AvrRpm1 or AvrB in the plant cell leads to phosphorylation of RIN4 , although neither of the type III effectors has sequence similarity to kinases [9] . RIN4 also interacts with , and is required for the function of , a second NB-LRR protein , RPS2 ( resistance to P . syringae protein 2 ) [10 , 11] . The corresponding type III effector , avirulence protein Rpt 2 ( AvrRpt2 ) , is an autoprocessed cysteine protease that is activated by a host cyclophillin after delivery [12 , 13] . AvrRpt2 cleaves RIN4 at two sites [14 , 15] , and the disappearance of RIN4 drives RPS2 activation [10 , 11] . One of the AvrRpt2 cleavage sites overlaps the AvrB binding site on RIN4 [15] . Thus , at least two independent type III effector proteins have evolved to target a small approximately 30–amino acid domain on RIN4 using at least two different biochemical mechanisms . It remains to be determined whether AvrRpm1 also targets this region of RIN4 . This region is shared among several otherwise unrelated Arabidopsis proteins of unknown function and may represent a common motif targeted by plant pathogens [14 , 15] . RPM1 , RPS2 , the type III effectors , and RIN4 have been demonstrated , or are predicted to be , localized to the inside of the plant plasma membrane; AvrB , AvrRpm1 , and RIN4 are acylated as a requirement for this localization [11 , 15–17] . Despite the wealth of genetic information for AvrB , its biochemical function remains elusive . A crystal structure of free AvrB revealed that it adopts a novel bilobal fold with no structural homologies to previously characterized proteins or functional domains [18] . A small upper lobe ( amino acid residues 123 to 217 ) contains three alpha-helices ( α5 , α6 , and α7 ) surrounding a five-stranded antiparallel beta-sheet ( β1-β5-β4-β3-β2 ) . A large lower lobe ( residues 28 to 122 and 218 to 317 ) is composed strictly of alpha-helices [18] . The junction of the two lobes forms a large solvent-accessible cleft with a volume of over 900 Å3 that extends into the lower lobe to form a pocket rich in conserved residues . Chimeras of AvrB with the closely related paralog avirulence protein C ( AvrC ) ( which does not activate RPM1 ) were used to demonstrate that the upper lobe of AvrB ( residues 126 to 216 ) is required for RPM1 activation [18] . The lack of sequence conservation in the upper lobe among AvrB paralogs from various plant pathogens that do not trigger RPM1 mediated responses ( see below ) further supports this contention . In contrast , the lower lobe and the interlobal cleft are highly conserved in sequence among the AvrB family members . The conserved cleft was hypothesized to be an enzymatic active site that binds to substrate and/or cofactor [18] . In this study , we present the structure of AvrB bound to RIN4 and we identify interacting residues in the upper lobe of AvrB that are required for both RIN4 binding and activation of RPM1 . We also identified a highly conserved nucleotide-binding pocket contained largely in the lower lobe of AvrB that is also required for activation of RPM1 . In addition , phosphorylation of AvrB occurs in the presence of a host cofactor or kinase and may represent a third prerequisite for recognition by RPM1 . Previous work using gel filtration and native gel electrophoresis demonstrated that AvrB bound a small RIN4 fragment consisting of amino acids 142 to 179 [15] . We cocrystallized AvrB with RIN4142–176 ( Figure 1A , 1C , and 1E; Table 1 ) . This fragment binds AvrB with affinity similar to that of the full-length protein ( kd = 3 μM versus 4 μM as determined by isothermal titration calorimetry [ITC]; see below ) . Thus , essentially all of the binding energy for interaction with AvrB is contained in RIN4142–176 . The structure of AvrB complexed to RIN4142–176 revealed that the peptide forms a “Z” shape with the N-terminal half contacting the upper lobe and the C-terminal half straddling the interface between the two lobes ( Figure 1A , 1C , and 1E; Table 1 ) . The interaction of AvrB with RIN4142–176 does not alter the overall structure of AvrB ( unpublished data ) . Using this cocrystal structure , we identified residues of AvrB that interact with RIN4142–176 and could potentially be important for binding affinity ( Figure 2A and 2C ) . The N-terminal half of RIN4142–176 interacts mainly through hydrophobic burial of RIN4 W154 plus a hydrogen bond between the indole nitrogen of RIN4 W154 and AvrB D213 ( Figure 2A ) . RIN4 W154 is part of an AvrRpt2 cleavage site on RIN4 [14] . Additionally , AvrB T182 contacts RIN4 Y151 , and AvrB V128 contacts RIN4 D155 , S161 , and G162 . The most extensive interactions of RIN4142–176 with AvrB involve the C-terminal half of the RIN4 peptide . First , there is a set of antiparallel beta-strand hydrogen-bonding interactions between main-chain AvrB residues 120 to 124 ( within strand β1 ) and RIN4 residues 167–171 . Second , there is a hydrogen bond between the main-chain of RIN4 N170 and the AvrB R209 guanidinium group . These interactions allow this region of RIN4 to form an additional strand of the beta-sheet in the upper lobe of AvrB ( Figure S1 ) . Importantly , in forming this strand , the RIN4 T166 side-chain is directed toward the interior of AvrB such that the hydroxyl group hydrogen bonds with the hydroxyl group of AvrB T125 , which in turn hydrogen bonds to the imidazole ring of AvrB H217 ( Figure 2C ) . Above this hydrogen-bonding arrangement , the aromatic rings of RIN4 Y165 and F169 as well as interleaving H167 are thrust against the surface formed from the last two turns of AvrB helix α7 ( Figure 2A ) . This “ring-stack” of RIN4 spans from the AvrB upper lobe ( R209 ) to the interlobe boundary ( R266 ) ( Figure 2A ) . These aromatic and indole side-chains stack in a nearly parallel arrangement with each other that is energetically very favorable . The guanidinium groups of AvrB R209 and AvrB R266 buttress the termini of the ring-stack , contacting RIN4 F169 and Y165 , respectively , via π–π interactions such that the guanidium groups are also almost parallel to the three stacked aromatic rings of RIN4 . In the middle of this ring stack , AvrB Q208 forms additional contacts with RIN4 H167 ( Figure 2A ) . AvrB Y210 may play a weak role in RIN4142–176 binding since additional electron density from RIN4142–176 appears to be contacting this residue ( Figure 1A ) . No additional electron density is observed for the last four residues of the RIN4 peptide ( sequence REER ) . RIN4 and AvrB interact in yeast two-hybrid assays and can be coimmunoprecipitated from plant tissue [9] . This interaction is direct , because both purified proteins comigrate as an in vitro complex on native gel filtration columns [15] . To test whether the aforementioned AvrB residues are involved in RIN4 binding ( listed in Table 2 ) , we mutated the relevant amino acids individually to alanine . These mutants were assayed for their ability to bind full-length RIN4 by in vitro gel filtration and/or in vivo by yeast two-hybrid assay ( Table 2 ) . In addition , the binding affinity of selected AvrB mutants and RIN4142–176 was measured by ITC ( Figure 3 ) . The cocrystal structure revealed that the side-chains of several AvrB upper lobe residues make contacts with the N-terminal region of RIN4142–176 . For example , D213 of AvrB interacts with W154 within the N-terminal portion of RIN4142–176 . However , substitution of D213 to alanine did not disrupt either binding to RIN4 or the ability to trigger RPM1-mediated responses . The substitution of nearby residues of AvrB ( i . e . , V128A and T182A ) also did not disrupt binding to RIN4 or activation of RPM1 ( Table 2; unpublished data ) . These AvrB residues contact the N-terminal region of RIN4142–176 that contains an AvrRpt2 cleavage [14 , 15] . Hence , our data suggest AvrB and AvrRpt2 target two distinct amino acid sequences of this short , 35–amino acid residue region of RIN4 . In contrast , disruption of AvrB residues contacting the C terminus of RIN4142–176 strongly affected RIN4 binding . AvrBT125A and AvrBH217A were unable to bind RIN4142–176 under our ITC conditions ( Figure 3 ) . Loss of RIN4 binding activity was not due to significant structural disruptions , since AvrBT125A or AvrBH217A were properly folded , as measured by circular dichroism ( unpublished data ) . Similar mutation of AvrB residues directly supporting the side-chains of the ring-stack also affected RIN4 binding . The single mutation AvrBR209A had very little effect on RIN4 binding ( Table 2 ) but a triple mutation of Q208 , R209 , and Y210 to alanine ( hereafter AvrBQRY/AAA ) significantly lowered the affinity for RIN4 ( kd = 9 μM for AvrBQRY/AAA ) and destabilized the AvrB/RIN4 complex ( Figure 3 and Table 2 ) . Overall , two regions of the upper lobe make distinct and functional contacts with the C-terminal region of RIN4142–176: ( 1 ) AvrB residues Q208 , R209 , and Y210 , which directly support the side-chains of the ring-stack , and ( 2 ) AvrB residues T125 and H217 , which interact with RIN4 T166 , between Y165 and H167 of the ring-stack . AvrB induces phosphorylation of RIN4 [9] , but it remained unclear whether AvrB must interact with RIN4 to trigger RPM1-mediated disease resistance and HR . We reasoned that if the RIN4–AvrB interaction is a prerequisite for RPM1-mediated HR or disease resistance , then AvrB mutants that do not bind to RIN4 should not trigger either phenotype . The preceding experiments identified AvrB mutants that varied in binding affinity to RIN4 ( Figure 3 and Table 2 ) . We expressed these avrB alleles in Pseudomonas syringae pv . tomato ( Pto ) DC3000 . All of the AvrB alleles that exhibited altered function , as described below , were expressed at essentially normal levels in P . syringae ( Figure S2D ) . Arabidopsis leaves were infected with Pto DC3000 strains expressing wild-type AvrB , each AvrB mutant , or an empty vector and assessed for RPM1-mediated HR using both trypan blue as a qualitative measure and the leakage of cellular ions into media and consequent changes in media conductivity over time as a quantitative measure ( Figures 4A , 4B , S2A , and S2C ) . We also quantified the RPM1-mediated restriction of pathogen growth for selected AvrB mutants ( Figure 4C ) . The results of these functional tests are summarized on the structures in Figure 2A and 2C . V128 , T182 , and D213 are in a region of the AvrB upper lobe that is not required for interaction with RIN4 ( Table 2 ) . Unsurprisingly , these mutants have no effect on the ability of AvrB to activate RPM1 ( Figure S2 ) . The residues of AvrB that directly support the ring-stack of RIN4 , especially R209 and Y210 ( Figure 3 and Table 2 ) , are more important for complex formation . Thus , while AvrBR209A bound RIN4 ( Table 2 ) , and only marginally diminished RPM1-mediated HR in both assays ( Figure 4A and 4B ) , disruption of several of the ring-stack interactions in AvrBQRY/AAA drastically diminished RIN4 binding activity in vitro ( Figure 3; Table 2 ) and resulted in significantly reduced RPM1-mediated HR ( Figure 4A and 4B ) . These levels of altered HR correlated with slightly reduced RPM1-mediated restriction of bacterial growth . Pto DC3000 ( avrBR209A ) and Pto DC3000 ( avrBQRY/AAA ) grew in planta more than bacteria expressing wild-type avrB ( Figure 4C ) . Furthermore , mutations that disrupt the hydrogen bonding between AvrB T125 and H217 and RIN4 T166 ( Figure 2A and 2C ) eliminated or greatly reduced binding to RIN4 and the ability of these AvrB mutants to trigger RPM1 function ( Table 2 and Figures 3 and 4 ) . Translocation assays confirmed that AvrBT125A was delivered into Arabidopsis cells ( as are other loss-of-function AvrB alleles described here; Figure S3 and Table 2 ) . Therefore , interactions that either directly or indirectly support the ring-stack of RIN4 are a major determinant for complex formation . Taken together , these results strongly suggest that binding of RIN4 by AvrB is a prerequisite for its ability to efficiently activate RPM1 . The AvrBR266A mutation was unable to trigger RPM1 function ( Figure 4 ) . The AvrB structure contains a cavity in the large lobe [18] . AvrB R266 lies at the interlobe boundary between the major RIN4-binding groove and this cavity ( Figure 2 ) . We hypothesized that this large cavity , rich in residues that are highly conserved across the AvrB protein family ( see below ) , could serve as a binding site for a cofactor for AvrB activity . Because the presence of AvrB renders RIN4 marginally hyperphosphorylated [9] , we wondered whether AvrB is an atypical kinase . We therefore examined binding to nucleotides , including adenosine triphosphate ( ATP ) , adenosine diphosphate ( ADP ) , guanosine diphosphate , and nonhydrolyzable ATP and guanosine diphosphate analogs , by soaking these into AvrB crystals . We found that crystallized AvrB could bind ADP within the lower lobe pocket of the large cavity ( referred to as the ADP pocket; Figures 1B , 1D , 1F , 2B , and 2D; Table 1 ) . We observed only weak density corresponding to other nucleotides in the lower lobe pocket ( unpublished data ) . Significant interactions with ADP involve AvrB residues Y65 , R99 , and R266 ( Figure 2B and 2D ) . Y65 stacks below the adenine ring and hydrogen bonds to the 2-OH group of the ribose , while R99 and R266 form salt bridges with the alpha- and beta-phosphates , respectively . N62 and F113 also contact the adenine base and phosphates , respectively ( Figure 2B and 2D ) . F113 also appears to contribute to proper positioning of R99 and R266 . The conserved AvrB residues Y131 and D297 located at the interlobe boundary are approximately 4 Å from ADP and appear to contact it only via bridging water molecules . Mutation of ADP-binding residues in AvrBY65A , AvrBR99A , and AvrBR266A resulted in complete loss of AvrB-induced RPM1 function ( Figure 4 ) . Mutation of ADP-interacting residues AvrBN62A and AvrBF113A resulted in partial losses of RPM1-mediated HR ( Figure S2B and S2C ) . Surprisingly , mutations that appear to contact ADP only via water bridges , AvrBY131A and AvrBD297A , also abrogated RPM1-mediated HR and bacterial growth restriction ( Figure 4 ) . Thus , all of the AvrB loss-of-function mutants in the ADP binding cavity eliminated the initiation of RPM1 function . We note , however , that they are each produced and properly folded ( Figure S2D and circular dichroism , unpublished data ) and translocated into host cells ( Figure S3 ) . Although AvrBY65A and AvrBD297A failed to bind RIN4 in ITC ( Figure 3 ) , they , along with AvrBR99A , did bind RIN4 in yeast two-hybrid assay , gel filtration , or both ( Table 2 ) . We discuss these data further below . Since RIN4 phosphorylation is induced by AvrB , it was hypothesized that AvrB may possess kinase activity . However , we detected no AvrB-dependent phosphorylation of RIN4 or RIN4142–176 using in vitro radiolabeling experiments in the presence or absence of Arabidopsis extracts . We also found no evidence for in vitro autophosphorylation of AvrB ( Figure 5A ) . We did , however , observe phosphorylation of AvrB in the presence of wild-type Arabidopsis extracts ( Figure 5A ) . AvrB phosphorylation was sensitive to ethylenediaminetetraacetic acid ( EDTA ) and heat denaturation of the plant extract by boiling prior to assays , suggesting that AvrB phosphorylation requires cations and a heat-labile plant factor ( unpublished data ) . Phosphorylation activity was specific for AvrB , the only member of this type III effector family demonstrated to trigger RPM1 function , since related paralogs ( Figure 5C ) did not or only weakly incorporated radiolabeled phosphate ( Figure 5A ) . In this assay , AvrB phosphorylation appears to be RIN4 independent , as it occurs in extracts from rin4 rpm1 rps2 mutant plants ( unpublished data ) . Titrating increasing amounts of purified full-length RIN4 protein into extracts from rin4 rpm1 rps2 did not alter the level of AvrB phosphorylation in vitro ( unpublished data ) . Furthermore , phosphorylation of AvrB does not require RIN4 binding , since AvrBQRY/AAA , AvrBT125A , and AvrBH217A were readily phosphorylated ( Figure 4A ) . These data show that RIN4 is not the plant cofactor that either directly or indirectly regulates AvrB phosphorylation in planta . In contrast , AvrB residues that directly contact ADP are important for phosphorylation ( Figure 5A ) . AvrBR266A and AvrBY65A were partially and completely compromised for phosphorylation , respectively ( Figure 5A ) . The requirement of ADP interacting residues for AvrB phosphorylation suggests that nucleotide binding by AvrB is critical for this event . On the other hand , AvrBY131A and AvrBD297A were phosphorylated to the same levels as wild-type AvrB ( Figure 5A ) . This correlates with the minor contribution of these residues to nucleotide binding observed from the crystal structure . Nevertheless , AvrB Y131 and D297 are required for the triggering of RPM1 ( Figure 4 ) , suggesting their involvement in another aspect of AvrB activation . Our data significantly extend previous observations defining the key functional regions of the type III effector protein AvrB: its upper lobe and interlobal cleft mediate contact with the Arabidopsis target protein RIN4 , and the lower lobe contains a pocket suitable for binding ADP or another similarly shaped molecule ( Figure 5B ) . We defined three correlates for triggering AvrB-dependent , RPM1-mediated disease resistance function in Arabidopsis . These are AvrB's interaction with RIN4 , its binding of nucleotide , or another small molecule of similar shape , and its likely phosphorylation in the presence of Arabidopsis extract . Our structure-based functional analysis of the AvrB-RIN4142–176 complex identified two main regions of interaction: AvrB T125 and H217 and AvrB Q208 , R209 , and Y210 . These AvrB residues interact with bulky and aromatic RIN4 residues Y165 , T166 , H167 , and F169 ( previously termed the AvrB binding site [BBS] [15] ) in a ring-stacking arrangement just C-terminal to the previously identified AvrRpt2 cleavage site ( RCS2; [14 , 15] ) . Mutation of these AvrB residues interferes with the ability to trigger RPM1 function , demonstrating that physical interaction of AvrB with RIN4 is required for recognition by RPM1 . AvrB residues Q208 , R209 , and Y210 are poorly conserved in other AvrB family members ( Figure 5C ) , suggesting that it might be the specificity determinant for RIN4 binding . The BBS and RCS are a functional , bipartite domain in RIN4 and approximately 11 additional proteins in Arabidopsis proteins ( pfam05627 ) . This RCS-BBS domain is widely distributed in multicellular plants evolutionarily distant from Arabidopsis , such as the moss Physcomitrella patens and the fern Cerapteris richardii ( http://plantta . tigr . org ) and thus may have conserved roles in the plant immune system . Within this domain , the RCS is highly conserved , while the BBS and the spacing between the RCS and the BBS is more variable . A number of other RCS-BBS proteins are cleaved by AvrRpt2 ( which targets the RCS [14 , 15] ) , yet it remains to be seen whether the additional 11 BBS-containing proteins are also bound by AvrB . It is noteworthy that the N-terminal RCS-BBS domain of RIN4 does not bind AvrB [15] , suggesting that the amino acid divergence between the N-terminal RCS-BBS and C-terminal RCS-BBS sequences of RIN4 is functionally relevant . AvrB is also recognized by a second resistance gene , Rpg1-b , in soybean [19] . A random mutational analysis of AvrB identified nine individual amino acids required for induction of both RPM1-mediated HR on Arabidopsis and Rpg1-b function on soybean [20] . Among these , AvrB T125 , Q164 , and I215 were required for the HR phenotypes on both RPM1-expressing Arabidopsis and Rpg1-b–expressing soybean . All of these residues are in the AvrB upper lobe . The hydrophobic I215 lies underneath T125 , and its mutation would likely disrupt the structural integrity of this region , and hence interaction with RIN4 . Q164 is not solvent exposed and is also likely to disrupt AvrB structure when mutated . S268 is also in the lower lobe , and its substitution to isoleucine abrogated RIN4 binding in yeast two-hybrid assays [20] . The polar side-chain of S268 is partially buried and its mutation to a residue with a bulkier , nonpolar side-chain could have detrimental effects on the structure of AvrB , thus resulting in loss of RIN4 binding . A RIN4 ortholog is present in soybean and possesses an intact BBS domain . Whether this soybean RIN4 ortholog interacts with AvrB and is required for Rpg1-b–mediated resistance remain to be determined . We also observed binding of ADP to the lower lobe pocket of AvrB ( Figures 2 and 5B ) . Nucleotide contact residues ( AvrB Y65 , R99 , and R266 ) are conserved between AvrB family members ( Figure 5C ) and are required for RPM1-mediated disease resistance responses . Thus , the ability of AvrB to trigger RPM1 function in Arabidopsis requires both interaction with RIN4 and an intact nucleotide binding pocket . Strikingly , Rpg-1b–mediated HR in soybean also required an intact AvrB nucleotide-binding pocket [20] . In these studies , AvrBR266A did not elicit an HR in soybean . In addition , AvrB G46 and A269 , which make contacts with ADP in our crystal structure , were also required for this activity in soybean [20] . We hypothesize that AvrB binds to ADP , or another similarly shaped nucleotide , and interacts with RIN4 to induce RIN4 phosphorylation in Arabidopsis . If so , then AvrB might have a kinase or protokinase activity required to phosphorylate RIN4 . Tertiary structure-matching programs such as DALI or MSD-Fold did not reveal significant structural similarity between AvrB and known kinases . However , the AvrB structure is similar to typical Ser/Thr protein kinases such as cAMP-dependent kinase , in that both contain a bilobal structure with a large lobe composed predominantly of alpha-helices and a small lobe with a mixed alpha-helix beta-sheet content [18] . We present a model of a ternary AvrB/RIN4/ADP structure created by superimposing the AvrB/RIN4 and AvrB/ADP coordinates and subsequent insertion of the ADP coordinates into the AvrB/RIN4 structure ( Figure 6A ) . In this model , the distance between the oxygen atoms of the beta-phosphate of ADP and the T166 Oγ atom is quite short ( 4 . 2 Å ) , indicating that RIN4 T166 is a strong candidate for phosphorylation by AvrB . We compared our modeled ternary complex to the structure of a ternary complex between cAMP-dependent kinase , AMPPNP , and an inhibitor peptide in which the acceptor serine 17 is replaced by an alanine [21] ( Figure 6B ) . This comparison revealed that AvrB residues necessary to elicit an RPM1-dependent HR correspond in position to residues critical for the kinase activity of this cAMP-dependent kinase . Also , the positions of the phospho-acceptor in the inhibitor peptide and the putative acceptor T166 of RIN4 are similarly positioned . In cAMP-dependent kinase , the nucleotide binds in a cavity between the small lobe and large lobe , with the small lobe making up the majority of the interactions , whereas in AvrB the nucleotide binds in the major cavity of the large lobe . As a result , the orientation of the nucleotide is different between the two structures . In cAMP-dependent kinase D168 deprotonates the acceptor serine/threonine residue and T201 positions D168 by hydrogen bonding . A similar role can be envisioned for AvrB H217 and T125 , respectively , with regard to the putative acceptor T166 of RIN4 . In addition , cAMP-dependent kinase K72 plays a role in phosphate binding and in stabilization of the transition state . It is analogous in position to R99 in AvrB . Although R266 in AvrB could also play this role , this residue is spatially most similar to D184 in cAMP-dependent kinase , which is involved in ligating a Mn2+ ion that chelates the terminal phosphate group . In the AvrB/ADP structure , we could not definitively observe a Mg2+ ion . The mechanism of metal binding in AvrB complexes remains to be elucidated . Finally , protein kinases contain an activation loop joining the two lobes whose phosphorylation is necessary for protein kinase activity . In cAMP-dependent kinase , phosphorylation of T197 in the activation loop ( residues 191 to 199 ) is necessary for kinase activity . In AvrB , residues 115 to 121 could form the activation loop , and three residues in this loop ( T118 , S119 , and T121 ) could serve as phosphorylation sites . Additionally , this loop also borders the region ( residues 120 to 125 ) that forms an antiparallel beta-sheet with residues 166 to 171 of RIN4 . Hence , phosphorylation of this loop region may also affect RIN4 binding . Despite this plausible similarity to kinases , pure AvrB neither autophosphorylates nor transphosphorylates pure RIN4 in vitro ( unpublished data ) . However , we found that AvrB is phosphorylated in the presence of Arabidopsis extracts , suggesting that AvrB's possible kinase activity would require accessory plant factors . In vitro , this phosphorylation event is RIN4 and RPM1 independent . The P . syringae type III effector avirulence protein Pseudomonas tomato ( AvrPto ) is also phosphorylated in the presence of plant extracts independently of its corresponding plant disease resistance proteins , Pto and Prf [22] . Additionally , NopL and NopP are TTSS effector proteins from Rhizobium sp . NGR234 that are phosphorylated in the presence of protein extracts from L . japonicus [23 , 24] . Hence , there is a class of type III effector proteins that are phosphorylated once delivered to the host cell . We anticipate that this modification is linked to effector activation in all of these cases . AvrB phosphorylation in the presence of Arabidopsis extract is dependent on the AvrB nucleotide-binding residues we defined , such as Y65 and R266 . While nucleotide binding residues are required for AvrB to be phosphorylated , they are not sufficient , because AvrC and Xanthomonas campestris campestris avirulence protein ( AvrXccC ) encode the conserved nucleotide-binding region and are not phosphorylated in the presence of Arabidopsis extract . These data suggest that nucleotide bound to AvrB is required for the recruitment and/or function of a host kinase . Alternatively , AvrB could act as a “protokinase” that lacks intrinsic phosphor-transfer activity that can be enhanced by association with plant accessory protein ( s ) . We speculate that each AvrB family member has evolved to usurp a plant species-specific protein that contributes to their activation by phosphorylation following delivery into host cells . The ability of AvrB to trigger RPM1 function requires its nucleotide-binding pocket , which in turn is required for AvrB phosphorylation induced by an unknown Arabidopsis cofactor ( s ) or kinase , and interaction with RIN4 . Neither AvrBY65A , AvrBY131A , nor AvrBD297A nor bound RIN4 in ITC ( Figure 3 ) , although all three did in gel filtration and/or yeast two-hybrid assays ( Table 2 ) . The most parsimonious explanation for these binding data requires very slow association and dissociation rates of complex formation for AvrB and RIN4 . For the wild-type proteins , these slow rates result in a modest equilibrium ( kd approximately 1 μM ) measurable by ITC and sufficient for isolation of AvrB–RIN4 complexes when the two proteins are incubated together prior to gel filtration chromatography . Similarly , the two proteins would likely interact when coexpressed during yeast two-hybrid analysis . We propose , however , that AvrBY65A , AvrBY131A , and AvrBD297A preferentially decrease the on-rate for AvrB–RIN4 complex formation . Hence , complexes could still form and be isolated by gel exclusion chromatography or inferred from yeast two-hybrid data due to the very slow off-rate . However , the resulting decrease in the overall equilibrium constant would prevent accurate affinity measurement by ITC . Implicit in this model is the likely requirement for a conformational change in AvrB that is slow and required prior to the binding of RIN4 . Importantly , Y131 and D297 reside at the interlobal boundary of AvrB , and their substitution can easily be envisaged to shift the conformational equilibrium of AvrB and lock it in a state unfavorable for RIN4 association . This interpretation is consistent with the finding of Ong and Innes ( 2006 ) that AvrBD297A enhanced binding of AvrB to RIN4 in their yeast two-hybrid system . Our data are consistent with the activity of the AvrB protein family ( Figure 5D ) . None of the AvrB homologs induce RPM1 function , although the Xanthomonas campestris protein AvrXccC interacts with RIN4 in yeast two-hybrid experiments and binds RIN4 weakly in vitro ( unpublished data ) . Most of the AvrB-RIN4 contact residues are poorly conserved , particularly those in α6 and α7 helices , including AvrB Q208 , R209 , and Y210 , which support the ring-stack of RIN4 . For example , these AvrB residues correspond to AvrC residues A239 , A240 , and S241 . AvrB T125 , located on β1 , is essential for RIN4 interaction and is conserved in all homologs of AvrB . Hence , the regions of AvrB that support the ring-stack of RIN4 appear to contribute to the functional specificity of AvrB for RIN4-dependent , RPM1-mediated HR . By contrast , the important ADP-contacting residues Y65 , R99 , and R266 in the lower lobe pocket are conserved in all AvrB homologs , suggesting that nucleotide binding is a core function for this entire type III effector family . However , an intact ADP binding cavity is not sufficient for phosphorylation of AvrB by Arabidopsis extracts , since the AvrC and AvrXccC possess them ( including N62 and F113 ) but are not readily phosphorylated ( Figure 5A ) . Furthermore , while AvrXccC interacts with RIN4 , at least in yeast , and possesses the nucleotide-binding pocket , it does not trigger RPM1 . This suggests that RIN4 interaction and nucleotide binding are not sufficient for the activation of RPM1 by AvrB family members . AvrB Y131 and D297 are 4 Å away from the nucleotide binding site and are located in the solvent-exposed region of the interlobe cleft . Many of the solvent-exposed residues of the interlobe cleft are highly conserved within the AvrB family ( Figure 5C ) . The electrostatic surface of the interlobe cleft and the sheer size of the cleft highly suggest that this might be an active site required for an as-yet-undefined activity of AvrB , or for the docking of an Arabidopsis protein that is required for AvrB phosphorylation . We speculate that the divergent sequences of the AvrB paralogs , centered on the interlobe cleft , are critical for recruitment of plant species-specific cofactors that enhance nucleotide turnover on each AvrB family member and/or align substrates of AvrB with potential catalytic residues in the cleft . We speculate that this set of atomic events would be required to trigger RPM1 ( or other NB-LR ) function in diverse plant species . Besides acting as an avirulence factor to trigger resistance in Arabidopsis and soybean , AvrB can also serve as a virulence factor in susceptible hosts . In the absence of a functional Rpg1-b gene , AvrB enhances the growth of P . syringae on susceptible soybean cultivars [19] Similarly , AvrB also induces a cytotoxic yellowing response on Arabidopsis plants lacking RPM1 that is attributable to either its function in virulence or a weak disease resistance response [17] . Mutation of AvrB T125 , R266 , or S268 to alanine abrogated the virulence phenotype in soybean and compromised the chlorosis phenotype in Arabidopsis [20] . Combined with our structural data , these results indicate that RIN4- and ADP-binding regions , as well as functions provided by the interlobe cleft , are required for the virulence activity of AvrB . Therefore , interaction with RIN4 , nucleotide binding , and host phosphorylation are correlated with both the virulence activity of AvrB and for its recognition by independent plant NB-LRR proteins . This corroborates the “guard” model hypothesis where NB-LRR proteins monitor the activity of type III effectors for recognition rather than direct interaction [4 , 6] . Our three-dimensional crystal structures of AvrB in complex with its host target RIN4 or the ADP , and the combined functional studies detailed here and in Ong and Innes ( 2006 ) , suggest a plausible series of events required for both AvrB virulence activity on susceptible hosts and for its ability to trigger disease resistance recognition in two plant species: AvrB is delivered by the TTSS of Pto DC3000 . Inside the host cell , AvrB binds to a nucleotide , or another small molecule of similar shape . The AvrB–nucleotide complex recruits a plant cofactor that transforms AvrB into a kinase capable of autophosphorylation . Alternatively , the nucleotide-bound form of AvrB mimics a substrate for an unknown plant kinase and becomes phosphorylated itself . Because soluble AvrB can be labeled in an Arabidopsis extract , we infer that phosphorylation of AvrB is independent of , and hence might precede , myristoylation and plasma membrane localization [17] . Phosphorylated AvrB becomes myristoylated and directed to the plasma membrane . At the plasma membrane , AvrB interacts with RIN4 , and its conformation is altered . This stable heterodimer guides RIN4 phosphorylation . The AvrB–RIN4 complex , and potentially the phosphorylated form of RIN4 itself , triggers RPM1-mediated activation of disease resistance . RIN4 functions as a negative regulator of basal defense [25] . Hence , in the absence of RPM1 , AvrB is similarly activated and subsequently interacts with , and indirectly induces post-translational modifications of RIN4 and other BBS-containing proteins [15] in order to curb basal defense responses and contribute to disease . For expression in P . syringae , AvrB-HA downstream of the AvrRpm1 promoter [17 , 26] was constructed by PCR amplification using primers that incorporated an XhoI site upstream of the AvrRpm1 promoter ( XhoI-AvrRpm1p ) and a BamHI site downstream of the HA epitope tag ( BamHI-HA ) . The resulting PCR product was digested with the restriction enzymes XhoI and BamHI and cloned into the broad host range vector pBBR1 MCS-2 [27] digested with the same enzymes . Mutations of AvrB were generated by PCR using PFU turbo high-fidelity polymerase ( Stratagene , http://www . stratagene . com ) . Overlapping primers incorporating the mutation of interest were synthesized , and PCR was conducted using the sense primer with BamHI-HA and the antisense primer with XhoI-AvrRpm1p . The resulting PCR products were gel purified , combined , and used as a template for a second PCR using the XhoI-AvrRpm1p and BamHI-HA primers . The resulting PCR product was cloned into the TOPO TA cloning vector ( Invitrogen , http://www . invitrogen . com ) and sequenced to ensure that no additional mutations had been introduced . The insert was then cleaved using the restriction enzymes XhoI and BamHI and cloned into pBBR1 MCS-2 digested with the same enzymes . pBBR1 MCS-2 containing the mutant AvrB-HA genes were then introduced into Pto DC3000 by triparental mating . AvrB alleles from X . campestris pv . campestris strain 8004 ( Xcc ) , P . syringae pv . glycinea race 0 ( AvrC ) , and P . syringae pv . syringae strain B728A were PCR amplified from the corresponding bacterial strains and cloned into TOPO-TA ( Invitrogen ) cloning vectors ( Z . Nimchuk and J . L . Dangl , unpublished data ) . To add an N-terminal glutathione-S-transferase ( GST ) tag , alleles were PCR amplified from TOPO-TA vectors using oligonucleotides that incorporated an N-terminal TEV cleavage site and subcloned into the pENTR D-TOPO vector ( Invitrogen ) . The genes were then recombined into pDEST-15 vector using the LR Clonase enzyme mix according to manufacturer's instructions ( Invitrogen ) . Similarly , mutated versions of AvrB were amplified from TOPO-TA and subcloned into pENTR D-TOPO followed by recombination into pDEST15 using LR recombination enzymes ( Invitrogen ) . AvrB in pProEX-HTa was induced with 0 . 75 mM isopropyl-β-d-thiogalactopyranoside ( IPTG ) at 18 °C for 6 h in BL21 Rosetta cells ( Stratagene ) . All protein purification steps were performed at 4 °C . Cell pellets were resuspended in buffer A ( 20 mM Tris [pH 8 . 0] , 300 mM NaCl , and 10 mM imidazole ) plus one “Complete EDTA-free” protease inhibitor tablet ( Roche , http://www . roche . com ) , a few crystals of Lysozyme ( Sigma , http://www . sigmaaldrich . com ) , and DNase ( Sigma ) . After resuspension , cells were lysed using an Avestin Emulsiflex-C5 ( Avestin , http://www . avestin . com ) and centrifuged for 45 min at 15 , 000 rpm in an SS-34 rotor . The supernatant was loaded on to a 5 ml High Trap chelating column ( GE Healthcare , http://www . gehealthcare . com ) preloaded with nickel as described in the manufacturer's instructions . The column was then washed with 10 column volumes of buffer A , followed by 10 column volumes of buffer A augmented with 50 mM imidazole . Specific elution of AvrB was performed with 5 column volumes of buffer A containing 400 mM imidazole . Relevant fractions were pooled and dialyzed overnight at 4 °C in low-salt buffer containing 20 mM Tris ( pH 8 . 0 ) and 100 mM NaCl in the presence of tobacco etch virus ( TEV ) protease to facilitate removal of N-terminal His tags . Removal of tags was verified by SDS-PAGE . The dialysate was loaded on an 8-ml Source Q ( GE Healthcare ) anion exchange column and eluted with a 0 to 400 mM NaCl gradient . If samples were not sufficiently pure at this point , relevant fractions were concentrated to approximately 10 ml and applied to a HighPrep 26/20 Sephacryl S200 ( GE Healthcare ) column equilibrated with 20 mM HEPES ( pH 7 . 5 ) , 150 mM NaCl , and 1 mM DTT . Purified protein was exchanged into 20 mM Tris ( pH 8 . 0 ) , 50 mM NaCl , and 3 mM DTT; concentrated to approximately 20 mg/ml and flash-frozen using liquid N2; and stored at −80 °C . All AvrB mutants were cloned into the PD15 plasmid ( GATEWAY; Invitrogen ) and thus isolated as TEV-cleavable GST fusions . Induction was also performed with 0 . 75 mM ITPG at 18 °C for 6 h in BL21 Rosetta cells ( Stratagene ) . Cell pellets were resuspended in buffer B ( 20 mM Tris [pH 7 . 5] , 300 mM NaCl , and 1 mM DTT ) and lysed as described for wild-type AvrB . Clarified lysates were loaded on a 5-ml High Trap glutathione column ( GE Healthcare ) . The column was then washed with 10 column volumes buffer B , followed by specific elution by 3 to 5 column volumes of buffer B plus 10 mM glutathione . Eluted protein was digested overnight at 4 °C with TEV protease , and completely digested protein was diluted 5-fold , loaded on an 8-ml Source Q column , and eluted with a 0 to 400 mM NaCl gradient . Relevant fractions were then concentrated to less than 1 ml , flash-frozen in liquid N2 in approximately 250-μl aliquots , and stored at −80 °C . RIN4 was cloned into pGEX-6P-1 as a GST-fusion cleavable with PreScission protease ( GE Healthcare ) and expressed in RIL codon-plus cells ( Stratagene ) . Cells were grown to an OD of approximately 0 . 4 at 37 °C , and then the temperature was decreased to 25 °C for 45 min , and cells were induced for 3 h with 0 . 5 mM IPTG . Cell pellets were resuspended in buffer C ( 20 mM sodium phosphate [pH 6 . 5] , 2 mM DTT , 1 mM EDTA ) plus one “Complete EDTA-Free” protease inhibitor tablet ( Roche ) , a few crystals of Lysozyme ( Sigma ) , and DNase ( Sigma ) . Cells were lysed as described for AvrB . Clarified lysates were then loaded on a hand-poured 20-ml Fast Flow S ( GE Healthcare ) column , washed with low-salt buffer , and then eluted with a 20 column volume gradient of buffer C plus 0 to 500 mM NaCl . The resulting broad peak was concentrated to 50 ml , 50 units/ml PreScission protease was added , and the mixture was dialysed overnight into buffer D ( 20 mM HEPES [pH 7 . 5] , 50 mM NaCl , 2 mM DTT ) . Completely digested protein , as verified by SDS-PAGE , was then loaded on a 8-ml Source S column ( GE Healthcare ) and eluted with a 0 to 400 mM NaCl gradient . Relevant fractions were then pooled and again dialyzed overnight in buffer C . The dialysate was then run on a 8-ml Source Q column ( GE Healthcare ) and eluted with a 0 to 400 mM NaCl gradient . The purity of the samples was verified by SDS-PAGE , concentrated to approximately 2 . 5 mg/ml , flash-frozen in liquid N2 in approximately 250-μl aliquots , and stored at −80 °C . Gel filtration experiments of the mutant AvrB proteins were performed by loading approximately 0 . 3 to 0 . 5 mg of protein either alone or with roughly equimolar RIN4 in a volume of 1 ml onto a hand-poured calibrated 16/70 Superdex S-75 column . Flow rate was 0 . 9 ml/min , and 3-ml fractions were collected from 30 to 80 ml over a 150-ml run . Circular dichroism experiments were run on a Pistar-180 Circular Dichroism/Fluorescence spectrophotometer ( Applied Photophysics , http://www . photophysics . com ) . Samples at approximately 0 . 1 mg/ml were exchanged into a buffer containing 20 mM potassium phosphate ( pH 7 ) , and placed in a 0 . 1-cm cuvette , and scans were taken from 185 to 260 nm with 0 . 2-nm increments and 30 , 000 repetitions per increment . ITC experiments were performed on a VP-ITC microcalorimeter ( MicroCal , http://www . microcalinc . com ) . To verify binding , we used concentrations of wild-type and variant AvrB ranging between 5 and 25 μM AvrB . RIN4142–176 peptide with concentrations ranging from 50 to 450 μM was titrated in 6-μl injections with stirring at 255 rpm . Experiments involving wild-type AvrB and full-length RIN4 used 6 μM and 120 μM , respectively . Once binding was confirmed ( for wild-type and QRY/AAA ) , experiments were repeated in triplicate . Nonbinding was confirmed by increasing the concentrations of both the proteins to as much as 25 μM and 450 μM , respectively . Nonbinding variants were confirmed by repeating the experiment twice . Thermodynamic parameters were fit to the data using Origin v 7 . 0383 software ( OriginLab , http://www . originlab . com ) . Conditions for crystallization of AvrB and AvrB/RIN4142–176 were similar to those reported for AvrB previously [18] . Free AvrB was crystallized by vapor diffusion at 4 °C of a 1:1 mix of protein ( 8 to 10 mg/ml ) with well solution ( 100 mM glycine [pH 9 . 0] , 20% to 30% polyethylene glycol 550 monomethyl ether [PEG 550 MME] ) . AvrB/RIN4142–176 was also crystallized by vapor diffusion at 18 °C of an approximately 1:2 to 3 ratio of protein to peptide , mixed with an equal volume of well solution ( 100 mM Tris [pH 7 . 5] , 20% to 30% PEG 550 MME ) . Initial crystals were of poor quality and were heavily twinned . This crystalline mass was resuspended in 50 μl of well solution , broken up and serially diluted 10- , 100- , and 1 , 000-fold , and used for microseeding . In general , seeds yielded suitable quality crystals within 2 d . AvrB/RIN4142–176 crystals belonged to space group P2 ( 1 ) with cell dimensions a = 45 . 9 Å , b = 58 . 2 Å , c = 119 . 8 Å , and β = 89 . 9° , which corresponds to a very different packing than the P6 ( 5 ) crystals found for crystals of free AvrB [18] . Soaks of nucleotide were performed by exchanging drop and reservoir solutions with 20 μl of 27% PEG 500 MME and 100 mM Tris 7 . 5 ( with and without 5 mM MgCl2 ) , followed by a final exchange in the drop of this solution plus 5 mM nucleotide . Nucleotides were soaked for approximately 1 d . Following soaks , crystals were found to pack in the P6 ( 5 ) space group , with cell dimensions a = b = 122 . 7 Å , c = 64 . 1 Å , which is within 2 . 5 Å of the cell dimensions reported for free AvrB [18] . All crystallization solutions described in this section are inherently cryoprotective , and no further cryoprotection was found to be necessary . AvrB/RIN4142–176 diffraction data were collected on a Rigaku RU-H3R ( http://www . rigaku . com ) rotating anode generator equipped with Osmic confocal “blue” optics , and diffraction intensities were recorded on an R-Axis IV++ image plate system . For the ADP-soaked crystals , diffraction data were collected at the SER-CAT beamline ( ID-22; Advanced Photon Source , http://www . aps . anl . gov ) . All structures were solved by molecular replacement using AMoRe [28] using the previously solved free AvrB structure ( PDB code 1NH1 ) [18] . Upon molecular replacement followed by rigid-body refinement , simulated annealing , and individual B factor refinement using CNS [29] , difference electron density could be found for both the peptide and nucleotide ( see Figure 1A and D ) . Peptide and nucleotide were then modeled into the resulting difference density using the program O [30] . Definitions of ADP torsions in O , as well as topology and parameter files for all nucleotides , Tris , and trifluoroacetic acid , were taken from the Hic-Up server ( http://xray . bmc . uu . se/hicup ) . This was followed by iterative cycles of simulated annealing , B factor refinement , and water picking to reach the results shown in Table S2 . In addition , restrained 2-fold noncrystallographic symmetry was used during refinement of the AvrB/RIN4142–176 structure . From two AvrB/RIN4142–176 complexes in the asymmetric unit , 5 , 389 atoms are modeled , including residues 26 to 53 and 56 to 321 of AvrB and residues 150 to 172 of RIN4 , in both complexes . In addition , 325 water molecules , four Tris molecules , and two trifluoroacetic acid molecules were included in the model . For the AvrB/ADP structures , there are 1 , 503 atoms , including residues 16 to 319 of AvrB , 1 nucleotide , one Tris molecule , and 82 water molecules . For Western blot analyses , 1 . 5-ml overnight cultures grown in KB with the appropriate antibiotics were pelleted , washed with hrp gene-inducing minimal media [26] , and resuspended to an OD600 of 0 . 1 in minimal media . Then , 2 . 5 ml of the 0 . 1 OD600 culture was induced overnight and spun down the next day . Pellets were resuspended in 250 μl of 20 mM Tris/HCl ( pH 8 . 0 ) and sonicated twice for 10 s with a 1-min interval between . The sonicated culture was spun down at 4 °C for 20 min at 20 , 000g . Then , 200 μl of the supernatant was removed carefully so as not to disturb the pelleted and centrifuged again at 4 °C for 20 min . Next , 150 μl of the supernatant was carefully removed and soluble protein quantified . And 20 μg of protein of soluble protein from the wild-type and mutant AvrB-HA expressing Pto DC3000 strains was loaded onto SDS-PAGE gels after equalizing volumes with 20 mM Tris/HCl ( pH 8 . 0 ) , 6× Laemmli buffer was added , and it was boiled for 5 min . Immunodetection was performed by standard methods using anti-HA antibodies ( Roche ) at a dilution of 1:1 , 000 . Lactophenol–trypan blue was used to visualize dead cells 5 h postinoculation with Pto DC3000 expressing wild-type and mutant AvrB-HA constructs as previously described [31] . Electrolyte leakage assays were carried out as previously described [31] . Briefly , fully expanded leaves from 3-wk-old plants were hand-inoculated with 0 . 1 OD600 ( approximately 5 × 107 cfu/ml ) Pto DC3000 DC3000 expressing wild-type or mutant AvrB-HA constructs . At 2 h after infection , 7 . 5-mm leaf discs were collected and washed extensively with distilled water for 1 h . Four leaf discs were placed in a tube with 6 ml of distilled water ( four replicates per treatment ) , and conductivity was measured over time with a conductivity meter ( model 130; Orion Research , http://www . thermo . com ) . Plant extracts were prepared by grinding two or three 2-cm2 leaves to a fine powder in liquid nitrogen using a mortar and pestle . Then , 1 ml of grinding buffer ( 20 mM Tris/HCl [pH 8 . 0] , 50 mM NaCl , 0 . 01% Triton X-100 , 5 mM DTT ) was added to the powder in a 1 . 5-ml microcentrifuge tube , and the mixture was vortexed for 30 s , followed by centrifugation for 10 min at 2 , 000g to remove large cell debris . The supernatant was collected and used as total plant extract . The 20-μl reactions contained 100 ng of purified AvrB allele or AvrB mutant , 1 μg of plant extract , 10 μM [γ32P]ATP ( 1 . 2 μCi; Amersham Biosciences , http://www . amershambiosciences . com ) , 100 μM ATP , and 10 mM MgCl2 . Reactions were allowed to proceed for 10 min and terminated by adding 5 μl of 5× Laemmli buffer and boiling for 5 min . Reactions were loaded onto 12% SDS-PAGE gels , and incorporated radiolabel was visualized by autoradiography . As an equal loading control for proteins used in the kinase reactions , the SDS-PAGE gels were stained by Coomassie blue after detection . Pto DC3000 strains containing the wild-type or mutant AvrB-HA constructs were streaked out onto King's medium B ( KB ) plates containing the appropriate antibiotics and incubated at 28 °C overnight . Bacteria were then scraped off the plate and resuspended to an OD600 of 0 . 0002 ( approximately 1 × 105 cfu/ml ) in 10 mM MgCl2 . Three-week-old plants were hand-inoculated with the diluted bacterial solution . Each sample was collected in quadruplicate using four leaves for each time point ( 16 discs per time point ) . Leaf discs were bored from the infiltrated area , ground in 10 mM MgCl2 , and serially diluted to quantify bacterial numbers . For yeast two-hybrid analysis , avrB and its mutant derivatives were cloned into the Gateway-compatible LexA binding domain ( BD ) fusion vector pEG202 using LR Clonase II enzyme mix ( Invitrogen ) . The LR reaction was left to proceed overnight at 16 °C . Yeast two-hybrid analysis was performed using the MATCHMAKER LexA system ( Clontech , http://www . clontech . com ) following the manufacturer's protocols . The yeast strains used in this study are EGY48 ( Clontech ) and RFY206 . RIN4 was expressed from the plasmid pJG4–5 as B42 activation domain ( AD ) fusions and transformed into yeast strain EGY48 ( MATα ) . avrB and its mutant derivatives were expressed from plasmid pEG202 and transformed into the yeast strain RFY206 ( MAT a ) carrying the lacZ reporter plasmid pSH18–34 ( +pSH18–34 ) . Preparation of highly competent yeast cells and small-scale lithium acetate transformations were performed using the Frozen-EZ Yeast Transformation II Kit ( Zymo Research , http://www . zymoresearch . com ) . The RFY206 ( +pSH18–34 ) transformants carrying pEG202:AvrB and its mutants were selected on minimal SD glucose agar base [0 . 7% yeast nitrogen base without amino acids and 2% bacto-agar supplemented with –Ura–His Dropout ( DO ) ] ( Qbiogene , http://www . qbiogene . com ) . The EGY48 transformants expressing pJG4–5 containing RIN4 were selected on minimal SD glucose agar base supplemented with –Trp DO ( Qbiogene ) . After plating , the plates were incubated for 3 to 4 d at 30 °C until colonies appeared . Pairwise matings were set up between RFY206 ( +pSH18–34 ) :LexABD-avrB strains and EGY48:B42AD-RIN4 or EGY48:B42AD . The standard yeast mating procedure was followed according to the manufacturer's protocols ( Clontech Yeast Protocols Handbook ) . A 100-μl aliquot of the mating culture was spread on SD Glucose Agar base supplemented with –Ura/–His/–Trp DO to select for diploids . Plates were incubated at 30 °C for 3 to 4 d to allow yeast cotransformants to form visible colonies . In the diploid strain , the two reporters are LEU2 and lacZ . To assay for protein–protein interactions , yeast cotransformants were replica plated onto two different selective media containing galactose ( Gal ) to induce the expression of B42AD-RIN4 protein . Plates were incubated at 30 °C for 3 to 4 d until growth was detected . 1 . SD Gal agar base supplemented with –Ura/–His/–Trp DO to confirm the nutritional phenotypes of the diploid by selecting for the LexABD , B42AD , and pSH18–34 vectors . 2 . SD Gal agar base supplemented with –Ura/–His/–Trp/–Leu/X-Gal DO to screen for Leu2 and lacZ reporter gene expressions . Growth and blue color were monitored based on activation of the reporter genes and were scored as a positive interaction between the fusion proteins . No interaction was scored if the replicates grew only on Gal/SD/–Ura/–His/–Trp DO plate and not on agar base supplemented with Gal/SD/–Ura/–His/–Trp/–Leu/X-Gal . To determine LexABD-AvrB accumulation in yeast , RFY206 yeast cultures were grown in selective medium overnight . The cultures were diluted to an OD600 = 0 . 15 – 0 . 2 . The cultures were continuously monitored until an OD600 of 0 . 4 to 0 . 6 was reached . The cells were pelleted and proteins were extracted and boiled in 50 mM sodium phosphate ( pH 7 . 0 ) , 25 mM 2-morpholinoethanesulfonic acid ( MES ) ( pH 7 . 0 ) , 3 M urea , 1% SDS , 10% β-mercaptoethanol ( BME ) , and 0 . 1% Bromophenol Blue supplemented with Complete Protease Inhibitor Pellets ( Roche ) . The boiled samples were spun briefly in the tabletop centrifuge to pellet cell debris . A volume equivalent to 0 . 5 total OD600 was loaded into each well on a 10% SDS-PAGE gel . After electrophoresis , the proteins were transferred from the SDS-PAGE gel to nitrocellulose membrane support . Western blots were done by standard methods . Anti-LexBD antibody was used at 1:100 ( Santa Cruz Biotechnology , http://www . scbt . com ) . Detection of LexA-AvrB was with the goat monoclonal antibody . Detection of the peroxidase signal of the secondary antibody-HRP conjugate was performed with ECL ( Amersham Biosciences ) . Selected AvrB mutants were fused to Δ79AvrRpt2 by cloning into Gateway-compatible pBBR1-MCS2 [32 , 33] using LR clonase and transformed into Escherichia coli DH5α . Each construct was introduced in Pto strain DC3000 by triparental matings . Infiltrations of Arabidopsis rpm1–3 were done as described [9] . The HR was scored 24 h after Pto DC3000 inoculations . Results were compared with leaves infiltrated with Pto carrying full-length avrRpt2 or an empty vector . Crystallographic coordinates are deposited at the RCSB Protein Data Bank ( http://www . rcsb . org/pdb ) with the codes 2NUD and 2NUN for the AvrB/RIN4 and AvrB/ADP complexes , respectively; and 1CDK for inhibitor peptide in which the acceptor serine 17 is replaced by an alanine . The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession numbers for the paralogs used in Figure 5 are AvrB ( P13835 ) , AvrC ( P13836 ) , AvrPphC ( AAV68743 ) , AvrB2 ( YP_272229 ) , AvrB4–1 ( YP_275207 ) , AvrB4–2 ( YP_273068 ) , AvrXccC ( XCC2109 ) , AvrB Psy1 ( AAN85189 ) , AvrB Psy2 ( AAF71496 ) , and AvrB3 ( YP_275207 ) .
Many bacterial pathogens use a specialized protein “injection needle” called a type III secretion system to help colonize cells of higher organisms . The type III secretion needle attaches to a host cell and is the delivery conduit for a variety of bacterial proteins that act inside of the host cell . These proteins are called type III effectors . They manipulate host cell biology in order to help the bacterial pathogen colonize the host . We studied one type III effector from plant pathogenic bacteria called Pseudomonas syringae . This effector , termed avirulence protein B ( AvrB ) , is targeted to the inner face of the plant cell plasma membrane , where it interacts with a membrane-bound host protein called RIN4 ( resistance to Pseudomonas maculicula protein–interacting protein ) . RIN4 is phosphorylated in the presence of AvrB and an as-yet-unknown additional host factor . We provide a structural basis for the binding of AvrB to RIN4 and a possible mechanism of action for AvrB inside the host . AvrB activation and its ability to bind RIN4 have evolved to help the pathogen , yet in Arabidopsis , the AvrB-dependent phosphorylation of RIN4 is sensed by the plant immune system , leading to a rapid halt in pathogen growth .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods", "Supporting", "Information" ]
[ "plant", "immune", "system", "plant", "biology", "type", "iii", "effector", "arabidopsis", "microbiology" ]
2007
Type III Effector Activation via Nucleotide Binding, Phosphorylation, and Host Target Interaction
We describe an innovative experimental and computational approach to control the expression of a protein in a population of yeast cells . We designed a simple control algorithm to automatically regulate the administration of inducer molecules to the cells by comparing the actual protein expression level in the cell population with the desired expression level . We then built an automated platform based on a microfluidic device , a time-lapse microscopy apparatus , and a set of motorized syringes , all controlled by a computer . We tested the platform to force yeast cells to express a desired fixed , or time-varying , amount of a reporter protein over thousands of minutes . The computer automatically switched the type of sugar administered to the cells , its concentration and its duration , according to the control algorithm . Our approach can be used to control expression of any protein , fused to a fluorescent reporter , provided that an external molecule known to ( indirectly ) affect its promoter activity is available . A crucial feature of biological systems is their ability to maintain homeostasis in spite of ever-changing environmental and intracellular conditions . In man-made systems , this ability can be engineered in devices ranging from the simple thermostat to the complex autopilot of a modern plane using “controllers” , which operate via a simple “negative feedback” mechanism ( Figure 1 ) : the quantity to be controlled ( ) is measured ( ) via a sensor ( whose dynamics are described by ) , then subtracted from the desired reference value ( ) ( i . e . negative feedback ) , and the resulting error ( ) is used by the controller to compute the “control action” ( ) to be implemented ( or actuated ) on the physical system ( e . g . switching on or off the heating , changing the angular position of the rudder ) . Control engineering has been applied as a powerful theoretical framework to elucidate the underlying principles driving gene networks [1]–[4] , to predict their dynamics and their robustness to noise [5] , [6] , and to theoretically demonstrate the possibility of steering gene network dynamics [7]–[9] . More recently , other groups have reported experimental applications of control engineering to drive gene expression from artificial inducible promoters by means of external stimuli ( e . g . light or osmotic pressure ) either in single cells , or across a cell population [10]–[12] . Toettcher and colleagues documented a successful application of closed-loop optogenetic control of membrane recruitment system in mammalian cells ( characteristic time of process in the order of seconds ) [11] . Milias-Argeitis and colleagues showed how population level control of a light-switchable gene system ( characteristic time in the order of minutes ) can be achieved by manually sampling a liquid culture of S . cerevisiae , then estimating target protein concentration via flow cytometry and control this quantity via Model Predictive Control and Kalman Filtering [12] . While this manuscript was under review , Uhlendorf and colleagues reported a successful attempt to drive gene expression from the Hog1 promoter in yeast using as control input changes in osmolarity [13] . These authors coupled an external feedback strategy to microscopy and microfluidics to achieve fully automated external control of the reporter fluorescent protein up to 15 hrs . Here we present , for the first time , the development and application of an automatic control system to regulate at will the level of expression of a protein from the GAL1 endogenous promoter in an exponentially growing population of yeast cells . We also demonstrated the ability of the control system to regulate at will protein expression from a complex synthetic transcriptional network . We controlled protein expression level by changing in real-time the concentration of a set of inducer molecules , known to modulate its expression ( i . e . galactose and glucose ) . We first demonstrated the ability of our control platform to regulate the level of expression of a reporter protein fused to the Gal1p protein from the endogenous GAL1 promoter ( Figure 2a ) . We then applied the same control platform to regulate the level of expression in a complex synthetic gene network ( Figure 2b ) , where the inducer molecule ( galactose ) directly activates the transcription factor , Gal4p , which in turns drives the expression of another transcription factor ( Swi5p ) , which finally binds the promoter driving the expression of the reporter protein ( Cbf1p-Gfp ) . Due to these multiple transcriptional/translational steps ( Gal4p Swi5p Cbf1-Gfp ) , the system is much slower compared to simple promoter-reporter systems , thus control becomes much more challenging due to the delay introduced by this indirect regulation . Our approach is applicable to a large class of gene networks to control expression of a protein of interest from an endogenous promoter , provided that: ( a ) an external molecule known to affect ( even indirectly ) the promoter activity is available; ( b ) a fluorescent reporter is fused to the protein; and ( c ) either a mathematical model of the gene network is available or dynamical properties of the bioprocess are compatible with a PI-PWM control configuration ( see “Control Objective and Control Strategy” and Figures S7–S8 ) . The microfluidic device presented in [14] has been used to trap cells and perform experiments . To this aim , a master mold has been produced using a ( ) silicon wafer as substrate ( Silicon Valley Microelectronics , US ) . In order to develop this device , we used multilayer soft-lithography with SU-8 ( Microchem , US ) as photoresist . Once the mold was ready we used ( Tridecafluoro-1 , 1 , 2 , 2-Tetrahydrooctyl ) -1-Trichlorosilane ( Sigma-Aldrich , US ) to prevent polymer from sticking to microstructures; at this point replica molding allowed us to obtain functional devices ( see Supplementary Information ) . The experimental setup is the same for both strains of cells used in this study ( yGIL337 and IC18 ) . Batch cultures were cultured for in Synthetic Complete + Galactose ( ) + Raffinose ( ) and repeatedly diluted . On the day designated for the actual control experiment , syringes featuring both Synthetic Complete + Glucose ( ) and Synthetic Complete + Galactose ( ) + Raffinose ( ) were connected to the device; syringes filled with ddH2O were attached to sink ports . Media and sugars filled syringes were attached to a computer controlled linear guide; the initial position for the syringes was above the level of the device , while the ddH2O syringes were set at . Hydrostatic pressure drove the flow of media in the device . We then loaded cells into the microfluidic device . Visual inspection at and magnifications allowed to exclude the presence of air bubles in the channels . At this point , the imaging field was set on the cell trap ( see [14] for references ) and the control algorithm was started . In depth details concerning this procedure are reported in Supplementary Information . Microscopy image acquisition has been carried out by with NIS Elements v . 3 . 22 software . Phase contrast and fluorescent images were acquired at intervals of . The control algorithm was synchronized with the acquisition process by running a polling routine that checked for the presence of new files in a predefined folder . Once a new set of images was found , the control algorithm run an image processing sub-routine meant to segment the phase contrast image ( see Supplementary Information for more details ) , locate the cells and obtain a binary mask that was used to select only pixels belonging to cells in the field . This mask was employed in the calculation of the population average fluorescence as reported in Supplementary Information . Our control scheme then used this value as a readout of the signal and then proceeded to the computation of the input action . For the in-vivo control implementation , we designed and implemented an integrated platform based on a microfluidic device , a time-lapse microscopy apparatus , and a set of actuated syringes , all controlled by a computer , as depicted in Figure 3 . At the core of this platform lies a microfluidic chip [14] . The chip has a micro-chamber ( height: ) which “traps” yeasts , which can only grow in a monolayer , thus making their automated image analysis easier . Once loaded in the device , yeasts can be exposed to any combination of two inducer compounds by simply modulating the difference in hydrostatic pressures at the two inlets , thanks to the Dial-a-Wave system [14] ( Supplementary Information ) . This can be easily achieved by varying the vertical position of syringes filled with sugar-supplemented media using motorized linear rails . A Finite State Automaton ( FSA ) , implementing the control logic , runs on a computer and , at intervals of 5 minutes , analyses the images automatically captured by the microscope hosting the microfluidic chip . A custom image processing algorithm locates the yeast cells in phase contrast and quantifies the population average Gfp intensity ( Supplementary Information and Figure S9 ) . This information is then used by the controller to compute the relative duration of Galactose/Glucose pulses , which must be applied to meet the control objective . The experimental results described here convincingly demonstrate that the expression of a protein can be controlled in vivo in real-time , using an inducer molecule acting directly or indirectly on protein expression , by applying principles drawn from classical control theory , and without requiring detailed quantitative knowledge of the process to be controlled , at least in the case of set-point regulation . An experimental control platform , sharing some similarity with our work , was presented as this manuscript was under review [13] . Differently from our approach , the control scheme proposed by the authors enabled regulation of a reporter protein expression from the Hog1 promoter using osmotic pressure as a control input . The authors implemented a model predictive control scheme which relied on a pre-existing quantitative model of the Hog1 promoter to be controlled , which may not always be available when applying the scheme to a different promoter . Since the authors , for their control scheme , exploited the osmolarity pathway , which shows adaptation , they needed to develop a model-based control approach to predict the system's behaviour . One of the advantages of our control scheme is that it can use as input any molecule and thus it may be easily transferred to the control of any other endogenous promoter , or gene network , whose dynamics can be elicited by external molecules and for which a measurable estimate of the output is available . Another useful feature is the use of a PI controller that requires minimal knowledge of the model of the system to be controlled . This generality , however , comes at a price: first , if the biological system to be controlled exhibits adaptation , or strong non-linear behaviors such as hysteresis , the PI controller is likely to fail , and a model-based control approach may be required [13] , [27] ( e . g . lack of controllability as investigated in Liu et al . [28] ) ; second , the need to construct a fusion protein with a fluorescent reporter , may disrupt the physiological function of the protein . In addition to providing an innovative platform to control protein expression in a completely automatic fashion , our results show also that binary digital pulses of an inducer molecule can be encoded and interpreted by the cell population to produce an “analog” response , i . e . a triangular wave of protein expression , or constant level of the protein . Digital-to-analog and analog-to-digital conversion are key features of signaling pathways . Gradients of extracellular stimuli are converted into an all-or-none responses by signaling pathways [29] . These digital responses , in turn , are decoded by the cells to generate analog time-varying transcriptional responses ( digital-to-analog conversion ) . Here we show that this core mechanism can be exploited by artificial control systems to modify at will gene and protein expression . In this context , we wish to emphasize that , while we focused on a pulse width modulation scheme alternative strategies can indeed be devised ( e . g . Pulse Amplitude or Pulse Frequency Modulation ) . The control quality obtained by our control scheme is remarkably good in the case of the Gal1 endogenous promoter , but it may seem unsatisfying in the case of the IRMA network when compared to classic control engineering approaches applied to engineering systems and devices . This is the first attempt to control gene expression in a complex network using feedback control in a noisy biological system . Indeed , the presence of cell-to-cell variability is one of the key obstacles when implementing control strategies for living systems . This is why here we aimed at controlling the average fluorescence level of the cell population , which is shown to converge towards the desired value . Moreover , the control scheme keeps biological noise from increasing and at a physiological level as estimated by the CV . Interestingly , in a related work [13] reporting the result of a control strategy applied to a simpler gene circuit ( a single promoter ) , the observed cell-to-cell variability is comparable to ours , demonstrating the inevitability of biological noise , and the challenges laying ahead for controlling gene expression in living cells . The microfluidics-based control strategy we developed enables control experiments using small volumes of reagents with minimal perturbations to the cells . It can be easily implemented with limited costs to fine tune the expression of a protein of interest from an endogenous promoter with minimal intervention ( i . e . introduction of a fluorescent reporter gene ) . We believe that experimental biologists will find new and clever ways to apply our approach to study trafficking or signalling pathways and the endogenous control mechanisms of a cell . Indeed the ability to simply overexpress a protein has led to innumerable new discoveries , and with our work we are providing a new ability which could be beneficial to many .
A crucial feature of biological systems is their ability to maintain homeostasis in spite of ever-changing conditions . In engineering , this ability can be embedded in devices ranging from the thermostat to the autopilot of a modern plane using control systems which operate via a negative feedback mechanism: the quantity to be controlled is measured then subtracted from the desired reference value , and the resulting error is used to compute the control action to be implemented on the physical system ( e . g . switching on or off the heating , changing the position of the rudder ) . Here , we developed and applied a method to regulate the expression level of a protein , in a growing population of cells over several generations , in a completely automatic fashion . We designed and implemented an integrated platform comprising a microfluidic device , a time-lapse microscopy apparatus , and a set of motorized syringes , all controlled by a computer . We tested the platform to force yeast cells to express a desired time-varying amount of a gene in yeast . Our method can be applied to control a protein of interest in vivo allowing to probe the function of biological systems in unprecedented ways .
[ "Abstract", "Introduction", "Materials", "and", "Methods", "Results", "Discussion" ]
[ "biotechnology", "bioengineering", "systems", "biology", "biological", "systems", "engineering", "control", "engineering", "engineering", "and", "technology", "synthetic", "biology", "biology", "and", "life", "sciences", "computational", "biology" ]
2014
In-Vivo Real-Time Control of Protein Expression from Endogenous and Synthetic Gene Networks
In West Africa , the principal vectors of lymphatic filariasis ( LF ) are Anopheles species with Culex species playing only a minor role in transmission , if any . Being a predominantly rural disease , the question remains whether conflict-related migration of rural populations into urban areas would be sufficient for active transmission of the parasite . We examined LF transmission in urban areas in post-conflict Sierra Leone and Liberia that experienced significant rural-urban migration . Mosquitoes from Freetown and Monrovia , were analyzed for infection with Wuchereria bancrofti . We also undertook a transmission assessment survey ( TAS ) in Bo and Pujehun districts in Sierra Leone . The majority of the mosquitoes collected were Culex species , while Anopheles species were present in low numbers . The mosquitoes were analyzed in pools , with a maximum of 20 mosquitoes per pool . In both countries , a total of 1731 An . gambiae and 14342 Culex were analyzed for W . bancrofti , using the PCR . Two pools of Culex mosquitoes and 1 pool of An . gambiae were found infected from one community in Freetown . Pool screening analysis indicated a maximum likelihood of infection of 0 . 004 ( 95% CI of 0 . 00012–0 . 021 ) and 0 . 015 ( 95% CI of 0 . 0018–0 . 052 ) for the An . gambiae and Culex respectively . The results indicate that An . gambiae is present in low numbers , with a microfilaria prevalence breaking threshold value not sufficient to maintain transmission . The results of the TAS in Bo and Pujehun also indicated an antigen prevalence of 0 . 19% and 0 . 67% in children , respectively . This is well below the recommended 2% level for stopping MDA in Anopheles transmission areas , according to WHO guidelines . We found no evidence for active transmission of LF in cities , where internally displaced persons from rural areas lived for many years during the more than 10 years conflict in Sierra Leone and Liberia . Lymphatic filariasis ( LF ) is a major cause of acute and chronic morbidity in humans in 73 countries in Asia , Africa , the Western Pacific and the Americas . Nearly 1 . 4 billion people are exposed to infection from three mosquito-borne filarial parasites ( Wuchereria bancrofti , Brugia malayi and B . timori ) [1] . These parasites have biphasic life cycles involving humans and various species of mosquito vectors from the genera Anopheles , Aedes , Culex , Mansonia and Ochlerotatus . Culex mosquitoes are the principal vectors of LF in Asia and the Americas but also play an important role in transmission in East Africa . The urban mosquito , Culex quinquefasciatus , is an important vector in the Tanzanian capital , Dar es Salaam , and the principal vector on the islands of Zanzibar in the same country in East Africa . Culex mosquitoes are common in large cities and urban areas in West Africa but their role in the transmission of LF is unclear . Despite the presence of W . bancrofti antigen positive individuals in many cities in West Africa , it has not been demonstrated that there is on-going transmission in these areas . In West Africa , LF is predominantly a rural disease and is transmitted by the Anopheles mosquitoes , with the members of the Anopheles gambiae complex being the major vectors [2] . Gbakima and colleagues [3] working in Ghana were unable to demonstrate active transmission of LF in Accra , and reported a very low potential for transmission in areas where Culex mosquitoes were the predominant human biting mosquitoes . Being a predominantly rural disease in West Africa , microfilaremic individuals are rarely seen in big cities in this sub-region . The question remains whether the influx of large numbers of people from rural to urban areas would have triggered transmission of the parasite in these cities , especially in post-conflict countries where massive rural to urban migration took place during the recent conflict period . LF is highly endemic in rural Sierra Leone where the disease occurs in all 12 provincial districts [4] , and the presence of W . bancrofti in Anopheles mosquito in Sierra Leone was first reported by Ronald Ross in 1900 . During the 10 years of civil conflict that started in 1991 , 47% of the pre-war population were internally displaced or took refuge in the neighboring countries of Guinea and Liberia [5] . Most of the internally displaced persons ( IDPs ) resided in camps and in urban centers . At the height of the conflict in1997 , Freetown was home to 1 . 2–1 . 5 million people up from its pre-war population of about 750 000 . An LF survey conducted in seven IDP camps in Freetown in 1997 revealed an antigen prevalence rate of 14 . 5% among IDPs [6] . This was followed by an LF mapping exercise carried out using the ICT in 2005 to determine the disease prevalence in Sierra Leone [4] . This exercise revealed an overall prevalence of 23 . 3% in Sierra Leone and 11 . 7% in Freetown but no microfilaria ( MF ) positive individuals were found in the capital . Based on an antigen positive rate of more than 1% and following the recommended WHO guidelines [7] , the Ministry of Health decided on an MDA campaign for the whole of the Western Area Province which means treatment for an additional one million people [8] . The decision to perform MDA in Freetown was not informed by evidence for active transmission of the disease . In Liberia , there is historical evidence of LF prevalence in the capital , Monrovia [9] , [10] . Poindexter [9] however reported that cases found in urban Monrovia ( the only area in which an organized mosquito eradication program was in operation ) were generally transient individuals from the provinces . The vectors of LF in Liberia have been identified as being primarily An . gambiae and An . melas [11] . In urban Monrovia , Culex and Aedes species were reported to be abundant , but of no importance in LF transmission [11] . A national LF mapping exercise in 2010–2011 showed that the disease is present in most counties , including the Monserrado County in which the national capital is located . While MDA in Liberia started in counties outside Monserrado in 2012 , the question remains whether MDA should be implemented in the national capital , Monrovia . It has been estimated that MDA for LF elimination is comparatively inexpensive in relation to most other public health programs [12] with country specific financial costs ranging from $0 . 06 to $2 . 23 . In 2010 , $132 , 000 ( not including running costs for the Ministry of Health and Sanitation program staff and DHMT staff , and vehicle expenditures ) was used to carry out MDA in Freetown when 1 , 404 , 407 were treated [13] . The aim of this study was therefore to establish whether there is an ongoing transmission of LF in the big cities of Sierra Leone ( Freetown , Bo and Pujehun ) and Liberia ( Monrovia ) . We tested the hypothesis that a transient population of microfilaremia carriers settling in urban areas is incapable of initiating LF transmission in an Anopheles transmission zone . Approval for this study was obtained from the IRB of the Liverpool School of Tropical Medicine and the Ethics and Scientific Review Committees of the Ministries of Health in Sierra Leone and Liberia . The urban communities , where mosquito sampling was done , were informed on the project and consent sought from the local authorities within each community . Consent was also sought from the head of the households where mosquito sampling was carried out . For the Transmission Assessment Surveys ( TAS ) , the communities where the schools were located were informed of the purpose of the study , in their local language . Due to low literacy rates , informed oral consent was obtained from the community leaders , as well as parents and guardians of each child participating in the study . The names of consenting parents and their children were recorded , and only the principal investigators of the study have access to this information . The data was analyzed and reported , to exclude any directly identifiable information , in order to maintain the anonymity of the parents and children . The study was conducted in three urban areas in Sierra Leone and Liberia , including the two biggest cities in Sierra Leone ( Freetown and Bo ) , and Pujehun town- the District capital of Pujehun District . In Liberia , the study was conducted in Monrovia , the capital . Pujehun town , the closest district capital to the Liberia border was a major hub for IDPs during the civil wars in Sierra Leone and Liberia . In Freetown and Monrovia , the transmission of LF was assessed through the examination of mosquitoes for the presence of W . bancrofti . The sentinel sites in Bo and Pujehun districts revealed MF rates of less than 1% after three MDAs with coverage rates of more than 65% [14] . Ongoing transmission was assessed in 1564 school children from 30 schools in Bo , and 1503 school children from 31 schools in Pujehun . The target population for MDA was 1 . 5 million people in the Freetown area [13] while Bo has an eligible urban population of 127 , 000 individuals ( http://www . citypopulation . de/SierraLeone . html ) . Pujehun on the other hand is a town with an eligible population of about 8500 people . Together , these cities account for more than 20% of the population targeted for MDA in Sierra Leone . The urban population of Monrovia is estimated at 939 , 524 according to the GeoNames geographical database ( http://population . mongabay . com/population/liberia/2274895/monrovia ) , accounting for 29% of the total population of Liberia . Mosquito collections were undertaken to obtain as many specimens as possible , influenced by budgetary , logistics and security constraints . In Freetown , mosquitoes were collected from high risk communities and slums where antigen positive individuals were detected during the mapping exercise . Two mosquito sampling surveys were undertaken in April–May 2009 and November–December 2009 . The first study was conducted during the wet season in Kroo Bay , before the start of MDA . The second follow-up study in the dry season was carried out in four additional communities , in other high risk areas , after the first MDA . A third and more elaborate study was undertaken over a 2 year period ( September 2010 to March 2012 ) , with collections done in the wet and dry seasons . For the third study , Freetown was divided into three zones across the city and . Slums dwelling and mosquito breeding sites were common in all three zones . In each zone two communities were selected , from which 10–30 houses were chosen for mosquito collection . Thus a total of 180 households were selected for the third study including the households from the previous studies . Information on the number of people sleeping in the rooms , the number who slept under ITNs the previous night and the number who received MDA was also collected . In all , 12 communities across Greater Freetown were sampled for all the three studies . These are: Aberdeen- Cape Road , Aberdeen-Crab Town , Aberdeen NDT , Kroo bay , Kissy Dockyard , Wellington-Portee , Wellington-Rokupa , George Brook and Goderich-Baoma , Goderich-Funkia , Goderich-Gbedembu and York . In each community , four collections were done to cover the major and minor rainy and dry seasons . The third study in Freetown was replicated in Monrovia . The Greater Monrovia District was divided into 3 zones and communities selected from each zone . These are Soniwein , Clara Town and New Kru Town communities ( Zone A ) , Togba Camp and Gbangay Town communities ( Zone B ) , King Gray and Kpelle Town communities ( Zone C ) . A total of 180 houses were selected for the study . 30 houses were selected from each community , except in Zone A where 20 houses each were selected per community . One collection was done in Monrovia , in 2011 . Indoor resting mosquitoes were collected early in the morning , between 5–9 am , by the knock-down , pyrethrum spray method [2] . The knocked down mosquitoes were collected into petri-dishes and labeled according to the house and sample numbers . The collected samples were identified based on their morphological characteristics . For each community , the female mosquitoes were separated according to species as well as their abdominal conditions , i . e . whether they are unfed , fed or gravid . They were then stored on silica gel and in pools , with a maximum of 20 mosquitoes per pool . Other mosquito species were also stored separately . The collected samples were sent to the Noguchi Memorial Institute for Medical Research , Ghana , for analyses . DNA was extracted from the pooled mosquitoes using the Qiagen DNeasy tissue kit ( Qiagen CA ) extraction method . This was followed by PCR to detect W . bancrofti DNA using the method of Ramzy and colleagues [15] . A positive and negative control was included in all reactions and samples testing positive for W . bancrofti were confirmed using a second PCR . Positive samples were also confirmed using the loop-mediated isothermal amplification ( LAMP ) method for detecting W . bancrofti DNA [16] . The LAMP method amplifies DNA with high specificity , efficiency and rapidity under isothermal conditions , unlike the traditional PCR method that requires the use of a thermal cycler . Amplification and detection of gene can be completed in a single step , by incubating the mixture of samples , primers , DNA polymerase with strand displacement activity and substrates at a constant temperature . It provides high amplification efficiency , with DNA being amplified 109–1010 times in about 1 hour . The resulting product is a turbid solution , indicative of product amplification . Sample confirmation can therefore be done visually . The LAMP assay protocol was performed , using the LAMP DNA amplification kit ( Eiken Chemical ) . Using the sequences provided by Takagi and colleagues [16] , the primers were synthesized by Eurofins MWG Operon . The LAMP assays were performed in a slightly modified protocol from Takagi and colleagues [16] to include 1 . 6 µM of each inner primer ( FIP and BIP ) , 0 . 2 µM of each outer primer ( F3 and B3c ) , 12 . 5 µl of reaction mix provided with the kit , 1 µl of fluorescent detection reagent , 8 U of Bst DNA polymerase and 2 µl of extracted DNA . The reaction mixture was topped up to 25 µl using double distilled water . The reaction mixture was incubated in a thermal cycler at 62°C for 70 minutes , followed by an enzyme inactivation step of 90°C for 10 min . Products were visualized for florescent detection under UV light directly in the eppendorf tubes . A positive and a negative control were included in the reactions . ICT card tests were performed to detect the presence of circulating filaria antigen in individuals residing in and around houses where positive mosquitoes were detected . These were performed according to the manufacturers' instructions . A school based antigenaemia prevalence survey using the TAS methodology described by WHO [17] , was conducted in Bo and Pujehun Districts . Prior to our school based surveys , sentinel site surveys involving 500 people from all age-groups from each district were conducted and no microfilaremic individuals were detected [14] . A total of 30 and 31 schools were randomly selected from all the schools in Bo and Pujehun districts respectively . Ten schools were surveyed in Bo town , and the remaining 20 schools from the surrounding villages . In the Pujehun District , 11 schools were surveyed in the town and the others from the surrounding villages . The survey was undertaken in school-aged children . Prior to the surveys , the schools were visited , and the head teachers and community elders were informed about the purpose of the study . Fifty to sixty children were randomly selected in each school , using a sampling interval of 2 . Their names , age and sex were recorded . Approximately 0 . 3–0 . 4 ml of blood was collected by finger prick from each child into an EDTA coated blood collection tube . The collected blood was assessed for LF using antigen detection by ICT . All ICT positive individuals were given the standard treatment of Ivermectin and Albendazole . Poolscreen v2 . 0 [18] was used to calculate the maximum likelihood of infection in the vector populations together with the associated 95% CIs . Biting rates were estimated by dividing the number of mosquitoes collected , by the number of individuals who slept in the rooms . From the ICT and TAS survey , the prevalence ( % ) of antigenemia was calculated as the number of antigen positive people with antigen divided by the number of people examined . A total of 1731 An . gambiae and 14342 Culex mosquitoes were analyzed . Table 1 shows the number of mosquitoes caught and analyzed in all the studies . Analysis of mosquitoes collected from the first ( Pre-MDA ) survey in Freetown showed no mosquito positive for W . bancrofti . Due to the low number of An . gambiae collected in the first study , the second study targeted communities near Anopheles breeding areas . Analyses of the An . gambiae collected in the second study also revealed none infected . Data collected during the third survey in Freetown ( 2010–2012 ) , revealed that 898 people resided in the houses surveyed . Of these , 235 used ITNs and 502 reported having taken Ivermectin and albendazole during the last MDA . The mosquito species collected during the third survey were An . gambiae ( 764 ) , An . funestus ( 3 ) , other Anopheline species ( 14 ) , Culex quinquefasciatus ( 6686 ) and Aedes species ( 11 ) . Together with the first two collections , the sampling yielded 12479 Culex quinquefasciatus and 972 An . gambiae . Of these , 11681 Culex and 960 An . gambiae were analyzed by PCR , with a pool range of 1–20 . The other mosquito species were not analyzed . No infected mosquitoes were detected from the communities except Goderich-Gbedembu . In this community , one unfed An . gambiae mosquito ( 249 An . gambiae tested in 21 pools , with a pool range of 1–20 ) and 2 pools of Culex mosquitoes ( 140 Culex tested in 14 pools , with a pool range of 1–20 ) were found positive . The Poolscreen v2 . 0 [18] calculation indicated a maximum likelihood of infection of 0 . 004% with a 95% confidence interval of 0 . 00012–0 . 021 for the An . gambiae , and a maximum likelihood of infection of 0 . 015% with a 95% confidence interval of 0 . 0018–0 . 052 for the Culex . Also , a total of 710 individuals slept in the rooms during the collection periods in Goderich-Gbedembu . The use of pyrethrum spray catches only permits an indirect estimation of the biting rate and this was calculated to be 0 . 31 bites/man/night for the An . gambiae and 0 . 32 bites/man/night for the Culex mosquitoes . For the entire collection of the third survey , 5880 individuals slept in the rooms and thus the biting rate was estimated to be 0 . 13 bites/man/night for the An . gambiae and 1 . 14 bites/man/night for Culex . ICT card tests on permanent residents , in the house and other adjoining houses where PCR positive mosquitoes were caught , failed to detect antigen positive cases . In Liberia , four mosquito species were collected; An . gambiae ( 771 ) , An . funestus ( 7 ) , Culex ( 2661 ) and Aedes ( 4 ) . All the An . gambiae and Culex were analyzed , with none positive for W . bancrofti . In Bo , a total of 1564 pupils were surveyed ( Table 2 ) . 603 pupils surveyed in 10 schools were from Bo town , and the remaining 961 were from the surrounding villages . Children in the 6–7 age categories were targeted . 1505 ( 96 . 2% ) of the students were in the 6–7 years group , and the remaining 59 ( 3 . 8% ) were 8–9 years old . 56 . 4% of the students were girls and the remaining 43 . 6% were boys . The results of the surveys in Bo district revealed only 3 female students positive for antigenemia , with a prevalence of 0 . 19% . All the positive children were from the surrounding villages . No mfs were detected in all 3 positive children . The critical cut-off value of 18 antigen positive cases , determined as the statistical power for the TAS using the WHO TAS survey tool [17] suggests that Bo has passed the TAS , and thus MDA can be stopped . In Pujehun District , 1503 children were surveyed . 56 . 2% were females and 43 . 8% were males . 492 pupils were surveyed from 11 schools in Pujehun town , and the rest were from the surrounding villages . 10 male students were found positive for antigenemia , with a prevalence of 0 . 67% . As observed in Bo , all of the positives were from villages around Pujehun town . Also , 4 of the antigen positive children were found positive for mf . Based on the prevalence of Antigenaemia and microfilaraemia observed in Pujehun and Bo districts , and following the WHO guidelines [17] , we can conclude that transmission cannot be sustained . Our knowledge of the transmission dynamics of LF in urban areas in West Africa is limited and it is not clear if MDA is required in many national capitals . The decision to initiate MDA to control and subsequently eliminate LF has relied on infection indicators in the human host . Implementation units , be they districts or counties , will become eligible for MDA if an LF mapping exercise , following WHO guidelines , reveals an infection rate of 1% or more [7] . Infection indicators like microfilaraemia or antigenaemia may persist after transmission has been interrupted . Interpretation of the significance of infection rates in humans is also confounded by large movements of infected individuals from endemic to non-endemic areas especially in areas of conflict like West Africa where a transient populations of internally displaced persons settle in large cities not directly affected by the conflict . Monitoring the presence of MF in humans , through the mosquitoes feeding on them ( Xenomonitoring ) provides an alternative way of demonstrating potential transmission in an area . It has been suggested as a tool for monitoring the impact of MDA on LF transmission [19] , [20] . We assessed the transmission potential of LF in urban Freetown and Monrovia using xenomonitoring . The results suggested that Culex mosquitoes , which are not known as vectors of LF in Sierra Leone [2] , [21] were capable of ingesting parasite material while feeding on MF positive individuals , demonstrating the potential of using non-vector species as a proxy for determining the presence of LF in human populations . Fischer and colleagues [22] showed through laboratory experiments that parasite DNA can be detected in both vector and non-vector mosquitoes for two weeks or longer after they ingest MF-positive blood . This study represents a field demonstration of xenomonitoring in non-vector species and as an indication of infection in an area . We were unable to demonstrate ongoing transmission of LF in our study sites based on infection rates in humans and mosquitoes . The presence of an infected vector mosquito using a diagnostic method that is not stage specific implies that people may be exposed to infective bites [15] . However , the maximum annual infective biting rate that could be derived from this infection rate ( 0 . 004 ) , assuming the mosquito was harboring infective larvae , is 44 infective bites per person per year based on the low human biting rates ( 0 . 31 bites/person/night ) observed for Anopheles mosquitoes in this study . Based on estimates for Culex quinquefasciatus by Hairston & De Meillon [23] about 15 , 500 infective bites of Culex quinquefasciatus were required to produce a new patent infection . Subsequently , a number of studies involving Culex , Anopheles and Aedes vectors in different parts of the world have provided data which allow estimates of this parameter ranging from 2700 to over 100 , 000 infective bites per new human case [24] . It is therefore unlikely that 44 infective bites person per night will enable transmission of LF in Freetown . Nonetheless , the positive mosquitoes demonstrate the presence of an MF carrier ( s ) in the Goderich-Gbedembu community which is dominated by an ethnic group emigrating from the northern districts of Sierra Leone where LF endemicity was highest [4] , before MDA commenced . However , the limited ICT card tests performed , in the community with positive mosquitoes , failed to detect antigen positive cases . The distribution of lymphatic filariasis in the world has been attributed to migration [25]–[27] and , the movement of infected IDPs to non-endemic areas may introduce infection into new areas . However , establishing and maintaining transmission of LF in new areas will depend on the availability of the appropriate vectors and their capability to sustain the transmission . In this case , the requirements for vector efficiency [28] must be met . In West Africa , LF is transmitted by Anopheles species and W . bancrofti does not develop well in Culex quinquefasciatus which is the main vector in urban areas in East Africa and Asia [2] , [21] . There is no evidence that Culex species play a role in LF transmission in West Africa . Also from our collections , Culex is the dominant mosquito species ( 89 . 4% ) , with An . gambiae accounting for only 10 . 2% of the mosquito population , in Freetown . A possible draw-back to our study is the relatively low abundance of Anopheles the study areas . Following MDA , mosquito infection prevalence rates have been shown to fall below 1% ( Goodman et al . , 2003; Farid et al . , 2007 ) [29] , [30] . As infection levels decline , increasing numbers of mosquitoes must be analysed in order to demonstrate a significant decline in infection prevalence ( Burkot and Ichimori , 2002 ) [31] . In Freetown , we analysed little less than 1000 mosquitoes and this gives us 63–92% chances of detecting a positive mosquito assuming infection prevalence as low as 0 . 1–0 . 25% , and over 95% chances with prevalence higher than 1% . Thus , while Anopheles abundance may be low in our study areas , the numbers analysed are still substantial in detecting very low infection prevalence . The outline provided by Katholi and Unnasch ( 2006 ) [32] can be used to guide the sampling process with respect to whether to screen individual insects or to screen pools , and if screening pools , how large should the pools be . The Anopheles-Wuchereria system is ecologically less stable in comparison to the culicine ( Culex and Aedes species ) -Wuchereria system and this has been attributed to the phenomenon of facilitation and limitation associated with the different vector-parasite relationships respectively . In areas where the transmission of LF by Anopheles mosquitoes was interrupted through vector control alone , transmission never resumed . House-spraying with residual insecticides led to sustained interruption of LF by the Anopheles punctulatus group in Solomon Islands [33] and parts of Papua New Guinea [34]; and by An . gambiae complex and An . funestus in Togo [35] , [36] . On the other hand there have been cases of recrudescence of LF transmission following the cessation of control programs in the areas where Culex mosquitoes are the vectors as experienced in the Nile Delta of Egypt [37] , India [38] and Haiti [39] . In this regard the cut-off points for TAS depends on whether transmission is by Anopheline , Culex or Aedes species . For Aedes species , which are more efficient transmitters of LF in comparison to Culex species , the target TAS threshold of <1% antigenaemia prevalence is half of that of Anopheles and Culex but Anopheles species are the least efficient [17] . In conclusion , we found no evidence that a transient population of from endemic rural areas settling in urban areas , through mass migration in post conflict countries can trigger LF transmission in an Anopheles transmission zone . Infection rates determined for both Culex and Anopheles mosquitoes in Freetown and Monrovia , were below the threshold associated with active transmission . Our school-based surveys showed prevalence rates indicative of transmission levels that can result in interruption in both Bo and Pujehun districts in Sierra Leone . This supports our findings of low transmission potential of the mosquito vectors as demonstrated by our xenomonitoring studies in the two national capitals . Initiation of MDA in big cities in West Africa should therefore be informed by evidence of active transmission demonstrated by the presence of 1% or more MF carriers in a sentinel site . Basing the decision to start MDA on antigen prevalence alone in urban areas may lead to treatment that may not be necessary .
There have been many arguments regarding the implementation of Mass Drug Administration ( MDA ) activities for elephantiasis control in urban areas , and especially in countries where the disease is mostly found in rural settings . Blanket MDA in implementation units in big cities , may be costly and unnecessary , without evidence for active transmission in urban areas . Over 1 million people were treated in Freetown during the first MDA carried out in 2010 . This represents hundreds of thousands dollars that may serve a better use in reducing the impact of elephantiasis in areas with established on-going transmission . This study was conducted to assess the evidence of transmission of elephantiasis in urban areas , as a result of rural to urban migration in West African countries that have experienced civil wars , and the displacement of people from rural to urban areas . The results showed that the main mosquitoes transmitting elephantiasis are in numbers not enough to support transmission . Testing of individuals also showed very few people to have infection . Together , the results show that elephantiasis infection in the urban areas , where the study was conducted , is not enough to justify the need for MDA in the national capitals . This study represents a strategy that can be adopted in many countries , to inform the decision for undertaking MDA activities in cities .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "medicine", "infectious", "diseases", "mosquitoes", "neglected", "tropical", "diseases", "vectors", "and", "hosts", "lymphatic", "filariasis" ]
2014
No Evidence for Lymphatic Filariasis Transmission in Big Cities Affected by Conflict Related Rural-Urban Migration in Sierra Leone and Liberia
Mutations affect individual health , population persistence , adaptation , diversification , and genome evolution . There is evidence that the mutation rate varies among genotypes , but the causes of this variation are poorly understood . Here , we link differences in genetic quality with variation in spontaneous mutation in a Drosophila mutation accumulation experiment . We find that chromosomes maintained in low-quality genetic backgrounds experience a higher rate of indel mutation and a lower rate of gene conversion in a manner consistent with condition-based differences in the mechanisms used to repair DNA double strand breaks . These aspects of the mutational spectrum were also associated with body mass , suggesting that the effect of genetic quality on DNA repair was mediated by overall condition , and providing a mechanistic explanation for the differences in mutational fitness decline among these genotypes . The rate and spectrum of substitutions was unaffected by genetic quality , but we find variation in the probability of substitutions and indels with respect to several aspects of local sequence context , particularly GC content , with implications for models of molecular evolution and genome scans for signs of selection . Our finding that the chances of mutation depend on genetic context and overall condition has important implications for how sequences evolve , the risk of extinction , and human health . In the genomes of all organisms , there is an inescapable risk of spontaneous mutations , which are seldom beneficial . This risk is reduced by cellular mechanisms that correct replication errors and repair DNA damage . If the degree of damage or the efficacy of repair is influenced by local sequence context , genetic background , or environmental conditions , then the number and kinds of mutations that ultimately occur would be similarly influenced . There is growing recognition of the potential for extensive context-dependent mutation , including in complex eukaryotes [1] . We were particularly interested in the relationship between the spontaneous germline mutation rate and the number of deleterious alleles already present in the genome , i . e . , genetic quality . If deleterious mutations are more likely to occur in genotypes that are “loaded” with deleterious alleles , then the resulting positive feedback loop would alter the equilibrium mutation rate , mean fitness , and the risk of extinction by mutational meltdown [2 , 3] . There is also increasing interest in the effects of local sequence context on mutation within genomes , which will allow for more realistic models of neutral molecular evolution . Such models are interesting in their own right and as a basis for null models in genome scans for signs of selection [4 , 5] . To investigate the effect of genetic quality on spontaneous germline mutation , we conducted a mutation accumulation ( MA ) experiment using Drosophila melanogaster in which focal 2nd chromosomes accumulated spontaneous mutations over 52 generations in the presence of either a wild-type “unloaded” 3rd chromosome or a 3rd chromosome “loaded” with known deleterious alleles . At regular intervals , we extracted focal chromosomes from the different backgrounds and assessed their fitness in a common genetic background . As previously reported , we found that focal chromosomes maintained in the loaded backgrounds declined in fitness almost three times faster than those maintained in unloaded backgrounds , providing indirect evidence that deleterious mutations arose at a greater rate in low-quality genotypes [6] . In addition , the effect of our genetic quality manipulations on mutational decline was highly correlated with the effect of the manipulations on body mass , suggesting that overall condition mediated the effect of genetic quality on the mutation rate [6] . However , fitness measures do not directly reveal the number or type of mutations occurring , which may provide insight into the mechanism through which genetic quality or condition affects mutation . One possibility is that condition affects the likelihood of error during DNA replication . DNA polymerases vary in fidelity [1 , 7] , and low condition might lead to greater use of low-fidelity polymerases , affecting the rate of single-nucleotide substitutions . Alternatively , the genome might be subject to a higher level of damage in low-condition individuals . For example , low-condition individuals could have elevated levels of endogenous mutagens , such as oxygen-centered free radicals [1 , 8] . However , in Caenorhabditis elegans , the mutagenic effect of oxidative stress seems to be minor compared to other sources of mutation [9] . Another idea is that high- and low-condition individuals could experience the same level of DNA damage but differ in the repair pathways they employ . DNA double-strand breaks ( DSBs ) are common and highly toxic to cells , and can be repaired through several mechanisms , which differ in their genomic consequences [10] . A DSB can be repaired by ligating the broken DNA ends through nonhomologous end-joining ( NHEJ ) , resulting in an insertion or deletion ( indel ) relative to the original sequence . An alternative is homology-directed repair , which restores the original sequence surrounding a DSB by copying from a homologous template . In D . melanogaster , the homologous chromosome is often used as a template , rather than the sister chromatid , which can result in allelic gene conversion [11] . This conservative approach to repair is more time consuming [12] , suggesting a possible trade-off between repair fidelity and energetic cost , and there is evidence that DNA repair is sensitive to diet quality in Drosophila [13 , 14] . We might therefore expect genetic quality to affect the prevalence of gene conversion events , which result from high-fidelity DSB repair , relative to indel mutations , which result from low-fidelity DSB repair . Finally , in Drosophila and many other organisms , the movement of transposable elements ( TEs ) can lead to new insertions and DSBs at excision sites , with the potential for significant fitness effects [15–17] . The movement of some elements can be regulated by the host genome , and there is evidence that stress , particularly in the form of high temperature , increases TE mobilization [18] . This suggests the possibility of different rates of TE insertions in individuals of different conditions . To explore these possibilities , we sequenced focal chromosomes from 112 of our MA lines , which accumulated mutations in seven different backgrounds ( one unloaded , six loaded ) for 52 generations , for a total of 5 , 824 MA generations . We used stringent criteria to call mutations in 38 pooled samples and estimated our power to detect true mutations given these criteria ( S1 Data ) . We compared the rates of several types of mutations between our loaded and unloaded treatments . In addition to mutational differences between genomes , we also examined the relationship between mutation rate and sequence context at multiple spatial scales . The rate of single-nucleotide substitution in our lines was 6 . 03 × 10−9 per base pair per generation ( 95% confidence interval [CI]: 5 . 57–6 . 50 × 10−9 ) , but there was no indication of a difference between loaded and unloaded lines ( Fig 1 ) . There was also no difference in the substitution spectrum between treatments ( Fig 2A ) . We found that 4% of the 786 substitutions were likely the result of multinucleotide mutation events , i . e . , multiple closely spaced substitutions , similar to previous observations [19] . Though substitutions are common , they may be less likely to cause fitness effects than other types of mutations . We also detected small insertions and deletions ( indels ) in our lines ( 37 deletions , 12 insertions; mean length 1 . 9 bp ) . As previously observed in flies [19 , 22] , there is a significant deletion bias among such events ( χ2 = 12 . 75 , p < 0 . 001 ) . In contrast to substitutions , indel rates were not the same across backgrounds: the rate of indel mutation was almost twice as high in loaded backgrounds compared with unloaded backgrounds ( Fig 1; unloaded rate: 3 . 38 × 10−10 , 95% CI 1 . 52–5 . 24 × 10−10; loaded rate: 6 . 65 × 10−10 , 95% CI 3 . 98–9 . 32 × 10−10 ) . This higher frequency of indels may have led to faster fitness decline in loaded lines , particularly if indels are more likely than substitutions to have relatively large fitness effects [23] . In accordance with this hypothesis , among mutations in coding regions , indels were significantly more likely than substitutions to be nonsynonymous ( substitutions: 135/184 , indels: 15/15 , Fisher’s exact test: p = 0 . 024 ) and to generate premature stop codons ( substitutions: 8/184 , indels: 13/15 , Fisher’s exact test: p = 2 . 38 × 10−13 ) . As intended , the accumulated mutations appear to be unbiased by selection . The observed frequency of substitutions and indels in genes ( 73 . 8% ) did not differ from the neutral expectation ( 74 . 2%; binomial test: p = 0 . 84; this was also the case considering substitutions and indels separately ) . Indels and substitutions did not differ from one another in their likelihood of occurring in genes ( indels: 80 . 0% , substitutions: 73 . 5% , Fisher’s exact test: p = 0 . 40 ) or in protein coding sequence ( indels: 30 . 6% , substitutions: 24 . 3% , Fisher’s exact test: p = 0 . 31 ) . Substitutions in protein coding sequence were not less likely to be nonsynonymous than expected by chance ( observed: 73 . 4% , expected: 74 . 4%; χ2 = 0 . 11 , df = 1 , p = 0 . 74 ) . Unlike most previous MA studies of Drosophila where mutations accumulated in homozygotes , in our experiment the focal chromosome was maintained in a heterozygous state by crossing MA males to females from a marked stock , “vg . ” This allowed us to detect 38 spontaneous mitotic homologous gene conversion events that occurred in these MA males ( which lack meiotic recombination ) by identifying regions of the focal chromosome containing substitutions matching SNPs found in the vg population . Previous evidence indicates that mitotic gene conversion tracts are exponentially distributed in length , with a mean of 1 , 463 bp [24] , and the minimum tract lengths we observed ( see Methods; mean 1 , 305 bp ) did not differ from this distribution ( Kolmogorov-Smirnov test , D = 0 . 134 , p = 0 . 5 ) . There was no difference in the distribution of tract lengths of events from loaded versus unloaded backgrounds ( Wilcoxon rank-sum test: W = 123 , p = 0 . 33; Kolmogorov-Smirnov test , D = 0 . 36 , p = 0 . 18 ) . The rate of gene conversion was over three times greater in the unloaded background compared with the loaded backgrounds ( Fig 1 ) . Although male D . melanogaster lack meiotic recombination , studies using transgenic constructs indicate that homology-directed repair can sometimes result in mitotic crossing over in addition to gene conversion [11] , and we found several cases of crossing over in our lines ( S2 Fig ) . Genomic regions found to have undergone exchange with the vg stock were excluded from all analyses , including those described above , except the analysis focusing specifically on mitotic crossing over . Just as unloaded lines had a higher rate of gene conversion , they were also more likely to experience crossing over ( unloaded: 4/52 lines tested , loaded: 1/132 lines tested , Fisher’s exact test: p = 0 . 023 ) . We hypothesize that these crossing over events occurred during mitotic DSB repair; our data on gene conversion suggest that unloaded lines repaired DNA breaks using the homologous chromosome at a greater rate than loaded lines , which may have led to a greater rate of crossing over in the unloaded lines . According to a previous estimate , 7% of gene conversion repair events are associated with crossing over [11] . Given our estimated rates of gene conversion , and accounting for the fraction of the second chromosome where the products of crossing over would not disrupt the markers for chromosome tracking ( i . e . , undetected crossing over during the MA experiment ) , the frequencies of crossover events we observed are not significantly different from those expected given the prediction in [11] ( binomial tests; unloaded: expected freq . = 8 . 0% , p = 1; loaded: expected freq . = 2 . 7% , p = 0 . 27 ) . Thus , the higher rate of crossing over in the unloaded treatment is consistent with the higher rate of gene conversion in that treatment . A reanalysis of fitness measures from [6] indicates that the patterns we reported previously remain significant ( and in fact become stronger ) when lines with crossing over are excluded ( S4 Text ) . In our original MA study [6] , there were ten different “loaded” backgrounds , and some had larger effects on the carrier’s condition ( assessed as body mass ) than others . We previously reported a relationship between fitness decline in a given background and the condition of individuals with that background . A background that caused a 10% decrease in body mass was inferred to cause 2-fold faster fitness decline in the focal chromosome [6] , suggesting that the effect of genetic quality on mutation rate was mediated by individual condition . Using the present dataset , which includes data from six loaded backgrounds and the unloaded background , we examined the relationship between indel or gene conversion rate and body mass across genetic backgrounds . We find that mass is a significant predictor of both types of event , with indels negatively associated with mass and gene conversion positively associated with mass ( Fig 3 ) . We similarly detect a significant relationship between mass and the difference between indel and gene conversion count per sample ( t = –3 . 00 , p = 0 . 005 ) . Certain stressors may lead to elevated transposition of mobile genetic elements [25] . We attempted to determine the number of TE insertions in our MA lines using PoPoolationTE [26] . We detected 226 putative insertions ( S1 Data ) , but there was no evidence that the rate of transposition differed between treatments ( Fig 1 ) . Our ability to correct for false negatives for this type of mutation is limited ( see Methods ) , but our best estimate of the rate of new insertions ( 0 . 069 insertions per haploid 2nd chromosome per generation; 95% CI = 0 . 057–0 . 081 ) is similar to previous findings obtained with very different methods when extrapolated to the haploid genome ( 0 . 17 versus 0 . 1–0 . 2; [16] ) . In addition to differences between genetic quality treatments , our data also provide insight into other dimensions of variation in the mutational spectrum . The substitution rate we observed is higher than some other estimates but intermediate between the two genotypes described by [19] ( Fig 2C ) . Those two genotypes differed from each other in their G:C to A:T transition rate and , notably , our G:C to A:T transition rate is intermediate between them ( S1 Fig ) . We find that the substitution rate is higher at G:C sites and influenced by adjacent bases ( Fig 2B ) , and that substitution and indel rates are influenced by local guanine-cytosine ( GC ) content in different ways in exploratory statistical models ( Fig 4 ) . The best models included the GC content within three sizes of windows surrounding a focal site for substitutions but two window sizes for indels . The GC content of the window encompassing the 26 bp to the left and right of focal sites was negatively associated with the occurrence of both substitutions and indels ( substitutions: Z = –2 . 88 , p = 0 . 004; indels Z = –6 . 40 , p < 1 × 10−4 ) , whereas a wider region was negatively associated with substitutions ( ±488 bp: Z = –2 . 54 , p = 0 . 011 ) but positively associated with indels ( ±498 bp: Z = 5 . 05 , p = 0 . 086 ) . Substitutions were positively associated with GC content within ±68 bp ( Z = 2 . 70 , p = 0 . 007 ) . The GC content of gene conversion tracts did not differ from the random expectation ( simulated: mean = 0 . 432 , 95% CI = 0 . 341 − 0 . 543; observed: mean = 0 . 427 , 95% CI = 0 . 342 − 0 . 480; t = 0 . 65 , p = 0 . 52 ) . A recent population genomic analysis found little evidence for GC-biased gene conversion in D . melanogaster [27] . Although our power to detect such a bias is limited , the spectrum of changes in gene conversion tracts did not differ from the neutral expectation ( χ2 = 2 . 80 , df = 5 , p = 0 . 73; S1 Data ) , consistent with a lack of GC-biased gene conversion . Dividing the chromosome into ~1-Mb windows , we found that gene conversion was more likely further from the centromere in both treatments ( all data: rs = 0 . 45 , p = 0 . 002; unloaded: rs = 0 . 33 , p = 0 . 031; loaded: rs = 0 . 34 , p = 0 . 025 ) . Combined with the positive , though nonsignificant , relationship between distance from the centromere and indel rate ( rs = 0 . 14 , p = 0 . 373 ) , this suggests that mitotic DSBs may be more likely to occur further from the centromere . An alternative explanation is that DSBs occur at equal rates across the chromosome , but those that occur closer to the centromere are less likely to get repaired , resulting in cell death , and so will go unobserved . No relationship with distance from the centromere was apparent for single-nucleotide substitutions ( rs = 0 . 02 , p = 0 . 922 ) . A unique aspect of this MA study is the comparison of mutational properties of loaded and unloaded lines . Though our data is among the largest sets of de novo mutations for a nonhuman animal , mutations are rare in an absolute sense , and this limits the statistical power for making contrasts . It is worth considering the totality of the evidence for mutational differences . In comparing sequence data from loaded and unloaded lines , we have considered four types of events: single-nucleotide substitutions , indels , gene conversions , and mobile element transposition , two of which show significant rate differences ( Fig 1 ) . If the null hypothesis was true for all four event types , the chance of observing two or more significant tests ( at α = 0 . 05 per test ) would be p = 0 . 014 , indicating we were unlikely to obtain our results by chance . Moreover , the separate analysis of mitotic crossing over using a larger set of lines provides additional support for homologous repair occurring at a higher rate in unloaded lines . Finally , the significant relationships of both indel and gene conversion rates with mass ( Fig 3 ) add a third line of support . This third line of support is not independent of the primary tests ( Fig 1 ) because loaded and unloaded backgrounds differ in body mass . Nonetheless , these relationships with mass could easily be nonsignificant if the primary contrasts between loaded and unloaded were significant by chance . If we limit ourselves only to the six loaded backgrounds , the point estimates for the correlations are in the expected directions ( ρindel , mass = –0 . 18; ρgene_conv , mass = 0 . 51 ) , though , unsurprisingly , nonsignificant given the small number of points . Taken together , these observations indicate important differences in mutational characteristics between flies with good and poor quality genetic backgrounds . Indel mutations and gene conversion events are outcomes of different DSB repair pathways: repair by NHEJ results in an indel , whereas homology-directed repair results in a gene conversion event [10 , 12] . The contrasting pattern we observed between loaded and unloaded lines ( Fig 1 ) suggests that these repair pathways were utilized at different rates in our experimental treatments , with the error-prone end-joining pathway employed more often in the loaded backgrounds and the conservative but time-consuming [12] homology-directed pathway employed more often in the unloaded background . A separate maximum likelihood analysis ( S5 Text ) indicates that the total number of indels plus gene conversion events did not differ significantly between loaded and unloaded backgrounds , suggesting that genetic quality did not affect the rate of DSBs , but rather the way in which DSBs were repaired , with gene conversion about two times more likely in the unloaded background than the loaded background . The genome sequences of our MA lines reinforce our earlier evidence of elevated mutation rates in low-quality genotypes [6] . The rate of indels relative to gene conversion in these lines appears to be related to genetic quality ( Fig 1 ) and overall condition ( Fig 3 ) . Condition-dependent use of alternative DSB repair pathways that differ in fidelity is a plausible explanation for these results , and for the faster fitness decline in low-quality genotypes we observed [6] , but more experiments will be required to confirm this . A previous study [14] used a reporter construct to examine how often three different pathways were used to repair induced DSBs in D . melanogaster of high versus low condition , created through a diet manipulation . In this construct , DSBs were always flanked by repeat sequences , allowing for repair via single-strand annealing ( SSA ) , resulting in the loss of one repeat . The majority of all DSBs were repaired using SSA for both high- and low-condition flies , though more so for high-condition flies ( see also [28] ) . For the remaining breaks , the usage rate of homologous repair ( HR-h ) compared to NHEJ was higher in low-condition flies than high-condition flies , although only later in life . This result seems inconsistent with our present results , but several factors complicate the comparison between studies . First , the major repair pathway in the reporter construct system , SSA , is unavailable for most spontaneous DSBs , because most do not occur between flanking repeats . Repair of DSBs is thought to be a competitive process among different pathways [29] . The availability of SSA in the construct study may substantially alter the competition between HR-h and NHEJ , and how this competition differs between high- and low-condition flies . Second , induced versus naturally-occurring DSBs could differ in their timing during the cell cycle , influencing which repair pathways are most likely to be used . Finally , the genomic location in which the reporter construct is inserted as well as the sequence of the construct itself may have some influence on repair pathway usage . While constructs are an invaluable tool , they only serve as a proxy for the subject of real interest: naturally occurring spontaneous events that lead to mutation , which are the subject of our study . If key aspects of the mutational process are indeed sensitive to condition , this will have important consequences for populations and individuals . We have studied germline mutations , but the same DNA repair processes are used in somatic cells , suggesting that the risk of cancer-causing mutations could depend on condition [30] . These results have important implications for public health if condition—either environmentally or genetically determined—mediates the mutation rate and spectrum in humans . There is evidence for variation in germline mutation rates among human families , but the sources of this variation are uncertain [31 , 32] . In any organism , adaptation to new environments could be accelerated if poorly-adapted individuals are more likely to transmit new mutations to their offspring [33] . Condition-dependent mutation also has implications for the genetic benefits of mate choice and sex-biased mutation rates [34] . We identified several additional dimensions of variation in the mutational spectrum . Although the rate of substitution was not affected by our genetic quality treatment , our data support the suggestion that the rate of G:C to A:T transitions may be elevated in some Drosophila genotypes relative to others , increasing the overall substitution rate ( [19]; Fig 2C , S1 Fig ) . Natural variation in mutation rates among genotypes has also been observed in nematodes [35] and algae [36] . We find that nucleotide context affects the rate of substitution at several scales , in accordance with observations from other taxa [36–39] . Beyond the elevated mutation rate at G:C sites relative to A:T sites , it is not clear to what extent these patterns are conserved . However , our finding that spontaneous indels tend to be flanked by low GC content is consistent with data on divergence between species [40] . Accounting for systematic variation in mutation rates within genomes will be necessary to develop accurate models of neutral molecular evolution , in order to correctly identify the effects of selection and other patterns of genome evolution such as codon bias [4 , 5] . The MA strategy combined with genome sequencing is a promising and relatively unbiased approach to identify patterns of variation in the mutational spectrum , both within and among genomes . Following MA , we crossed pairs of independent MA lines from the same ( 3rd chromosome ) treatment , creating genotypes that were heterozygous for new mutations , and froze these flies at –80°C . Using the Qiagen DNeasy Blood and Tissue Kit insect tissue protocol with minor modifications , we extracted genomic DNA from 56 heterozygous genotypes selected at random from seven genetic quality treatments , using at least 28 males , and on average 47 males per heterozygous genotype . We sequenced either heterozygous genotypes or pooled samples of two heterozygous genotypes so that the expected frequency of new mutations was 0 . 5 or 0 . 25 , respectively . See S1 Data for coverage information . We also sequenced the stock population of flies ( carrying the “vg” chromosome ) used to maintain the MA lines , to identify gene conversion events; in this case , DNA was extracted from 200 flies , frozen at generation 52 , and average coverage was 60X . Library preparation and multiplexed paired-end 100 bp sequencing was conducted at the University of British Columbia Biodiversity Research Centre ( Vancouver , BC ) or at The Centre for Applied Genomics ( The Hospital for Sick Children , Toronto , ON ) , using Illumina HiSeq technology . Sequencing was conducted in four blocks ( samples 1–16 , 17–26 , 27–36 , 37–38 ) , with additional sequencing runs performed for some blocks because of poor initial coverage . We conducted multiple alignment steps to reduce mapping error . Reads from each sample were mapped to the D . melanogaster reference genome ( v . 5 . 56 ) , using BWA ( v . 0 . 5 . 9 ) [41] and Stampy ( v . 1 . 0 . 21 ) [42] . Duplicate reads were removed using Picard tools ( http://picard . sourceforge . net ) , and the data were remapped using IndelRealigner in GATK ( v . 2 . 3 . 9 ) [43] . We combined data from all samples using Samtools ( mpileup; v . 0 . 1 . 16 ) [44] , considering only the focal chromosome . We used stringent mapping and calling procedures to avoid false positives [19 , 21] and attempted to account for the number of true mutations that were excluded by this procedure ( false negatives ) , as described below . For each site , we examined the total number of reads in each sample using an in-house Perl script . If sequences from duplicated loci are mapped to a single reference locus , this mapping error may lead to unusually high coverage in multiple samples and potentially the false appearance of mutations at intermediate frequency . We considered a sample to have high coverage if the number of reads was >2 . 5 times the median coverage for that sample across sites and discarded any site where >25% of samples had high coverage . At the remaining sites , we considered only those base calls with quality scores denoting accuracy > 99 . 9% . We discarded sites where >20% of samples had coverage <10 . To infer the most likely state of the common ancestor at each site , we determined the majority base call of the reads in each sample , allowing for ties , and then determined the majority of those calls across samples . The site was discarded if >20% of samples had a majority allele that differed from this overall consensus , a problem that can arise from mapping error . We considered remaining sites to be “callable . ” At each callable site , we only considered those samples with coverage of at least nine , with at least one read on each strand . The number of callable sites in each sample is given in S1 Data . We recorded the forward and reverse coverage of each of these callable samples for later analyses . We identified putative substitutions in callable samples as cases with at least five nonconsensus base calls , with at least one nonconsensus base call on each strand . This and other elements of the calling criteria are sensitive to both coverage and the expected mutant frequency ( 25% or 50% ) but are accounted for in our assessment of detection power used to estimate rates . When a putative mutation was called , we collected additional information about the focal sample , as well as other samples at the site , for further analysis . We discarded cases with evidence of mapping error ( S1 Text ) , as well as cases where a base matched the homologous , non-MA chromosome “vg , ” which we dealt with in the gene conversion analysis described below . Following previous authors [20] , each remaining putative substitution was examined in IGV for other possible problems . We discarded additional cases of mismapping , which were primarily due to indels or SNPs in the consensus sequence or repetitive sequence in the reference . We tested nine putative substitutions and two putative multinucleotide mutations by Sanger sequencing and confirmed them all . To call small indel events , we used the Pindel pipeline [45] following alignment with BWA . As with substitutions , we only retained cases where the putative indel was supported by at least five reads , with at least one supporting read on each strand . As with substitutions , a signature of mapping error was the presence of the same putative indel in multiple samples . We discarded cases where a putative indel appeared in more than five reads from other samples , or where the mutant frequency was unusually low ( binomial probability <0 . 001 ) , and we visually examined the remaining cases in IGV . We tested 21 putative indels by Sanger sequencing and confirmed them all . At sites not considered callable , e . g . , due to high depth , our power to detect mutations is zero in all samples . Similarly , we also have zero power to detect mutations at a site in a sample where the coverage is too low , though the site could be callable in other samples . For a sample in which the site is callable with forward coverage of nF > 0 and reverse coverage of nR > 0 , we estimated the probability of detecting a true mutation as Pdetect=∑i=1nF∑j=1nRXi , jBB[i , nF , fmut , ρnF]BB[j , nR , fmut , ρnR] where Xi , j = 1 if i + j ≥ 5 ( the minimum number of reads required to call a mutation ) and zero otherwise , fmut is the expected frequency of new mutations in the sample ( 0 . 25 or 0 . 5 ) , and BB is the beta-binomial density function with overdispersion ρ . Pdetect is reduced below its maximum value of 1 as total coverage ( nF + nR ) decreases , or as coverage becomes biased towards the forward or reverse strand . We incorporated overdispersion because of evidence that the number of mutant calls in a heterozygous sample can be overdispersed relative to a binomial distribution [20] . We estimated overdispersion ( ρ ) for each sequencing block by maximum likelihood ( S2 Text ) . Estimates of ρ were small ( 1 . 01–1 . 17 ) and not significantly different from 1 ( no overdispersion ) in most blocks . Nevertheless , we used these ML values when calculating Pdetect for subsequent analyses . For each sample , we calculated Ω as the sum of Pdetect across all sites and multiplied by the number of MA lines within the sample ( 2 or 4 ) . Ω represents the effective number of sites called in a sample across all MA lines present in that sample ( 2 or 4 ) , weighted by the relative opportunity for mutation . For example , if the data for a sample consisted of x sites sequenced at very high coverage ( so Pdetect ≈ 1 for these sites ) , then Ω ≈ 2x ( or 4x ) if the sample was comprised of 2 ( or 4 ) MA lines . Detection power ( Pdetect ) was about half as large for samples consisting of 4 MA lines ( where mutant frequency was 25% ) compared to samples consisting of 2 MA lines ( where mutant frequency was 50% ) . Consequently , Ω did not differ between samples with 2 versus 4 MA lines , reflecting the trade-off between sequencing more MA lines in a single sample and sequencing each MA line within a sample with greater coverage . As expected , the number of substitutions detected was highly correlated with Ω across samples ( r = 0 . 87; N = 38; p < 1 × 10−11 ) . Although indel mutations will sometimes involve more than one site , it is likely that Ω is nevertheless a good approximation for our power to detect these events in our data , because the indels we detected were generally small ( mode = 1 bp , mean = 1 . 9 bp , max = 4 bp ) , and because we applied the same coverage criteria for calling indels and substitutions , which are more stringent than the criteria used by Pindel . The number of indels detected was positively correlated with Ω across samples ( rSpearman = 0 . 57; N = 38; p < 0 . 001 ) . We therefore used Ω to assess our power to detect indels and estimate the rate of indels . Nonetheless , we caution that Ω should be regarded as a cruder approximation of power for detection of indels than for substitutions . It will be sufficient for our main goal of comparing indel rates between loaded and unloaded backgrounds , as the method is applied to both treatments and sequence coverage levels are similar between treatments . We downloaded gene locations and sequences from FlyBase ( https://flybase . org ) . We simulated >27 , 000 random point mutations and determined our power to detect mutations at each site and whether simulated mutations occurred in genes . We also determined whether each observed substitution and indel occurring in coding sequence was synonymous or nonsynonymous . A mutation was considered nonsynonymous if it altered the amino acid sequence or length of at least one known protein isoform . Finally , we also tested whether the frequency of nonsynonymous versus synonymous substitutions differed from the neutral expectation by simulating random substitutions in the relevant genes ( >1 . 2 × 106 simulations total ) , assuming a transition bias of 2:1 , and ignoring variation in our power to detect mutations . During MA , the focal second chromosome was maintained in heterozygous males . The homologous chromosome , “vg , ” was derived from a stock bearing recessive markers introgressed onto a wild-type background . This stock differed from the MA consensus sequence at 1 in every 272 callable sites ( after weighting by alternate allele frequency ) , but the stock itself was genetically variable . We can detect gene conversion events if they occur in a region where the focal chromosome and the vg chromosome differ , because the MA chromosome will carry SNPs that are commonly found on the vg chromosome . Our approach to calling gene conversion events and assessing power is described in detail in S3 Text . Briefly , we first identified sites where vg differed from the MA consensus , which we refer to as vg-SNPs . We then identified sites in each MA sample with reads that matched a vg-SNP . Using this list of matching sites , we called a gene conversion event in a given sample when there were at least three sites , each with at least three high-quality base calls matching a vg-SNP , within a 5-kb window . On average , there were 10 . 7 matching sites per event we detected , with an average of 6 . 8 matching calls per site . In a separate analysis , we also applied less stringent criteria by including cases where only one site had at least five high-quality base calls matching vg-SNPs; our results are qualitatively unchanged by the inclusion of such cases . All putative gene conversion events were examined in IGV . We tested three putative gene conversion events by Sanger sequencing and confirmed them all . To assess power , we simulated random events of various tract lengths and determined the probability that each event would be detected given our calling criteria , the expected frequency of a new mutation in the MA sample , and the list of vg-SNPs . Previous studies indicate that gene conversion tracts are exponentially distributed in length , with a mean of approximately 1 , 463 bp [24] . Although we cannot determine the exact lengths of the gene conversion events in our dataset we examined approximate lengths based on the distance between the leftmost and rightmost sites within a tract with two or more base calls matching a vg-SNP , which will underestimate the true length . This distribution did not differ significantly from an exponential distribution with mean 1 , 463 bp ( see Results ) . We calculated power-corrected gene conversion rates assuming as exponential distribution of tract lengths with mean λ = 1 , 463 bp ( see S3 Text ) , but our main conclusions are unchanged when values of λ ~30% shorter or longer ( e . g . , λ = 1 , 000 , 1 , 900 ) are assumed instead . In the first block of genome sequencing , we found evidence that crossing over occurred between the focal MA chromosome and the homologous vg chromosome in samples 1 , 2 , and 16 . Specifically , large numbers of substitutions were identified on the left arm of the chromosome , which were of the expected mutant frequency ( 0 . 25 for these samples ) , and matched polymorphisms on vg , indicating that crossing over occurred in one of the MA lines comprising each of these samples ( S2 Fig ) . We excluded these regions from all analyses ( all of 2L for samples 1 and 2 , and the distal 3 Mb in sample 16 ) . Prior to sequencing blocks 2–4 , we tested additional MA lines for crossing over with vg by Sanger sequencing two loci on the distal end of chromosome 2L that include nine SNPs found in the vg population . Based on the location of the markers used in the MA experiment , recombination effects on 2R would have been excluded during MA , whereas those on 2L would have been undetectable . In addition to the three lines in the dataset presented here , we found evidence for crossing over in two additional lines ( out of 184 tests in total ) . We excluded these lines from genome sequencing in blocks 2–4; the additional lines chosen for sequencing were chosen more or less at random , given the availability of material . There was no genomic evidence of crossing over in the 2nd chromosomes we fully sequenced that tested negative for crossing over based on Sanger sequencing ( S2 Fig ) . We identified TEs in our data using PoPoolationTE [26] , which aligns reads to known TE sequences , along with a genome reference where these sequences are masked . Read pairs that map partially to a genomic sequence and partially to a TE sequence provide an indication of the location and frequency of TEs in the genome . We downloaded TE sequences from FlyBase ( http://flybase . org ) and NCBI ( http://www . ncbi . nlm . nih . gov ) . PoPoolationTE is known to be sensitive to the distribution of insert sizes and coverage , and our data are not ideally suited for detection of TEs . We use it here only to get a crude sense of TE activity and to test if there are obvious differences in TE activity between treatments . We identified putative TEs in each sample independently and then clustered TEs by family based on their putative locations . A novel insertion will appear at intermediate frequency in one sample only . An excision of a consensus TE sequence will appear at intermediate frequency in one sample and be fixed in the remaining samples . In addition to these criteria , we only considered putative TE insertions and excisions that were supported by at least five reads , with at least one read on each strand , and with presence/absence information from at least nine reads in total . We did not identify any TE excisions . Some of these criteria for calling TEs are similar to our criteria for calling substitutions and indels , and the number of TEs detected was significantly correlated with Ω across samples ( r = 0 . 76; N = 38; p < 1 × 10−7 ) , indicating that Ω may account for much of the variation among samples in our power to detect TEs . The rates of TE insertion we report are calculated by assuming that our highest value of Ω corresponds to a TE detection probability of 1 and thus represent lower bound estimates of the TE insertion rate . We tested the effect of genetic quality ( loaded or unloaded ) on the rates of single-nucleotide substitution , indels , gene conversion events , and TE insertions by fitting GLMs with N = 38 samples in R [46] . Because our initial goal was to compare loaded and unloaded backgrounds , we do not distinguish among different types of loaded backgrounds in these analyses . We also ran these models including “background genotype” as a random effect , but the random effect term was nonsignificant . In addition to a main effect of background , we included power as a covariate to account for differences in power and the number of MA lines within each sample . Multinucleotide mutations were each treated as single events . For indels and TEs , we found that the number of MA lines within a sample had a significant effect on the observed number of events , beyond the effect of power per se , and so we included it as an additional covariate in these cases . Each model used a Poisson link function , and we tested for possible overdispersion using the R package AER . We detected marginally significant overdispersion in the case of gene conversion ( p = 0 . 0497 ) . Because opinions differ on how to test fixed effects when there is overdispersion , we fit both a quasi-Poisson model ( Wald t test for an effect of treatment ) , and a generalized linear mixed model ( GLMM ) with an observation-level random effect ( likelihood ratio test [LRT] for an effect of treatment ) , which gave the same conclusions . For other mutation types , we tested for an effect of treatment using a Wald Z test . In addition , we tested for an effect of sequencing block by incorporating a random effect of block in GLMMs , but the random effect variance for block was found to be negligible in all cases , and was not included in our final analyses . Our mutation rate estimates are based on predicted values from these models ( quasi-Poisson in the case of gene conversion ) for a sample with average power , accounting for power and the number of MA generations . To test for an effect of body mass on indel and gene conversion rates , we fit Poisson GLMs as above , with mass instead of treatment as a main effect . We initially included a random effect of genetic background ( seven levels ) to account for overdispersion , but found that the variance attributed to this factor was negligible . Further , we found that a model using only “background mass” was much better than a model using only “background genotype” based on AIC scores , suggesting that variation in mutational properties among backgrounds is more simply accounted for by their effect on mass than by their specific genotypes . For all models , detection power was a significant positive predictor of observed mutation number . To examine the effect of local context on substitution rates , we determined our context-specific power to detect substitutions at the central position in each of the 32 possible 3-bp contexts in each sample . We also considered the effect of GC content in a wider region on the rate of substitutions and indels by first finding the GC content of sites with substitutions , and of the 6 bp centered on the midpoint of each indel , and then determining the GC content in the 1 kb surrounding each of these focal sites or 6-bp regions ( data to the left and right of each site were averaged ) . In addition to observed mutations ( excluding multinucleotide substitutions ) , we conducted this procedure for >86 , 000 randomly chosen sites , across samples , where we simulated twice as many events for those samples representing 4 versus 2 MA lines , and retained each simulated event according to the probability that it would be detected . We divided these focal sites based on the GC content of the focal site ( two categories for substitutions [GC versus AT] , three categories for indels [%GC < 1/3 , 1/3 ≤ %GC ≤ 1/2 , %GC > 1/2] ) . We calculated average GC content in a 51-bp overlapping sliding window for mutant sites relative to nonmutant sites . We then plotted the weighted average across categories of focal site , with weights based on the GC content of random sites on chromosome 2 . We also searched for the combination of window sizes for which GC content best predicted the presence of mutations using logistic models that also included the GC content of the focal site and a random effect of sample ( preliminary tests indicated that interaction effects were absent ) . We determined the AIC score for models with 1 , 2 , or 3 nested windows , with the condition that each window was at least 1 . 25 times the size of the next smallest window , by first testing >1 , 000 random combinations of window sizes and then testing a grid of window combinations surrounding the combination with the lowest AIC score from the original random set . The best model involved three windows for substitutions and two windows for indels . To test for possible effects of chromosomal location on mutation rates , we determined our power to detect events in each of 44 ~1-Mb regions across the chromosome , based on all callable sites for substitutions and indels and 104 simulated gene conversion events with tract lengths of 5 kb , and used this to calculate power-corrected mutation and gene conversion rates for each region . We then examined the rank correlation between these region-specific rates and that region’s distance from the centromere . We also examined the GC content of gene conversion tracts in the following manner . We first determined the approximate midpoint of each observed tract as halfway between the leftmost and rightmost sites matching vg-SNPs , considering only sites with at least two matching calls , and examined the GC content of the consensus sequence in the 1 , 463 bp surrounding each midpoint , where 1 , 463 bp is the expected mean tract length [24] . We then simulated >28 , 000 gene conversion events in random locations on chromosome 2 , with exponentially distributed tract lengths with mean 1 , 463 bp . We simulated twice as many events for those samples representing 4 versus 2 MA lines , and retained each simulated event according to the probability that it would be detected . We then compared the GC content of the 1 , 463 bp surrounding the midpoints of simulated gene conversion tracts to that of the observed tracts . To assess the possibility of GC-biased gene conversion , we used vg-SNP sites to determine the spectrum of changes expected in gene conversion tracts in the absence of bias , accounting for the observed frequency of each vg-SNP , to compare with the observed spectrum ( S1 Data ) . Our power to detect bias is limited , because the locations of heteroduplex regions of gene conversion tracts are uncertain , and we cannot detect cases where mismatches are resolved in favour of the consensus base .
The replication and maintenance of genomes is essential to all organisms , and multiple cellular pathways serve to correct replication errors and repair DNA damage . The use of these repair pathways can vary among individuals , and we hypothesized that those in poor condition may be less capable of effectively repairing their DNA . We used genome sequencing to study new mutations in experimental fruit fly lineages , some of which had reduced condition due to previously-existing mutations in their genomes . Based on the new mutations we observed , we conclude that flies in poor condition repaired breaks in their DNA less effectively , leading to mutations that reduced the fitness of their offspring . We also found that some areas of the genome were more likely to mutate than others , altering predictions for how genome sequences evolve . If the presence of deleterious genetic variants increases the mutation rate , as our results indicate , this is expected to increase the risk of extinction in small populations , but could also accelerate adaptation to new environments . Our results further imply that individuals in poor condition are at increased risk of acquiring cancer and transmitting genetic disorders to offspring .
[ "Abstract", "Introduction", "Results", "Discussion", "Methods" ]
[ "invertebrates", "insertion", "mutation", "invertebrate", "genomics", "animals", "animal", "models", "mutation", "substitution", "mutation", "drosophila", "melanogaster", "model", "organisms", "dna", "recombination", "dna", "gene", "conversion", "drosophila", "research", "and", "analysis", "methods", "chromosome", "biology", "animal", "genomics", "insects", "arthropoda", "biochemistry", "cell", "biology", "nucleic", "acids", "gene", "identification", "and", "analysis", "genetics", "mutation", "detection", "biology", "and", "life", "sciences", "dna", "repair", "genomics", "organisms", "chromosomes" ]
2016
Low Genetic Quality Alters Key Dimensions of the Mutational Spectrum
Mycobacterium tuberculosis ( MTB ) is the causative bacterium of tuberculosis , a disease responsible for over a million deaths worldwide annually with a growing number of strains resistant to antibiotics . The development of better therapeutics would greatly benefit from improved understanding of the mechanisms associated with MTB responses to different genetic and environmental perturbations . Therefore , we expanded a genome-scale regulatory-metabolic model for MTB using the Probabilistic Regulation of Metabolism ( PROM ) framework . Our model , MTBPROM2 . 0 , represents a substantial knowledge base update and extension of simulation capability . We incorporated a recent ChIP-seq based binding network of 2555 interactions linking to 104 transcription factors ( TFs ) ( representing a 3 . 5-fold expansion of TF coverage ) . We integrated this expanded regulatory network with a refined genome-scale metabolic model that can correctly predict growth viability over 69 source metabolite conditions and predict metabolic gene essentiality more accurately than the original model . We used MTBPROM2 . 0 to simulate the metabolic consequences of knocking out and overexpressing each of the 104 TFs in the model . MTBPROM2 . 0 improves performance of knockout growth defect predictions compared to the original PROM MTB model , and it can successfully predict growth defects associated with TF overexpression . Moreover , condition-specific models of MTBPROM2 . 0 successfully predicted synergistic growth consequences of overexpressing the TF whiB4 in the presence of two standard anti-TB drugs . MTBPROM2 . 0 can screen in silico condition-specific transcription factor perturbations to generate putative targets of interest that can help prioritize future experiments for therapeutic development efforts . Tuberculosis ( TB ) remains a major global health challenge with a need for enhanced drug development efforts . The current standard treatment for drug-susceptible tuberculosis involves treating for 6 months with a cocktail of first-line drugs that have serious side-effects [1] . Antibiotic resistance , drug toxicity , and the long treatment duration renders it difficult to treat patients successfully [2] . Despite this , the current first-line therapy recommended by the World Health Organization is decades old [2] . Mycobacterium tuberculosis ( MTB ) has the ability to tune its physiology to adapt and survive in the broad range of conditions within the host , altering its drug susceptibility in the process . A promising approach for discovering novel drug targets has been to develop computational models that recapitulate the organismal phenotypes of interest . For example , to identify candidate metabolic drug targets , genome-scale mechanistic models of metabolism have been used to simulate growth and metabolic phenotypes under gene deletion conditions for multiple pathogens [3] . Another important biological network for which mechanistic models have been constructed is gene regulation[4 , 5] . Transcriptional regulatory networks ( TRNs ) orchestrate organismal responses to condition-specific perturbations . Although TRNs and metabolic networks have been modeled mainly using different approaches , regulatory and metabolic responses are intrinsically interconnected . Modeling the metabolic consequences of regulatory perturbations in MTB will refine our understanding of its physiology and potentially lead to novel candidate drug targets . Thus far , the effects of gene regulation have been integrated into constraint-based metabolic modeling with three major strategies: ( 1 ) modifying the objective function of a metabolic model to impose an implicit regulatory goal [6 , 7] , ( 2 ) overlaying gene expression information to impose condition-specific flux constraints on the metabolic model [7–11] , and ( 3 ) integrating transcriptional regulatory information explicitly with the metabolic model [12–16] . Overlaying gene expression directly onto metabolic models has been used as a proxy for explicit reconstruction of gene regulatory networks to constrain the space of possible metabolic flux states . For example , the iMAT method uses ‘ON’ vs . ‘OFF’ gene expression states to guide which corresponding metabolic reactions are active vs . inactive [9 , 10] . As this approach applies the effects of perturbations apparent in the gene expression data directly as reaction constraints upon the metabolic model , a model representing the perturbation of a transcription factor ( TF ) based on directly overlaying gene expression data requires , as input , transcriptional profiles measuring the perturbation of the specific TF of interest . In the absence of such condition-specific gene expression data , efforts at integrating information about regulation and metabolism have reconstructed known regulatory interactions between transcription factors , metabolic target genes , and environmental conditions . These approaches do not require the generation of transcriptional profiles directly probing transcription factor perturbations , and thus are applicable to new conditions . Most of these approaches ( e . g . rFBA and SR-FBA ) involve manually defining a set of Boolean regulatory rules based on literature information [12–15] . A significant drawback to these manually generated rules is the difficulty to scale the rule generation process to capture the complete set of regulatory interactions for an entire organism . Regulatory constraints have also been interfaced in a semi-automated manner with the Probabilistic Regulation of Metabolism framework ( PROM ) [16] . PROM simulates the metabolic effect of a TF knockout by estimating from gene expression data the conditional probability that each TF knockout would disrupt the expression of a corresponding metabolic target gene , based on the following equation: P ( Gene = ON | TF = OFF ) ≈Number of samples with Gene = ON AND TF = OFFNumber of samples with TF = OFF . This probability is mapped onto the reactions catalyzed by the protein encoded by the target gene , and it is used to modulate the maximum reaction flux bounds . Notably , the first integrated metabolic-regulatory model for MTB was constructed using PROM [16] . In this work , we present a significantly improved regulatory-metabolic model for MTB based on the PROM framework . MTBPROM2 . 0 encapsulates a substantially expanded knowledge base of underlying metabolic and regulatory mechanisms , featuring a regulatory network for 104 TFs based on ChIP-seq interactions that interface with a refined genome-scale metabolic model with 810 genes . We also extended the PROM framework to enable the prediction of metabolic consequences of TF overexpression . Using this expanded model , we can generate both knockout and overexpression phenotype predictions for 3 . 5 times as many TFs while exposed to a greater range of environmental and genetic perturbations . MTBPROM2 . 0 has improved agreement between predictions and experimental datasets assessing gene essentiality and overexpression growth defects compared to alternative methods , and it has successfully predicted synergy between TF perturbations and anti-TB drugs . We have reconstructed an updated regulatory-metabolic model for MTB using the PROM framework . The updated model , MTBPROM2 . 0 , incorporates a knowledge expansion in both the metabolic and regulatory components . Fig 1 summarizes the updated information incorporated into the model ( additional information is provided in Table 1 , S1 Text , and S1 Table ) . To expand the knowledge base of metabolism represented by MTBPROM2 . 0 , we updated the metabolic component by integrating gene-associated reactions with literature evidence from the multiple existing genome-scale metabolic models and supplementing with additional reaction information from literature ( see S1 Text for details ) [20–23] . The updated metabolic model , Mycobacterium tuberculosis iSM810 , includes a greater number of genes and reactions with literature evidence compared to the metabolic component used in MTBPROM1 . 0 , which was iNJ661 ( see S1 Table for the details of the properties of iSM810 and S1 File for both the SBML format and COBRA Toolbox implementation of iSM810 ) . Moreover , iSM810 demonstrates an improved ability to predict MTB growth , as quantified by the Matthews Correlation Coefficient , which is a performance metric that reduces bias for uneven category sizes ( MCC , see Methods for details ) . Across 91 different metabolite utilization conditions , iSM810 had an MCC = 0 . 44 , precision = 0 . 71 , and recall = 0 . 84 , whereas iNJ661 had an MCC = 0 . 25 , precision = 0 . 79 , and recall = 0 . 26 . For metabolic gene knockout conditions , iSM810 had an MCC = 0 . 52 , precision = 0 . 83 , and recall = 0 . 59 over 810 genes , whereas iNJ661 had an MCC = 0 . 27 , precision = 0 . 72 , recall = 0 . 44 over 661 metabolic genes ( see S1 Text for detailed growth simulation results ) . The transcriptional regulatory network component represents the most substantial expansion of knowledge in MTBPROM2 . 0 ( see S1 Text for a visual comparison of the networks ) . We leveraged a greatly extended regulatory network based on transcription factor ( TF ) binding measured using genome-scale ChIP-seq data generated for 190 transcription factors ( 89% of an estimated 214 MTB TFs ) ( described in [19] ) . For the purposes of integrating with the regulatory-metabolic model , we used the subset of interactions from the ChIP-seq binding network wherein the TF binding footprint is located proximally to transcriptional start sites ( i . e . region spanning 150 bp upstream to 70 bp downstream of start site ) , and included indirect interactions based on information from the TBDB operon browser [24 , 25] . We also considered only interactions between the TFs and the genes in the metabolic model iSM810 . The resulting updated regulatory component includes 2555 interactions for 104 TFs ( 49% ) that link to 647 metabolic genes ( 80% of the metabolic genes in iSM810 , 16% of all MTB genes ) . Importantly , the expanded , data-rich coverage of transcriptional regulatory interactions to metabolic genes facilitates systems modeling of a broader set of metabolic consequences to TF perturbations . The original PROM framework was designed to simulate the metabolic effects of TF knockouts . To infer the strength of each regulatory interaction for each TF knockout simulation , the conditional probability that each target metabolic gene is expressed in the absence of each transcription factor was calculated using microarray gene expression data ( see Methods for details ) . We used as input a dataset that measured the gene expression profiles resulting from overexpressing each of 206 TFs ( which include 103 of the 104 TFs in the regulatory component of our model ) , as described [26] . This dataset is particularly suited to inferring the strength of each TF-target gene interaction because it measures the consequences of systematic regulatory gene perturbations . These inferred regulatory influences were mapped onto the metabolic model to simulate the consequent growth phenotype ( see Methods for details ) . We used the updated regulatory-metabolic model MTBPROM2 . 0 to simulate the effect of TF knockouts on MTB growth rates across multiple media conditions ( see S2 Table for complete set of simulation results ) . We compared the growth rate predicted for each TF knockout to the corresponding growth rate of the wild-type under the same simulated environmental conditions , and we designated TFs with simulated knockout growth rates less than 0 . 95 of wild-type as having growth defect . To validate the extent to which MTBPROM2 . 0 can predict growth defects of transcription factor knockouts , we compared the simulated TF knockout predictions with experimentally derived gene essentiality data generated by Griffin et al . [27] . Our evaluation criterion was whether the model simulated knockout growth rate ratios could distinguish between the TF knockout strains that are highly confident essential ( Griffin score < 0 . 1 , 13 TFs ) from TF knockouts that are highly confident non-essential ( Griffin score > 0 . 9 , 29 TFs ) . MTBPROM2 . 0 had an MCC = 0 . 33 ( precision = 0 . 59 , recall = 0 . 77 ) . This improved markedly upon the overall performance of the original model , MTBPROM1 . 0 , which had a MCC = 0 ( precision = 0 . 50 , recall = 0 . 29 ) on this new kind of data for comparison . MTBPROM2 . 0 performance remained higher than MTBPROM1 . 0 when other essentiality thresholds were tested as well ( See S1 Text for detailed analysis ) . To extend the predictive scope of the PROM framework , we modified the simulation to enable prediction of transcription factor overexpression growth phenotypes , using the same input gene expression dataset to train conditional probabilities ( see Methods for details ) . As validation , we compared the MTBPROM2 . 0 predicted overexpression growth ratios to experimentally measured doubling time ratios of the TF overexpression strains with and without the induction of overexpression [26] . Fig 2 , Panel A shows experimentally measured overexpressed vs . not overexpressed doubling time ratios of the TFs predicted by MTBPROM2 . 0 , where a higher doubling time ratio indicates a greater growth defect upon TF overexpression . The bars are color-coded based on whether the MTBPROM2 . 0 simulation predicted a growth defect upon the overexpression of each TF . Using the 85th percentile ( corresponding to a doubling time ratio of 3 . 3 ) as an experimental cutoff threshold to delineate growth defect vs . no defect , we evaluated the ability of MTBPROM2 . 0 to correctly distinguish between these groups . The overall MCC was 0 . 2 , with precision = 0 . 23 , and recall = 0 . 69 . To boost the utility of the MTBPROM2 . 0 TF overexpression predictions , we used various network properties to train a logistic regression model to estimate the confidence that each TF overexpression prediction by MTBPROM2 . 0 would be correct ( see Methods for details and S3 Table for condition-specific predictions ) . Of the variables we tested , we found two that contributed significantly to the logistic regression model: ( 1 ) whether the MTBPROM2 . 0 prediction for a particular TF matched the prediction generated by the iMAT method [9 , 10 , 28] , and ( 2 ) the average number of regulators that each essential target gene had ( see S1 Text for complete list ) . We performed ten-fold cross-validation on our logistic regression model to evaluate the ability of the model to predict TFs where growth effects were correctly simulated by MTBPROM2 . 0 ( see Methods and S1 Text for details ) , and found an average cross-validation MCC of 0 . 56 ( precision = 0 . 81 , recall = 0 . 86 ) . This performance implies that the logistic regression model can not only be used to help prioritize the TFs that should be followed-up in further experiments , but it can also identify properties of TFs that make their phenotypes more challenging to predict by MTBPROM2 . 0 . Fig 2 , Panel B shows the MTBPROM2 . 0 predictions of overexpression growth defect for the 46 TFs with high confidence scores based on the logistic regression model . Using the same 85th percentile cutoff threshold for growth defect as for evaluating all the TFs , MTBPROM2 . 0 predicted growth defect with a MCC of 0 . 47 ( precision = 0 . 45 , recall = 0 . 71 , p < 0 . 01 Fisher’s exact test ) ( see Fig 3 ) . MTBPROM2 . 0 achieved an improved performance compared to predicting growth defect based on the whether the TF significantly repressed essential metabolic genes ( MCC = 0 . 31 , precision = 0 . 26 , recall = 0 . 88 ) or based on condition-specific metabolic models generated by overlaying overexpression microarray data with iMAT [9 , 10 , 28] ( MCC = 0 . 24 , precision = 0 . 31 , recall = 0 . 50 ) ( see Methods for details on these alternative prediction strategies ) . Finding effective new drug combinations is a major challenge in the TB field . MTBPROM2 . 0 can be integrated with condition-specific metabolic models to predict effects of combinatorial perturbations on the growth of MTB . To assess our ability to predict synergies with drugs , we exploited test compounds with demonstrated anti-TB activity [29] as well as current anti-TB agents . We exposed MTB to minimum inhibitory concentration ( MIC ) levels of each compound for 16 hours , less than one doubling for this organism and before any cell death was evident . RNA was isolated and applied to arrays provided by TIGR under the NIAID contract N01-AI-15447 using published protocols [30] ( the data are accessible from the Gene Expression Omnibus , accession number GSE71200 ) . We used these transcriptional response data with the iMAT method to constrain iSM810 and generate a series of drug-specific models that represent the metabolic state of MTB when exposed to each of the antibacterial agents . We integrated each of these drug-specific MTB metabolic models into the updated regulatory network component of MTBPROM2 . 0 and simulated the growth outcome of each TF knockout and overexpression event . The simulations predicted multiple TF knockouts and overexpression conditions that would have a growth defect in the presence of the drug but not under standard culturing conditions ( see Supplemental S1 Text and S4 Table for full results ) . To experimentally validate , we tested the overexpression of the TF whiB4 ( Rv3681c ) , which MTBPROM2 . 0 predicted to have a growth defect when exposed to each of four agents: ethionamide ( ETH ) , isoniazid ( INH ) , a coumarin analog ( IMTB009 ) , and a guanosine analog ( IMTB0044 ) . We compared the growth ( by OD600 ) and metabolic activity ( by Alamar Blue Assay [31] ) of wild-type H37Rv with a strain overexpressing whiB4 in the presence of each drug ( see Methods for details ) . TF overexpression did not alter sensitivity to IMTB009 or IMTB044 ( data not shown ) , but did synergize with the inhibitory activity of ETH and INH . Fig 4 shows representative growth and metabolic activity time-course profiles of the wild-type strain and the strain overexpressing whiB4 from one of three experiments ( each performed with three biological replicates ) . While no appreciable growth difference was detected between the two strains in the absence of ETH , dosing the strains with 3μM of ETH ( approximately 0 . 5x the MIC ) resulted in significantly more growth inhibition ( 3-fold lower OD600 at 14 days post drug , Fig 4 , Panel A ) and less metabolic activity ( Fig 4 , Panel B ) in the strain overexpressing whiB4 compared to wild-type . Similarly , dosing the strains with 2μM of INH ( approximately 0 . 6x the MIC ) resulted in significantly more growth inhibition ( 2-fold lower OD600 at 14 days post drug , Fig 4 , Panel C ) and less metabolic activity ( Fig 4 , Panel D ) in the strain overexpressing whiB4 compared to wild-type . In addition , we tested four drugs predicted by the model not to synergize with whiB4 ( IMTB001 , IMTB031 , IMTB036 , and IMTB041 ) , and observed no differential growth upon exposure to these compounds ( data not shown ) . Models that leverage knowledge of biological circuitry to generate accurate predictions of phenotype can synergize with experimental efforts to improve understanding of biology and identify novel intervention strategies . In this work , we have generated an updated mechanistic regulatory-metabolic model for Mycobacterium tuberculosis that incorporates expanded and improved knowledge of the metabolic and regulatory systems . The refined model , MTBPROM2 . 0 , can predict metabolic consequences of TF knockout or overexpression under different environmental conditions , and suggest hypotheses of underlying molecular mechanisms that contribute to consequent phenotypes . This work has shown that we can predict growth phenotypes resulting from overexpressing or disrupting transcription factors with a mechanistic regulatory-metabolic model of MTB , and that integrating more knowledge of underlying metabolism and regulation into mechanistic models improves predictive ability . Moreover , we have demonstrated the utility of MTBPROM2 . 0 in predicting TF perturbations that synergize with the activity of drugs . Our model minimizes complicating assumptions , and with this simplified rule set , a significant proportion of predictions generated can be successfully validated . Therefore , using mechanistic models to screen and generate hypotheses for interesting targets can help to prioritize experiments that further extend knowledge of the underlying biology . Expanding the PROM framework to enable prediction of TF overexpression phenotypes opens a new dimension of in silico screening that can be followed up with experiments , and to the best of our knowledge , MTBPROM2 . 0 is the first explicitly reconstructed regulatory-metabolic model to incorporate overexpression simulations . Given that the experimental tools are already in place to control and monitor the effects of TF overexpression in MTB [26] , introducing this predictive capability further facilitates the capacity to translate PROM predictions into tractable experiments . Transcription factor overexpression is also a more physiological perturbation than knockout , since most transcription factors will undergo differential expression under exposure to different conditions , whereas complete gene knockout would typically arise only from genetic mutation . Being able to exploit overexpression to perturb MTB phenotype may open therapeutic possibilities for different modes of drugs . Leveraging the ability of MTBPROM2 . 0 to simulate a broader set of genetic perturbations under a greater range of conditions to guide experiments , we can begin to gain a better understanding of the condition-specific genetic sensitivities of MTB . Incorporating additional regulatory and metabolic reaction information expanded the scope of the predictions , enabling predictions on a broader range of transcription factors than MTBPROM1 . 0 . Moreover , the improvement in gene essentiality predictive performance of observed with MTBPROM2 . 0 compared to MTBPROM1 . 0 suggests that additional representation of biochemical and regulatory knowledge captured by the integrated model further drives improvements to predictive ability . Continuing efforts to improve the representation of biochemistry and regulation in the metabolic and regulatory model components—as well as inclusion of other important processes—will likely generate more comprehensive predictions that are more reflective of MTB behavior . Finding effective combinatorial therapies is an important challenge in TB drug development . While drug combinations can be more effective and less likely to promote MTB resistance , identifying viable combinations by experimental efforts alone is hindered by the large search space . Integrating MTBPROM2 . 0 with drug-specific metabolic models informed by transcriptional responses in MTB , we successfully predicted synergistic interactions between overexpression of the transcription factor whiB4 and the drugs ethionamide and isoniazid . This synergy suggests that the regulatory targets of whiB4 include potential drug targets that can enhance the activity of these agents . While two other predicted whiB4-drug interactions were not validated , the ability of the model to identify synergistic TF-drug interactions in 50% of cases and to identify correctly drugs that do not synergize with the TF in four of four instances that we tested argues that the model is capturing a significant portion of the mechanism ( s ) that underlie the synergistic phenotypes . Applying this approach to other anti-TB agents could contribute to rationally informing identification of novel candidates for synergistic targets . There remain substantial gaps in the knowledge and representation of regulation and metabolism that lie beyond the scope of the current PROM framework . Determining which phenotypes can be modeled with by rough representations of mechanism and which phenotypes require more detailed , in-depth models can shed light into the complexity of the phenotypes themselves and inform future modeling efforts . Our logistic regression model enabled us to distinguish between TFs that are amenable to MTBPROM2 . 0 predictions and those that are not based on the network properties . This approach gave a network-based criterion with which to rank predictions that were most promising to follow-up in experiments . Although in silico predictions are easy to make in high throughput , it is often difficult to follow-up on more than a handful of predictions with in-depth experimental characterization . Therefore , having an accurate means to prioritize the predictions that are most likely to yield meaningful follow-up results will streamline the iterative process of investigation . Moreover , the ability to identify properties that make a TF hard to predict by the current regulatory-metabolic model also can direct method development towards improvements that can address the current limitations . In the case of using MTBPROM2 . 0 to predict TF overexpression growth phenotypes , the factors that could stratify the TFs suggest that being able to account for more complex regulatory mechanisms would improve predictive ability . Future method development to address combinatorial regulation and the effects of both activation and repression may help to improve overall predictive accuracy for regulatory-metabolic models . Growth phenotype simulations were performed on the genome-scale metabolic models using the flux balance analysis tools in the COBRA Toolbox [28 , 32] . Growth rates were generated by calculating the optimal value of the flux of the biomass generation reaction , which was set as the objective function in these simulations . Single gene deletion simulations calculated the growth rates with the reactions that map to each individual gene set to have flux rates of zero . To evaluate the ability of the metabolic and regulatory-metabolic models to predict gene knockout growth defects , single gene deletion growth simulations for each of the models were compared to an experimental essentiality screening dataset generated by Griffin et al . [27] . The Griffin essentiality dataset screened for essential genes from transposon mutagenesis libraries by assessing the set of transposon insertions detected after growing the pooled transposon mutants for a period of time [27] . The authors summarized their findings with an essentiality confidence score for each gene that ranges from 0 to 1 that represents the probability that particular gene is non-essential ( low confidence scores indicate that a gene is likely essential , and high confidence scores indicate that a gene is likely non-essential ) . We used the experimental data to separate the genes into two groups: those that have a defect when perturbed and those that do not . We considered genes with Griffin essentiality confidence scores of less than 0 . 1 to be highly confident essential , and we evaluated the ability of the models to successfully simulate growth rates that can distinguish these essential genes . For TF overexpression prediction validation , we compared growth predictions against experimental doubling time ratio data of strains of MTB with overexpression induced vs . uninduced ( described in [26] ) . For all gene perturbation predictions , performance was evaluated based on how well the prediction method could correctly separate the effects of the TF perturbations , where a true positive is a TF that is correctly predicted to cause a defect , and a true negative is a TF that is correctly predicted to cause no growth defect . Performance was summarized by precision TPTP+FP , recall TPTP+FN and the Matthew’s correlation coefficient ( MCC ) , a metric that reduces bias for uneven category sizes and reports values between -1 ( low performance ) and 1 ( high performance ) MCC = TP*TN-FP*FNTP+FP*TP+FN*TN+FP* ( TN+FN ) [33] . In each metric , TP represented the ‘true positives’ , TN represented the ‘true negatives , ’ FP represented the ‘false positives , ’ and FN represented the ‘false negatives . ’ To evaluate the ability for the metabolic models to predict growth under different environmental conditions , we simulated growth under different carbon and nitrogen sources using the COBRA Toolbox by adjusting the exchange reaction fluxes to allow input of the desired metabolite . Growth predictions on diverse carbon and nitrogen sources were compared to experimental growth condition data reported in [23] . We additionally included palmitate , oleate , carbon monoxide , and cholesterol as carbon sources that have been experimentally reported elsewhere [34–36] . In our evaluation of carbon and nitrogen utilization predictions , we considered only the metabolites that were represented in the genome-scale metabolic models . When possible , we fixed the exchange reaction fluxes of the metabolites that we were not adjusting to match the constituents in Sauton’s defined media [37] , ensured that removal of the nitrogen or carbon source would result in no growth , and determined whether each metabolic model would grow in the presence of the different carbon and nitrogen sources . One of the metabolic models we tested could not grow with Sauton’s media-defined conditions . Therefore for this case , we set the exchange fluxes to match the components of Middlebrook 7H9 media [37] . When the carbon source was varied , the Sauton’s media nitrogen sources were used , and when the nitrogen source was varied , the Sauton’s media carbon source was used . In evaluating predictive performance , we calculated the MCC , precision , and recall by designating true positives as the number of metabolites correctly predicted to allow growth , and true negatives as the number of metabolites correctly predicted to not allow growth . The original PROM approach to estimating the influence of a regulatory interaction in the event of a TF knockout was to calculate from a gene expression dataset the conditional probability that a target gene is ‘ON’ in the absence of the transcription factor: P ( Gene = ON | TF = OFF ) ≈Number of samples with Gene = ON AND TF=OFFNumber of samples with TF=OFF ( 1 ) The expression threshold that delineated between the ‘ON’ and ‘OFF’ states could be either set externally or calculated to be a desired quantile from the input expression data . These conditional probabilities were then used to constrain the maximal fluxes of the reactions ( above which a penalty was incurred ) catalyzed by the gene products in the metabolic model . To predict the regulatory effects of TF overexpression , we adapted the conditional probability calculation to estimate the probability that a target gene is ‘ON’ when the expression of the transcription factor is above a threshold value denoted ‘OVEREXPRESS’: P ( Gene = ON | TF = OVEREXPRESS ) ≈Number of samples with Gene = ON AND TF=OVEREXPRESSNumber of samples with TF=OVEREXPRESS ( 2 ) This formulation required setting two expression thresholds: one that delineated between ‘ON’ and ‘OFF’ states , and another that delineated between ‘ON’ and ‘OVEREXPRESS , ’ which transformed the numerical expression values into the three expression states . This formulation is compatible with any gene expression datasets , not just profiles of TF overexpression . It is important to note also that this model only takes into account the effect of genes that are repressed by transcription factors . The effects of target genes that were activated by the overexpression of a transcription factor fell outside the scope of this model . The code for simulating TF knockouts and overexpression is available in S2 File , with necessary input files provided in S3 File . Given that the calculation of the conditional probabilities is dependent on the gene expression dataset being used , we used a sampling approach to estimate the uncertainty of the conditional probabilities . We used microarray data profiling individual TF overexpression perturbations [26] to estimate conditional probabilities . This approach is particularly suited for our sampling approach because it includes at least three biological replicates that measure the overexpression of each TF . Therefore , we sampled the dataset 500 times by randomly selecting for each TF three replicates measuring the overexpression and calculated the conditional probability from the resulting assembled data . The MATLAB code for simulating TF knockouts and overexpression is available in S2 File . We compared the ability of MTBPROM2 . 0 to predict transcription factor overexpression phenotypes using condition-specific metabolic models generated using the iMAT algorithm [9 , 10] . We used the TF overexpression microarray dataset to generate iMAT models to simulate the metabolic state of each TF overexpression condition [26] . For each TF , the overexpression microarray data were binarized such that a gene was designated ‘ON’ if it had a positive fold change value in at least 75% of the samples , and was designated as ‘OFF’ otherwise . These binarized data were then used to generate a condition-specific iMAT model and reaction flux profile from the COBRA Toolbox implementation of iMAT [9 , 10 , 28] . The growth ratio of each TF overexpression condition was calculated by taking the ratio of growth rate derived from the iMAT flux profile and the growth rate simulated from the wild-type model , iSM810 . To simulate drug-specific metabolic models , we applied iMAT to constrain iSM810 based on the transcriptional profiles of MTB response to different drugs measured by arrays provided by TIGR under the NIAID contract N01-AI-15447 . To binarize the expression data , genes with log2 fold change < -1 upon exposure to drug were designated as ‘OFF . ’ Drug-specific iMAT models were generated and used to simulate TF perturbations by integrating with the MTBPROM2 . 0 regulatory framework . To estimate a confidence score for how likely the MTBPROM2 . 0 prediction of a particular TF was likely to be correct , we trained a logistic regression model with the R package “stats” [38] using different properties associated with the regulatory network architecture and the target genes . Logistic regression is a supervised machine-learning model [39] designed to learn the probability that a particular response Y belongs to a particular category given the state of input predictor features ( X1 , … , Xp ) based on the following relation: P ( Y=category|X1…Xp ) =exp ( β0+β1X1+…+βpXp ) 1+exp ( β0+β1X1+…+βpXp ) ( 3 ) In this case , the category we predicted was whether the MTBPROM2 . 0 prediction for a TF overexpression growth defect would be ‘TRUE’ or ‘FALSE , ’ where ‘TRUE’ indicates either a true positive or a true negative prediction , and ‘FALSE’ indicates either a false positive or a false negative prediction . The predictor features we used to train the initial model were the number of metabolic target genes , the fraction of target genes that were highly confident essential based on Griffin data , and the average number of additional regulators that the target genes had . To refine the logistic regression model , we retrained the model using only the features that were evaluated to be significant by the z-statistic [39] . To test the predictive performance of the refined logistic regression model , we performed ten-fold cross-validation using the R package “cvTools” [40] , and calculated the average MCC across the iterations . The MTB strain H37Rv and the strain containing an ATc-inducible expression vector for the TF gene of interest , whiB4 , were cultured as described previously [26 , 30] . Briefly , the strains were grown in Middlebrook 7H9 with the ADC supplement ( Difco ) , 0 . 05% Tween80 at 37°C with constant agitation . The strain containing the ATc- inducible expression vector was grown with the addition of 50 μg/mL hygromycin B to maintain the plasmid . All experiments were performed under aerobic conditions and growth was monitored by OD600 . At an OD600 of approximately 0 . 1 , the cultures were supplemented with either the drug of interest in DMSO solution or DMSO as no-drug control . Concurrently , expression of whiB4 was induced using an ATc concentration of 100 ng/mL culture . Growth was monitored via OD600 for a period of 14 days post drug supplementation and induction of whiB4 overexpression . MTB metabolism was measured by the extent of conversion of the oxidation-reduction dye , Alamar Blue [31] . Ten percent by volume of Alamar Blue reagent was added to wells , and , after 2 and 24 hours of incubation , results were measured by fluorescence ( excitation 544nm , emission 590 nm ) . Percentage of reduction of AlamarBlue was calculated according to the manufacturer's instructions .
Tuberculosis remains a major global health challenge with a need for enhanced drug development efforts . Drug development would be aided by understanding more about the bacteria that causes the disease , Mycobacterium tuberculosis ( MTB ) , and how it adapts to survive the broad range of conditions within hosts . To help this effort , we extended a computational model that uses our understanding of how the MTB transcriptional regulatory network ( genes that interact to control the abundance of target genes ) influences the metabolic network ( genes that drive biochemical reactions ) . Using this model , MTBPROM2 . 0 , we were able to successfully predict whether disrupting or boosting the action of regulatory genes would cause a growth defect in MTB . By applying these predictions , across many environmental conditions , this tool can help find potential new drug targets for more effective MTB treatments .
[ "Abstract", "Introduction", "Results", "Discussion", "Methods" ]
[]
2015
Integrated Modeling of Gene Regulatory and Metabolic Networks in Mycobacterium tuberculosis
Human immunodeficiency virus type 1 ( HIV-1 ) resistance to protease inhibitors ( PI ) results from mutations in the viral protease ( PR ) that reduce PI binding but also decrease viral replicative capacity ( RC ) . Additional mutations compensating for the RC loss subsequently accumulate within PR and in Gag substrate cleavage sites . We examined the respective contribution of mutations in PR and Gag to PI resistance and RC and their interdependence using a panel of HIV-1 molecular clones carrying different sequences from six patients who had failed multiple lines of treatment . Mutations in Gag strongly and directly contributed to PI resistance besides compensating for fitness loss . This effect was essentially carried by the C-terminal region of Gag ( containing NC-SP2-p6 ) with little or no contribution from MA , CA , and SP1 . The effect of Gag on resistance depended on the presence of cleavage site mutations A431V or I437V in NC-SP2-p6 and correlated with processing of the NC/SP2 cleavage site . By contrast , reverting the A431V or I437V mutation in these highly evolved sequences had little effect on RC . Mutations in the NC-SP2-p6 region of Gag can be dually selected as compensatory and as direct PI resistance mutations , with cleavage at the NC-SP2 site behaving as a rate-limiting step in PI resistance . Further compensatory mutations render viral RC independent of the A431V or I437V mutations while their effect on resistance persists . The Human Immunodeficiency virus type 1 ( HIV-1 ) protease ( PR ) is a key enzyme in viral replication and a major target for therapeutic intervention . Protease inhibitors ( PI ) are the backbone of some of the most active combinations of antiretroviral drugs used in the treatment of HIV-infected patients . The long-term efficacy of these compounds , however , is threatened by the emergence of viral resistance and subsequent spread of resistant virus . HIV-1 resistance to PIs is promoted by gradual accumulation of amino-acid substitutions in PR , resulting in altered PI binding [1]–[5] . Resistance-promoting changes in PR generally also decrease viral replicative capacity ( RC ) due to decreased processing of the natural substrate . Accordingly , additional mutations accumulate in PR over time that mainly compensate for these losses in RC , but may also contribute to resistance directly [1]–[3] , [6] . The biological effect of PI resistance mutations thus has to be viewed as the product of their effect on enzyme inhibition ( resistance ) and on enzyme activity ( RC ) . Besides mutations directly affecting PR , several mutations in the Gag polyprotein , the main substrate of PR , have been found to play a significant role in the evolution of PI resistance [7]–[13] . These mutations were generally classified as compensatory mutations that restore activity of the mutated PR for its natural substrate [7]–[9] , [12] , [14] , [15] . Particular attention has been turned to mutations in the region surrounding the NC-SP2-P6 cleavage sites at the C-terminus of Gag . Figure 1 gives an overdrive of these mutations and of their position in the NC-SP2-p6 region of HIV-1 Gag . The most frequently observed cleavage site mutations in this region are substitution A431V , located at position P2 of the NC-SP2 cleavage site , mutation L449F at position P1' of the SP2-P6 cleavage site , and mutations K436R and I437V , situated immediately downstream of the NC-SP2 site . Interestingly , some of these mutations appear to depend upon the presence of specific mutations in PR: Mutation A431V is mostly observed in PI-resistant viruses carrying mutations V82A and/or M46I in PR [16] . Mutation L449F is frequently seen in viruses with mutation I84V in PR [16] . Neither of these two substitutions are seen in wild-type , protease inhibitor-naïve viruses . Substitution P453L is a polymorphism found in some inhibitor-naïve viruses . It is , however , seen with significantly higher frequency in resistant viruses carrying the I84V mutation in PR [16] and also specifically seen in resistant viruses carrying the I50V PR mutation , in association with L449F [12] . In this context , two main questions remain to be answered . First , although some studies have identified Gag polymorphisms outside of cleavage sites that appeared to be important for the replicative capacity of viruses with PR mutations [15]–[20] , the extent to which changes in the matrix ( MA ) , capsid ( CA ) , nucleocapsid ( NC ) proteins and in adjoining cleavage sites participate in evolution of viruses under PI pressure in vivo has not been fully evaluated . Second , the potential impact of Gag cleavage site mutations on viral resistance , in addition to their well established compensatory effect on viruses carrying PI resistance mutations , remains to be established . Nijhuis et al . [13] recently reported the emergence of viruses carrying mutations K436E and I437V ( Figure 1 ) within the SP2 linker peptide of Gag following in vitro selection for HIV-1 resistance to an experimental PI . These mutations in Gag preceded the detection of mutations in PR , and decreased HIV susceptibility to several PIs . It is currently not clear , however , whether mutations in Gag have a selective impact on the level of resistance under PI pressure in vivo , and whether this impact could be independent of the effect of these mutations on viral RC . In this study , we have constructed recombinant viruses carrying different Gag-Pol segments from the plasma of HIV-1 infected subjects in whom viral resistance had evolved to high levels following prolonged antiretroviral treatment failure . Recombinant viruses were tested phenotypically for resistance and replicative capacity . In most cases , the loss of RC resulting from resistance mutations in PR was partially compensated by the NC-SP2-P6 region of Gag while addition of the MA and CA domains had no effect . The presence of patient-derived NC-SP2-P6 resulted in strong increases in the IC50 of most PIs , establishing the important role of this region in determining resistance . Selective reversion of mutations A431V or I437V in a subset of these recombinant viruses produced a strong reduction in resistance , but only a minor effect on replication capacity . The effects on viral resistance correlated with processing at the NC-SP2 cleavage site , revealing the importance of this cleavage event for HIV-1 resistance to PIs . Our results suggest a second pathway of resistance in patients failing a PI-containing regimen , establish a direct effect of Gag cleavage site mutations on antiviral resistance beyond their previously described compensatory role , and identify NC-SP2 cleavage as a limiting step in resistance development in vivo . Gag-Pol HIV-1 sequences from 6 patients were studied . The PR and RT sequences of these viruses contained multiple resistance mutations , as defined according to the IAS-USA table , and are shown on Figure 2A ( PR ) and Figure 2B ( RT ) . The mean number of mutations in PR was 8 . 8 ( range: 6–11 ) . All 6 viruses also carried mutations in the NC-SP2-P6 cleavage region of Gag ( Figure 2C ) . Mutation A431V , at the P2' position of the NC-SP2 cleavage site was found in 4 viruses . Mutation I437V , immediately downstream of that cleavage site , was found in 2 viruses , and mutation L449H , at the P1 position of the SP2-p6 cleavage site was found in one virus , in association with the A431V mutation . Interestingly , mutations A431V and I437V were found to be mutually exclusive in these 6 viruses , which was also the case in the 20 other patients of the ANRS 109 study ( data not shown ) . For each of these 6 viruses , 4 different Gag-Pol recombinant clones were constructed ( Figure 3 ) . Clones BS contained the whole of Gag , PR and RT from patient virus , down to the junction between RT and RNAse H . Clones AS carried patient-derived C-terminal half of NC , SP2 spacer peptide and p6 , in association with PR and RT from the same patient . Clones XS had only PR and RT sequences from patient virus in association with NL4-3 Gag . Finally , clones BX carried the whole Gag from patient viruses , in association with NL4-3 PR and RT . It is noteworthy that in the latter , the transframe region of the Gag-Pol polyprotein was patient-derived , while the last 12 amino acids at the C-terminus of the Gag polyprotein were from NL4-3 . For each virus , the AS , XS and BX clones were derived from a single BS clone . All 4 clones from each of the 6 patients were tested phenotypically for resistance to PIs . The results of these analyses , representing mean±SD of at least 3 independent experiments , are shown on Table 1 , where resistance is expressed as a fold-change in IC50 relative to NL4-3 . Six distinct PIs were tested: indinavir ( IDV ) , nelfinavir ( NFV ) , amprenavir ( APV ) , saquinavir ( SQV ) , lopinavir ( LPV ) and atazanavir ( ATV ) . Viruses carrying patient-derived PR , RT and complete Gag sequences ( BS clones ) were strongly resistant to most PIs tested , in accordance with clinical failure of the respective patients . In contrast , resistance was found markedly lower in viruses carrying only patient-derived PR and RT in absence of any patient-derived Gag sequences ( XS clones ) . This finding emphasizes the critical importance of HIV-1 Gag in viral resistance to PIs . When a shorter segment of Gag encompassing the NC-SP2-P6 region was added to the same highly mutated PR and RT sequences ( AS clones ) , resistance levels were comparable to those measured with clones carrying the full Gag coding sequence ( BS clones ) , strongly suggesting that most of the resistance impact of Gag is restricted to the NC-SP2-p6 region . Therefore , the MA and CA proteins , together with their flanking cleavage sites , do not appear to play a significant role in PI resistance selection in vivo , at least for this group of heavily pre-treated patients . Interestingly , all BX clones , which carried patient-derived Gag but wild-type PR and RT , exhibited increased IC50 levels relative to NL4 . 3 , an effect that ranged from 1 . 6-fold to 5 . 6-fold , suggesting that even in the presence of wild-type PR , changes in Gag occurring during the course of HIV evolution under selective pressure by PIs in vivo exert a clear effect on HIV-1 suseptibility to these compounds . As expected , the presence of patient-derived Gag sequences had a strong positive impact on the RC of the multiply resistant viral clones . The RC of XS clones , which lacked patient-derived Gag , was markedly lower than that of BS clones , carrying the whole Gag sequence from patient plasma: the mean RC of XS clones was 5 . 6% of NL4-3 , and the mean change in RC for XS clones relative to cognate BS clones was a 19 . 5-fold decrease . The greatest such effect was seen with the VIS13 virus ( 53 . 6-fold decrease ) and the lowest with VIS16 ( 2 . 2-fold decrease ) . Similar to what was seen for resistance , the RC of most BS clones did not differ from that of cognate AS clones , again emphasizing the absence of a significant impact of MA and CA on resistance-associated loss of RC . It is noteworthy that substantial variation in RC was seen among the BS clones from different patients , with the lowest ( VIS16 ) at 31 . 9±6 . 0% and the highest ( VIS13 ) at 117 . 9±30 . 3% . Similarly , marked variation in RC was observed among BX clones , which only carried patient-derived Gag in absence of mutated PR and RT . For 4 of the clones , the RC of BX clones was higher than that of the cognate BS clone , suggesting that resistance mutations in PR and/or RT continued to exert some deleterious effect on RC in spite of compensatory mutations in Gag . For 2 viruses , however , the RC of the BX clone was lower than that of the BS clone ( VIS13 , VIS18 ) . In these cases , the RC of the BX clone was also lower than that of the AS clone , whereas the RC of BS and AS clones were similar . Taken together , these findings are consistent with the possibility that these viruses carried mutations in the NC-SP2-p6 region able to facilitate replication of viruses with resistance mutations in PR and RT , but whose presence is deleterious for replication of viruses carrying wild-type PR and RT sequences . Overall , the increase in RC resulting from the presence of patient-derived Gag sequences in clones carrying highly mutated PR and RT sequences was almost strictly parallel to the increase in resistance observed with the same clones . In general , however , the effect measured on RC was stronger than the resistance effect . Thus , for example , the mean change in IDV resistance for XS clones relative to BS clones was a 5 . 2-fold decrease , compared to the 19 . 5-fold loss in RC . Having established that the determinants in Gag able to exert an enhancing effect on HIV-1 RC and resistance to PIs are essentially restricted to the NC-SP2-p6 region , we sought to evaluate the precise role of NC-SP2 mutations A431V and I437V in this phenomenon . We therefore selected XS clones from 3 patients and introduced the A431V ( seen in VIS13 and VIS18 viruses ) or the I437V ( seen in VIS16 ) mutations into the NL4-3-derived Gag sequences , yielding the VIS13XS431V , the VIS16XS437V and the VIS18XS431V mutants ( Figure 4 ) . In parallel , these NC-SP2 cleavage site mutations were reverted back to wild-type in the patient-derived Gag sequences of the corresponding BS clones , yielding the VIS13BS431A , VIS16BS437I and VIS18BS431A mutants . As seen in Figure 5 , the reversion of mutations A431V or I437V in BS clones from patients VIS13 and VIS16 resulted in a strong decrease in the IC50 of the three PIs tested , corresponding to a loss of resistance . In these two viral backgrounds , the decrease in IC50 resulting from reversion of the NC-SP2 cleavage site mutations was equivalent to that seen when replacing the whole Gag region from patient virus with that of NL4-3 ( i . e . , in the XS clones ) . Coherent with this observation , when mutations A431V and I437V were introduced in the XS clones , a strong increase in IC50 was seen , back to levels close to those seen with the BS clones . This observation strongly suggests that in highly resistant viruses , the resistance impact of the NC-SP2-p6 region is essentially carried by NC-SP2 cleavage site mutations . A slightly different and less clear picture was seen with virus VIS18 , where the IC50 effect of reversion of A431V was relatively modest , in particular with IDV , and where this effect did not seem to be quite as pronounced as that seen with the XS clone . Nonetheless , introduction of A431V in VIS18XS produced a strong and significant increase in IC50 to all 3 tested drugs , to levels comparable to those seen with VIS18BS . We next examined the effect of these mutations on RC ( Figure 6 ) . Reversion of NC-SP2 cleavage site mutations resulted in a moderate or no reduction in RC , contrasting with the much more dramatic effect of these changes on the resistance phenotype . Furthermore , the effect on RC was far less pronounced than that of the total removal of patient-derived Gag sequences , as seen in XS clones . In VIS13 , reversion of A431V from VIS13BS reduced RC by about half , a reduction that was strikingly lower than that seen with VIS13XS . In VIS18 , the reversion of A431V from VIS18BS did not significantly change RC , contrasting with the strong reduction in RC seen in VIS18XS . In VIS16 , the change in RC produced by reversion of I437V was again marginal , but in this virus , only a small change in RC was seen when comparing the BS and the XS clone . These results show that NC-SP2 mutations are not sufficient to explain the losses of RC that characterize clones with highly mutated PR and RT sequences and in which patient-derived Gag sequences are absent . They suggest that sequence determinants in NC-SP2-P6 outside of the NC-SP2 cleavage site account in part for this phenomenon , confirming the published results of Myint et al . [19] . Remarkably , however , when NC-SP2 cleavage site mutations were introduced in the XS clones , in which all Gag sequences are derived from NL4-3 , we observed almost complete restoration of RC back to levels measured in BS clones . This surprising result shows that in viruses having evolved under prolonged selective pressure by PIs , RC is dependent upon complex interactions between NC-SP2 cleavage site mutations and their environing sequences , while single Gag mutations have a much stronger effect on RC when introduced into an otherwise wild-type background . Kinetic analyses of HIV-1 Gag cleavage by wild-type PR have revealed that NC-SP2 is one of two sites , together with CA-SP1 , where cleavage occurs with slowest kinetics . The importance of NC-SP2 cleavage in infectivity is unclear and has been recently challenged by results showing that mutations obliterating cleavage at this site had little effect on HIV-1 infectivity [21] . Given the remarkable effect of NC-SP2 cleavage site mutations on the RC and on the levels of resistance expressed by viruses with highly resistant PR , we examined extent that these phenotypic changes were reflected by differences in PR cleavage at this particular site . Virions from clones VIS13BS , VIS13BS431A , VIS13XS , and VIS13XS431V were produced by HeLa cells in the absence or in the presence of lopinavir at two concentrations . The protein content of purified virions was analyzed by quantitative western blotting using antibodies against CA and NC . Figure 7 shows representative results from one out of three independent experiments . Analysis of the CA-reactive products revealed a strong inhibition of wild-type HIV-1 NL4-3 Gag processing at 50 nM LPV , while processing of VIS13BS derived virus was much less inhibited even at a concentration of 2 , 5 µM . No significant difference in CA processing was observed when VIS13XS or the derivatives with alterations in position 431 were compared ( Figure 7A ) . In contrast , processing of the NC-SP2 cleavage site was strongly dependent on the amino acid in position 431 ( Figure 7B ) . This was reflected by the relative amounts of fully processed NC and the intermediate cleavage product NC-SP2 , while only very small amounts of the NC-SP2-p6 product were detectable in all cases . For virus VIS13BS , which carried all patient-derived Gag sequences including mutation A431V , fully processed NC amounted to ∼80% of all NC-reactive products in the presence of 0 , 5 µM LPV ( Figure 7C ) . Furthermore , mature NC was still present at >60% of all NC-reactive species in the presence of 2 , 5 µM LPV , a concentration amounting to approximately twice the IC50 of VIS13BS . Reversion of A431V in VIS13BS ( clone VISBS431A ) produced a clear change in this pattern with NC-SP2 amounting to ∼65% of all NC-reactive species at 2 , 5 µM LPV ( Figure 7C ) . A very similar pattern was seen with VIS13XS , in which all Gag sequences were derived from NL4-3 . In this case , NC-SP2 amounted to >80% of all NC-reactive species at 2 , 5 µM LPV and 60% at 0 , 5 µM LPV . Introducing the A431V mutation into this background reverted the NC/NC-SP2 ratio yielding a NC processing profile comparable to that of VIS13BS at all LPV concentrations . In this study , we addressed the contribution of Gag to the selection of HIV-1 resistance to PIs in vivo . Our results reveal that mutations in the NC-SP2-p6 region of Gag directly contribute to PI resistance in addition to their compensatory role , an effect that correlates with the extent of proteolytic processing of the NC-SP2 cleavage site . In contrast , in these samples from patients having failed multiple lines of antiretroviral therapy , the upstream MA-CA-SP1 region of Gag does not appear to influence PI resistance . These results suggest a new paradigm of resistance evolution in vivo that is further supported by recent results from in vitro selection experiments [13] . First , we investigated the extent that cleavage site mutations in Gag can promote resistance per se , together with their ability to partly compensate for losses of RC that result from mutations in PR . Coevolution of HIV PR and its substrates in PI resistance has been well established for specific mutations in the NC-SP2-P6 region of Gag [7]–[9] , [12] , [16] , [22] . Most studies have emphasized the important role of these mutations in partial compensation of the loss of HIV-1 RC that often results from PR resistance [8] , [9] , [12] , [14] , [15] . The mechanism of this compensation has been documented for mutation A431V , which emerges mostly in viruses carrying resistance mutation V82A in PR . Interestingly , this Gag mutation does not act through direct steric compensation of the structural change in the substrate-binding domain of PR , but creates an additional point of contact between the substrate and the substrate-binding cavity of the enzyme . This contact is independent of the presence of resistance mutations , thereby producing a non-specific increase in enzymatic activity at the mutated site [23] . Comparison of recombinant viruses carrying whole patient-derived Gag sequences together with their cognate PR and RT sequences ( BS viruses ) with recombinant viruses carrying patient-derived PR and RT only ( XS viruses ) revealed a remarkable contribution of Gag cleavage site mutations A431V and I437V in the NC-SP2-p6 region to the emergence of PI resistance in vivo ( Table 1 ) . When the cleavage site mutations were removed by site-directed mutagenesis in clones carrying whole patient-derived Gag-PR-RT sequences , 2 to 5-fold reductions in resistance were observed , at least for certain drugs . Furthermore , inserting these mutations into viruses carrying patient-derived PR-RT sequences , but NL4-3-derived Gag sequences usually resulted in viruses whose resistance was equivalent to , or even higher , than that observed for viruses carrying only patient-derived Gag-Pol sequences . The observation that cleavage site mutations A431V and I437V were actually acting as resistance mutations in these highly evolved , highly resistant clinical viruses , is complementary to a recent study [13] , which reported in vitro selection for resistance to an experimental PI using a laboratory strain of HIV-1 leading to emergence of mutations in SP2 at position 436 and 437 before any changes in PR . In contrast , the impact of the cleavage site mutations A431V or I437V on viral RC was more nuanced . When cleavage site mutations were removed by site-directed mutagenesis in clones carrying patient-derived Gag-PR-RT sequences , RC was impaired in some cases , but not to the extent that resistance was impaired . Furthermore , a considerably greater loss of RC was observed when the entire patient-derived Gag was replaced by Gag sequences from the reference NL4-3 virus . Thus , although the introduction of cleavage site mutations into viruses carrying NL4-3 Gag sequences and patient-derived PR-RT sequences fully restored viral RC in all cases , the reversion of the same cleavage site mutations from a patient-derived Gag sequence had , at most , only a small impact on RC . Taken together , these findings suggest that other , as yet undefined , mutations had occurred in gag in these patient-derived viruses that partially ( VIS 13 , VIS16 ) , or completely ( VIS18 ) compensated for the loss of fitness resulting from protease resistance mutations , at least in the absence of PI treatment . These findings are consistent with those of Myint et al . [19] , who showed that changes in the p6 region of Gag could participate in partial correction of resistance-associated loss of RC . Furthermore , the context-dependent effects of NC-SP2 cleavage site mutations on RC indicate that these changes can be dually selected: ( i ) as compensatory mutations restoring RC of PR mutated viruses without further changes in gag and ( ii ) as direct PI resistance mutations . Upon prolonged treatment , further mutations in Gag outside the cleavage site may render viral RC independent of the A431V or I437V mutations while the effect of these mutations on resistance and thus their selective power in vivo persists . The nature of the Gag mutations outside of cleavage site that appear to contribute to viral RC was not investigated in this study . As seen on Figure 2C , numerous polymorphisms , including insertions and duplications in the p6 region of Gag , differed among the viral clones studied here , most of which being frequently observed in HIV-1 sequence databases . Of note , our clones did not carry the polymorphisms described by Myint et al . as being able to exert a compensatory effect on HIV-1 RC . We therefore hypothesize that the RC effect seen in some of our clones following reversion of cleavage site mutations is likely mediated by complex combinations of non-unique natural polymorphisms , which may vary from one virus to another according to their overall Gag genetic context . The mechanism through which mutations in PR substrates can promote resistance by themselves , independently of their effect on RC , either in the presence or in the absence of resistance mutations in PR has yet to be explained . As discussed earlier , viral resistance to PIs involves a balance between two competing PR ligands: the inhibitor and the Gag or Gag-Pol polyproteins . In vitro , the affinity constant of PIs for their target is most often in the sub-nM range , while the affinity constant of Gag or Pol cleavage sites for PR , which varies according to the cleavage site , has been evaluated as being in the mM range [24] . How an increase in the affinity constants of the latter ligand can displace binding of the former is unclear . One explanation , however , may relate to the different stoichiometry of PR relative to its natural substrates at the site of HIV virion maturation [25] , compared to that of PR relative to free , active , unbound PIs at these sites . Clearly , further work , involving precise evaluation of intracellular concentrations of PIs at the site of viral assembly , would be needed in order to better understand these phenomena . The other main observation of our study is that changes in Gag that promote increases in resistance and in RC in highly evolved , highly resistant , viruses are essentially restricted to the NC-SP2-p6 region of Gag . In our panel of viruses , little effect of MA-CA-SP1 regions was measured either in resistance or in RC . The notable interstrain variability in some domains of HIV-1 MA and CA , and in particular the strong variability in the CA-SP1 cleavage site , do not appear to play a significant role in resistance . Two viruses constituted possible exceptions . In virus VIS09 , the RC of the AS clone , carrying only patient-derived NC-SP2-p6 in combination with cognate PR and RT patient sequences , was significantly different from that of the BS clone , which carried whole Gag sequences from patient plasma . The resistance levels of the VIS09 AS clone , however , were not significantly different from those measured with the BS clone . In virus VIS20 , the same phenomenon was observed , albeit on a smaller scale , and the difference in RC between AS and BS clones did not reach statistical significance . These observations further support the idea that mutations other than NC-SP2-p6 cleavage site mutations can help compensate for losses of viral RC associated with PI resistance , and further suggest that mutations occurring outside of the NC-SP2-p6 region can participate in this process . These mutations do not , however , appear to have appreciable impact on drug resistance . The absence of effect of MA-CA-SP1 sequences on drug resistance , and their only modest impact on viral fitness , strongly suggests that cleavage events in the NC-SP2-p6 region have a critical impact on resistance and fitness , and may constitute rate-limiting events for HIV virion maturation both in the absence and in the presence of PIs . This conclusion was confirmed by western blot experiments examining the extent of cleavage of Gag proteins in a series of VIS13-derived recombinant and mutants viruses . We first observed that increasing LPV concentration had a similar effect regarding CA processing in all viruses carrying resistance mutations in PR . This was different from the recent in vitro selection experiments , where mutations in SP2 at position 436 and 437 increased overall Gag processing [13] . We found , however , that adding or removing the NC-SP2 cleavage site mutation A431V in different VIS13-derived clones did have an effect on the amount of fully processed NC relative to its partially cleaved precursor NC-SP2 . This effect was seen both in the absence or in the presence of inhibitor , consistent with the expectations from the structural analysis of the A431V mutant peptide with wild-type and mutant PR [23] . Importantly , the NC/NC-SP2 processing efficiency fully correlated with the resistance phenotypes of the respective viruses . This finding is in apparent contradiction with the results of mutagenesis experiments reporting that mutant virions lacking cleavage at the NC-SP2 site were close to being fully infectious [21] . Several explanations may be proposed to reconcile our findings with those of Coren et al . First , the assay systems used to monitor infectivity are not fully comparable and may differ in their sensitivity to small changes in viral infectivity . Second , these authors have essentially evaluated the effect of mutagenesis of NC-SP2 with wild-type HIV PR from a laboratory molecular clone in absence of protease inhibitor . It can be hypothesized that the rate-limiting nature of cleavage at this site may be more important in the context of primary PR sequences , of resistant PR enzymes , and/or in virions assembled and matured in the presence of protease inhibitors . In this regard , it is noteworthy that the NC-SP2 cleavage site sequence is highly conserved among HIV-1 strains that have never been exposed to PIs , and that in vitro , mature NC has been found to exert optimal chaperone function when compared to other Gag cleavage products , including NC-SP2 [26] . In our experiments , the dramatic effects of mutation A431V on resistance , its context-dependent impact on RC , and its strong influence on the ratio of NC to NC-SP2 , strongly argues in favor of a critical role of this cleavage event in vivo , in viruses having escaped to pharmacological pressure by protease inhibitors . HeLa cells and P4 cells ( HeLa-CD4 LTR-LacZ ) were cultivated in Dulbecco's modified Eagle's medium supplemented with 10% fetal calf serum and antibiotics . P4 cells were cultured in the presence of geneticin ( 500 µg/ml ) . P4 cells were used as indicator target cells for HIV-1 infection both in resistance and in viral replicative capacity assays [3] , [8] , [27]–[29] . Infection of P4 cells by HIV-1 virions was monitored in a single cycle of infection , based on the expression of ß-galactosidase , which , in this cell line , is strictly dependent upon induction by the HIV-1 Tat transactivator protein . HIV-1 Gag-Pol plasma sequences from 6 patients with multiple drug resistance were PCR-amplified and cloned into pNL4-3 . The patients were recruited from the ANRS 109 “Vista” study , a pilot study aimed at evaluating the virological and immunological consequences of a treatment simplification in patients with multiple antiretroviral drug resistance and no options for suppressive therapy [30] . All 6 patients had a history of failure of multiple lines of combination antiretroviral therapy , which included several protease inhibitors . The recombinant viruses carried different segments of Gag , and/or PR-RT sequences from plasma virus ( up to RNAse-H junction ) in an NL4-3 background ( Figure 3 ) . The GenBank accession numbers for the sequences are VIS09: bankit1176725 FJ649603; VIS13: bankit1176730 FJ649604; VIS15: bankit1176735 FJ649605; VIS16: bankit1176737 FJ649606; VIS18: bankit1176743 FJ649607; VIS20: bankit1176745 FJ649608 . RNA was isolated using a Qiagen extraction kit , and the whole-gag protease and reverse transcriptase encoding regions were reverse transcribed and amplified by nested PCR . Nested PCR primers were designed to contain one enzyme restriction site each : BssHII+ ( 5′ TGC TGA AGC GCG CAC GGC AAG A ) and SnaBI- ( 5′ CCC ATC TAC GTA GAA AGT TTC TGC ) . The PCR product was digested by BssHII and SnaBI and used to replace the corresponding fragment of pNL4-3 , generating a full-length clone ( BS clone ) carrying the complete gag-protease-RT sequences derived from each patient ( between base pairs 711 and 3870 of pNL4-3 ) . As shown on Figure 3 , these 6 BS clones were then used as the source of the gag-pol gene for the construction of three different types of recombinant viruses : i ) AS clones , which contained patient-derived NC/p1/p6 protease and reverse transcriptase sequences between the ApaI and SnaBI restriction sites; ii ) XS clones , which contained patient-derived PR and RT sequences between the XbaI site and the SnaBI site in association with NL4-3-derived Gag; iii ) BX clones , carrying patient-derived Gag between BssHII and XbaI , in association with NL4-3 PR and RT sequences . Introduction of patient-derived sequences into pNL4-3 using the indicated restriction enzymes was made possible by PCR amplification of the corresponding segments of viral genome with primers displaying unique restriction sites . Primers ApaI+ ( 5′GAA ATT GTA GGG CCC CTA GGA AAA AG ) and SnaBI- were used to introduce the restriction sites ApaI at the 5′ and and SnaBI at the 3′ end of the 1862 bp AS fragment . By the same process , XbaI ( 5′ GGA GCC TCT AGA CAA GGA ACT GTA TCC T ) and SnaBI restriction sites were added to the 5′ and 3′ ends of the PR-RT fragment and BssHII and XbaI restriction sites were added to the 5′ and 3′ ends of the Gag fragment . Six other recombinant viruses were constructed by site-directed mutagenesis . First , to remove and replace the cleavage site mutations ( A431V , I437V ) of the patient-derived Gag-PR-RT fragment with the wild type pNL4-3 amino acid pattern and second , to add these mutations in the recombinant viruses containing the patient-derived PR-RT fragment . The oligonucleotides used for these mutagenesis experiments are listed in Table S1 . HeLa cells cultivated in 25 cm2 flasks were transfected with 5 µg of HIV plasmid DNA by the calcium phosphate precipitate method . After 18 hours of incubation , transfected cells were subcultured in triplicate in 96 well plates in the presence of serial dilutions of 6 protease inhibitors . After 30 hours , viral supernatants containing 1–2 ng of p24 antigen were used to infect subconfluent P4 cells in 96 well plates in the presence of DEAE-dextran ( 15 µg/ml ) . Forty hours later the single cycle virus titer was determined by quantification of the ß-galactosidase activity in the P4 lysates , using a colorimetric assay based on the cleavage of chlorophenol red-ß-D-galactopyranoside ( CPRG ) . Optical densities in the reaction wells were read at 570 nm with a reference filter set at 690 nm . 293T cells cultivated in 25 cm2 flasks were transfected with 3 µg of HIV plasmid DNA by the calcium phosphate precipitation method . After 18 hours , the precipitate was removed by gentle washing and fresh medium was added . After a further 24 hours of culture , the supernatant was clarified by centrifugation at 500× g for 15 min and quantified for HIV-1 p24 content by Elisa . P4 indicator cells were infected with two-fold dilutions of the supernatant , representing a range of p24 concentrations from 0 . 78 ng/ml to 50 ng/ml , in the presence of DEAE-dextran ( 15 µg/ml ) . Forty hours later , ß-galactosidase activity in P4 cells was measured by CPRG assay as described above , optical density values plotted as a function of the p24 content in the viral inoculum , and the slope determined by linear regression . Replication capacity was then calculated as the ratio between the slope measured for the tested virus to that of reference virus NL4-3 and expressed as a percentage . 293T cells were transfected using the calcium phosphate method in 6-well plates using 2 µg DNA per well . Medium was changed 6 h after transfection and cells and particles were harvested 48 h after transfection . Particles were recovered from clarified culture medium by centrifugation for 1 h at 44 , 000 rpm through a 20% sucrose cushion in a TLA45 rotor . Cell and particle lysates were separated either on 12 . 5% standard polyacrylamide gels or on 14% acrylamide tricine gels for superior resolution of smaller proteins as described [31] . Proteins were transferred to Immobilon FL ( Millipore ) and reacted with a mixture of polyclonal rabbit anti-CA ( 1∶5 , 000 ) and goat anti-NC ( 1∶3 , 000; kind gift of Dr . J . Lifson , NCI ) followed by a mixture of donkey anti-rabbit IRdye700CW ( Licor; 1∶20 , 000 ) and donkey anti goat IRdye800CW ( Licor; 1∶20 , 000 ) . Blots were scanned using the Licor Odyssey Infrared Imaging System and quantified with the Licor software as specified by the manufacturer . IC50 values and fold-changes in IC50 were compared using a one-way ANOVA test . A p<0 . 05 was considered to be statistically significant . When groups differed significantly , a Bonferroni multiple comparison post-test was performed to make two by two comparisons .
Protease inhibitors are among the most active antiviral drugs used in the treatment of Human immunodeficiency virus type 1 ( HIV-1 ) infection . The efficacy of these compounds , however , can be threatened by the emergence of viral resistance , the result of the gradual accumulation of specific mutations in the viral protease . HIV-1 resistance to protease inhibitors often results in impaired protease function and in the loss of the replicative capacity of the virus , an effect that can be partially corrected by selection of compensatory mutations in one of the natural substrates of the protease , the Gag protein . In this study , we have found that Gag mutations not only correct viral replicative capacity but also play a major and direct role in resistance . We observed that this effect is essentially mediated by mutations in the C-terminal region of Gag , and that it correlates with the extent of cleavage downstream of the Gag nucleocapsid protein . Our results establish that mutations in Gag constitute a second and important pathway of HIV-1 resistance to protease inhibitors in patients failing antiretroviral treatment .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "virology/mechanisms", "of", "resistance", "and", "susceptibility,", "including", "host", "genetics", "virology/immunodeficiency", "viruses", "virology/antivirals,", "including", "modes", "of", "action", "and", "resistance" ]
2009
Gag Mutations Strongly Contribute to HIV-1 Resistance to Protease Inhibitors in Highly Drug-Experienced Patients besides Compensating for Fitness Loss
Damaged cardiac valves attract blood-borne bacteria , and infective endocarditis is often caused by viridans group streptococci . While such bacteria use multiple adhesins to maintain their normal oral commensal state , recognition of platelet sialoglycans provides an intermediary for binding to damaged valvular endocardium . We use a customized sialoglycan microarray to explore the varied binding properties of phylogenetically related serine-rich repeat adhesins , the GspB , Hsa , and SrpA homologs from Streptococcus gordonii and Streptococcus sanguinis species , which belong to a highly conserved family of glycoproteins that contribute to virulence for a broad range of Gram-positive pathogens . Binding profiles of recombinant soluble homologs containing novel sialic acid-recognizing Siglec-like domains correlate well with binding of corresponding whole bacteria to arrays . These bacteria show multiple modes of glycan , protein , or divalent cation-dependent binding to synthetic glycoconjugates and isolated glycoproteins in vitro . However , endogenous asialoglycan-recognizing clearance receptors are known to ensure that only fully sialylated glycans dominate in the endovascular system , wherein we find these particular streptococci become primarily dependent on their Siglec-like adhesins for glycan-mediated recognition events . Remarkably , despite an excess of alternate sialoglycan ligands in cellular and soluble blood components , these adhesins selectively target intact bacteria to sialylated ligands on platelets , within human whole blood . These preferred interactions are inhibited by corresponding recombinant soluble adhesins , which also preferentially recognize platelets . Our data indicate that circulating platelets may act as inadvertent Trojan horse carriers of oral streptococci to the site of damaged endocardium , and provide an explanation why it is that among innumerable microbes that gain occasional access to the bloodstream , certain viridans group streptococci have a selective advantage in colonizing damaged cardiac valves and cause infective endocarditis . Infective endocarditis ( IE ) remains a disease with considerable morbidity and mortality [1] , [2] . Of the numerous bacteria that have the opportunity to enter the bloodstream , three major genera of Gram-positive pathogens ( streptococci , staphylococci and enterococci ) dominate in IE . Streptococci and staphylococci account for 80% of IE cases [3] , and viridans group streptococci , including Streptococcus gordonii and Streptococcus sanguinis , make up to half of such cases [4] . Unlike the intrinsically virulent staphylococci , streptococci causing IE are commensal species that normally reside in the oral cavity and show relatively weak or no pathogenicity . However , they have the potential to cause life-threatening IE when they enter the bloodstream through lesions in the oral epithelia or after dental procedures [5] . The question arises as to why these particular organisms have such a selective advantage in causing IE . Answers to this question are of importance in preventing and treating this serious disease . The pathogenesis of IE is complex but bacterial adherence to damaged heart valves is a required event [1] , [6] . Platelets appear to play a crucial role in pathogenesis , as the streptococci associated with IE can bind to platelet components to initiate adherence to damaged human heart valves [7] . Several streptococcal adhesins are known to bind human platelets [8]–[15] . Among the best characterized are GspB and Hsa of S . gordonii and SrpA of S . sanguinis , all belonging to a conserved family of serine-rich repeat ( SRR ) glycoproteins [13]–[15] . The SRR glycoproteins and corresponding specialized secretion systems are being defined in a growing number of pathogens , indicating their important roles in the behavior of Gram-positive organisms [16] , [17] . However , we are only beginning to understand the functions of this interesting family of proteins . All human cell surfaces are covered by a dense and complex layer of glycans [18]–[20] . Glycan-mediated host-pathogen interactions are involved in various human disease processes [21] . Sialic acids ( Sias ) are a diverse group of α-keto acids with a shared nine-carbon backbone [22] , [23] . Given their presence at terminal positions of cell surface and secreted glycoconjugates , they mediate or modulate various biological interactions [24] , [25] . Interestingly , GspB , Hsa and SrpA are all Sia-binding adhesins . GspB , the SRR adhesin from S . gordonii strain M99 , was recently found to contain a subdomain in its binding region ( BR ) [26] , with the topology and strand inserts similar to the V-set Ig-like fold adopted by mammalian sialic acid binding immunoglobulin-like lectins ( Siglecs ) [27] . Each of the other two homologous SRR adhesins , Hsa from S . gordonii strain DL1 and SrpA from S . sanguinis strain SK36 , also contains a Siglec-like subdomain in their BRs [26] . Taken together , existing data suggests that the Sia-binding capabilities of GspB , Hsa and SrpA are conferred by their Siglec-like modules , and that such binding assists interactions with platelets . This surface property may help targeting the bacteria to the coagulum made of platelets and other components on damaged cardiac valves and act as a contributory factor in the pathogenesis of IE . However , detailed characterization of cognate ligands of such adhesins is lacking . It is also not known whether these bacteria could selectively target platelets in fluid human whole blood , a process that is under-explored but may contribute to the pathogenesis of IE [1] . This question is of particular interest also because the endovascular system is an environment where numerous potential sialoglycan competitors exist [28] . Such selectivity would thus play an essential role for the successful delivery and adhesion of bacteria to damaged valvular endocardium , and provide an explanation to the selective advantage of certain types of bacteria in causing IE . Here we utilize a number of complementary techniques and novel assays to study the role of the Siglec-like domain-containing SRR-adhesins in bacterial recognition of host sialoglycans . We explore mechanisms of these bacterial interactions with saliva and in whole blood . Our finding extends previous understanding of the mechanisms of infective endocarditis . The novel insights gained help us to better understand the contribution of Sia-binding adhesins in the pathogenesis of IE . In particular , we address questions including the following: 1 ) What are the detailed sialoglycan-binding characteristics of GspB-BR , Hsa-BR , and SrpA-BR ? ; 2 ) Are the Siglec-like BRs of these adhesins responsible for whole bacterial recognition of sialoglycans ? ; 3 ) Are Siglec-like domain-containing SRR adhesins common among oral streptococci , and is there a good phylogeny-function relationship ? ; 4 ) How do these oral streptococci interact with saliva from the oral cavity , and with blood components from the bloodstream ? ; and 5 ) Can these bacteria preferentially recognize platelets in the setting of whole human blood via Sia-adhesin interactions , despite numerous potential sialoglycoprotein competitors from plasma and surfaces of other blood cells ? In addressing the last issue , we have developed a novel whole blood-whole bacterium flow cytometry assay that is most relevant to bona fide human conditions . For the first time , we have demonstrated that oral streptococci can indeed selectively target platelets in human whole blood . It appears that circulating platelets may act as inadvertent Trojan horse carriers of oral streptococci to the site of damaged endocardium . By serendipity , certain sialoglycan-binding properties that facilitate normal oral commensalism seem to have set these bacteria up to be inadvertent , yet highly specific causative agents of endocarditis , via platelet intermediaries . As a proof of concept , we have also shown that soluble recombinant bacterial adhesin binding region proteins can block the preferred platelet-bacterial interactions in whole blood . The knowledge gained may contribute to the development of novel preventive or therapeutic approaches against infective endocarditis . Glutathione S-transferase ( GST ) -tagged BRs of GspB , Hsa , and SrpA ( see Table S1 for details ) , were tested for binding to various sialoglycans , using a recently developed slide microarray . This unique sialoglycan microarray presents over 70 synthetically recreated naturally-occurring oligosaccharide structures with diverse sialic acid forms , glycosidic linkages , and underlying glycans , representing the broadest range of such targets available to date [23] , [29] . All three proteins exclusively bound to glycans terminated with sialic acids but not non-sialylated ones ( Figure 1A–1D ) . However , they also showed distinct specificities . GspB-BR interacted with a very narrow spectrum of sialoglycans , primarily Neu5Acα2-3Galβ1-3GalNAcα1- ( Glycan #15 , the sialyl T antigen , sTa ) and related structures ( Figure 1A and 1D ) . 9-O-Acetylation of the Neu5Ac moiety of sTa ( Glycan #9 , compared to #15 ) did not influence binding significantly . Sulfation of underlying glycans also showed no effect ( Glycans #57 , 58 and 62 , 63 , compared to #55 , 56 , and 11 , 12 , respectively ) . In contrast , Hsa-BR bound a broad range of Sias with different underlying glycans ( Figure 1B and 1D ) . Conspicuously , it recognized all α2-3-linked Sias but not any α2-6- or α2-8-linked ones . This finding expands on previous studies , which showed that Hsa could bind α2-3-linked Sias with three different underlying glycans , specifically , sTa , Neu5Acα2-3Galβ1-4GlcNAcβ1- ( 3′SLn ) and Neu5Acα2-3Galβ1-4Glcβ1- ( 3′SL ) structures [30] , [31] . Our microarray displays Sias with over 10 different underlying glycans ( Table S2 ) , and it appears that every α2-3-linked Sia present on the array could be bound by Hsa-BR ( Glycans #1 , 2 , 7-16 , 21 , 22 , 25 , 26 , 29 , 30 , 33-36 , 39 , 40 , 55-58 , and 60-63 ) ( Figure 1B and 1D ) . Similar to GspB-BR , 9-O-acetylation on Sia did not block Hsa-BR binding . In contrast to GspB-BR , Hsa-BR could not only recognize tri- and oligo-saccharide Sias , but also di-saccharide Sias ( Glycans #25 , 26 , 29 , 30 ) . Sulfation significantly increased binding ( Glycans #57 , 58 and 62 , 63 , compared to #55 , 56 , and 11 , 12 , respectively ) . Notably , SrpA-BR recognized sialoglycans in a manner resembling Hsa-BR more than GspB-BR ( Figure 1C ) , showing binding to both di-saccharide and tri-/oligo-saccharide Sias . Sulfation also increased binding . Moreover , we tested the maltose-binding protein ( MBP ) -tagged Hsa-BR , which showed the same binding characteristics as GST-tagged Hsa-BR ( Figure S1 ) , indicating that tagging methods did not alter adhesin binding . To test whether the SRR adhesin-BR fusion proteins faithfully represent the binding properties of the corresponding whole organisms , we assessed direct binding of strains M99 , DL1 , and SK36 to the same microarray . To confirm that binding was due to expression of the adhesin , we also tested isogenic variants of each strain , in which the gene encoding the SRR glycoprotein had been deleted ( M99ΔgspB , DL1Δhsa , and SK36ΔsrpA ) . Prior glycan microarray studies using whole bacteria were characterized by high background noise or no success at all ( http://www . functionalglycomics . org/glycomics/publicdata/selectedScreens . jsp ) . However , our current efforts at optimization yielded good signal-to-noise ratios ( Figure S2 ) . Notably , DL1 and M99 showed nearly identical binding profiles as their respective adhesins , Hsa and GspB , on the array ( Figure 1D ) . In keeping with the relatively low binding of SrpA to the arrayed Sias ( see mean relative fluorescence intensities in Figure 1A–1C ) , the corresponding bacteria SK36 showed minimal binding . Importantly , all adhesin-deficient mutant strains showed no binding to the sialosides on the microarrays . Thus , whole bacterial binding to the immobilized sialoglycans was SRR adhesin-dependent . The SRR glycoproteins comprise a large family of adhesins in Gram-positive bacteria [16] . To determine the prevalence of Siglec-like domain-containing adhesins in the family , we searched public databases and gathered additional sequence data from our lab collection ( strains PS478 , 72-40 , and G9B ) . Seventeen additional predicted SRR homologs of GspB , Hsa , and SrpA were identified based on their Siglec-like domain-containing BR sequences ( Figure 2A ) . While GspB clustered with five other SRR proteins , Hsa and SrpA appeared in a different branch , and were more closely related to each other than to GspB . The phylogenetic relationship agreed with their functional relationships in terms of ligand repertoire ( Figure 1A–1C ) . To further demonstrate this phylogeny-function relationship , we produced additional GST-tagged adhesin-BR proteins ( GspBPS478BR , GspB72-40BR , and GspBG9BBR ) , which differ from GspBM99BR by only a few amino acid residues ( Figure S3 ) . We then labeled the corresponding whole bacteria ( strains PS478 , 72-40 , and G9B ) , and tested the organisms together with their adhesin-BRs on the same sialoglycan microarray . The new adhesin-BRs displayed binding properties that were nearly identical to those of GspBM99BR ( Figure S4 ) . The strains also showed binding patterns nearly identical to that of their adhesins , as well as to strain M99 , whereas an adhesin-deficient mutant of 72-40 ( PS1070 ) [32] showed no binding to any sialoglycan . Moreover , the glycan-binding properties of all above-mentioned adhesin-BR fusion proteins were confirmed by a conventional enzyme-linked lectin assay [26] , using two model glycans , sTa and 3′SLn ( Figure 2B and 2C ) . Taken together , the data show that the Siglec-like domain-containing bacterial adhesins are prevalent in S . gordonii and S . sanguinis species , which are among the most common causative agents of IE . The data also indicate that GspB , Hsa and SrpA studied in the present work are representatives of many Sia-binding SRR proteins . The results and methods from this work should thus be broadly applicable . Streptococci normally reside in the oral cavity , where they use multiple adhesins for commensal interactions [33] , and only a subset of strains are known to become opportunistic pathogens in the endovascular environment , causing diseases such as IE . We thus wanted to probe the broader range of adhesins on DL1 , M99 and SK36 , asking if they use the same adhesins in the oral cavity for colonization and in the bloodstream . We used a dot blot assay to assess bacterial binding to whole human saliva , salivary ductal secretions , isolated and purified glycoproteins , and related glycoconjugates . In addition , we examined whether binding required divalent cations . Under divalent cation-chelating conditions ( +EDTA ) ( Figure 3A , see substrate info in Figure S5 ) , wild type DL1 bound saliva samples A1 , A3 , and A5 , but not de-sialylated samples A2 , A4 and A6 . Similarly , DL1 interacted with various sialylated glycoproteins but showed much reduced or no binding to corresponding desialylated glycoproteins . These included , for example , salivary mucins MUC5B ( B1 ) and MUC7 ( B3 ) . When the adhesin-deficient strain DL1Δhsa was used under the same conditions in the bacterial overlay , binding was not seen . For M99 and SK36 , the Sia-dependent interactions were also absent when corresponding adhesin-deficient strains were used ( Figure S5 ) . These data show that the Siglec-like SRR adhesins are involved in bacterium-saliva and various sialoglycoprotein interactions , and confirm that they interact with their Sia-ligands in a Ca2+/Mg2+ independent manner [26] . In contrast , the binding profiles changed considerably in the presence of Ca2+/Mg2+ ( Figure 3B ) . Additional binding activities that did not involve Sias were observed and those interactions largely obscured the Sia-dependent patterns seen in EDTA . Most conspicuously , DL1 bound to N-acetylgalactosamine ( GalNAc ) -terminated structures ( G5 , K1 , K3 and K4 ) , and these interactions were still present with the Hsa-deficient mutant . This fits previous findings [34] , indicating that lectin-like adhesins apart from the Sia-binding ones are also expressed on such oral streptococci . Similarly , M99 bound to terminal β-galactose ( Gal ) glycan structures ( I6 , and K6 ) , while SK36 also interacted with GalNAc-terminated structures , as was seen with DL1 ( G5 , K1 , K3 and K4 ) ( Figure S5 ) . Taken together , the data indicate that these bacteria express additional adhesins beyond Sia-binding ones . These multiple adhesins likely evolved to interact with salivary and oral mucosal glycoproteins and/or other glyco-epitopes , e . g . the ones present in Gal/GalNAc receptor polysaccharides on coaggregating bacterial strains [35] , which could be essential for their biofilm formation and colonization in the oral cavity . Moreover , the asialoglycan-binding adhesins require divalent cations for action , unlike the Sia-binding SRR-adhesins . When oral streptococci enter the bloodstream , they encounter a drastically different environment , with regard to terminal glycan sequences available for adhesin binding . Unlike the case in the oral cavity , terminal Gal and GalNAc residues are not tolerated on plasma- or blood cell surface glycoconjugates , as they are immediately recognized for clearance by receptors on hepatocytes or macrophages [36] , [37] . Instead , the endovascular system is an environment where numerous sialoglycans exist [28] . Thus , the Sia-binding SRR adhesins should theoretically become the more prominent determinants of glycan-mediated binding in the bloodstream . To test this hypothesis we probed interactions of DL1 and DL1Δhsa with human RBCs in traditional hemagglutination assays , in the presence or absence of EDTA ( Figure 3C–3F ) . Hemagglutination was comparable under all conditions when untreated RBCs were used ( Figure 3C , all three rows ) , i . e . , independent of divalent cations . Moreover , no hemagglutination was observed with DL1Δhsa under any condition ( Figure 3D , all three rows ) . Thus , GalNAc- or Gal-binding adhesins do not contribute significantly to bacterial hemagglutination , which is predominantly mediated by Sia-recognition . In contrast , when the RBCs were sialidase-treated and underlying glycans were exposed , DL1 bound avidly to such RBCs in the presence of Ca2+/Mg2+ ( Figure 3E , third row ) . Furthermore , this was independent of the Sia-binding adhesin Hsa ( Figure 3F , third row ) . These hemagglutination reactions were most likely mediated by GalNAc- and/or Gal-binding adhesins expressed on DL1 , when their ligands on RBCs became exposed by sialidase treatment , and in the presence of divalent cations . Indeed , neither DL1 nor DL1Δhsa showed any hemagglutination of sialidase-treated RBCs when Ca2+/Mg2+ were absent ( Figure 3E and 3F , first two rows ) . Taken together , the results fit the finding that normal RBCs are primarily covered by sialoglycans without many exposed GalNAc or Gal residues [38] . Although DL1 possesses both the Sia-binding adhesin and other adhesins including other types of lectin-like adhesins , the bacterium depends primarily on the former to bind RBCs . In the oral cavity , where these streptococci normally reside , they likely utilize many kinds of adhesins evolved for colonization [34] , [39] . However , when they enter the blood stream , an environment where Sias dominate the terminal glycome , they become primarily dependent on their Sia-reactive SRR adhesins to mediate binding . We then systematically compared binding of the three isogenic pairs of strains to blood cells , including RBCs and platelets ( Figure 4A and 4B ) . Hemagglutination assays confirmed that the sialic acid-mediated interactions of DL1 , M99 and SK36 with human RBCs were dependent on their respective SRR adhesins , Hsa , GspB and SrpA ( Figure 4A ) . An immobilized platelet adhesion assay showed similar trends among the three strains ( Figure 4B ) . Notably , the SRR adhesin-deficient mutant strains displayed residual binding to platelets , indicating that other adhesins expressed on such strains could contribute to the platelet-bacterial interactions . We also tested bacterial binding to immobilized whole human plasma glycoproteins by dot blot ( Figure 4C ) . Both DL1 and M99 showed clear Sia- and Hsa-dependent binding to whole plasma . In contrast , SK36 showed minimal binding to whole plasma under the conditions tested . Although binding of the different strains to separate human blood components has been studied by us and others , evaluation of such binding in human whole blood has not been addressed to date . However , the latter approach is most biomedically relevant . Bacteria entering the bloodstream would encounter ∼2 mM concentration of competing sialoglycoproteins in the plasma [40] and over 100-fold larger total RBC surface area than that of platelets ( average single RBC surface area: 2S = 2×πr2 = 2×3 . 14× ( 4 µm ) 2 , and average RBC count: 4 . 40–6 . 00×106/µL; average single platelet surface area: 2S = 2×πr2 = 2×3 . 14× ( 1 . 5 µm ) 2 , and average platelet count: 0 . 14–0 . 40×106/µL ) . Thus the question arises as to whether these bacteria can preferentially detect platelets under such conditions . To address this question we developed a novel whole bacterium-whole blood flow cytometry ( WBWB-FC ) assay , in which fluorochrome-labeled bacteria and antibodies were mixed into fresh anti-coagulated human blood , allowed to interact for short time , and then rapidly diluted before immediate analysis by flow cytometry . Two distinct populations of cells were seen when the mixture was analyzed by forward ( FSC ) and side scatter ( SSC ) ( Figure 5A , 5D , 5J and 5M ) . Erythrocytes were identified by the expression of glycophorin A ( CD235a ) ( Figure 5E ) , and platelets by GPIIb ( CD41a ) ( Figure 5F ) . Minimum non-specific interactions were seen as a result of careful selection of antibodies and optimization of assay conditions . Bacteria showed no interaction with the antibodies used ( Figure 5G–5I ) . When wild type DL1 was added in whole human blood , it preferentially recognized platelets as compared with RBCs ( Figure 5K and 5L , note the percentage values of Q2/Q1 ) . When DL1Δhsa was used , the preferred platelet-bacterial interaction was lost ( Figure 5N , compare to 5K and 5O ) . The M99 and SK36 isogenic pairs gave similar results , albeit lower extent of preferential platelet binding by wild type M99 or SK36 was observed compared to by DL1 . We next determined the effect of divalent cations on whole bacterium-whole blood bindings ( Figure 6A ) . Interactions were largely comparable in either EDTA or heparin anti-coagulated blood , indicating that divalent cations did not influence binding events much . In addition , an anti-P-selectin antibody was used to monitor platelet activation during the course of whole blood-bacterial binding experiments . Minimum activation was observed , indicating that these bacteria interacted with platelets in their resting state ( Figure S6 ) . Thus , for the first time , we have demonstrated that the viridans group streptococcal strains can utilize their Siglec-like domain-containing SRR adhesins to preferentially bind platelets in the setting of human whole blood , despite numerous potential binding competitors , e . g . , plasma- and other blood cell surface sialoglycoproteins . Divalent cations in whole blood also showed minimal effect on these bacterial interactions with blood cells , supporting the dominant roles of Sia-binding adhesins for streptococcal recognitions in the blood stream . To pursue future studies in this area it would be important to know if the recombinant adhesin-BR proteins can also preferentially detect platelets in the setting of human whole blood . As a proof of concept , we tested Hsa-BR . Notably , it indeed clearly displayed a strong preference to bind human platelets compared to RBCs ( Figure 6B ) . Also , it increasingly adhered to platelets when more protein was used ( Figure 6D–6F and Figure S7 ) . As a control , secondary antibody alone did not distinguish platelets from RBCs ( Figure 6C ) . These data indicate that sialic acid ligand density , accessibility , and/or particular sialoglycan presentation [41] on platelets play an essential role in mediating the preferred platelet-bacterial binding . Based on the above data , we further hypothesized the recombinant adhesin-BR proteins would be capable of inhibiting the preferred platelet-bacterial interaction in human whole blood . We thus tested Hsa-BR in inhibiting DL1-platelet binding . It was found that the favorable DL1-platelet interaction was gradually inhibited by increasing concentrations of added soluble recombinant Hsa-BR protein ( Figure 7 ) . In keeping with this blocking of platelet-bacterial interactions , increasing numbers of un-bound bacteria were detected with increasing amounts of recombinant Hsa-BR added ( Figure 7C–7E , quadrant 3 ) . Furthermore , the adhesin-blocking effect was verified by platelet adhesion assay using washed and immobilized platelets ( Figure S8 ) . The incidence of and mortality from IE has not reduced over the past three decades , despite significant improvements seen in most other diseases during the same period of time [4] . The incidence of IE is about 1 . 4–12 . 7 per 100 , 000 person-years [1] , and mean in-hospital mortality is as high as 20% and one-year mortality up to 40% [4] . Without treatment , IE is often lethal . In short , IE remains an important medical and socioeconomic burden to date . Bacterial adhesins play critical roles in host-microbe interactions , mediating host signaling events and affecting bacterial uptake or invasion [42] . Sialic acid-binding adhesins of viridans group streptococci are among the first identified lectin-like adhesins [43] , [44] . This is not surprising considering the ubiquity of sialic acids on host cell surfaces and in mucosal secretions , and their usual occurrence at terminal positions of various glycoproteins and glycolipids [25] . Notably , 70%–90% of streptococcal strains from a collection of S . sanguinis , S . gordonii , and S . oralis species were reported to express sialic acid-reactive adhesins , unlike the other streptococcal species such as the S . anginosus [45] . This is in intriguing coincidence with independent findings that S . sanguinis , S . gordonii , and S . oralis are among the leading causative organisms of streptococcal IE [46] . Taken together , the Sia-adhesin mechanism we have elaborated in the current study might play a much more prevalent role in the pathogenesis of IE than currently recognized . Of course , such a mechanism may not be restricted to SRR type adhesins that we emphasize in the present work . On a broader scale , SRR glycoproteins constitute an important family of conserved cell surface proteins associated with Gram-positive pathogenesis . Among the important questions arising , it is imperative to achieve full characterization of cognate ligands for these adhesins [16] . In this regard , glycan microarrays serve as a valuable tool . It allows simultaneous screening of hundreds of glycan-protein interactions in a high-throughput manner and only requires minimal materials . For the first time , we characterize in detail the glycan-binding spectra of a series of serine-rich repeat adhesins of oral streptococci . This is a highly conserved family of glycoproteins that contribute to virulence for a broad range of Gram-positive pathogens [16] . The information may help us to understand tissue and site tropism of the streptococcal adhesion within the oral cavity and elsewhere . Useful glycoanalytical probes are also identified . The bacterial adhesin Hsa-BR fusion protein shows great promise in replacing plant lectins MAL-I and MAL-II as a broad-range probe for α2-3-linked sialic acids . For instance , to our knowledge , there was not a good lectin for glycan microarray quality control to probe all α2-3-linked sialic acids . Hsa-BR will thus prove useful in such efforts as well as in other applications including cell and tissue staining . It is also gratifying to have achieved successful whole bacterial binding onto sialoglycan microarrays , which enables direct comparison of glycan-binding properties between whole bacteria and their corresponding adhesins . This approach not only confirms that adhesin-BRs faithfully represent the Sia-binding properties of corresponding whole organisms , but also demonstrates that the Siglec-like domain-containing SRR adhesins are responsible for the observed binding . Whole bacteria bind defined sialoglycoproteins in a Sia- and SRR adhesin-dependent manner as well . The evaluation of divalent cation effects on adhesin binding also provides valuable information for understanding differential contribution of bacterial adhesins in different biological niches . In the oral cavity , where free divalent cation concentrations in saliva usually exceed 1 mM in healthy individuals [47] , the multiple Ca2+/Mg2+-dependent adhesins on different bacteria bind their receptors and obscure the Ca2+/Mg2+-independent Sia-adhesin recognition events . In human blood , the normal plasma level of divalent cations typically exceeds 2 mM [47] . However , the Ca2+/Mg2+-independent sialic acid-reactive SRR-adhesins become dominant in recognition events in the bloodstream . Animal models of IE have been used for decades as tools for examining this disease [48]–[50] . However , there are general concerns about merely relying on animal models to infer human relevance [51] , [52] . For example , studies of sepsis in mice versus humans have shown markedly different outcomes , which were corroborated by markedly different gene expression profiles [53] . Also , marked differences among human and animal glycomes exist [54] , [55] . In this regard , our whole bacterium-whole human blood evaluation is highly complementary to animal studies and most relevant to the native human conditions . Compared to conventional in vitro studies , several advantages are evident with the WBWB-FC assays developed here . For example , these assays faithfully present all components involved in real human conditions ( other than endothelium ) , a feature incomparable by any other experimental system . Minimal sample manipulation is required . No RBC lysis , wash or separation steps are required , rendering the assays fast and facile . Minimizing manipulations of whole blood not only avoids loss of cells , but also ensures minimal alterations to blood cell phenotypes so that results obtained reflect more accurately the in vivo situations . In addition , the method allows routine analysis using whole blood volumes of merely 10 µL per tube , a considerable reduction compared to standard methods . Finally , the assay is simple , sensitive , and reproducible . The WBWB-FC assays in the present work provide the first definitive evidence that these oral streptococci can preferentially recognize platelets in intact human whole blood , despite the numerous alternative sialoglycoprotein ligands in plasma and on other blood cells . This preferential platelet-bacterial interaction readily occurs in the fluid whole blood , and may play a significant role in the pathogenesis of IE . Platelets may thus act as discriminative vehicles to deliver particular Sia-binding bacteria to damaged valvular endocardium . In other words , these bacteria inadvertently hitch a ride on circulating platelets and the platelets act as Trojan horse carriers of oral streptococci to the site of damaged endocardium . Aside from novel mechanistic insights , the present work also provides valuable information from a biomedical point of view . Recent guidelines on the prevention of infective endocarditis have greatly reduced the indications for antimicrobial prophylaxis , in part because of concerns that the risks of these agents may outweigh the benefits [56] , [57] . Targeting virulence has been proposed as a promising means to reduce antibiotic use and circumvent increasing antibiotic resistance [58] , [59] . The various sialoglycan ligands identified by our glycan microarray screening might be used to produce glycan-based inhibitors against bacterial adhesion , and the rigorously verified Sia-binding adhesin-BR proteins could be potentially developed into prophylactic drugs to block the host target which the opportunistic pathogens exploit to cause IE . As a proof of concept , we have shown that Hsa-BR can successfully block corresponding DL1 binding to platelets in intact human whole blood . Alternatively , these adhesin-BR proteins might be explored as vaccines to boost immunity and generate blocking antibodies against such streptococcal infections . Moreover , the WBWB-FC assay provides a novel method for determination of susceptibility of culprit pathogens to potential virulence inhibitors . Meanwhile , our data provide an explanation why it is that among numerous microbes that can gain access to the bloodstream , certain viridans group streptococci have a selective advantage in colonizing damaged cardiac valves . Unlike the common situation where pathogens systematically evolve virulence by optimizing binding to specific host targets , this is a case wherein sialoglycan-binding properties that aid normal oral commensalism inadvertently predispose these bacteria to become serendipitous , yet highly specific pathogens . All research involving human participants have been approved by our Institutional Review Board ( IRB ) or an equivalent committee . Each healthy donor was individually informed and gave his/her written consent for using the collected samples in a scientific study . Collection and use of blood samples included in this study was approved by the Human Research Protections Program ( HRPP , #080677X ) , and saliva samples by the Health Sciences Institutional Review Board ( HSIRB , #ORB0511008E ) . Allophycocyanine ( APC ) -conjugated CD41a , fluorescein isothiocyanate ( FITC ) -conjugated CD62P , and control IgG1-FITC were obtained from Becton Dickinson ( San Diego , CA , USA ) . FITC-conjugated CD235a ( Glycophorin A ) was obtained from eBioscience ( San Diego , CA , USA ) . Anti-GST SureLight APC was obtained from Columbia Biosciences ( Maryland , MD , USA ) . Alexa Fluor 555-conjugated goat anti-rabbit IgG ( H+L ) and Glutathione S-transferase rabbit IgG antibody fraction were obtained from Molecular Probes ( San Diego , CA , USA ) . Anti-MBP antiserum was obtained from New England Biolabs ( San Diego , CA , USA ) . All chemicals were obtained from Sigma-Aldrich ( St . Louis , MO , USA ) , unless otherwise stated . The bacterial strains and plasmids used in this study are listed in Table S1 . Streptococci were grown in Brain Heart Infusion ( Becton Dickenson ) at 37°C in a 5% CO2 environment for 18 h with no agitation . Antibiotic selection was not required for strains carrying chromosomally integrated plasmids or DNA fragments . Strain PS1070 secretes a truncated form of GspB into the culture medium , but has no detectable GspB associated with the cell wall . The gspB BR was amplified from chromosomal DNA of S . gordonii strains 72-40 and PS478 using primers RGspBgst2 and RGspBgst3 , and then cloned in pGEX3X ( GE Healthcare ) as described for cloning of the BR of strain M99 [60] . The BR coding region of S . gordonii strain G9B was similarly amplified from chromosomal DNA using the forward primer 5′AAGGGGATCCCAGAAGCTTCTAGTCAGACAGGCCGG and reverse primer 5′GTGCCTTGCAGAATTCGAAGTACTTCCTTCTCTTGT ( BamHI and EcoRI sites are underlined ) , and then cloned into pGEX3X . Engineered versions of the HsaBR and SrpABR coding regions , with codons optimized for expression in E . coli , were subcloned from plasmids pSV278-HsaBR and pSV278-SrpABR , respectively , into pGEX5X . Cultures of E . coli strain BL21 carrying the pGEX expression plasmids were grown in LB with 50 µg/mL carbenicillin until on OD600 of approximately 0 . 6 . Cultures were placed on ice for 20 min , and the expression of GST fusion proteins was induced by the addition of IPTG to a final concentration of 0 . 1 mM . Cultures were incubated for 16 h at 18°C . Cells were harvested by centrifugation and lysed by sonication , and the GST fusion proteins were purified using Glutathione Sepharose 4B ( GE Healthcare ) according to the manufacturer's instructions . The eluted proteins were exchanged into DPBS and stored at −80°C . Labeling was performed as previously described [61] , except that cyanine 3 ( Cy3 ) mono-reactive NHS ester ( GE Healthcare ) was chosen as the fluorescent label . Cy3-labeled bacteria were washed and resuspended in PBS before use in bacterial overlay , sialoglycan microarray and whole blood binding assays . Glycan microarrays were fabricated using epoxide-derivatized slides as previously described [29] . Printed glycan microarray slides were blocked by ethanolamine , washed and dried , and then fitted in a multi-well microarray hybridization cassette ( AHC4X8S , ArrayIt , Sunnyvale , CA , USA ) to divide into 8 subarrays . The subarrays were blocked with Ovalbumin ( 1% w/v ) in PBS ( pH 7 . 4 ) for 1 h at RT , with gentle shaking . Subsequently , the blocking solution was removed by aspiration and diluted adhesin-BR samples were added to each subarray . After incubating the proteins for 2 h at RT with gentling shaking , the slides were extensively washed to remove non-specifically bound proteins . Anti-GST rabbit IgG fraction was added to the subarrays , incubated at RT for 1 h , and washed . Anti-MBP antiserum was used when MBP-tagged Hsa-BR was tested . Fluorescently labeled antibody ( Alexa Fluor 555-labeled goat anti-rabbit IgG ( H+L ) , Molecular Probes ) was then applied and incubated for 1 h . Following final washes and drying , the developed sialoglycan microarray slides were subjected to scanning by a Genepix 4000B microarray scanner ( Molecular Devices Corp . , Union City , CA , USA ) immediately . For Cy3-labeled whole bacteria , the glycan glass slides were blocked with Ovalbumin ( 3% w/v ) in PBS ( pH 7 . 4 ) for 1 h at RT , bacteria were subsequently added to the arrays and allowed to interact with the glycans at RT for 2 h . After extensive washing and drying , the bacterium-bound slides were scanned . Data analysis was done using the Genepix Pro 7 . 0 analysis software ( Molecular Devices Corp . , Union City , CA ) . All adhesin-BR fusion proteins and bacterial strains were tested and compared at various concentrations on the arrays . The bacterium-bound glass slides were also imaged by a Keyence microscope ( BIOREVO , BZ-9000 , Keyence USA ) . Homologues of the Siglec-like BRs were identified by BLAST search of databases using the GspB signal peptide ( residues 1 to 90 ) , a highly conserved region of the S . gordonii and S . sanguinis SRR glycoproteins . The binding assay was performed following previously reported protocol [26] . GST-BR fusion proteins were diluted to 0 . 5 µM in DPBS , and 50 µL applied to microtiter wells . Plates were incubated for 18 h at 4°C , unbound protein was removed by aspiration , and the wells were rinsed three times with DPBS . Multivalent biotinylated carbohydrates ( GlycoTech Corporation ) were diluted to the indicated concentrations in DPBS containing 1× Blocking Reagent ( Roche ) , 50 µL was added to each well , and the plate was incubated for 3 h at RT with vigorous rocking . Unbound biotinylated carbohydrates were removed by aspiration , and wells were washed three times with 100 µL DPBS . Fifty microliters of streptavidin-conjugated horseradish peroxidase ( 0 . 1 µg per mL in DPBS ) was added to each well and the plate was incubated for 1 h at RT . The wells were washed twice with 100 µL DPBS , and then 200 µL of a solution of 0 . 4 mg OPD per mL phosphate-citrate buffer ( Sigma ) was added to each well . After approximately 15 min , the absorbance at 450 nm was measured . Glycoarray dot blots for bacterial overlay were prepared by immobilizing human salivary samples , naturally occurring glycoconjugates ( Table S4 ) and neoglycoconjugates ( Table S5 ) as dots containing 1 µg of protein on nitrocellulose ( 0 . 45 µm pore size , Whatman Protran BA 85 , Fisher Scientific ) . The dot blots for examining streptococcal binding to whole plasma were prepared by spotting three-fold serial dilutions of human whole plasma from their original concentrations ( ∼68 mg/mL ) as dots on nitrocellulose . Samples were collected as previously described [62] , except that here un-stimulated WS samples were then spun at 12 , 000× g for 15 min at 4°C to remove debris . The resultant clear supernatant was transferred into a separate polypropylene microtube and stored at −80°C until further use . Samples treated with sialidase from Clostridium perfringens ( Type X , Sigma-Aldrich ) and Arthrobacter ureafaciens ( ProZyme , Hayward , CA ) at a final concentration of 0 . 05 U/mL at 37°C for 30 min were also included in the dot blots where indicated . The overlay method was performed as previously described [61] . In brief , blots were blocked in Tris-buffered saline ( 0 . 15 M NaCl , 20 mM Tris HCl and 0 . 1% NaN3 ) containing 5% BSA ( TBS-BSA ) for 2 h and then overlaid with labeled bacteria suspended in TBS-BSA ( 108 bacteria per mL ) for 2 h at 4°C in the dark . Next , the blots were washed four times at 4°C for 5 min on a rotary shaker with Tris-buffered saline ( 0 . 15 M NaCl , 20 mM Tris HCl and 0 . 1% NaN3 ) containing 0 . 05% Tween-20 and dried on gel blot paper ( GB003 , Whatman ) . Fluorescence signals of adherent bacteria were detected by a fluorescence scanner ( TYPHOON 9400 imaging system , GE Healthcare ) . For conditions where the overlay was done in the presence of divalent cations , 1 mM CaCl2 and 1 mM MgCl2 were included in all buffers; while for conditions where the overlay was done in the absence of divalent cations , 5 mM EDTA was included . Blood was collected in Vacutainers ( Becton Dickenson ) containing 3 . 2% sodium citrate as anticoagulant . Erythrocytes ( RBC ) were washed and resusupended in PBS containing 2 mg/mL bovine serum albumin ( Sigma-Aldrich , St . Louis , MO ) ( PBS-BSA ) to a final concentration of 10% ( v/v ) . For the removal of sialic acids , RBCs were treated for 1 h at 37°C with sialidase from Clostridium perfringens ( Type X , Sigma-Aldrich ) and Arthrobacter ureafaciens ( ProZyme , Hayward , CA ) at final concentration of 0 . 05 U/mL in PBS . Thereafter , ∼1×106 washed RBCs were used in each well , with two-fold serial dilutions of each bacteria suspension starting from ∼3×108 bacteria per well , in round-bottom wells of a 96-well microtiter plate ( Costar , Corning Incorporated , Corning , NY ) . The endpoints of titrations were determined after overnight incubation at 4°C . Images of hemagglutination were taken with a digital camera ( Canon EOS 50D ) . Bacterial binding to immobilized human platelets was assessed as described [13] . In brief , bacterial strains were grown for 18 h , washed twice with DPBS , sonicated briefly to disrupt aggregated cells , and then diluted to approximately 1×107 bacteria per mL . Fifty microliters of the bacterial suspensions were then applied to microtiter plate wells containing platelet monolayers ( ∼6×106 platelets ) that had been treated 1 h at RT with 100 µL of a blocking solution ( 1× Blocking Reagent [Roche] ) to reduce non-specific adherence . In the case of adhesin-blocking experiment , different concentrations of Hsa-BR were added and incubated for 1 h with the platelet monolayers , prior to the addition of DL1 . The plates containing bacteria and platelets were incubated at RT for 3 h with vigorous rocking . Unbound bacteria were then removed by aspiration and washing with DPBS , and the platelet-bound bacteria were released by trypsinization . The number of input and bound organisms was determined by plating serial dilutions of the bacterial suspensions on sheep blood agar , and binding was expressed as the percent of the input bacteria that bound to the platelet monolayers . Immunolabeling was performed immediately after blood draw . Fresh whole blood was mixed with Cy3-labeled bacteria in different ratios and the mixtures were incubated at room temperature for 0 . 5 h . Aliquots of the mixtures ( 10 µL ) were added to 5 mL polystyrene round bottom tubes ( BD Falcon ) , followed by adding anti-CD41a-APC and/or anti-CD235a-FITC . After mixing gently , the mixtures were incubated at room temperature for another 0 . 5 h , and then diluted by PBS ( without Ca2+/Mg2+ ) to 1 mL total volume for immediate analysis using a FACS Calibur flow cytometer ( BD Biosciences ) . For platelet activation analysis , Cy3-labeled bacteria , anti-CD41a-APC , anti-CD62P-FITC and IgG1-FITC control antibody were used . For adhesin-BR binding , different amounts ( 6 pmol , 24 pmol , and 96 pmol ) of GST-HsaBR were used together with anti-GST-APC . In adhesin inhibition of whole bacterial binding studies , Hsa-BR was added to whole blood samples and the mixtures were incubated at room temperature for 0 . 5 h before Cy3-labeled bacteria and anti-CD41a-APC were added . The resulting mixtures were incubated at room temperature for another 0 . 5 h before flow cytometric analysis . All results are expressed as percentage of positive cells , either bacterium-bound or adhesin-bound . Statistical analyses were performed with GraphPad Prism 6 . 0 software . Bacterial adhesion to immobilized platelets was statistically analyzed using paired t test , bacterial and Hsa-BR binding in whole human blood using 2way ANOVA , and Hsa-BR blocking of DL1 binding in whole blood using 1way ANOVA . P values <0 . 05 were considered statistically significant .
Bacterial infective endocarditis remains a disease with considerable morbidity and mortality . Of the numerous bacteria that can enter the bloodstream , certain oral commensal viridans group streptococci are among the major causative organisms of endocarditis . However , mechanisms underlying this selectivity are incompletely understood . Interactions between adhesins of such bacteria and human platelet sialoglycans are believed to play an important role in this selectivity , by facilitating bacterial adherence to damaged heart valves . Nevertheless , the molecular requirements for these interactions are not fully explored . Particularly , it is unclear whether selective targeting of platelets by these bacteria actually occurs in fluid human whole blood , an environment where numerous potential sialoglycan competitors exist . In the present work , we have addressed these important issues . We characterize in detail the glycan-binding spectra of a series of serine-rich repeat adhesins of oral streptococci . For the first time , we demonstrate that oral streptococci can indeed selectively target platelets in whole human blood . As a proof of concept , we also show that soluble recombinant bacterial adhesin binding region proteins can block the preferred platelet-bacterial interactions in whole blood . The knowledge gained from this study may help the development of novel preventive or therapeutic approaches against infective endocarditis .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "medicine", "and", "health", "sciences", "chemical", "characterization", "cardiovascular", "physiology", "microbiology", "glycocalyx", "chemical", "biology", "opportunistic", "infections", "biochemical", "analysis", "bacterial", "diseases", "histochemistry", "and", "cytochemistry", "techniques", "bioassays", "and", "physiological", "analysis", "fluorescent", "analogue", "cytochemistry", "cytometry", "bacteria", "bacterial", "pathogens", "research", "and", "analysis", "methods", "infectious", "diseases", "cell", "adhesion", "medical", "microbiology", "oral", "medicine", "streptococcus", "microbial", "pathogens", "binding", "analysis", "biochemistry", "cell", "biology", "physiology", "biology", "and", "life", "sciences", "glycobiology", "organisms" ]
2014
Oral Streptococci Utilize a Siglec-Like Domain of Serine-Rich Repeat Adhesins to Preferentially Target Platelet Sialoglycans in Human Blood
Measures that bolster the resolution phase of infectious diseases may offer new opportunities for improving outcome . Here we show that inactivation of microbial lipopolysaccharides ( LPS ) can be required for animals to recover from the innate immune tolerance that follows exposure to Gram-negative bacteria . When wildtype mice are exposed to small parenteral doses of LPS or Gram-negative bacteria , their macrophages become reprogrammed ( tolerant ) for a few days before they resume normal function . Mice that are unable to inactivate LPS , in contrast , remain tolerant for several months; during this time they respond sluggishly to Gram-negative bacterial challenge , with high mortality . We show here that prolonged macrophage reprogramming is maintained in vivo by the persistence of stimulatory LPS molecules within the cells' in vivo environment , where naïve cells can acquire LPS via cell-cell contact or from the extracellular fluid . The findings provide strong evidence that inactivation of a stimulatory microbial molecule can be required for animals to regain immune homeostasis following parenteral exposure to bacteria . Measures that disable microbial molecules might enhance resolution of tissue inflammation and help restore innate defenses in individuals recovering from many different infectious diseases . A great deal is known about how animals kill bacteria but very little about what happens to the corpses . Most bacterial structures seem to be broken down by phagocytes [1] , [2] , yet much of the catabolic machinery is made by the bacteria themselves ( phospholipases [3] , peptidoglycan hydrolases [4] , autolysins ) , which in some cases produce agonists that can elicit inflammation . Host enzymes may also participate , attacking peptidoglycan ( lysozyme , peptidoglycan binding proteins [5] , [6] ) , lipids ( phospholipases [3] ) , chitin [7] , proteins ( cathepsins , others ) , lipopolysaccharides ( LPS ) and , presumably , DNA . It is likely that the breakdown products are excreted , retained within phagocytes ( e . g . , in lymph nodes or arterial walls [8] , [9] ) or in discrete extracellular deposits [10] , or recycled by the host . The seminal studies of Cohn [1] , [11] , Elsbach [2] , [3] and Schwab [12] established that degradation of ingested bacteria by phagocytes can be incomplete . More recently , deposits of microbial antigens have been discovered in animals following infection with B . burgdorferii , the etiological agent of Lyme Disease [10] . A key question raised by these findings has remained unanswered to date: to what extent does the inactivation of stimulatory microbial molecules influence the outcome of infectious diseases or host reactions to microbe-rich environmental exposures ? Here we show that for one important agonist , bacterial lipopolysaccharide ( LPS ) , persistently active molecules can greatly delay immune recovery in vivo . Exposure to Gram-negative bacterial LPS induces humans and many other mammals to enter a transient state of altered immune responsiveness known as cellular reprogramming or tolerance [13]–[15] . The phenomenon has also been produced in animals by peritonitis [16] , influenza virus infection [17] and bacterial lipopeptides [18] , [19] and in cultured macrophages using muramyl dipeptide [20] , lipoteichoic acid [21] , and flagella [22] . For a period that may last from a few hours to a few days after exposure to the microbial agonist , tolerant animals and cells respond to a second exposure by producing reduced amounts of many pro-inflammatory cytokines while maintaining , or even increasing , their production of certain anti-inflammatory and anti-infective molecules . Tolerance wanes as inflammation resolves and the animal regains normal immune responsiveness . Long considered a mechanism for preventing inflammation-induced injury , innate immune tolerance may also be immunosuppressive [23] . Acyloxyacyl hydrolase ( AOAH ) , an enzyme produced by macrophages , neutrophils , and dendritic cells , inactivates bioactive LPSs by removing two of the six fatty acyl chains that are present in the bioactive hexaacyl lipid A moiety [24] . In mice that lack AOAH , a single intraperitoneal exposure to as little as 80 ng of hexaacyl LPS induces tolerance that lasts for several weeks , much longer than that seen in wildtype animals that have received much larger doses [25] . Tolerant animals respond sluggishly to Gram-negative bacterial challenge and are unable to prevent bacterial multiplication in vivo [25] . The mechanism ( s ) by which fully acylated LPS maintains reprogramming in vivo have been uncertain . Here we considered several possibilities . First , it seemed likely that bioactive LPS would remain within or on Aoah−/− macrophages for prolonged periods and render them tolerant via cell-intrinsic signaling , despite the existence of mechanisms that promote LPS efflux from macrophages [26] , [27] . Second , it was possible that extracellular bioactive LPS , released from tolerant macrophages and/or other in vivo reservoirs , could prevent tolerant macrophages from recovering and induce tolerance in recruited naïve monocytes . A third consideration was that mediators produced by tolerant cells , even in the absence of LPS , could induce tolerance in themselves and other cells in vivo [21] . Finally , it was possible that LPS-stimulated Aoah−/− macrophages might undergo long-lived , stable reprogramming that persisted even in the absence of bioactive LPS [28]–[32] . A combination of these mechanisms was also considered . The studies described here indicate that macrophage tolerance can be maintained for long periods in vivo by the presence of small amounts of fully acylated extracellular LPS . The source of the LPS seems to be extrinsic to the tolerant cell , coming from the fluid in which macrophages live or the LPS-containing cells they contact in vivo . We found that cell-associated LPS can be released , bind to other cells , and induce or maintain their tolerant state . Importantly , LPS-exposed , tolerant macrophages regained normal responsiveness when transferred to a LPS-free environment . Furthermore , in vivo inactivation of LPS by administering recombinant AOAH partially prevented tolerance . These results identify persistence of bioactive LPS in both cells and cell-extrinsic reservoirs as a primary mechanism that drives prolonged macrophage tolerance in vivo . They suggest that measures to inactivate LPS in these reservoirs might shorten the period of macrophage unresponsiveness that follows many Gram-negative bacterial diseases . They also provide evidence that inactivation of microbial molecules can be an essential element of the resolution/recovery phase of infectious illnesses . As previously reported [25] , 14 days after LPS injection , Aoah+/+ mice have largely recovered from tolerance while Aoah−/− mice are still reprogrammed . To identify phenotypic markers of tolerance , we injected Aoah−/− and Aoah+/+ mice i . p . with LPS or PBS and isolated their peritoneal macrophages 14 days later . Peritoneal macrophages ( F4/80+ cells as determined by flow cytometry ) from LPS-injected Aoah−/− mice had lower SSC ( granularity ) , less surface F4/80 ( macrophage marker , EGF-TM7 member of the adhesion-GPCR family ) , and less surface CD86 ( costimulatory molecule , also reduced during innate immune tolerance in humans [33] ) when compared with their LPS-exposed Aoah+/+ counterparts ( Fig . 1 A–C ) . Each of these parameters correlated with the cells' responses to LPS ex vivo ( Fig . 1 D–F , Fig . S1 ) . When compared with naïve Aoah−/− or LPS-exposed Aoah+/+ macrophages , macrophages from LPS-exposed Aoah−/− mice also had less surface CD11b ( macrophage marker , Integrin αM chain ) , CD69 ( early activation marker ) , CD16/32 , CD32 ( Fcγ receptors ) , and Ly6C/G ( Gr1 ) and slightly higher CD40 ( costimulatory molecule ) expression ( Table S1 ) . They did not have higher expression of two markers found in alternatively activated macrophages , arginase or FIZZ 1 ( Table S1 ) . Based on these findings , we used SSC and surface F4/80 and CD86 expression to identify the tolerant phenotype in subsequent experiments . We previously found that both Aoah+/+ and Aoah−/− peritoneal macrophages retain LPS for at least 10 days in vivo [25] . Whereas the LPS in Aoah+/+ macrophages had been partially deacylated ( i . e . , it had lost two of the six fatty acyl chains from lipid A ) , that in Aoah−/− macrophages was fully acylated [25] . The cells' ability to produce TNF in response to a second exposure to LPS was related inversely to their LPS content; Aoah+/+ macrophages were almost 20-fold more responsive than were Aoah−/− macrophages . It thus seemed likely that cell-associated LPS , if fully acylated , could maintain macrophage tolerance for long periods in vivo . To localize the cell-associated LPS , we injected FITC-labeled LPS i . p . to Aoah−/− and Aoah+/+ mice and harvested their peritoneal macrophages 10 days later . Anti-FITC antibodies were used to detect cell-associated LPS . We found that the majority of the LPS was intracellular ( Fig . 2 A–C ) . Aoah+/+ macrophages contained more LPS per cell than did Aoah−/− macrophages , because there were more macrophages in Aoah−/− mouse peritoneum after i . p . LPS injection [25] . Anticipating that there would be differences in the intracellular localization of acylated and partially deacylated LPS , we then studied the macrophages using immunofluorescence microscopy . We found LPS co-localized with the lysosome marker , LAMP1 ( Fig . 2 D–H ) , but not with markers for ER ( Calnexin ) , cis and medial Golgi ( Giantin ) , trans-Golgi ( TGN46 ) or early endosomes ( Rab5a ) ( not shown ) [34] . There was no evident difference in the intracellular location of acylated LPS ( in Aoah−/− cells ) and partially deacylated LPS ( in Aoah+/+ cells ) . Thus , both Aoah+/+ and Aoah−/− peritoneal macrophages contain LPS in endolysosomes for at least 10 days after i . p . injection; at this time Aoah−/− macrophages are tolerant and Aoah+/+ macrophages are not , consistent with the tolerant state being determined by LPS acylation status rather than by differential LPS localization within the cells . We then tested whether prolonged tolerance in vivo is due to retention of bioactive LPS in Aoah−/− peritoneal macrophages or conferred by the peritoneal environment in which the macrophages reside . In these and subsequent transfer experiments , donor and recipient macrophages were identified by their surface expression of CD45 . 1 or CD45 . 2 using flow cytometry . We transferred Aoah+/+ or Aoah−/− peritoneal cells to Aoah+/+ or Aoah−/− recipient mice and injected LPS i . p . 24 hours later . Fourteen days after injection , we harvested peritoneal cells and stimulated them ex vivo with LPS while blocking protein secretion with Brefeldin A . We then used flow cytometry to identify F4/80+ macrophages , gated to distinguish CD45 . 1 cells from CD45 . 2 cells , and measured macrophage intracellular IL-6 and TNFα as indices of LPS responsiveness ( see example in Fig . S2 ) . We found that Aoah+/+ macrophages were tolerant 14 days after they were transferred into Aoah−/− mice , whereas Aoah−/− macrophages exhibited reduced tolerance after they were transferred into Aoah+/+ mice ( Fig . 3A ) . Aoah+/+ donor macrophages transferred into LPS-injected Aoah−/− mice also had lower surface levels of F4/80 and CD86 when studied 14 days after LPS injection , confirming that Aoah+/+ macrophages gained the tolerant phenotype in LPS-injected Aoah−/− peritoneum ( Fig . 3B , C ) . The findings were similar whether we transferred CD45 . 1 donor cells to CD45 . 2 recipient mice or vice versa . The presence of reprogramming 14 days after i . p . LPS injection was thus determined by the recipient environment , not by donor macrophage expression of AOAH or the lack thereof . If macrophage tolerance is maintained for prolonged periods by environmental cues , removing tolerant Aoah−/− macrophages from an LPS-containing environment should allow them to regain responsiveness to LPS . We injected Aoah−/− mice i . p . with LPS to produce tolerant macrophages . Fourteen days after injection , the peritoneal cells were harvested , washed and transferred to the peritoneal cavity of a naïve Aoah+/+ or Aoah−/− mouse . Seven days after transfer , we tested whether the donor Aoah−/− macrophages remained tolerant . We found that the donor Aoah−/− macrophages recovered from tolerance after they were transferred into either Aoah−/− or Aoah+/+ mice ( Fig . 3D–F ) ; removal from an LPS-containing environment thus allowed macrophage recovery even in the absence of AOAH . The results again suggest that the in vivo environment plays a pivotal role in determining the behavior of peritoneal macrophages . We then asked how the peritoneal environment determines the fate of macrophages . Although bioactive LPS might still be present in the peritoneum , LPS also induces a broad array of inflammatory mediators , some of which ( e . g . , IL-10 , TGF-β , IL-1 receptor antagonist ) may promote macrophage reprogramming [15] . To find out whether wildtype macrophages can become tolerant in an Aoah−/− environment that lacks LPS-induced mediators , we transferred Aoah+/+ CD45 . 1 peritoneal cells into mice that lack both AOAH and TLR4 ( Aoah−/−Tlr4−/− CD45 . 2 ) and can neither deacylate , nor respond to , LPS . Fourteen days after i . p . LPS injection , the donor Aoah+/+ CD45 . 1 macrophages were tolerant ( Fig . 4A ) ; since the recipient mice do not produce LPS-induced mediators , this observation suggested strongly that such mediators are not required to maintain prolonged tolerance in vivo . In another approach , CD45 . 2 Aoah−/−Tlr4+/+ or Aoah−/−Tlr4−/− mice were given LPS i . p . Fourteen days later , CD45 . 1 naïve Aoah+/+ peritoneal cells were introduced into the peritoneal cavity . One day after transfer , the donor macrophages had become tolerant in both Aoah−/−Tlr4+/+ and Aoah−/−Tlr4−/− recipients ( Fig . 4B ) . These findings suggested that bioactive LPS is present for a prolonged period in the LPS-exposed Aoah−/− peritoneum and that this LPS can induce and maintain macrophage tolerance in the absence of LPS-induced host mediators . To obtain direct evidence for the presence of LPS in the peritoneum many days after i . p . injection , we injected 10 µg [3H/14C]LPS into the peritoneal cavities of Aoah−/− and Aoah+/+ mice and measured 14C and 3H in the peritoneal flush medium , peritoneal cells , mesenteric membranes , and fat 10 days later . The distribution of 14C dpm , a marker for the LPS carbohydrate backbone , was similar in the presence and absence of AOAH: about 0 . 3% of the injected LPS was found in cell-free peritoneal flush fluid; from 6 to 9% of the injected 14C LPS was recovered from intraperitoneal fat and 2 to 3% from the mesentery ( Fig . 5A ) . We reported previously that 1–4% of the injected LPS could be recovered from peritoneal cells [25] . Based on the ratio of 3H to 14C in the samples [35] , 99% ( Aoah+/+ ) and 19% ( Aoah−/− ) of the recovered LPS had been deacylated ( Fig . 5B ) , in keeping with the results found for peritoneal cells [25] . Is the intraperitoneal acylated LPS bioactive ? In other experiments , we gave 10 µg LPS i . p . to Aoah+/+ and Aoah−/− mice and flushed their peritoneal cavities with culture medium 10 days later . The cell-free peritoneal flush medium from Aoah−/− mice activated naïve Tlr4+/+ macrophages but not Tlr4−/− macrophages , suggesting that bioactive LPS was present in the medium ( Fig . 5C ) . When we re-challenged the macrophages with LPS , we found that the flush medium from Aoah−/− mice could also render naïve Tlr4+/+ macrophages tolerant ( Fig . 5D , E ) . We conclude that ( 1 ) free LPS is present in the peritoneal fluid of Aoah−/− mice 10 days after i . p . injection; ( 2 ) this LPS is fully acylated; and ( 3 ) the acylated LPS is bioactive , able to activate naïve Tlr4+/+ macrophages and reprogram them . If LPS that is taken up by macrophages can be released to act on other cells , fully acylated LPS released from Aoah−/− cells should be able to activate naïve cells and reprogram them . We first tested this idea in vitro . We injected Aoah−/− mice with LPS i . p . and harvested and washed their peritoneal cells 10 days later , when almost all of the LPS was associated with macrophages [25] . We co-cultured the peritoneal cells ( including tolerant macrophages ) with naïve Tlr4+/+ or Tlr4−/− peritoneal cells ( including naïve macrophages ) for 18 hours . Significantly higher IL-6 and IL-10 ( Fig . 6A , B ) levels were found in the culture medium of Tlr4+/+ peritoneal cells co-cultured with tolerant Aoah−/− cells than in the medium of Tlr4−/− cells co-cultured with tolerant cells , suggesting that LPS from tolerant cells stimulated naïve Tlr4+/+ cells to produce these cytokines . After incubation for 18 hours , the co-cultured cells were washed twice with cRPMI and the adherent macrophages were treated with LPS for 6 hours ( Fig . 6C–E ) . Naïve macrophages co-cultured with tolerant cells produced less TNF and IL-6 , but similar amounts of IL-10 , suggesting that they had become reprogrammed during the 18 hour incubation period . When we co-cultured naïve Tlr4+/+ peritoneal cells with Tlr4−/−Aoah−/− peritoneal cells that had been exposed to LPS in vivo , the naïve cells could also be activated , confirming that LPS from previously exposed macrophages is sufficient to activate naïve macrophages ( data not shown ) . Co-culture of naïve Tlr4+/+ peritoneal cells with LPS-exposed Aoah+/+ cells ( again , harvested 10 days after i . p . LPS injection , sufficient time for LPS inactivation to occur in AOAH-sufficient animals ) did not induce IL-6 or IL-10 production ( data not shown ) . To find out whether direct cell-cell contact is required for naïve macrophage activation during co-culture , we separated naïve peritoneal cells from LPS-exposed peritoneal cells in transwell cultures . This significantly decreased IL-6 and IL-10 production by the naïve macrophages ( Fig . 6A , B ) , suggesting that direct cell-cell contact enables optimal delivery of LPS from one macrophage to another . To test whether LPS-laden macrophages can release LPS to act on other macrophages in vivo , we injected 20 µg LPS-FITC i . p . to CD45 . 2 Aoah−/−Tlr4−/− mice . Seven days later , peritoneal cells were harvested , washed , and transferred i . p . to CD45 . 1 Aoah−/− naïve mice . After 7 days , we found that the transferred F4/80+ macrophages had lost 2/3 of their LPS-FITC and that recipient macrophages had gained small amounts of LPS-FITC , demonstrating that LPS can be released from donor macrophages and taken up by recipient macrophages in vivo ( Fig . 7A , B ) . In addition , we found that the recipient macrophages became tolerant ( Fig . 7C–E ) , indicating that they had been exposed to bioactive LPS ( the Tlr4−/− donor cells should not produce tolerizing mediators ) . These results show that macrophages can release the LPS that they contain and that the released LPS can act on other cells in vivo . If the LPS remains bioactive , it can induce tolerance in other macrophages . Bioactive LPS is thus present in the peritoneum of LPS-injected Aoah−/− mice , where it is sufficient to prevent macrophages from regaining responsiveness . Do LPS-induced mediators also play a role in maintaining tolerance ? We transferred CD45 . 2 Aoah−/−Tlr4−/− peritoneal cells to CD45 . 1 Aoah+/+ or Aoah−/− ( both Tlr4+/+ ) recipient mice , injected LPS i . p . , and asked whether the Aoah−/−Tlr4−/− donor macrophages became tolerant in the peritoneum of tolerant Aoah−/− recipient mice . Fourteen days after LPS injection , we harvested recipient peritoneal cells and treated them ex vivo with Micrococcus luteus plus poly I:C ( LPS-exposed Aoah−/− macrophages express hetero-tolerance [36] to M . luteus [TLR2 agonist] and poly I:C [TLR3 agonist] [25] ) . Aoah−/−Tlr4−/− donor macrophages ( F4/80+ ) harvested from LPS-injected Aoah−/− recipients had significantly lower IL-6 and TNF responses to ex vivo re-challenge ( Fig . 8A ) , and they also had less surface F4/80 and CD86 expression ( Fig . 8B , C ) . Since Aoah−/−Tlr4−/− donor macrophages cannot sense LPS , these results suggested that LPS-induced mediators in the peritoneum are also important for prolonging tolerance in Aoah−/− mice . Aoah−/−Tlr4−/− donor cells were less tolerant than were the recipient's Aoah−/−Tlr4+/+ macrophages , again reinforcing the prominent direct role played by fully acylated LPS . IFN-γ can improve monocyte function in septic patients and both IFN-γ and GM-CSF can restore responses of LPS-desensitized ( tolerant ) monocytes in vitro [37] , [38] . We next tested whether Aoah−/− tolerant macrophages can recover their responses to LPS by treating them with rhAOAH ( recombinant human AOAH ) , IFN-γ , GM-CSF or a combination of these agents for 18 hours ex vivo ( Fig . S3A–C ) . rhAOAH only slightly increased the macrophages' responses , possibly because deacylation occurs slowly and would not be expected to reach completion within 18 hours [39] . GM-CSF preferentially restored the IL-6 response while IFN-γ mainly boosted the TNF and RANTES responses . Combining the three agents largely restored the macrophages' responses . Aoah−/− tolerant macrophages , like desensitized human monocytes [38] , could thus be rescued by treating them with IFN-γ and GM-CSF in vitro . To test whether providing AOAH to mice can prevent or reverse prolonged tolerance in vivo , we gave Aoah−/− mice LPS i . p . on day 0 , followed by rhAOAH or carrier protein BSA i . p . daily from day 1 to day 13 . Peritoneal cells were harvested on day 14 , plated , and adherent macrophages were rechallenged ex vivo with LPS . LPS-stimulated RANTES and IL-6 were at naïve Aoah−/− macrophage levels in macrophages from animals that had received rhAOAH treatment ( Fig . 9A–C ) . TNF production is the most sensitively reprogrammed component of tolerance in these cells; here the TNF level in culture medium overlying macrophages from LPS-exposed rAOAH-treated mice was significantly lower than the naïve macrophage level but 10-fold higher than the levels produced by macrophages from mice that received carrier protein BSA instead of rhAOAH . Recombinant AOAH was thus able to ameliorate prolonged tolerance in vivo in Aoah−/− animals . Treatment with IFN-γ or antibody to IL-10 receptor did not rescue Aoah−/− animals from prolonged endotoxin tolerance ( data not shown ) , in line with the conclusion that bioactive LPS plays a dominant role in maintaining prolonged tolerance in vivo . Patients who experience severe sepsis develop a state of immune tolerance that may last for many weeks and is thought to be immunosuppressive [40] . The phenomenon has been characterized principally by studying peripheral blood leukocytes , whose responses to LPS and other microbial agonists are typically altered to diminish pro-inflammatory cytokine production while maintaining or increasing production of anti-inflammatory molecules such as IL-10 and IL-1 receptor antagonist . LPS-induced tolerance in mice mimics the human phenomenon in many ways ( reduced monocyte-macrophage CD86 , reprogrammed cytokine production ) but not in others ( e . g . , murine macrophages do not decrease class II molecule expression ) [23] . Although some Gram-negative bacteria can modify the acylation structure of their LPS in ways that may alter its ability to trigger signaling via MD-2—TLR4 [41] , LPS is also disabled by host enzymes , either on mucosal surfaces ( alkaline phosphatase ) [42] or in tissues ( AOAH ) [24] . In addition to experiencing prolonged tolerance , mice that lack AOAH respond to small subcutaneous or intravenous doses of LPS by producing high titers of polyclonal antibodies [43] and developing massive , prolonged hepatomegaly [44] , [45] . Animals have many other mechanisms for neutralizing hexaacyl LPS in plasma and tissues [46] , yet none of these is able to prevent these striking reactions in animals that cannot deacylate LPS . Transgenic mice that produce greater than normal amounts of AOAH are protected from live E . coli challenge [47] , again emphasizing the importance of AOAH mediated LPS inactivation in optimizing anti-bacterial immune responses . Continued exposure to microbial agonists can prolong the activation of cultured cells . Hume et al . reported that continuous exposure to LPS induced a sustained activation state in a macrophage cell line [48] , and Hedl et al . found that prolonged exposure to muramyl dipeptide , a ligand for Nod2 , promoted tolerance in human monocyte-derived macrophages [20] . Tolerance has also been described in human cells that may be exposed repeatedly to LPS in vivo , such as the alveolar macrophages of tobacco smokers [49] and blood monocytes from patients with uncontrolled gram negative bacterial infection [50] or cystic fibrosis [51] . Tolerance lasts for weeks in patients who have chronic pyelonephritis with active bacterial urinary tract infection [52] , as well as in volunteers with typhoid fever [53] . Individuals who inhale endotoxin-rich agricultural dusts may also develop chronic macrophage activation or tolerance [54] , [55] . Here we show that tolerance can be maintained in vivo for long periods by the presence of small amounts of bioactive LPS , raising the possibility that the rate and/or extent of LPS inactivation might influence the rate of recovery from many Gram-negative bacterial diseases . The mammalian MD-2—TLR4 LPS receptor is activated most sensitively by LPSs that have a lipid A moiety that contains 6 acyl chains . Such “hexaacyl” LPSs are produced by many of the Gram-negative bacterial commensals and pathogens that can inhabit the mucosal surfaces of the upper respiratory and gastrointestinal tracts [56] , [57] . Since AOAH inactivates these LPSs , AOAH deficiency might be associated with greater susceptibility to , or duration of , Gram-negative bacterial diseases that involve mucosal surfaces . To date , genetic linkage studies have found associations between polymorphisms in the AOAH gene and rhinosinusitis ( confirmed in 2 populations of different ethnic composition [58] , [59] ) as well as asthma [60] in humans . In addition , two studies of large populations [61] , [62] have independently found that AOAH mRNA expression is associated in trans with polymorphisms in HLA-DRB1 that , in turn , have been strongly linked to colitis and primary biliary cirrhosis . How AOAH influences disease expression , if it does , remains uncertain . The present studies identified the presence of cell-associated and extracellular ( cell-extrinsic ) LPS as the primary determinant of prolonged LPS tolerance in macrophages in vivo . This conclusion is supported by the observations that 1 ) prolonged tolerance could be induced in either Aoah−/− or Aoah+/+ macrophages that had been transferred into LPS-injected Aoah−/− mice , and 2 ) Aoah−/− macrophages did not exhibit prolonged tolerance when they were transplanted into Aoah+/+ hosts , indicating that LPS inactivation by AOAH in the host environment , not in the tolerant cell itself , determines the tolerant phenotype . Tolerance could be induced in naïve macrophages either by direct contact with cells that had taken up LPS or by extracellular LPS acquired within the peritoneal environment . In addition , administration of rAOAH to Aoah−/− mice partially prevented tolerance , and tolerance could be induced in naïve macrophages when they were transferred into LPS-exposed Aoah−/−Tlr4−/− mice or co-cultured with LPS-exposed Aoah−/−Tlr4−/− peritoneal cells , which do not produce LPS-induced mediators but can release fully acylated LPS into their environment [26] , [63] , [64] . Although these studies identify bioactive LPS as essential for maintaining tolerance , we also found that LPS-induced paracrine mediators further promoted tolerance , perhaps in part by extending the phenotype to cells that do not express TLR4 . These results also do not exclude the possibility that the epigenetic changes shown to be induced by short-term exposure to LPS [28]–[32] contribute to maintaining prolonged tolerance in macrophages , but these changes evidently do not persist ( or maintain dominance ) in an environment that lacks extracellular LPS; they can also be overcome by soluble mediators such as interferon-γ or GM-CSF . Where does LPS deacylation occur in vivo ? LPS may be deacylated extracellularly , as was shown using purulent ascites fluid [65]; both LPS-binding protein and CD14 can present LPS to extracellular AOAH in a manner that promotes its deacylation [66] . Alternatively , LPS may be taken up by macrophages , neutrophils , or dendritic cells and inactivated by AOAH , or conceivably LPS could be deacylated by AOAH in cells that have acquired AOAH via mannose-6-phosphate receptors [67] . Recent studies found that the LPS in circulating LPS-HDL ( high density lipoprotein ) complexes undergoes deacylation in the liver [68] , where AOAH is produced by Kupffer and dendritic cells [44] . AOAH-deficient mice cannot deacylate LPS in ascites or in cells within , or on the walls of , the peritoneal cavity , and these cells or membranes may then become reservoirs that release small amounts of bioactive LPS over time . This LPS then could maintain macrophage tolerance or induce tolerance in monocytes that newly arrive in the peritoneal fluid . The LPS “depot” includes the peritoneal membrane and/or mesenteric fat , since we found significant quantities of radiolabeled , fully acylated LPS in these sites . Naïve macrophages may also acquire LPS from other macrophages by direct cell-cell contact . Innate immune reactions to microbes are typically short-lived . Mobilizing an animal's antimicrobial armamentarium usually promotes microbial eradication and clearance within hours or a few days . Potentially harmful inflammation then resolves as the battlefield is cleared and defenses are restored [69] . Recovery is thought to involve both anti-inflammation ( preventing inflammation-induced damage ) and resolution ( clearing the battlefield and promoting return of homeostasis ) . Known tissue resolution mechanisms include neutrophil apoptosis , macrophage emigration and efferocytosis of dead cells , and the production of lipoxins , resolvins [and other lipids] [70] , proteases , and gaseous signals that promote restoration of homeostasis in tissues [69] , [71] , [72] . Here we present evidence for another essential component of resolution: inactivating the microbial molecules that tell the host that microbes are present . A host's ability to remove or disable bioactive microbial molecules from an infected tissue may influence the ultimate outcome of many host-microbe encounters , since inactivating these molecules removes an important obstacle to resolution of inflammation and restoration of innate host defenses . Aoah−/− C57BL/6J mice were generated as described [35] . Tlr4−/− ( B6 . B10ScN-Tlr4lps-del/JthJ ) mice were purchased from Jackson Laboratory . These mice have a 7 kb deletion in the TLR4 gene; the mutation was backcrossed to C57Bl/6J for at least 6 generations . C57BL/6J CD45 . 1 ( B6 . SJL-Ptprca Pepcb/BoyJ ) mice were also from Jackson . Aoah−/− Tlr4−/− and Aoah−/−CD45 . 1 mice were obtained by crossing Aoah−/− mice with Tlr4−/− or B6 CD45 . 1 mice , respectively . All mice were housed in a specific pathogen- and murine norovirus-free facility . All mice were studied using protocols approved by the Institutional Animal Care and Use Committee ( IACUC ) . IACUC of the University of Texas Southwestern Medical Center ( permit number: A3472-01 ) approved the Animal Protocol Number 0028-07-08-2 . The IACUC of the National Institutes of Allergy and Infectious Diseases ( permit number A4149-01 ) approved the Animal Study Protocol LCID 11E . Both protocols adhered to the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . E . coli O14 LPS was prepared by the phenol-chloroform-petroleum ether method [73] . E . coli O111 LPS was purchased from Sigma . Salmonella typhimurium Rc LPS ( [3H/14C]LPS; 3H-labeled fatty acyl chains and 14C-labeled glucosamine backbone ) was prepared from S . typhimurium PR122 as described [74]; 1 µg had ∼150 , 000 dpm 3H and ∼10 , 000 dpm 14C . Experiments using radiolabeled LPS followed the requirements of the Radiation Safety department , UT-Southwestern Medical Center . FITC-LPS was prepared as described by Tobias et al . [25] . Micrococcus luteus cells and poly I:C were obtained from Sigma . Recombinant human AOAH was produced by ZymoGenetics , Inc . Recombinant mouse IFN-γ and recombinant mouse GM-CSF were from R&D Systems . Antibodies used for FACS were anti-F4/80 ( clone BM8 , eBioscience ) , anti-CD86 ( clone GL1 , BD ) , anti-FITC ( polyclonal rabbit IgG , Invitrogen ) , anti-IL-6 ( clone MP5-20F3 , eBioscience ) , anti-TNF ( clone MP6-XT22 , eBioscience ) , anti-CD45 . 1 ( clone A20 , BD ) , and anti-CD45 . 2 ( clone 104; BD ) . Antibodies used for microscopy were anti-CD107a ( LAMP-1 ) ( clone 1D4B , BD ) , anti-Rab5a ( clone 15/Rab5 , BD ) , anti-Giantin ( rabbit polyclonal , Abcam ) , anti-TGN46 ( rabbit polyclonal , Abcam ) , anti-Calnexin ( rabbit polyclonal , Abcam ) . Mice were injected i . p . with various doses of LPS in 300 µl of PBS . Fourteen days later , the mice were euthanized using CO2 and their peritoneal cells were harvested by flushing the peritoneum with 5 ml of PBS containing 5 mM EDTA . Cells were stained with antibodies and analyzed using flow cytometry . To measure cell responses to re-challenge , peritoneal cells were washed and resuspended in cRPMI medium ( RPMI 1640 containing 10% heat-inactivated FBS ( endotoxin <0 . 06 EU/ml; Hyclone ) , 100 µM nonessential amino acids , 100 U/ml penicillin , 0 . 1 mg/ml streptomycin , 2 mM L-glutamine , 10 µM sodium pyruvate , 25 mM Hepes , pH 7 . 4 , and 50 µM 2-mercaptoethanol ) and plated with 1×106 cells/well in 12-well-plates . After incubation for 18 hours ( 37°C , 5% CO2 , 80% humidity ) , the floating cells were washed away and the adherent macrophages were treated with 1 µg/ml E . coli O111 LPS for 6 hours . The culture medium was used for ELISA and the cells were washed with PBS and lysed with PBS containing 0 . 1% Triton X-100 to measure protein ( Biorad ) . Mice were injected i . p . with 10 µg LPS-FITC . Ten days after injection , peritoneal cells were harvested and stained with anti-F4/80 Ab to identify macrophages . The cells were then washed and fixed with 4% paraformaldehyde ( PFA ) or fixed and permeabilized with cytofix/cytoperm ( BD ) . Anti-FITC PE antibody was used to detect cell-surface or total LPS-FITC by FACS . For microscopy , peritoneal cells were plated in culture dishes for 18 hours , then the floating cells were washed away and the adherent macrophages were fixed with 4% PFA before being blocked and permeabilized with 1% BSA , 25% goat serum , 0 . 05% saponin in PBS . Cells were stained with primary antibodies at 4°C overnight and with the secondary reagents at room temperature for 1 hour . 4′ , 6-diamidino-2-phenylindole ( DAPI , Sigma ) was used to stain nuclei . After washing , FluorSave aqueous mounting medium ( EMD Chemicals ) was applied and then coverslips were affixed . Stained sections were examined using a Leica SP5 X-WLL confocal microscope and analyzed using LAS AF Lite ( Leica ) software . Donor mice were euthanized and their peritoneal cells were harvested , washed , and resuspended in PBS . Cells from mice of the same genotype/treatment were pooled and an aliquot containing 2×106 cells in 300 µl PBS was injected i . p to each recipient mouse . Because naïve Aoah+/+ , Aoah−/− , Aoah−/−TLR4−/− , LPS-injected Aoah−/− mice and Aoah−/−TLR4−/− mice have similar per cent distribution of macrophages [25] , all recipient mice received approximately the same number of donor macrophages ( 8×105 ) . Twenty four hours after transfer , half of the recipient mice from each group received 1 µg LPS O14 i . p . Fourteen days later , recipient mice were euthanized and their peritoneal cells were harvested , washed , and resuspended in cRPMI medium containing 1 µg/ml LPS 0111 at 37°C for 4 hours ( Tlr4+/+ macrophages ) or 40 µg/ml Micrococcus luteus plus 2 . 5 µg/ml poly I:C for 8 hours ( Tlr4−/− macrophages ) in the presence of 3 µg/ml Brefeldin A ( eBioscience ) . The ex vivo stimulations were performed in non-tissue culture-treated V bottom 96-well plates ( Sarstedt ) to minimize macrophage adhesion . After stimulation , peritoneal cells were stained with anti-F4/80 antibody to identify macrophages and CD45 . 1 and CD45 . 2 antibodies to differentiate donor and recipient cells ( see Fig . S2 ) . The cells were then fixed and permeabilized ( eBioscience ) and stained with antibodies to IL-6 and TNF α to measure intracellular responses to ex vivo stimuli . We used the per cent of the total F4/80+ cells that were IL-6 , TNF double positive as a measure of macrophage responsiveness . Approximately 1–10×104 donor macrophages were recovered from each recipient mouse . Fewer donor macrophages were recovered from mice that had been injected with LPS , especially Aoah−/− mice; this reflected the known migration of stimulated cells from the peritoneum [75] . In cases when few donor macrophages were present , at least 100 donor macrophages were measured by flow cytometry and results were compiled from at least 3 different mice in each group . To exclude the possibility that macrophages became tolerant when cultured ex vivo with tolerant macrophages or vice versa , we harvested naïve ( CD45 . 1 ) and tolerant ( CD45 . 2 ) peritoneal cells , mixed them at different ratios and stimulated them with LPS for 4 hours ex vivo . The responsiveness of macrophages did not change during ex vivo co-culturing . In some experiments ( see Fig . 4B ) , we injected recipient mice i . p . with 1 µg LPS . Fourteen days later , naïve peritoneal cells ( including naïve macrophages ) were transferred i . p . to LPS-exposed mice or naïve mice . After 24 hours , peritoneal cells were harvested and the responsiveness of donor macrophages was analyzed ex vivo as described above . We also obtained tolerant macrophages from Aoah−/− mice that had been injected i . p with 0 . 5 µg LPS and transferred 2×106 peritoneal cells ( including approximately 8×105 tolerant macrophages ) to naïve Aoah−/− or Aoah+/+ mice . Seven days later , we analyzed whether the tolerant macrophages had regained responsiveness in the naïve hosts ex vivo ( Fig . 3D–E ) . In other experiments ( see Fig . 7 ) , CD45 . 2 Aoah−/−TLR4−/− donor mice were injected with 20 µg LPS-FITC i . p . After 7 days , peritoneal cells from LPS-FITC injected mice or control naïve mice were harvested , washed , and 2×106 peritoneal cells ( including approximately 8×105 macrophages ) were transferred i . p . to CD45 . 1 Aoah−/− mice . Seven days after transfer , peritoneal cells were collected from recipient mice . The donor and recipient macrophages' LPS-FITC content , F4/80 and CD86 expression , and ex vivo IL-6 and TNF responses were measured by flow cytometry . IL-6 , TNF and IL-10 ELISA kits were purchased from BD , RANTES ELISA kit was from R&D system . Manufacturer instructions were followed . We injected 10 µg [3H/14C]LPS i . p . to Aoah−/− or Aoah+/+ mice . Mice were euthanized 10 days later . Five ml PBS containing 5 mM EDTA was used to flush each peritoneal cavity . The peritoneal fat , mesentery and livers were harvested and homogenized in PBS . Aliquots were solubilized in 1 ml 0 . 5% SDS with 25 mM EDTA and 5 ml Bio-safe II scintillation cocktail ( Research Products International Corp ) , and counted with quench and spill-over correction ( Packard Tri-Carb 2100TR; Perkin-Elmer ) . Mice were given 10 µg LPS i . p . Ten days later , they were euthanized using CO2 and 2 ml of cRPMI was used to flush the peritoneal cavity . The flush medium was centrifuged and the cell-free supernatant was collected . Peritoneal cells from naïve mice were cultured in cRPMI medium for 4 hrs to allow macrophages to adhere . The floating cells were washed away and the adherent macrophages were cultured in flush medium for 18 hours ( 37°C , 5% CO2 , 80% humidity ) before the medium was removed and saved for ELISA . The cells were then washed with cRPMI twice and the adherent macrophages were challenged with 1 µg/ml E . coli O111 LPS in cRPMI for 6 hours at 37°C . In co-culture experiments , 106 tolerant cells were mixed with 106 naïve cells in 12-well plates for 18 hours; the culture medium was collected for cytokine ELISA . The co-cultured cells were then washed with cRPMI twice and the adherent macrophages were stimulated with LPS for 6 hours . The culture medium was used for ELISA . To measure cytokines produced by naïve cells in the co-culture system and exclude a contribution from tolerant cells , 106 tolerant cells were cultured in separate wells and treated in the same manner as were co-cultured cells . The low levels of cytokines produced by tolerant cells were subtracted from those produced by co-cultured cells . To separate tolerant cells from naïve cells , 106 tolerant cells were cultured in permeable Transwell inserts ( Corning ) overlying 106 naïve cells in 12-well plates for 18 hours . The control was 106 naïve cells in permeable inserts co-cultured with 106 naïve cells . The culture media were collected after 18 hours of co-culture . Aoah−/− mice were given 1 µg LPS i . p . on day 0 . From day 1 to day 13 , mice were injected i . p daily with 0 . 3 µg rhAOAH or carrier protein BSA in 300 µl PBS . On day 14 , the peritoneal cells were harvested and macrophages were challenged with 1 µg/ml E . coli O111 LPS for 6 hours at 37°C . Cytokine levels were measured in the culture medium . Flow cytometry analysis was done on FACS Calibur or LS Rortessa ( BD ) . BD and FlowJo software was used to analyze data . Unpaired Student's t test ( two-tailed ) was used for comparisons between groups . Linear regression was used to perform correlation analysis . In all figures , error bars indicate one SE .
We showed previously that mice lacking acyloxyacyl hydrolase ( AOAH ) , the host enzyme that inactivates Gram-negative bacterial lipopolysaccharides ( LPS ) , are unable to regain normal immune responsiveness for many weeks/months after they are exposed in vivo to a small amount of LPS or Gram-negative bacteria . The many possible explanations for slow recovery included long-lasting epigenetic changes in macrophages or other host cells , chronically stimulated cells that produce certain mediators , and persistent signaling by internalized LPS within macrophages . Using several in vivo techniques to study peritoneal macrophages , we found that none of these mechanisms was correct . Rather , prolonged recovery is caused by intact LPS that remains in the environment where macrophages live and can pass from one cell to another in vivo . This is the first evidence that the persistence of a bioactive microbial agonist , per se , can prevent resolution of inflammation in vivo . It also identifies the stimulatory microbial molecule as a realistic target for intervention – in further support , we found that providing recombinant AOAH can be partially preventive . In a larger sense , showing that chemical inactivation of one important microbial signaling molecule is required for full recovery should encourage efforts to find out whether disabling other microbial agonists ( chitin , lipopeptides , flagella , others ) also benefits infected animals .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "medicine", "infectious", "diseases", "immune", "cells", "immunity", "innate", "immunity", "immune", "defense", "immunology", "immune", "suppression", "biology", "immunity", "to", "infections", "microbiology", "immune", "tolerance", "immune", "response" ]
2013
Persistently Active Microbial Molecules Prolong Innate Immune Tolerance In Vivo
We identified a functional single strand origin of replication ( sso ) in the integrative and conjugative element ICEBs1 of Bacillus subtilis . Integrative and conjugative elements ( ICEs , also known as conjugative transposons ) are DNA elements typically found integrated into a bacterial chromosome where they are transmitted to daughter cells by chromosomal replication and cell division . Under certain conditions , ICEs become activated and excise from the host chromosome and can transfer to neighboring cells via the element-encoded conjugation machinery . Activated ICEBs1 undergoes autonomous rolling circle replication that is needed for the maintenance of the excised element in growing and dividing cells . Rolling circle replication , used by many plasmids and phages , generates single-stranded DNA ( ssDNA ) . In many cases , the presence of an sso enhances the conversion of the ssDNA to double-stranded DNA ( dsDNA ) by enabling priming of synthesis of the second DNA strand . We initially identified sso1 in ICEBs1 based on sequence similarity to the sso of an RCR plasmid . Several functional assays confirmed Sso activity . Genetic analyses indicated that ICEBs1 uses sso1 and at least one other region for second strand DNA synthesis . We found that Sso activity was important for two key aspects of the ICEBs1 lifecycle: 1 ) maintenance of the plasmid form of ICEBs1 in cells after excision from the chromosome , and 2 ) stable acquisition of ICEBs1 following transfer to a new host . We identified sequences similar to known plasmid sso's in several other ICEs . Together , our results indicate that many other ICEs contain at least one single strand origin of replication , that these ICEs likely undergo autonomous replication , and that replication contributes to the stability and spread of these elements . Horizontal gene transfer , the ability of cells to acquire DNA from exogenous sources , is a driving force in bacterial evolution , facilitating the movement of genes conferring antibiotic resistance , pathogenicity , and other traits [1] . Conjugation , a form of horizontal gene transfer , is the contact-dependent transfer of DNA from a donor to a recipient , generating a transconjugant . During conjugation , the DNA to be transferred is processed and protein machinery in the donor mediates transfer to the recipient . The proteins involved in DNA processing and conjugation are encoded by a conjugative element . Integrative and conjugative elements ( ICEs , also called conjugative transposons ) , appear to be more prevalent than conjugative plasmids [2] . The conjugation machinery ( a type IV secretion system ) encoded by ICEs is homologous to that of conjugative plasmids , and much of what is known about the mechanisms of transfer have come from studies of conjugative plasmids [3 , 4] . The defining feature of ICEs that distinguish them from conjugative plasmids is that ICEs are typically found integrated into a host chromosome and are passively propagated during chromosomal replication and cell division . ICEBs1 from Bacillus subtilis ( Fig 1 ) is an integrative and conjugative element that is easily manipulated and can be activated in the vast majority of cells in a population [5–10] . Like most other ICEs , ICEBs1 resides integrated in the host chromosome and most of its genes are repressed [5 , 11 , 12] . ICEBs1 gene expression is induced in response to DNA damage during the RecA-dependent SOS response and following production of the cell- sensory protein RapI [5 , 6 , 13] . DNA damage and RapI independently cause proteolytic cleavage of the ICEBs1 repressor ImmR [13] , leading to de-repression of ICEBs1 gene expression and production of proteins needed for excision and transfer . Excision from the chromosome results in the formation of a circular plasmid form of ICEBs1 . If appropriate recipients are present , the ICEBs1-encoded conjugation machinery can mediate transfer , presumably of linear single-stranded DNA ( ssDNA ) from host ( donor ) to recipient , generating a transconjugant . ICEBs1 then integrates into the chromosome of the transconjugant . ICEBs1 can be induced in >90% of cells in a population simply by overexpression of the regulator rapI [5 , 6] . The proteins that transfer ICEs are homologous to those that transfer conjugative plasmids , including the F plasmid from Escherichia coli [14] and pCF10 from Enterococcus faecalis [4] . Prior to transfer , a relaxase nicks one DNA strand at the origin of transfer ( oriT ) and becomes covalently attached to the 5' end . Based on analogy to the F and Ti plasmids , the DNA is unwound after nicking and the nicked ssDNA with the attached relaxase is transferred to the recipient [15–18] . Once in a recipient ( now a transconjugant ) , the relaxase attached to the transferred linear ssDNA is believed to catalyze the circularization ( ligation ) of ssDNA with the concomitant release of the relaxase [19–21] . Conversion of this circular ssDNA to dsDNA in the transconjugant should be needed for efficient integration into the chromosome of the new host because the substrate for the ICE recombinase ( integrase ) is typically dsDNA [22] . Also , following the same rationale , it is likely that integration of the ICE back into the host chromosome from which it originally excised requires that the ICE be double-stranded . The nicking and unwinding of conjugative DNA for transfer is similar to the early events in rolling circle replication ( RCR ) used by many plasmids and phages [reviewed in 23] . RCR plasmids encode a relaxase that binds a plasmid region called the double strand origin ( dso ) and nicks a single DNA strand ( the leading strand ) . Following association of a helicase and the replication machinery , leading strand DNA replication proceeds from the free 3’ end using the circular ( un-nicked ) strand as template . Once the complement of the circular strand is synthesized , the relaxase catalyzes the release of two circular DNA species: one dsDNA circle , and one ssDNA circle . The circular ssDNA is converted to dsDNA , typically using an RNA primer to a region of the circular ssDNA . Priming of the ssDNA circle allows DNA polymerase to synthesize the complementary strand , followed by joining of the free DNA ends by host DNA ligase [23] . Three general mechanisms for initiation of RNA primer synthesis for RCR and conjugative plasmid complementary strand synthesis have been described: 1 ) recruitment of host RNA polymerase; 2 ) recruitment of host primase; and 3 ) use of a plasmid-encoded primase [3 , 24 , 25] . Recruitment of the host RNA polymerase or primase typically requires a region of the plasmid that is revealed and active only when single-stranded ( that is , after nicking and unwinding by helicase ) . This region is referred to as a single strand origin of replication ( sso ) and has been defined for many plasmids and phages that replicate by the rolling circle mechanism [24] . Sso activity also contributes to plasmid stability [26–29] . Here , we use "sso" to indicate a DNA sequence that is orientation-specific and promotes second strand synthesis of elements that use rolling circle replication . Virtually nothing is known about how the transferred ssDNA of ICEBs1 , or any other ICE , is converted to dsDNA . During conjugation , the form of ICEBs1 DNA that is transferred to recipients is likely ssDNA with an attached relaxase [7 , 8] , analogous to conjugative transfer of other elements [15–17] . Furthermore , when activated in host cells , ICEBs1 replicates autonomously by the rolling circle mechanism and this replication is required for stability of the excised element in a population of growing cells [30] . It is not known how dsDNA is synthesized from the ssDNA that is generated during rolling circle replication of ICEBs1 in host cells . We postulated that ICEBs1 has an efficient mechanism for converting ssDNA to dsDNA , both during rolling circle replication of ICEBs1 in host cells and following conjugative transfer of ssDNA to recipients ( that is , in transconjugants ) . Since the ICEBs1 relaxase does not appear to have a primase domain , we postulated that ICEBs1 might have a functional sso . We identified a single strand origin of replication in ICEBs1 , which we named sso1 . sso1 is similar to the sso's of several characterized plasmids . We found that sso1 was sufficient to correct replication defects in a plasmid that uses rolling circle replication and that otherwise did not have an sso , indicating that sso1 was functional . Analyses of an sso1 mutant of ICEBs1 indicated that there is at least one other region in ICEBs1 that is able to promote second strand synthesis . Our results indicate that Sso function in ICEBs1 was important for the stable acquisition of ICEBs1 by transconjugants and for maintenance of ICEBs1 following excision in host cells . These findings highlight the importance of autonomous replication in the ICE lifecycle and likely extend to many , and perhaps most , functional ICEs . We searched [31] ICEBs1 for sequences that are similar to known sso's from plasmids that replicate in B . subtilis by rolling circle replication . We found that an intergenic region in ICEBs1 immediately downstream of nicK ( the gene for relaxase ) is 76% identical to the sso of B . subtilis RCR plasmid pTA1060 [32 , 33] ( Figs 1A and 2 ) . This sequence in ICEBs1 is also similar to the sso's of related RCR plasmids pBAA1 , pTA1015 , and pTA1040 [33] ( Fig 2 ) . Experiments described below demonstrate that this region of ICEBs1 functions as an sso . Therefore , we named it sso1 . ICEBs1 sso1 contained conserved features known to be important for pBAA1 sso activity . Functional studies of the pBAA1 sso defined three stem-loop structures that were important for activity [35] . Single-stranded ICEBs1 sso1 was predicted to form three stem-loop structures that were similar to those of pBAA1 on the levels of sequence and secondary structure ( Fig 2 ) . Based on these analyses , we predicted that sso1 was functional . Single strand origins are known to increase the stability of RCR plasmids . They also cause a reduction in the amount of ssDNA that accumulates from RCR plasmids . We tested the ability of sso1 from ICEBs1 to function as an sso using four different assays , one assay for stability of an RCR plasmid and three types of assays for ssDNA . pHP13 is an sso-deficient RCR plasmid that replicates in B . subtilis [37] . pHP13 and other sso-deficient RCR plasmids are relatively unstable and lost from the population without selection for the antibiotic to which the plasmid confers resistance . However , the plasmids are stabilized if they contain a functional sso [e . g . , in 26 , 38] . We found that sso1 increased the stability of pHP13 . We cloned a 418 bp region containing the putative ICEBs1 sso1 into pHP13 , generating pHP13sso1 ( pCJ44 ) , and compared stability of plasmids with and without the putative ICEBs1 sso1 . A single colony of B . subtilis cells containing either pHP13 or pHP13sso1 ( strains CMJ77 and CMJ78 , respectively; Table 1 ) was inoculated into rich ( LB ) liquid medium containing 2 . 5 μg/ml chloramphenicol to select for each plasmid . Exponentially growing cultures were diluted ~1/50 in antibiotic-free LB medium . Cultures were diluted as needed in non-selective medium to maintain exponential growth . After approximately 20 generations of growth , cells were plated on antibiotic-free LB agar . Individual colonies were picked and tested for growth on selective ( antibiotic-containing ) and non-selective agar plates to calculate the percentage of antibiotic-resistant ( plasmid-containing ) clones . Approximately 11% ( 43/400 ) of the colonies tested had lost pHP13 . In contrast , only 0 . 5% ( 2/400 ) of the colonies tested had lost pHP13sso1 . We found that this activity of sso1 was orientation-specific . pHP13 containing sso1 in the reverse orientation , pHP13sso1R , ( pCJ45 ) ( strain CMJ102 ) was not stabilized . After approximately 20 generations of non-selective growth , 13% ( 13/100 ) of colonies tested had lost pHP13sso1R . These measurements of stability of pHP13 and derivatives are consistent with previous reports analyzing the stability of pHP13 and its sso+ parent plasmid pTA1060 [39] . Our results are most consistent with the notion that the fragment from ICEBs1 cloned into pHP13 functions as an sso . However , our results might also indicate that the cloned sequence could function as a partitioning site , or increase plasmid copy number , or somehow stabilize the plasmid by a mechanism separate from a possible function as an sso . To further test the function of sso1 from ICEBs1 , we visualized ssDNA in living cells using a fusion of the B . subtilis single strand DNA binding protein ( Ssb ) to green fluorescent protein ( Ssb-GFP ) . Cells contained either no plasmid , pHP13 , pHP13sso1 , or pHP13sso1R . We measured the intensity and area of the Ssb-GFP foci and calculated the percentage of cells containing at least one large intense focus ( Materials and Methods ) . Under the growth conditions used , virtually all plasmid-free cells contained at least one focus of Ssb-GFP ( Fig 3A ) , most likely associated with replication forks , as described previously [40 , 41] . Of these plasmid-free cells with foci of Ssb-GFP , approximately 5% ( 344 total cells observed ) contained a large bright focus ( evaluated using ImageJ with defined intensities described in Materials and Methods ) . In contrast to the plasmid-free cells , approximately 38% of cells ( of 830 total cells observed ) containing pHP13 ( no sso ) had a large bright focus of Ssb-GFP ( Fig 3B ) in addition to the smaller foci found in plasmid-free cells . Like the plasmid-free cells , only ~4% of cells ( 521 total cells counted ) containing pHP13sso1 had large foci of Ssb-GFP ( Fig 3C ) . This is consistent with the expectation that there should be less ssDNA in cells with the plasmid with an sso . In contrast , approximately 45% of cells ( of 939 total cells counted ) containing pHP13sso1R had a large focus of Ssb-GFP ( Fig 3D ) . Based on these results we suggest that the large foci were due to the accumulation of pHP13 ssDNA bound by Ssb-GFP , that ICEBs1 sso1 reduces accumulation of single-stranded plasmid DNA , and that the function of sso1 is orientation specific . We verified that Ssb-GFP was bound to plasmid DNA using chromatin immunoprecipitation followed by quantitative PCR ( ChIP-qPCR ) . We crosslinked protein and DNA using formaldehyde , immunoprecipitated Ssb-GFP with anti-GFP antibodies , and measured plasmid DNA in the immunoprecipitates with PCR primers specific to part of pHP13 ( Materials and Methods ) . We found that the relative amount of plasmid DNA associated with Ssb-GFP was 25-30-fold greater in cells containing pHP13 ( no sso ) than that in cells containing pHP13sso1 ( Fig 4A ) . This activity of sso1 was also orientation specific as the amount of pHP13sso1R DNA associated with Ssb-GFP was similar to that of pHP13 ( without an sso ) . The inserts did not significantly alter the relative quantity of plasmid DNA in medium containing antibiotic ( Fig 4B ) , consistent with previous analyses of pHP13-derived plasmids [39] . To further test the function of sso1 , we used Southern blots to compare the amounts of single and double-stranded plasmid DNA in cells containing pHP13 , pHP13sso1 , and pHP13sso1R . An RCR plasmid without an sso generates a greater fraction of ssDNA than the same plasmid with a functional sso [29] The approach to distinguish dsDNA and ssDNA is to compare two Southern blots , one in which the DNA is denatured , and the second in which the DNA is not denatured , prior to transfer from gel to membrane . Both dsDNA and ssDNA are detected in the blot that was denatured whereas only ssDNA is detected in the blot that was not denatured . The probe used to detect the plasmids was a 32P-labeled ~1 kb DNA fragment complementary to cat in pHP13 . The probe was labeled on the strand complementary to the template strand for second strand ( sso1-driven ) synthesis . We detected plasmid DNA in Southern blots with DNA that had been denatured prior to transfer to membranes ( Fig 5A ) . There was one major DNA species from cells containing pHP13 ( Fig 5A , lane 1 ) or pHP13sso1R ( Fig 5A , lane 3 ) . This species was not detectable in cells containing pHP13sso1 ( Fig 5A , lane 2 ) . However , slower-migrating DNA bands were detected from pHP13sso1-containing cells ( Fig 5A , lane 2 ) . To measure single-stranded plasmid DNA , we probed DNA that had not been denatured before transfer . As expected , cells containing pHP13 had readily detectable levels of single-stranded plasmid DNA ( Fig 5B , lane 1 ) . This band corresponded to the major band observed in the denaturing blot ( Fig 5A , lane 1 ) . In contrast , cells containing pHP13sso1 had barely detectable levels of single-stranded plasmid DNA ( Fig 5B , lane 2 ) , consistent with a drop in the proportion of ssDNA of the plasmid with the sso . pHP13sso1 plasmid DNA was detectable under denaturing conditions ( Fig 5A , lane 2 ) , demonstrating that the low signal of pHP13sso1 ssDNA was not due to lack of plasmid DNA in the sample . The effect of sso1 on ssDNA content was orientation specific as cells with pHP13sso1R had single-stranded plasmid ( Fig 5B , lane 3 ) , comparable to that detected in the denaturing blot ( Fig 5A , lane 3 ) . These data indicate that ICEBs1 sso1 , when present on pHP13 , reduced accumulation of the ssDNA replication intermediate of pHP13 . The findings are consistent with the analyses of Ssb-GFP foci and association with plasmid DNA . Based on this combination of data , we conclude that sso1 from ICEBs1 is a functional single strand origin of replication that enables second strand DNA synthesis to an RCR plasmid in an orientation-specific manner . To test the function of sso1 in the context of ICEBs1 , we constructed a deletion of sso1 ( Fig 1B , Δsso1 ) and measured conjugation efficiency . Disappointingly , the conjugation frequency of ICEBs1 Δsso1 was indistinguishable from that of ICEBs1 sso1+ ( ~1% transconjugants per donor for both ICEBs1 Δsso1 and ICEBs1 sso1+ ) . This result could indicate that sso1 does not function during ICEBs1 conjugation , or that there is at least one other way to convert ssDNA to dsDNA in transconjugants . If there are other regions of ICEBs1 that provide the ability to synthesize the second strand of DNA , then removal of such regions should uncover a phenotype for sso1 in ICEBs1 . We found that removal of ICEBs1 DNA from ydcS to yddM ( Fig 1 ) revealed the role of sso1 in the ICEBs1 life cycle . We made two versions of this deletion derivative of ICEBs1 , one with and one without sso1 , referred to as mini-ICEBs1 sso1+ ( Fig 1C ) and mini-ICEBs1 Δsso1 ( Fig 1D ) . These mini-ICEBs1's contain the known regulatory elements in the left end ( Fig 1A ) , the origin of transfer ( oriT ) and ICEBs1 genes needed for nicking and replication , and genes and sites needed for excision and integration . The mini-ICEBs1 is functional for excision and can transfer to recipient cells using transfer functions provided in trans from a derivative of ICEBs1 that cannot excise or transfer [6] . Experiments described below ( summarized in Fig 1G ) indicate that single strand origin function is important for both stable acquisition by transconjugants and for maintenance in host cells following excision from the chromosome . We tested the ability of mini-ICEBs1 sso1+ and mini-ICEBs1 Δsso1 to be stably acquired by recipients in conjugation . We mated mini-ICEBs1 sso1+ ( encoding kanamycin resistance ) into a wild type recipient ( streptomycin resistant ) , selecting for resistance to kanamycin and streptomycin . Donor cells also contained a derivative of ICEBs1 integrated at thrC that is able to provide conjugation functions , but that is unable to excise and transfer [6] . In these experiments , the mini-ICEBs1 is mobilized by the transfer machinery encoded by the non-excisable element at thrC . Recombination between the element at thrC and the mini-ICEBs1 would result in loss of kan from the mini-ICEBs1 and would not yield kanamycin-resistant transconjugants . The conjugation ( mobilization ) efficiency of mini-ICEBs1 sso1+ was ~0 . 2% transconjugants per donor . Transconjugants on selective medium produced normal looking colonies ( Fig 6A ) that stably maintained kanamycin resistance even after propagation under non-selective conditions . We picked 50 transconjugants , streaked each on nonselective plates ( LB agar , no antibiotic ) to single colonies , and then picked a single colony from each isolate and restreaked , testing for resistance to kanamycin ( LB agar with kanamycin ) . Each isolate tested ( 50/50 ) was still resistant to kanamycin . These results indicate that mini-ICEBs1 sso1+ is transferred and stably maintained in the transconjugants , analogous to the properties of wild type ICEBs1 . Results with donor cells containing mini-ICEBs1 Δsso1 ( strain LDW179 ) were different from those containing mini-ICEBs1 sso1+ . The apparent conjugation ( mobilization ) efficiency was similar to that for mini-ICEBs1 sso1+ , ~0 . 3% transconjugants per donor . However , there were at least two types of colonies , large and small , on the original plates ( LB with kanamycin and streptomycin ) selective for transconjugants ( Fig 6B ) . The large colonies were similar in appearance to the transconjugants from mini-ICEBs1 sso1+ ( Fig 6A ) . In contrast to the transconjugants with the mini-ICEBs1 sso1+ , many of the transconjugants receiving mini-ICEBs1 Δsso1 appeared to be unable to stably retain this element . That is , these transconjugants were no longer resistant to kanamycin after growth under non-selective conditions . We picked all the transconjugants ( 153 ) from an LB agar plate containing kanamycin and streptomycin , streaked for single colonies on non-selective plates ( LB agar without antibiotics ) , and then tested a single colony from each of these for resistance to kanamycin . Of the 153 isolates tested , 86 ( 56% ) were sensitive and 67 ( 46% ) were resistant to kanamycin . Usually , but not always , the small colonies generated cells that were sensitive to kanamycin and had apparently lost mini-ICEBs1 Δsso1 . The larger colonies typically generated cells that were resistant to kanamycin , indicting the stable presence of mini-ICEBs1 Δsso1 . These results indicate that the mini-ICEBs1 Δsso1 was unstable in >50% of the transconjugants . We postulate that the mini-ICEBs1 Δsso1 is unstable in the small colonies because it is unable to integrate before the initial transconjugants grow and divide . Furthermore , we postulate that there was some conversion of ssDNA to dsDNA independent of sso1 such that there was integration in some of the transconjugants . In addition , it seems likely that the initial kanamycin resistance of the transconjugants was due to expression of the kanamycin resistance gene ( kan ) , and presumably the gene must be double-stranded to be efficiently transcribed . If this is true , it implies that integration takes time and that there is considerable cell growth and division before the mini-ICEBs1 Δsso1 can integrate . It is also possible , although we believe unlikely , that the mini-ICEBs1 Δsso1 is not converted to dsDNA . In this case , there is some other mechanism for the transconjugants to be initially resistant to kanamycin and for integration of single-stranded ICEBs1 DNA , perhaps by a relaxase- [19] or integrase- [44 , 45] mediated recombination event . Our results indicate that the conversion of ICE ssDNA to dsDNA in transconjugants is important for the stable acquisition of the element . The initial molecular events in the recipient likely include entry of the linear single-stranded ICE DNA with the relaxase covalently attached to the 5' end , followed by relaxase-mediated circularization of the ssDNA . The presence of sso1 on this circular ssDNA likely enables efficient synthesis of a primer for second strand DNA synthesis . In the absence of sso1 , second strand synthesis is likely less efficient , leading to loss of ICEBs1 from many of the cells . ICEs that are transferred by a type IV secretion system are all thought to enter the recipient as linear ssDNA with an attached relaxase [4] . If true , then an efficient mechanism for priming second strand DNA synthesis is likely to be critical for the stable propagation and spread of these elements . After induction of ICEBs1 gene expression and excision from the chromosome , it takes several generations to reestablish repression and for re-integration into the chromosome [30] . Therefore , after excision from the chromosome , autonomous replication of ICEBs1 is required for its stability in host cells during growth and cell division [30] . We tested the contribution of sso1 to the stability of ICEBs1 in host cells following excision from the chromosome . We induced gene expression and excision of mini-ICEBs1 sso1+ , mini-ICEBs1 Δsso1 , and replication-defective ICEBs1 ΔnicK by expressing rapI from a xylose-inducible promoter ( Pxyl-rapI ) . By two hours after expression of rapI , all three derivatives of ICEBs1 had excised normally as indicated by a >90% decrease in attL , the junction between the left end of ICEBs1 and chromosomal sequences ( Fig 7A ) . At that time ( two hours post-induction , 0 generations in Fig 7 ) , rapI expression was repressed by removing xylose and adding glucose . We then monitored the kinetics of reintegration of mini-ICEBs1 sso1+ , mini-ICEBs1 Δsso1 , and ICEBs1 ΔnicK into the host chromosome using qPCR to monitor the formation of attL . If the plasmid form of the element cannot replicate , then we expect a decrease in the proportion of cells that contain an integrated element ( a decrease in formation of attL ) compared to that in cells with an element that can replicate . After ~4 generations ( 6 hours of rapI repression ) , mini-ICEBs1 sso1+ had reintegrated into the chromosome in ~80% of cells ( assuming 100% integration before induction of ICEBs1 gene expression ) . In contrast , mini-ICEBs1 Δsso1 had reintegrated in ~5% of the cells ( Fig 7A ) . This amount of integration was significantly less than that of mini-ICEBs1 sso1+ , but greater than that of ICEBs1 ΔnicK ( Fig 7A ) , indicating that mini-ICEBs1 Δsso1 is inefficiently maintained in dividing host cells but that it is more stable than the ICEBs1 mutant that is completely unable to replicate . We also determined the fraction of colony forming units ( CFUs ) that were kanamycin-resistant after four generations of growth . Cells were plated non-selectively ( without antibiotic ) and then individual colonies picked and tested for resistance to kanamycin . Since each of the ICEBs1 derivatives contains kan , this should be a good indication of stable integration of ICEBs1 . Consistent with qPCR results , mini-ICEBs1 sso1+ was present in >95% ( 36/37 ) of cells ( CFUs , plated at generation = 4 ) as judged by resistance to kanamycin . In contrast , <5% of cells ( 1/25 ) plated after 4 generations contained mini-ICEBs1 Δsso1 . That is , only one of the 25 colonies tested had robust growth on kanamycin . Of the 24 cells that did not form robust colonies , 12 did not have any detectable growth , and 12 grew poorly on kanamycin . Since ICEBs1 DNA must presumably be double-stranded in order to integrate into the chromosome by Int-mediated site-specific recombination , and based on the results above showing that sso1 functions as a single strand origin of replication in a plasmid ( pHP13 ) , the simplest model is that sso1 also functions as a single strand origin of replication in the mini-ICEBs1 , and in ICEBs1 . Results presented above indicate that , in the context of mini-ICEBs1 , sso1 has a function and causes a phenotype . However , in the context of an intact ICEBs1 , loss of sso1 caused little if any detectable phenotype . Together , these results indicate that there is a region downstream of sso1 in ICEBs1 that somehow enables synthesis of the second strand of DNA . We used derivatives of ICEBs1 with different amounts of DNA downstream from sso1 to determine the location of at least one of these regions . We found that the region between yddB and conG ( Fig 1A ) was at least partly functionally redundant with sso1 . We compared the deletion derivative ICEBs1 Δsso1 Δ ( yddB-yddM ) that is missing sso1 and sequences from yddB through yddM ( Fig 1E ) to ICEBs1 Δsso1 Δ ( conG-yddM ) that is missing sso1 and sequences from conG through yddM and contains the sequences from yddB through conG ( Fig 1F ) . As above , we measured the ability of these elements to function in conjugation and to reintegrate in host cells following induction of gene expression and excision . Our results indicate that sso1 in ICEBs1 is functional . Based on the life cycle of ICEs , it seems likely that many ( most ) other functional ICEs also have at least one region capable of functioning as an Sso . Although there is relatively little sequence similarity between sso's from different plasmids and other RCR elements [24] , we found that the sso of the RCR plasmid pC194 [46] from Staphylococcus aureus is identical to a region in several different ICEs from clinical isolates of Streptococcus pneumonia ( Table 2 ) . In addition , we found that an ICE from Mycoplasma fermentans has a region similar to the sso from the plasmid pT181 from S . aureus [47] and an ICE from Streptococcus suis has a region similar to the sso from the plasmid pUB110 from S . aureus [48] . The simplest notion is that each of these sequences functions as an sso for the cognate ICE . In contrast to the examples of sequence similarity ( identity ) described above , the function of an sso likely depends on its structure in addition to or instead of its primary sequence . For example , replacement of the sso of pBAA1 with a different primary sequence that retained secondary structure resulted in a functional sso [35] . Identification of sso's in other ICEs will likely require a combination of analyses of sequence and predicted structures and direct functional tests . RCR plasmids and ICEs share many functional properties . Like ICEBs1 [30] and members of the SXT/R391 family from Gram-negative bacteria [49] , other ICEs may be capable of autonomous replication via a rolling circle mechanism {[30 , 49] and references therein} . In addition , the ICEBs1 oriT and its conjugative relaxase NicK also serve as double-stranded origin and a replicative relaxase , respectively , supporting autonomous rolling circle replication . Furthermore , some RCR plasmid replicative relaxases can serve as conjugative relaxases [50] . We have now shown an additional similarity between RCR plasmids and ICEs: the importance of Sso activity in stability of the element . Functional ICEs appear to have a conserved lifecycle . Therefore , we suspect many other ICEs contain a single strand origin of replication to support both autonomous ICE replication in host cells and stable establishment in transconjugants . Preliminary bioinformatic analyses revealed that several ICEs contain sequences with high identity to characterized sso's from RCR plasmids ( Table 2 ) . These findings support the notion that many ICEs likely undergo autonomous rolling circle replication . We propose that they use oriT as a dso , the conjugative relaxase as a replicative relaxase , and an sso for second strand synthesis following conjugative transfer to a new host and during autonomous replication in the original host . The location of sso1 relative to oriT in ICEBs1 could increase the probability of successful chromosome re-integration . sso1 in ICEBs1 is downstream of oriT , the double-stranded origin of replication ( dso ) . In contrast , the sso in most RCR plasmids is upstream of the dso [23] , although there are some exceptions [e . g . , 28] . The positioning of an sso upstream of the dso in plasmids ensures that the sso is not single-stranded ( and thus active ) until leading strand synthesis from the dso is almost complete . However , the location of ICEBs1 sso1 relative to oriT ensures that the attachment site in the circular ICEBs1 ( attICE , previously referred to as attP ) is not double-stranded ( and thus a substrate for site-specific recombination into the chromosome ) until ligation and recircularization at the nic site ( in oriT ) occurs . Initiation of second strand synthesis from an sso upstream of oriT could result in replication of attICE before recircularization , and integration could result in a double-strand break in the chromosome ( Fig 8 ) . Thus , the location of sso's in ICEs may be an adaptation to the ICE lifecycle to prevent premature integration and possible damage to the host . RCR plasmids and conjugative plasmids have evolved multiple strategies to initiate second strand synthesis [3 , 24] . The F plasmid of E . coli and many RCR plasmids from Gram positives contain an sso that , when single-stranded , produces a folded structure that is recognized as a promoter by the host RNA polymerase . Transcription initiates and a short RNA serves as a primer for DNA replication [51–55] . Some plasmids ( from both Gram negative and positive bacteria ) contain sites that recruit the host primase DnaG , and these sites are important for plasmid replication [25 , 38] . The mobilizable plasmid ColE1 contains a primosome assembly site that is thought to be involved in priming of the transferred strand [3 , 56] . The sso of RCR plasmid pWV01 is RNA polymerase-dependent in B . subtilis [27] but has RNA polymerase-independent priming activity in Lactococcus lactis . This RNA polymerase-independent priming requires a region of the sso similar to the primosome assembly site in the phage ø174 [27] . Lastly , several conjugative or mobilizable plasmids from Gram negative bacteria encode a primase that can synthesize RNA primers on the transferred ssDNA [57 , 58] , and primase activity can be important for conjugative transfer [59–61] . The plasmids RSF1010 and R1162 each have a primase that recognizes the plasmid origin of replication ( oriV ) [62 , 63] . The primases of ColIb and RP4 can prime a variety of ssDNA templates [58] and appear to have general primase activity that is not specific to their cognate plasmid [57 , 64] . Despite the loss of regions that contribute to second strand DNA synthesis in ICEBs1 , limited or inefficient second strand synthesis most likely occurs . Inefficient second strand synthesis also occurs in many RCR plasmids that are missing an sso . Plasmids without an sso can be maintained in cells and some are even used as cloning vectors [for example , 37 , 65] . Although the mechanisms for this sso-independent second strand synthesis are not known , one possibility is that RNA fragments that can hybridize to ssDNA could serve as primers for DNA replication . Even though elements that use rolling circle replication can function without an sso , any element with an sso , and thus an efficient mechanism for priming and completing second strand synthesis , should have a significant evolutionary advantage over elements lacking this function . We suspect that the RCR plasmids and ICEs use similar mechanisms to prime second strand synthesis when there is not an sso . Based on our results , we conclude that ICEBs1 has at least two mechanisms for efficient second strand synthesis , one of which utilizes sso1 . We postulate that these multiple mechanisms might increase propagation of ICEBs1 under different conditions in B . subtilis and perhaps broaden its ability to function in other hosts . For example , there might be growth stages or conditions in which use of one mechanism is inefficient . In this case , the presence of a second mechanism for second strand synthesis could allow for more efficient propagation of ICEBs1 . We also suspect that multiple modes of initiating second strand synthesis could enable ICEBs1 to transfer and propagate efficiently to multiple hosts . For example , whereas sso1 works efficiently in B . subtilis , it might not work efficiently in another organism , and a different mechanism for initiating second strand synthesis could enable spread and maintenance of ICEBs1 in such hosts . Some sso's are known to function in only one or a few host species , and host specificity is determined , in part , by the strength of host-specific RNA polymerase-sso interactions and/or the presence of other host factors [23 , 54] . For example , RCR plasmid pMV158 contains two sso's that are functionally redundant in Streptoccus pneumoniae . However , deletion of one of the sso's , ssoU , decreases conjugative transfer of pMV158 from S . pneumoniae to Enterrococcus faecalis by 300-1000-fold [28 , 66] . The conjugative module from pMV158 composed of its cognate MobM relaxase , oriT and two sso’s is widely conserved in plasmids from several Gram positive species [66] . Similarly , the primases of conjugative plasmids ColIb and RP4 are important for conjugation into some species but disposable for conjugation into others , indicating that second strand synthesis mechanisms differ between hosts [3] . We speculate that many ICEs might have two different regions that allow for conversion of ssDNA to dsDNA , perhaps enabling stable acquisition by and maintenance in different host species and thereby broadening the ICE host range . B . subtilis strains were derived from JH642 ( pheA1 trpC2 ) [67 , 68] and are listed in Table 1 . Most were constructed by natural transformation . Conjugation experiments utilized recipient CAL85 that is cured of ICEBs1 ( ICEBs10 ) and is resistant to streptomycin ( str-84 ) [5] . To induce ICEBs1 gene expression in host cells , rapI was overexpressed from amyE::{ ( Pxyl-rapI ) spc} [9] or amyE::{ ( Pxyl-rapI ) cat} [43] . Pxyl is a xylose-inducible promoter that is also repressed in the presence of glucose [69] . Construction of the non-excisable ICEBs1 in thrC325::{ ( ICEBs1-311 ΔattR::tet ) mls} ) has been described previously [7] . Most ICEBs1 derivatives contained kan , conferring resistance to kanamycin . The Δ ( rapI-phrI ) 342::kan allele in LDW21 and LDW22 has been described [5] . The Δ ( conG-yddM ) 39::kan and Δ ( yddB-yddM ) 41::kan mutations have the same endpoints as alleles described previously [7] . Plasmids pCAL316 and pCAL317 containing kan and ~1 kb of DNA flanking the deletion were linearized and transformed into the ICEBs1 markerless Δsso1 mutant ( Δsso1-13 ) to generate ICEBs1 Δsso1-13 with Δ ( conG-yddM ) 39::kan and Δ ( yddB-yddM ) 41::kan , respectively . PCR was used to confirm the mutations in ICEBs1 and verify that they were produced by double crossover recombination . Δsso1-13 is a 194-nucleotide markerless deletion that fuses the 43rd and 238th nucleotides of the intergenic region between nicK and ydcS . Two 1 . 5 kb DNA fragments containing DNA flanking the deletion site were PCR amplified . The two fragments were fused and inserted into the BamHI and EcoRI sites of pCAL1422 ( a plasmid that contains E . coli lacZ ) [8] via isothermal assembly [70] . The isothermal assembly product was integrated by single crossover into B . subtilis strain IRN342 ( ICEBs1 Δ ( rapI-phrI ) 342::kan ) [5] . Transformants were screened for loss of lacZ , indicating loss of the integrated plasmid , and PCR was used to identify a Δsso1 ( LDW22 ) and wild type ( LDW21 ) clone . Mini-ICEBs1 sso1+ {ΔICEBs1-93 Δ ( ydcS-yddM ) 93::kan} and mini-ICEBs1 Δsso1 {ΔICEBs1-177 Δ ( sso1-yddM ) 177::kan} are large deletion-insertions that leave the left and right ends of ICEBs1 intact . Both deletions contain a kanamycin resistance gene that interrupts part of yddM as previously described [6] . The Δ ( ydcS-yddM ) 93::kan deletion-insertion begins 19 bp upstream of ydcS . The Δ ( sso1-yddM ) 177::kan deletion-insertion begins 6 bp downstream of nicK . Splice-overlap-extension PCR [71] was used to fuse a ~1 kb fragment of genomic DNA upstream of the deletion endpoint to a DNA fragment containing kan and the kan-yddM junction amplified from ΔICEBs1-205 [6] . We constructed two pHP13 derivatives to test sso1 function . Both plasmids contain sso1 ( based on conservation to the Sso of plasmid pTA1060 ) and additional flanking sequence from ICEBs1 . We used PCR to amplify a 418 bp fragment of ICEBs1 from 78 bp upstream of the 3' end of nicK to the first 76 bp of ydcS , including sso1 ( Fig 1 ) . The PCR product was ligated into the lacZ alpha complementation region of pHP13 [37 , 39] ( NCBI accession DQ297764 . 1 ) with the multiple cloning sites , between the BamHI and EcoRI sites ( pCJ44 ) or SalI and EcoRI sites ( pCJ45 ) . pCJ44 contains sso1 in a functional orientation and pCJ45 contains sso1 in the reverse orientation ( sso1R ) , relative to the direction of leading strand DNA synthesis [37] . The ssb-mgfpmut2 fusion is driven by the rpsF promoter and inserted by double crossover at lacA , as described previously [40] . Strains with this fusion also contain wild type ssb at the normal chromosomal location . Bacillus subtilis cells were grown in LB or in MOPs-buffered S750 defined minimal medium [72] . ICEBs1-containing strains were grown in minimal medium containing arabinose ( 1% w/v ) as the carbon source , and ICEBs1 gene expression was induced by the addition of xylose ( 1% w/v ) to induce expression of Pxyl-rapI . Cells containing pHP13-derived plasmids were grown in liquid medium containing 2 . 5 μg/ml chloramphenicol to select for maintenance of the plasmid . Chloramphenicol was omitted from the growth medium when testing for maintenance of pHP13-derived plasmids as described in the text . Antibiotics were otherwise used at the following concentrations: kanamycin ( 5 μg/ml ) , chloramphenicol ( 5 μg/ml ) , spectinomycin ( 100 μg/ml ) , tetracycline ( 10 μg/ml ) , streptomycin ( 100 μg/ml ) , and a combination of erythoromycin ( 0 . 5 μg/ml ) and lincomycin ( 12 . 5 μg/ml ) to select for macrolide-lincosamide-streptogramin ( mls ) resistance . Conjugation experiments were performed essentially as described [5 , 6] . Briefly , donor and recipient cells were grown in defined minimum medium containing 1% arabinose . Xylose ( 1% ) was added to donors to induce expression of Pxyl-rapI , causing induction of ICEBs1 gene expression and excision . After two hours of growth in the presence of xylose , equal numbers of donor and recipient cells were mixed and collected by vacuum filtration on a nitrocellulose filter . Filters were incubated at 37°C for 3 hours on 1 . 5% agar plates containing 1X Spizizen’s salts ( 2 g/l ( NH4 ) SO4 , 14 g/l K2HPO4 , 6 g/l KH2PO4 , 1 g/l Na3 citrate-2H2O , 0 . 2 g/l MgSO4-7H20 ) [73] . Cells were washed from the filters , diluted and plated on LB agar containing streptomycin and kanamycin to select for transconjugants . Donor cell concentration was determined at the time of cell mixing ( after growth in xylose for two hours ) by plating donor cells on LB agar containing kanamycin . Conjugation efficiency was calculated as the ratio of transconjugants per donor . Microscopy was performed essentially as described [74 , 75] . Briefly , mid-exponential phase cells were placed on pads of 1% agarose . Images were taken on a Nikon E800 microscope equipped with Hamatsu CCD camera and 100X DIC objective . Chroma filter set 41012 was used for GFP . The contrast and brightness of fluorescent images were initially processed using Improvision Openlabs 4 . 0 Software . We measured the intensity and area of Ssb-GFP foci using ImageJ ( http://imagej . nih . gov/ij/ ) . A high ( conservative ) global threshold was applied to every image to separate intense Ssb-GFP foci ( pixel intensity ≥ 200 , 8-bit image ) from background . The area of each intense Ssb-GFP focus was measured using the automatic particle analysis tool . We then analyzed the distribution of the area of each focus , and used 4 pixels as a cutoff for a “large” focus ( 4 pixels was the third quartile for intense foci in control strain CMJ118 ) . Finally , we counted the number of cells in the DIC image , and calculated the number of cells with at least one large , intense focus . Mid-exponential phase cultures were fixed in an equal volume of ice-cold methanol . Cells were washed in NE buffer ( 100 mM NaCl , 50 mM EDTA , pH 8 . 0 ) and lysed in NE buffer containing 0 . 5 mg/ml lysozyme for 30 min at 37°C . Sarcosyl ( 1% final , Sigma ) and proteinase K ( 90 μg/ml final , Qiagen ) were added , and the suspension was incubated for 20 min at 70°C . Equal volumes of phenol and chloroform were added , and the suspension was vortexed vigorously . Following centrifugation , the aqueous layer was removed and total nucleic acids were precipitated from the aqueous layer by addition of 0 . 1 volume of 3 M sodium acetate and 2 . 5 volumes of ice-cold ethanol . The precipitate was washed once with 70% ethanol and resuspended in ddH2O overnight at room temperature . Equal amounts of nucleic acid ( ~40 μg per sample ) were separated on two 0 . 8% agarose gels . Following electrophoresis , one of the gels was soaked in an alkaline solution ( 0 . 5 M NaOH , 1 . 5 M NaCl ) for 30 min to denature the DNA . Both gels were also soaked in neutralization buffer ( 2 . 5 M NaCl , 0 . 5 M Tris HCl ) for 30 min . DNA was transferred to nicrocellulose membranes ( Whatman ) by capillary transfer , essentially as described [76] . DNA was then fixed by baking the membranes for 2 hours at 80°C . Prior to probing , the membranes were incubated for 1 h at 37°C in rotating tubes containing prehybridzation buffer with formamide [76] . We used 32P-labeled probe to detect plasmid DNA . Primer CLO377 ( 5’-AGCACCCATT AGTTCAACAA ACG-3’ , complementary to part of cat on pHP13 ) was end-labeled with ( gamma-32P ) -ATP ( Perkin-Elmer ) using T4 polynucleotide kinase ( New England Biolabs ) . Labeled oligonucleotides were separated from unincorporated ATP using Centri-Spin 10 spin columns ( Princeton Separations ) . A region of cat from pHP13 was PCR amplified using labeled CLO377 and unlabeled primer oLW39 ( 5’-AGTCATTAGG CCTATCTGAC AATTCC-3’ ) , thereby producing dsDNA with one strand labeled . The PCR product was separated from excess primers using the Qiagen PCR Purification Kit and diluted in hybridization buffer [76] . The PCR product was denatured by boiling for 1 min and immediately put on ice . Equal amounts of probe were applied to each blot ( denatured and non-denatured ) . Membranes and probe were incubated overnight at 37°C in rotating tubes containing hybridization buffer . Excess probe was removed from the membranes by serially washing in 2X—0 . 1X SSC and 0 . 5%- 0 . 1% SDS . The 32P-labeled DNA was detected using a Typhoon FLA 9500 phosphorimager . ChIP-qPCR was used to measure the association of Ssb-GFP with pHP13-derived plasmid DNA and was carried out essentially as described [77 , 78] . Briefly , DNA-protein complexes were crosslinked with formaldehyde . Ssb-GFP was immunoprecipitated with rabbit polyclonal anti-GFP antibodies ( Covance ) . qPCR was used to determine the relative amount of plasmid DNA that was bound to Ssb-GFP [42] . We used primers specific to the cat gene in the pHP13 backbone to amplify DNA in both immunoprecipitates and in pre-immunoprecipitation lysates ( representing the total input DNA ) . Values from immunoprecipitates were normalized to those of total input DNA ( % of input ) [42] . We also determined the amount of total plasmid in each strain relative to control strain CMJ129 using the ΔΔCp method [79] . DNA was amplified from pre-immunoprecipitated lysates , and values obtained for plasmid gene cat were normalized to chromosomal locus ydbT . Primers to cat were oLW104 ( 5’-GCGACGGAGA GTTAGGTTAT TGG-3’ ) and oLW107 ( 5’-TTGAAGTCAT TCTTTACAGG AGTCC-3’ ) . Primers to ydbT were described [8] . We used qPCR to determine if ICEBs1 was integrated into the chromosomes of transconjugants . We measured attL , the junction between the chromosome and the left end of ICEBs1 and attB , the chromosomal attachment site without ICEBs1 using primer pairs specific for each region [5 , 30] . We also used qPCR to measure reintegration of ICEBs1 into the chromosome of cells from which it originally excised . In these experiments , host cells with ICEBs1 integrated in the chromosome at attB were grown in defined minimal medium with 1% arabinose . Expression of Pxyl-rapI was induced with 1% xylose , causing induction of ICEBs1 gene expression and excision . After two hours of growth , cells were pelleted and resuspended ( to an OD600 of 0 . 05 ) in minimal medium with 1% glucose ( and no xylose ) to repress expression of Pxyl-rapI and eventually restore repression of ICEBs1 gene expression . DNA was extracted at various times after repression of Pxyl-rapI from 1–2 ml of cell culture using the Qiagen DNEasy tissue kit protocol for Gram-positive bacteria . We determined the amount of reintegrated ICEBs1 relative to uninduced cells ( before expression of Pxyl-rapI ) using the ΔΔCp method [79] . ICEBs1 reintegration was determined by quantifying the amount of attL , the junction between the chromosome and the left end of ICEBs1 [30] , relative to the amount of the nearby chromosomal locus ydbT . Values were normalized to uninduced cells in which ICEBs1 is integrated in a single copy in the chromosome .
Mobile genetic elements facilitate movement of genes , including those conferring antibiotic resistance and other traits , between bacteria . Integrative and conjugative elements ( ICEs ) are a large family of mobile genetic elements that are typically found integrated in the chromosome of their host bacterium . Under certain conditions ( e . g . , DNA damage , high cell density , stationary phase ) an ICE excises from the host chromosome to form a circle . A linear single strand of ICE DNA can be transferred to an appropriate recipient through the ICE-encoded conjugation machinery . In addition , following excision from the chromosome , at least some ( perhaps most ) ICEs undergo autonomous rolling circle replication , a mechanism used by many plasmids and phages . Rolling circle replication generates single-stranded DNA ( ssDNA ) . We found that ICEBs1 , from Bacillus subtilis , contains at least two regions that enable conversion of ssDNA to double-stranded DNA . At least one of these regions functions as an sso ( single strand origin of replication ) . ICEBs1 Sso activity was important for the ability of transferred ICEBs1 to be acquired by recipients and for the ability of ICEBs1 to replicate autonomously after excising from its host’s chromosome . We identified putative sso's in several other ICEs , indicating that Sso activity is likely important for the replication , stability and spread of these elements .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[]
2015
Identification of a Single Strand Origin of Replication in the Integrative and Conjugative Element ICEBs1 of Bacillus subtilis
Leaky integrate-and-fire ( LIF ) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli , tasks or dynamic network states . However , neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential ( LFP ) . Given that LFPs are generated by spatially separated currents across the neuronal membrane , they cannot be computed directly from quantities defined in models of point-like LIF neurons . Here , we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks . To search for this best “LFP proxy” , we compared LFP predictions from candidate proxies based on LIF network output ( e . g , firing rates , membrane potentials , synaptic currents ) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional ( 3D ) network model of multi-compartmental neurons with realistic morphology , spatial distributions of somata and synapses . We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy , accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations . This proxy performed well over a broad set of conditions , including substantial variations of the neuronal morphologies . Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation , and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo . Models of recurrently connected networks of leaky integrate-and-fire ( LIF ) neurons are well established tools for studying brain function [1 , 2] . The equations describing the single LIF neuron are simple and can be easily adapted to generate complex dynamics [3 , 4] . Despite their simplicity , LIF network models have proved able to describe a wide spectrum of different cortical dynamics and cortical functions , from the emergence of up and down states [5–7] , working memory [8–10] , attention [11 , 12] , decision making [13] , rhythmogenesis [14] , and sensory information coding [11 , 15 , 16] . In some cases it is possible to describe the dynamics of LIF networks analytically [17 , 18] , thus providing deeper insights into how spiking neuronal networks may implement the basic cerebral computational mechanisms [19] . Models can only be properly tested against experimental evidence when they can predict empirical measures quantitatively . Local cortical activity is often recorded in vivo or in vitro using the local field potential ( LFP ) , a measure obtained by low-pass filtering ( below a few hundred hertz ) the electrical potential recorded from extracellular electrodes . The LFP signal reflects mass neural activity arising within a few hundred micrometers or more from the recording electrode [20–25] . This spatial scale is highly relevant for LIF network models , which typically aim to describe the activity of thousands or tens of thousands of cells . The recording of LFPs has a prominent role in systems neuroscience , and such recordings have been used extensively to investigate cortical network mechanisms involved in sensory processing [26] , motor planning [27] , and higher cognitive processes [28] . LFP is generated by transmembrane currents in the neurons in the vicinity of the recording electrode [23] and depends on morphological features of the contributing cells , the positioning of synapses , as well as the correlation level of synaptic inputs [20 , 21 , 29 , 30] . Under reasonable assumptions about the extracellular milieu the cellular LFP contributions can be computed as a weighted sum of the transmembrane currents in multi-compartment neuron models [31–34] . This allows for detailed numerical investigations of spatial , as well as spectral features of the LFP signals [35] . In particular , such simulations of large populations of morphologically detailed neurons have provided insight into how the neuronal activity at the population level influences the spatial reach and laminar variation of the LFP signal in vivo [20 , 21 , 33 , 34 , 36] the relative importance of active and passive currents [37] , and the population LFP signal measured from cortical slices in microelectrode arrays ( MEAs ) [38 , 39] . However , it has been unclear how best to use LIF networks to model and provide understanding of LFP recordings . This is because extracellular potentials arise in biological tissue due to a spatial separation of inward ( sinks ) and outward ( sources ) transmembrane currents of the neurons , and neuron models used to compute an LFP signal must thus have a minimum of two spatially separated compartments in order to generate a potential [32] . In LIF models , however , a single compartment is typically used as an approximation of the entire neuron , including the spatially extended dendritic structure , and individual cells within a population are not assigned to a specific spatial position . One possible way to compute LFPs from LIF network is to project the spike times generated by the LIF network under consideration onto morphologically detailed 3D neuron models and then compute the field that the currents flowing through these 3D networks generate . However , this approach would require the modeler to set up a cumbersome and computationally expensive network model based on multi-compartment model neuron . As a much simpler alternative , we here instead search for a general and easy-to-use proxy to predict the time course of the LFP based on variables available directly from the LIF network simulations . Here we investigate and evaluate different strategies to compute an LFP proxy directly from the output of standard LIF network simulations without the use of multi-compartment neuronal morphologies . Our approach is as follows: we first simulate an LIF point-neuron network model and record the output spiking activity , membrane potentials , and synaptic currents . Next , we compute a realistic ground-truth estimate of the LFP that the same LIF network activity would generate . We do this by injecting distributed synaptic currents corresponding to the stored LIF synaptic events , onto a population of multi-compartment neurons with realistic distributions of dendrites and synapses ( we call this population the “3D network” ) . We then compare this simulated ground-truth LFP signal to a number of LFP proxies computed directly from measures of activity of the point-neuron LIF network . These proxies include those previously proposed in the literature ( e . g . , the average firing rate [11 , 14 , 40] , the average membrane potential [24 , 41–44] , the sum of synaptic currents [7 , 45] , and the sum of absolute values of synaptic currents [15] ) , as well as others proposed here . By separating the spiking dynamics generated by the LIF network from the LFP generated by the 3D network , we are also able to investigate how different assumptions regarding cell morphology , synaptic distributions and recording positions influence the accuracy of the different LFP proxies . We find that a simple linear combination of excitatory ( AMPA ) and inhibitory ( GABA ) synaptic currents extracted from the point-neuron LIF network provides a proxy for the LFP that closely matches the temporal features of the signals resulting from the morphologically realistic LFP model generated by the 3D network . Even with a small set of fixed parameters this LFP proxy is able to account for the LFP signal with a high degree of precision under most investigated conditions . Our goal was to understand how to compute a simple yet accurate approximation ( denoted as “proxy” in the following ) of the LFP that would be generated by the time series of synaptic activity of an LIF network if its neurons had a realistic spatial structure and arrangement . We therefore first simulated an LIF network ( known to reproduce several features of cortical dynamics ) . Next , we injected the synaptic activity it generated into a synthetic three-dimensional network model ( 3D network ) of a layer of a cortical column that employed multi-compartmental neurons with realistic morphology , spatial distributions of somata and synapses , and computed the extracellular potentials generated by this synaptic activity . We selected an LIF network ( adapted from [14] and refined in [15 , 16 , 46 , 47] ) that has been shown to reproduce a number of important features of the dynamics of visual primary cortical neural population recorded in vivo during naturalistic sensory stimulation , including a realistic spectrum of cortical dynamics and of its modulation with the visual stimuli , including low-frequency ( 1–12 Hz ) and gamma ( 50–100 Hz ) oscillations [15 , 46] . Moreover , when using a simple proxy ( which is demonstrated below to perform well ) to compute an LFP from synaptic currents , this LIF network reproduced quantitatively several important properties of recorded extracellular potentials , including LFP power spectra and spectral information content [15] , and cross-frequency and spike-field relationships [16 , 46] . Thus , the LIF network seemed to generate a sufficiently realistic dynamics to provide synaptic input for the generation of biologically plausible LFPs in the 3D network . The LIF network model ( Fig 1A ) was composed of 4000 excitatory and 1000 inhibitory LIF neurons that were randomly connected with a pair-wise connection probability of 0 . 2 ( for further details see Methods ) . The LIF network received two kinds of external inputs: a “thalamic” synaptic input thought to carry the information about the external stimuli and a stimulus-unrelated input representing slow ongoing fluctuations of activity . Synaptic dynamics and parameters are reported in Tables 1 and 2 , and further details can be found in the Methods . Importantly , as is the case for most LIF network models to date , our LIF network did not have any spatial structure: the individual neurons were not assigned to a specific spatial position and consequently the connectivity had a random and sparse structure . The LFP signal that would result from the time series of spikes generated by the LIF network provided the postsynaptic neurons had biologically plausible dendritic structures , was computed by injecting the LIF synaptic activity into a 3D network of morphologically detailed multi-compartmental model neurons ( Fig 1B , see Methods ) . A summary of the properties of the 3D network is reported in Table 3 , while the synaptic parameters are listed in Table 4 ( see Methods for further details ) . In order to set up the 3D network we were required to make additional assumptions regarding the spatial positioning of cells , the shape and size of their dendritic structures , as well as the synaptic distributions . We focused on computing the LFP generated by one cortical layer ( in terms of soma positions ) that comprised both inhibitory and excitatory neurons . In our default setting , we assumed all neurons in the 3D network to be inside two cylinders with 250 μm radius and 250 μm height that were stacked one above the other to resemble the vertical structure of layer 2/3 ( Fig 1B ) . Note that this spatial scale is similar to the size of the neuronal pool contributing to the recorded LFP , the so-called spatial reach , in the case of uncorrelated synaptic activity driving a neuronal population [20 , 21 , 35] , and resulted in a neuronal density consistent with known estimates of 50000 neurons per mm3 in the cortex [48] . While our two model populations most directly resemble a pair of excitatory and inhibitory populations in cortical L2/3 , we show in subsection “Dependency of the LFP signal on dendritic morphology” that our results also pertain to the LFP generated by neuron morphologies found in other cortical layers . Given these geometrical constraints , we created the multi-compartmental cell models in the 3D network in the following way: soma locations for all cells were homogeneously distributed within the lower cylinder ( Fig 1B ) . Next , we placed artificial straight axons that were distributed at random cortical depths and random orientations within both cylinders . They served as targets in an algorithmic generation of dendrites , through which pyramidal cell dendrites were connected to all axons within a specified reach distance while optimizing the following wiring conditions: short conductions times , short total cable length and synaptic democracy ( i . e . , equal impact of synaptic inputs at the site of dendritic integration [49 , 50] ) . This procedure has been shown previously to reproduce pyramidal-cell-like dendrites [51] . The number of axons and their length were set so that the resulting cell morphologies matched the membrane surface distribution of real cortical layer 2/3 pyramidal cell reconstructions [52] within the constraints of the simplified columnar arrangement that was chosen for this study . This procedure also provided good matches for total cable lengths and number of branch points ( compare membrane surface distribution in S1 Fig and see Methods for more details ) . Note that the virtual axons used for the generation of the morphologies were subsequently discarded . Since the membrane area ( and consequently the transmembrane current ) of the axons is very small compared to the dendrites , we expected them to have a negligible contribution to the present 3D network LFP generation . Stellate cell dendrites were generated in a similar manner , but were only connected to axons in the lower cylinder . This resulted in stellate cell morphologies with realistic bush-like dendrites . Fig 1C illustrates the overall structure of the resulting 3D network . To further validate the simulation results obtained with these morphologies , we also built an alternative 3D network with anatomically reconstructed morphologies ( see Methods ) and checked that the results were essentially the same as for algorithmically grown morphologies ( see subsection “Performance of LFP proxies in different dynamic network states” ) . Finally , AMPA synapses were homogeneously distributed over the whole neuronal surface while GABA synapses were located only in the lower cylinder , closer to the soma ( Fig 1B , see Methods for details ) . Alternative synaptic distributions are explored in the “Dependency of the LFP signal on the distribution of synapses” subsection . For each neuron in the LIF network we randomly assigned a multi-compartmental neuron model with a unique dendritic structure in the 3D network . The connectivity of the LIF network determined which postsynaptic spikes in the LIF network simulation should serve as input spikes for each multi-compartment neuron . We then used these spike times together with the external input ( see above ) to activate synaptic currents in the 3D network ( see Methods ) . In this way we assured that the synaptic input in a multi-compartment neuron was identical to its LIF neuron counterpart . The synaptic dynamics in the 3D network was identical to that in the LIF network . In a subsequent step , we took into account all transmembrane currents in the neurons of the 3D network to compute the LFP by means of well-established volume conduction theory and the so called line-source method [31 , 34] ( see Methods ) . Fig 1 shows a half-second excerpt of results for an example simulation using the spiking activity generated by the LIF network ( Fig 1D ) in response to a 1 . 5 spikes/ms stimulus ( see Methods for details ) to calculate the corresponding LFP signal along the vertical axis of the cylinder at different electrode depths from the 3D network ( Fig 1E ) . The temporal fluctuations of the LIF signal were strongly correlated across depth , albeit with a sign shift around the depth just between the two cylinders ( which we from now on will refer to as the inversion point ) . The sign of the baseline ( DC ) LFP was negative above the inversion point while it was positive below it . This reflects that the LFP was dominated by the perisomatic inhibitory synapses generating a net source current close to the soma and sink return currents in the apical branches . The excitatory synapses contributed less due to their homogeneous distribution ( Fig 1B ) , giving only a weak current dipole [29] , as will be discussed in more detail in the next sections . We defined the amplitude of LFP fluctuations at each depth as the standard deviation of the signal over time , and further assigned it the same sign as the LFP baseline , i . e . , negative/positive above/below the inversion point . The magnitude of the LFP amplitude was largest around the middle of each cylinder ( Fig 2A ) , decreased steeply close to the inversion point and more smoothly beyond the vertical boundaries of the network . The decrease of the amplitude of the LFP fluctuations when the electrode was moved away from the center of the 3D network is shown in Fig 2B for all depths . This decrease in LFP power was consistent with results of [20]: inside the 3D network ( X/R < 1 , where X is the displacement of the electrode from the center and R is the radius of the cylinder ) differences were small , but when the electrode was placed outside the 3D network ( X/R > 1 ) the decrease was steep . Note that the region around the inversion point where the potential is very small , broadened with the distance from the center . We observed that all power spectra recorded outside this noise-dominated region had similar shapes ( Fig 2C and 2D ) , suggesting that LFP fluctuations could be roughly approximated by the same time series rescaled by the numerical value of the LFP amplitude shown in Fig 2B . The observation that LFPs recorded in different spatial positions had similar temporal behavior and differed mainly by a scaling factor , suggested that a single LFP proxy could work for recordings at different depths and positions in the horizontal plane , provided that it is properly scaled . Such a factorization of spatial and temporal dimensions can be expressed ( see [30] ) as LFPproxy ( r , d , t ) =fproxy ( r , d ) *gproxy ( t ) ( 1 ) where d is the depth and r the distance from the population center . The term fproxy ( r , d ) then gives the amplitude of the signal as a function of the electrode position ( as in Fig 2B ) while the dimensionless gproxy ( t ) has variance equal to one and describes the temporal features of the LFP signal . We first focused on finding the optimal gproxy ( t ) for an LFP signal recorded at selected depths along the central vertical axis ( X/R = 0 ) of the 3D network . However , we found ( see subsection “New class of LFP proxies” ) that the identified optimal LFP proxy was applicable also to other depths and radial distances of the populations ( given an appropriate overall scaling of the signal amplitudes , cf . Fig 2B ) . The contribution to the LFP signal from synaptic inputs onto the interneurons ( and their associated return currents ) was negligible both in amplitude ( Fig 3A ) and in determining the LFP spectrum ( Fig 3B ) . This was due to the different morphologies of the two types of neurons: consistently with what was shown previously for stellate cells with symmetrically placed synapses [20] ( i . e . , a so-called close-field arrangement [53] ) , the contribution from the interneurons to the LFP was negligible ( Fig 3 ) . Further , the associated power spectrum of this contribution was closer to colored noise and did not display gamma fluctuations . We investigate this in detail in the subsection “Dependency of the LFP signal on dendritic morphology” . Since we obtained a very similar LFP when we only simulated the contribution from synaptic inputs onto the pyramidal neurons , all the results shown in the following will , unless otherwise stated , consider only the contributions from pyramidal neurons to LFP . Likewise , the LFP proxies will be based only on input onto excitatory neurons ( as done previously [15] ) . However , the inhibitory neurons obviously play a key role ( i ) in generating the dynamics and ( ii ) in providing the GABA currents of synapses onto pyramidal neurons that contribute strongly to the LFP . We first tested six LFP proxy candidates ( Fig 4A ) : AMPA currents , GABA currents , the average firing rate FR , the average membrane potential Vm , and the sum of these synaptic currents ∑I as well as their absolute values ∑|I| . Note that the "AMPA currents" and "GABA currents" proxies are defined as the sum of the post-synaptic currents for each type of synapse over all pyramidal neurons ( see Table 1G ) . These currents have depolarizing and hyperpolarizing effects , respectively , on the postsynaptic neurons . We thus here use the convention that assigns a positive sign to AMPA currents and a negative sign to GABA currents . Because of the opposite signs assigned to the AMPA and GABA currents , the sum of the absolute values of the currents ∑|I| is equivalent to the difference between the currents . For several reasons , i . e . , synaptic delay and dendritic filtering , we expected the best proxy for the LFP time course to possibly involve time-delayed measures of LIF network variables . To assess the best values of these delays we first computed the cross-correlation function between the ground-truth LFP and the considered LFP proxy obtained from the LIF network , and found the delay at which the absolute value of the correlation was largest ( for half of the recording depths the correlation is negative due to LFP inversion ) . The LFP proxy that we chose was the z-scored ( i . e . , baseline-subtracted and normalized to have variance equal to one ) and time-shifted LIF network variable that maximized the fraction of variance explained , R2 . Finding the best delay and rescaling factor was done separately for each depth , but we found that the differences in the observed best values of the delay across depth , were minor ( see S2B Fig ) . Fig 4B and 4C shows the comparison between the 3D network LFP signal at two different electrode depths and the LFP proxy given by the sum of absolute values of the synaptic currents ∑|I| , that given our sign convention simply becomes the difference between the currents , i . e . , LFP∑|I| ( r , d , t ) =f∑|I| ( r , d ) *Norm[∑pyrAMPA ( t−τ ) −∑pyrGABA ( t−τ ) ] ( 2 ) where Norm[] indicates the mean-subtracted , normalized version of the time series between square brackets . Fig 4D and 4E shows the cross correlation between the 3D network LFP signal and proxy for the two depths . A comparison of the average fraction of variance explained by all the LFP proxies displayed in Fig 4A across different depths ( Fig 4F and 4G ) shows that the best one was the sum of absolute values of synaptic currents ∑|I| ( <R2> = 0 . 83 ) followed by the negative of the GABA currents ( <R2> = 0 . 81 ) and then the AMPA currents ( <R2> = 0 . 78 ) . The negative of the sum of synaptic currents ∑I and membrane potential Vm performed in a similar way ( <R2> = 0 . 69 ) , while the firing rate FR gave a poor fit ( <R2> = 0 . 51 ) . The R2 is slightly larger for depths about 100 μm from the inversion point , probably due to stronger synaptic and return currents . We found two results to be of particular interest . The first was that a proxy based on GABA currents alone gave clearly a better match for the simulated LFP signal than the AMPA currents alone . The second was that the ∑|I| gives the best fit which suggests that the magnitude of the AMPA currents locally sums with the magnitude of the GABA return currents . Thus the two types of synaptic currents contribute to the LFP with the same sign . This feature is partly due to the fact that AMPA synapses are distributed over the whole surface of pyramidal neurons , while GABA synapses are located only in the lower cylinder close to the soma ( Fig 1B ) . This will be further investigated in the “Dependency of the LFP signal on the distribution of synapses” subsection . The fits above were computed by averaging the time-varying variables over the set of excitatory neurons in the LIF network . However , we also tested the quality of the fit obtained by averaging over all the neurons in the LIF network or only over inhibitory neurons . The results for each variable and depth are shown in S2A Fig , together with the associated optimal delays . The relative ranking of the candidate proxies remains unaltered . Further , proxies obtained by averaging the firing rate , the membrane potential , or the synaptic input currents over the excitatory neurons ( as above ) performed better than proxies obtained by averaging the same variables over the inhibitory neurons set and roughly the same as proxies obtained averaging over all neurons ( S2A Fig ) . Since AMPA and GABA currents contributed differently to the LFP signal we investigated a novel proxy , the weighted sum between AMPA and GABA currents ( WS ) , that uses a linear combination of AMPA and GABA synaptic currents where we introduce a factor α describing the relative contribution of the two currents and a specific delay for each type of current: LFPWS ( r , d , t ) =fWS ( r , d ) *Norm[∑pyrAMPA ( t−τAMPA ) −α ( ∑pyrGABA ( t−τGABA ) ) ] ( 3 ) Note that the two proxies ∑|I| and ∑I are particular cases of the above equation in which the delays are the same , and α is equal to 1 and -1 respectively . We first tested the WS proxy with the electrode located in the center of the 3D network for different depths . The optimal value of α was always positive , but varied across depths ( Fig 5A ) . The optimal delays were always in the range [5–7] ms for τAMPA ms and in the range [-1 1] ms for τGABA . This implies that the optimal LFP proxy was achieved by subtracting the GABA PSCs ( postsynaptic currents ) from the AMPA PSCs occurring around 6 ms in the past . Performance was very high for all depths ( up to 93% of variance explained , see Fig 5B ) . Since the optimal values of α , ( Fig 5A ) τAMPA and τGABA ( S2B Fig ) were relatively stable across depths , we defined a new proxy: the reference weighted sum LFP proxy ( RWS ) . The structure of the RWS proxy is the same as the WS proxy but the variables are fixed: α is set to the average accross depths of the optimal values for WS ( 1 . 65 , see Fig 5A ) and the delays to τAMPA = 6 ms and τGABA = 0 ( S2C Fig ) . This results in LFPRWS ( r , d , t ) =fRWS ( r , d ) *Norm[∑pyrAMPA ( t−6ms ) −1 . 65 ( ∑pyrGABA ( t ) ) ] ( 4 ) We found that the performance of this proxy was almost indistinguishable from the single-depth optimized values across depths ( Fig 5B ) and largely outperformed all other proxies . Moreover , we found the performance of a proxy with α = 1 . 65 to be very good ( >80% of variance explained ) for a broad range of other AMPA- and GABA-current delays ( S2C Fig ) . We next tested the performance of the proxies for different distances of the electrode from the center of the 3D network: Fig 5C compares the fraction of variance explained by WS , RWS and the other proxies mentioned above for LFPs measured at different distances from the center of the 3D network . The depicted results are found from averaging across all depths . The Standard Error of the Mean of R2 across depths was <1% for all proxies and all lateral displacements and is not displayed in the figure since it would not be visible . Values for explained variance were very stable for different lateral electrode positions: in particular , for all lateral displacements RWS performances were similar to WS and outperformed all other proxies ( Fig 5C ) . The average optimal value of α across depths was always close to the reference value 1 . 65 ( Fig 5D ) . Given that the RWS proxy was much simpler than WS ( see below ) and able to explain more than 90% of the variance of the LFP time course at a wide range of electrode recording positions , we tentatively propose this as the best proxy for the LFP signal computable directly from LIF network variables . The proxies given by the combination of two synaptic parameters ( WS and RWS ) have four free parameters ( scale as described by the function f in Eq 1 and following , AMPA and GABA delays , relative amplitude of AMPA and GABA contribution ) while the other proxies have only two free parameters ( scale , delay ) . We assessed by means of the Bayesian Information Criterion ( BIC , [54] , see Methods for details ) whether the benefit in terms of improved performance of the models based on the linear combinations of synaptic currents was worth the increase in model complexity due to the higher number of parameters . We found that , according to this model selection criterion , RWS outperforms all previous proxies and WS outperforms RWS and all other proxies ( S3 Fig ) , demonstrating the power of the RWS and WS models . However , the optimal WS parameters are by construction different for each recording position and , as we will see in the following , for various network structures and states . Thus comparison of LFP predictions from use of the WS proxy requires detailed knowledge about recording position as well as the characteristics of the underlying network , and will thus have limited practical use . On the other hand , as the parameters of the RWS proxy are fixed , it can be used directly for all locations in space . As seen in the following , the RWS proxy performs well for a broad set of conditions ( input intensity , neuron morphology , synaptic distribution ) , and this means crucially that the proxy can be used also under weak assumptions about the spatial structure of the underlying network . We thus conclude that RWS is the best LFP proxy based on LIF network variables . In the following we will test its robustness for different dynamic network states , spatial architectures and synaptic properties . So far we investigated the LFP proxies using LIF networks in a state exhibiting weakly synchronized oscillations in the spiking dynamics , stimulating the LIF network at a relatively low intensity ( 1 . 5 spikes/ms ) . However , LIF networks can generate a variety of different dynamic network states when the frequency of external inputs is varied [14 , 15 , 18] . In order to test LFP proxies in different dynamic network states , we stimulated the LIF network with a wide range of input intensities , covering both much higher and much lower intensities than the one tested in above . Fig 6A shows , from left to right , a raster plot of a subset of neurons in the LIF network for a low-intensity input ( 0 . 5 spikes/ms ) , the default input level ( 1 . 5 spikes/ms ) , and a high-intensity input ( 6 spikes/ms ) . Shown below ( Fig 6B ) is the LFP signal generated in the 3D network at the reference depth of 100 μm for these three cases together with their corresponding WS fits . For external stimulation with 0 . 5 spikes/ms , recurrent activity in the LIF network was almost absent , with all pyramidal neurons and most interneurons being silent . The LFP amplitude was very small and the signal very noisy . For an input of 1 . 5 spikes/ms , firing was sparse with coexisting slow and high-frequency LFP fluctuations , and for 6 spikes/ms the dynamics were dominated by high-frequency LFP gamma oscillations also visible in the LIF network spiking activity . With an input frequency of 0 . 5 spikes/ms , none of the candidate proxies was able to account for the LFP ( Fig 6C ) . This was presumably because in these low-firing conditions , randomly occurring , uncorrelated synaptic inputs onto the neurons close to the electrode dominated the LFP signal . Such activity does not give a strong dipolar LFP pattern [21] and is apparently more difficult to capture with the global LIF network variables considered in the proxies . For the larger inputs ranging from 1 to 6 spikes/ms , however , the WS proxy was able to explain more than 91% of the variance . RWS was able to explain 88–91% of the variance between 1 to 3 spikes/ms with a small decrease to 87% for an input of 6 spikes/ms ( Fig 6C ) . For inputs of 1 spikes/ms or more the sum of the absolute values of the synaptic currents explained 81–85% of the variance , the membrane potential 70–79% , the sum of the currents 70–79% , and the firing rate only 51–60% . Overall , the ranking of the proxies regarding their R2 values remained the same for all dynamic network states and the RWS provided an excellent proxy in all cases . As shown in Fig 6D the relative weighting between AMPA and GABA currents as given by the parameter α for the WS proxy was stable and close to the reference values 1 . 65 chosen for RWS for input stimulus intensities , except for the case of very low-intensity input in which the LFP signal is almost absent and the fit is poor . A key property of the LIF network is that it exhibits a prominent gamma-band activity ( 30–100 Hz ) in the overall firing activity when the input intensity is increased as indicated by an increased peak in the power spectral density ( PSD ) [15] . We therefore investigated how this is reflected in our simulated LFP signal and how well the LFP proxies capture these properties of the LFP signal . Fig 7A shows the power spectra for three different input frequencies . All proxies except for the membrane potential tended to underestimate the low frequency LFP fluctuations and to overestimate frequencies in the gamma range . WS and RWS proxies both produced a nearly perfect fit of the LFP spectrum in the gamma-band range while exhibiting the smallest error in the low frequency components among all proxies . In the 1–3 spikes/ms input range the modulation of the LFP gamma power was well approximated by all proxies , while for 6 spikes/ms input , WS and RWS underestimated it ( Fig 7B ) . All proxies essentially predicted the correct peak LFP gamma frequency ( Fig 7C ) for all input levels above 1 spikes/ms . We hypothesized that the negligible contribution of inhibitory neurons was due to the weak dipole moment created by the symmetrically placed synapses on the dendrites of stellate cells [29] . To test this hypothesis we investigated in the following the effect of neuron shape on the LFP generation by systematically altering the morphology of the interneuron population while keeping its inputs fixed . This manipulation also tested the robustness of the LFP proxies to the specific choice of the neuronal morphology . We started with two overlapping cylinders ( distance = 0 μm ) describing the stellate cell morphology . Then we progressively increased their “pyramidalness” , i . e . , the distance between the two dendritic bushes and generated a new interneuron population for each cylinder distance ( Fig 8A; see Methods for details ) . The generated morphologies ranged from pure stellate cells ( the interneuron used in the reference case ) , to cells corresponding to layer 2/3 pyramidal cells where the two cylinders were juxtaposed ( the pyramidal neuron used in the reference case ) , to cells where the two areas were parted by several hundred micrometers ( as in layer 5 pyramidal neurons ) . In all cases GABA synapses were distributed only on dendrites located inside the lower cylinder , while AMPA synapses were distributed over the entire dendritic tree ( Fig 1B ) . We found that the LFP signal from the 1000 interneurons was very weak for cylinder distances less than about 100 μm , corresponding to a 40% overlap between the two cylinders ( see Fig 8B and 8C ) . The amplitude of the LFP signal increased with the cylinder distance together with the current dipole moment ( Fig 8C and 8D; see Methods ) . The “transition distance” of about 100 μm is seen to be associated with the appearance of an inversion point in the LFP ( Fig 8C ) and with the establishment of a sizable dipole moment ( Fig 8D ) . Above this transition distance the LFP became larger with larger cylinder separations , yet saturating somewhat for distances above about 250 μm , corresponding to our reference model of layer 2/3 pyramidal cell . This demonstrates that the lack of a sizable contribution to the overall LFP from our interneurons in the reference model was due to their stellate morphologies . Below the inter-cylinder transition distance all proxies performed poorly with average fraction of variance explained across depths smaller than 70% ( for 100 μm the range was <R2> between 0 . 37 and 0 . 64 ) , but <R2> quickly saturated as soon as the dipole appeared ( Fig 8E ) . <R2> was smaller for all proxies compared to the reference case ( since the noise was larger due to the smaller number of neurons , i . e . , 1000 neurons versus 4000 neurons for the reference case ) , but the ranking of performances for different proxies remained roughly the same: above the transition distance the fraction of variance explained by WS was 83% , RWS and the sum of absolute values of currents both explained 80% , the membrane potential and sum of synaptic currents 59% , while firing rate explained only 47% of the variance . Note that for inter-cylinder distances above the transition distance , the stable performance of the proxies were accompanied by stable values of the optimal coefficient α ( Fig 8F ) . This result implies that the RWS we have found for populations of layer-2/3-like pyramidal cells , likely also can be applied to pyramidal cell populations with different morphologies , as long as they produce a dipolar LFP . In order to verify that the assumptions we made to algorithmically construct the neuronal morphologies in the 3D network did not bias the results , we also did simulations using realistic morphologies obtained from anatomical reconstructions ( see Methods ) . The spatiotemporal dynamics of these LFP signals was found to be qualitatively very similar to the one previously shown , and the agreement with proxies was even higher , with RWS reaching R2 = 0 . 95 ( S4 Fig ) . This result indicates that our conclusions are not strongly dependent on the detailed branching patterns within the basal and apical dendritic bushes . In the reference case ( Fig 1B ) GABA synapses were distributed only in the lower cylinder while AMPA synapses were distributed homogeneously across all dendrites . In order to test how our results depended on this distribution we therefore evaluated all LFP proxies for a variety of synaptic distribution patterns . Fig 9A illustrates the three main different synaptic distributions tested: ( 1 ) a case where all synapses were distributed homogeneously , ( Hom . ) ( 2 ) the reference case ( Ref . ) , and ( 3 ) a case where AMPA synapses were located only in the upper cylinder ( AM Up ) , leading to a complete separation between AMPA and GABA synapses . We further considered two conditions where ( 4 ) AMPA synapses were located only in the lower bush leaving the upper bush empty ( AM down ) and where ( 5 ) AMPA cortical synapses were located in the upper bush while thalamic AMPA inputs were distributed homogeneously ( AMr Up ) . Even though the parameters in the LIF network and thus the output activity remained precisely the same as before in these different situations , the corresponding LFP signal was dramatically altered by the choices of synaptic distributions ( Fig 9B ) . The amplitude of the fluctuations was strongly affected , while the spatiotemporal features were only moderately altered . Note , however , that the position of the thalamic synapses only marginally affected the LFP fluctuations , and only the mean value of the LFP was affected . As a rule of thumb , we found that the more spatially segregated AMPA and GABA synapses are , the larger are the LFP fluctuations ( Fig 9C ) . We further observed that the variation of the LFP amplitude on the synaptic distribution directly reflected changes in the magnitude of the current dipole moment ( Fig 9D ) . The individual contributions to the LFP from AMPA and GABA synapses were strongly dependent on the spatial distributions ( Fig 9E ) : when synapses were distributed homogeneously , the contribution of their currents to the LFP signal was small as compared to when the synapses were segregated . Moreover , the AMPA contribution was larger when synapses were confined to the upper than to the lower cylinder . When the synapses were distributed homogeneously , the LFP signal was very weak resulting in poor performances for all LFP proxies ( Fig 9F ) . When the cortical AMPA synapses were confined to the upper bush , the performance of the WS proxy was not affected , but a small decrease of 0 . 07 in the <R2> value was observed for both RWS and the sum of the absolute values of synaptic currents . For the same situation there was a larger decrease of 0 . 17 in the <R2> value to a global value of only 0 . 51 for both the membrane potential and the sum of synaptic currents . However , in the configuration in which AMPA synapses were confined to the lower bush and the LFP amplitude was small , the <R2> for the membrane potential and the sum of synaptic currents rose to 0 . 81 and 0 . 79 respectively , a value comparable to results for the WS and RWS proxies ( 0 . 80 and 0 . 78 ) . This suggests that the advantage of using the WS and RWS proxies over , e . g . , a membrane-potential proxy is particularly large when the AMPA and GABA synapses are spatially separated so that a large current dipole moment and a large amplitude LFP is generated ( Figs 8C and 9D ) . The best coefficients for WS strongly depended on the synaptic distribution ( Fig 9G ) : When AMPA synapses were confined to the upper cylinder forming a strong current dipole moment , the optimal AMPA coefficients became larger than the GABA ones . Therefore , although the R2 value of RWS was still 0 . 82 under these conditions , a better result could be achieved with a proper tuning of the coefficients . To keep the consistency with the LIF network in which the synapses were current-based ( see Methods ) , all LFP simulations considered until now were done using current-based synapses in the 3D network . However , some neuronal features may be better approximated by conductance-based synaptic models in which the postsynaptic currents ( PSCs ) depend on the local membrane potential and do not have a fixed shape as in the case of current-based synapses . To test this situation , we repeated our simulations by introducing conductance-based synapses in the 3D network . Synaptic time constants were left unchanged , while the peak conductance values were scaled to obtain PSC amplitudes equivalent to current-based synapses for the reference stimulus intensity 1 . 5 spikes/ms [47] . While the simulated LFP amplitude was smaller when using conductance-based instead of current-based synapses ( compare the three panels in Fig 10A with the three panels in Fig 6B and note the different y-axis scales ) , the time course was similar . We found that the explanatory power of the proxies was similar or better in all cases compared to the situation with LFPs computed with current-based synapses ( Fig 10A ) : the R2 values for the RWS were in the range 0 . 91–0 . 93 for inputs between 1 and 3 spikes/ms , and 0 . 88 for 6 spikes/ms . We hypothesize that the main reason for the increase in performance was that the LFP contributions from different neurons were more correlated when synapses were conductance-based [47] . Note that in the case with conductance-based synapses , the performance of the membrane potential proxy is in the very low 0 . 5–0 . 6 range for R2 for all stimuli above 1 spikes/ms . This can be understood given that the membrane potential no longer depends linearly on synaptic input currents as in the case with current-based synapses . The WS proxy coefficients for 1 spikes/ms inputs were rather similar to the current-based case , but when the input frequency was increased , the optimized value of the coefficient α , describing the ration of GABA to AMPA currents in the WS proxy , increased ( Fig 10C ) . This likely reflects that for stronger stimuli the neurons were more depolarized , so that the average membrane potential was closer to the AMPA reversal potential and further away from GABA reversal potential . Consequently , the GABA versus AMPA PSC-amplitude ratio increased . Nevertheless , the RWS still performed well for all inputs ( Fig 10B ) . A major difference between the accurate LFP proxies using synaptic currents ( sum of currents , WS , RWS ) compared to the less accurate proxy based on firing rates is that a spike is a very local event in time , while the postsynaptic current following after a spike ( as well as the contribution to the LFP ) lasts for many milliseconds . So an instantaneous firing rate proxy like the ones we are considering based on firing rates cannot be expected to perform well ( even with a fixed delay ) . In laminar population analysis ( LPA , [30] ) the LFP time course was rather assumed to be given by the measured firing rates convolved with a suitable ( i . e . , delayed exponential ) kernel , the rationale being that spikes causally drive synaptic currents which in turn set up the LFP . The present RWS proxy is similarly constructed , effectively corresponding to a suitable weighted sum of exponentially convolved presynaptic spike rates corresponding to excitatory and inhibitory synaptic currents . The postsynaptic soma membrane potentials following presynaptic spiking is more low-pass filtered than the synaptic currents ( and also the transmembrane return currents in the case of multicompartmental models ) [29] , and LFP proxies based on this dynamical variable will generally fail to predict the most rapid temporal changes in the LFP . An interesting result is that all the proxies tested here displayed largely the same modulation of the LFP gamma power as a function of input intensity , both in terms of relative power modulation and peak frequency ( Fig 7D and 7E ) . This is encouraging since we did not specifically aim to find a good prediction of the power spectrum when constructing the LFP proxies and estimating their parameters . We note however that no proxy is fully able to account for the low-frequency end of the spectrum ( Fig 7A and 7B ) , which is overestimated by the membrane potential proxy and underestimated by the other proxies . If one is interested in a highly detailed reproduction of the whole LFP spectrum , preliminary results hint to the possibility of designing a WS fit optimized to match the spectrum instead than the spatiotemporal features and to define an LFP proxy that slightly differs from the RWS discussed above . However , the fraction of spectral variance explained by the RWS is already 0 . 91 ( average over all stimulus intensities above 0 . 5 spikes/ms , standard morphology and synaptic condition ) which likely is sufficient for most purposes . In the present work we have focused on how the relationship between LIF variables and ground-truth LFP change when the 3D model features change , keeping the LIF model fixed . While different LIF networks would generate different activity and hence different synaptic currents , we expect roughly the same relationship between these synaptic currents and the generated LFP . Therefore , for any LIF network generating enough correlated activity to result in a sizeable LFP , we expect RWS to be a good proxy . Our strategy had the advantage that we could vary the assumptions in the LFP-generating model , e . g . , the distribution of synapses or neuronal morphologies , without affecting the spiking dynamics . The disadvantage of this approach is , however , that the 3D network does not match the LIF network in every respect; for instance , even though the synaptic input currents were identical in the two models , the resulting soma potentials in the multi-compartmental neurons were not identical to those in the LIF neurons ( due to passive dendritic filtering ) . It is , however , unlikely that imposing identical somatic potentials , or identical currents entering the soma , in the two models would result in a more realistic LFP since large synaptic currents would be needed to overcome the passive filtering for distant synapses . Instead one could consider changing the synaptic weight distribution in the LIF network simulation to make the two models match better . Our focus here was to use LIF models as commonly used in the literature ( typically using homogeneous weight distributions ) , but it would be an interesting topic for future studies to extract effective point-neuron synaptic weight distributions from the multi-compartmental population and use these in the LIF network simulations in order to make the two simulation environments even more similar . We did not test different LIF network architectures or sizes , but we expect the RWS proxy to be applicable as long as the network displays a sufficient level of correlation . We have found in previous modeling studies [20 , 21] that correlated synaptic activity is necessary to create a sizable LFP signal , and in this case all cells in the dominant LFP-generating population will contribute . Making the network size larger or altering its connectivity would therefore likely not qualitatively change the form of the best LFP proxy ( as long as a sufficient level of spiking correlations is maintained in the network ) . The LFP generated by larger populations , however , should be tested in further studies taking into account the summed effect of several cortical populations , across layers as well as heterogeneous spatial structure in the horizontal direction . A limitation of the presented simulation setup is that it models only AMPA and GABA synapse contributions . However , most of our results pertaining to the proxy do not depend on the particular feature of the synapses and are therefore likely to extend to different synapses as well . For instance , it should not matter for the quality of our suggested proxy whether or not the synaptic weights are changing due to plasticity since the weight changes will be reflected in the synaptic currents extracted from the LIF network as well . Including slower synapses , such as NMDA synapses in the model setup , will on the other hand affect the LFP frequency content , particularly at low frequencies . This effect could be captured by a proxy including NMDA in the sum of synaptic currents with a weight depending on the number and spatial distribution of NMDA synapses . As with the synaptic weight distributions discussed above , the inclusion of NMDA synapses when computing the LFP proxy presupposes that it is also included in the LIF network model ( which was beyond the scope of this study ) . Moreover , we did not model subthreshold active dendritic conductance [55] , nor the active channels behind spike generation . The contributions from the latter is expectedly negligible for at least the low frequencies of the LFP [56] ( but see [57–59] ) , while the effect of the former should be explored in future projects . The present suggested proxy assumes the LFP contribution following spikes to be spatiotemporally separable , i . e . , factorizable into a product of a function of time with a function of space [30] . Due to , for example , the intrinsic filtering effect [29 , 36 , 60] this is not strictly true as the spatial distribution of the transmembrane currents setting up the LFP depends to some extent on the frequency . However , if warranted the present proxy can be extended , for example by assuming a more detailed proxy consisting of a sum of such spatiotemporally separable kernels . Recently , we presented an analytical method to estimate the LFP spectrum from the dynamics of a LIF network [61] using as LFP proxy the sum of the absolute values of synaptic currents . By fitting a recurrent excitatory-inhibitory LIF network model to LFP recordings from monkeys presented with visual stimuli , we were able to estimate the LIF model that best fitted the observed LFP , and to predict at least in part the observed firing rate and some of the visual features in the receptive field that elicited the observed neural activity . In this recent work [61] , the time evolution of the LFP was computed analytically from the LIF network as a function of the external input by applying linear response theory to the mean-field approximations of each kind of synaptic current separately and then summing their absolute values over pyramidal neurons ( as in [15] and in Eq 2 ) . In principle , it is possible to extend this analytical calculation by using the more efficient proxy presented here by simply changing the coefficients in the final sum of the synaptic currents . This paves the work for obtaining realistic analytical estimations of LFPs from recurrent LIF networks . As discussed in [35] , an efficient analytical approach could be at the heart of the development of model-based analysis methods for performing inferential statistics of network models on LFPs , analogous to the role played by Dynamic Causal Modelling [62 , 63] in the analysis of EEG and fMRI recordings . Here we studied proxies for the LFP produced by a local 3D network , corresponding to a single cortical layer . Experimentally recorded LFPs , however , are most likely containing contributions from several layers [20] . Therefore , a natural extension of this work would be to study the LFP generated by several connected 3D networks forming a full cortical columns [64 , 65] and determine how LFP proxies should be designed in this context . Since electrical potentials in the nervous tissue are assumed to add linearly , we expect LFP proxies to be constructed in largely the same manner as presented here , by summing synaptic contributions from different cortical layers , possibly with a weighting depending on the recording depths . Constructing the LFP signal from a full cortical column model [65] is the topic for a separate ongoing project [66] . We expect our proxy to also work well for other brain structures where pyramidal neurons are elongated and arranged in an almost parallel way , such as the CA1 and CA2 regions of the hippocampus . On the other hand , many subcortical structures have a neuronal architecture so different from the cortex that we that we cannot a priori expect the present rules of LFP prediction to be applicable . A possible future line of research will be to apply the combination of LIF dynamics and 3D morphology we used in this work to investigate such areas to find a compact way to study the mechanisms generating the LFP observed there . We focused in the present study on the LFP signal , but finding good models for relating activity in spiking network models and experimentally measured signals is relevant also for other types of commonly recorded signals such as the EEG , MEG and VSD . Since the biophysical mechanisms generating these signals are in principle known , we believe our framework could be extended to also study other measurement modalities in the future . We summarize here the structure of the LIF network that generated the spiking dynamics . We refer to [15 , 46] for full details . The network was composed of LIF neurons with current-based synapses whose time evolution was modeled as difference between exponentials ( see below ) , fixed threshold , fixed refractory time [67] , and fixed conduction delay of 1 ms . Subthreshold dynamics for each neuron were given by τmV˙m ( t ) =−Vm ( t ) +∑PSCsyn ( t ) ( 5 ) where τm corresponded to the membrane time constant due to the leak , Vm was the membrane potential , and PSC were the occurring synaptic events as a function of time t . When the membrane potential Vm crossed a threshold value of 18 mV above resting potential , a spike occurred , the potential dropped to a reset value of 11 mV above the reset potential and no spike could be emitted for a refractory time of 2 ms . Post-synaptic currents ( PSCs ) were determined by the spikes emitted by the pre-synaptic neurons in the LIF network as well as by the external inputs . The time course of PSCs was described by the difference of two exponentials simulating the opening and closing process of the synapse . The equation can be written with two first order differential equations introducing the auxiliary variable x: τdsynPSC˙ ( t ) =−PSC ( t ) +x ( t ) ( 6 ) τrsynx˙ ( t ) =−x ( t ) +τm ( Jsyn∑synδ ( t−tsyn−τl ) ) ( 7 ) where τr/dsyn indicate the rise and decay times of the synapses , and Jsyn indicates the synaptic strength . The latency time of the synapses τl was set to 1 ms . Compound synaptic currents were the linear sum of contributions induced by single pre-synaptic spikes occurring at time tsyn . We included two types of synapses: AMPA and GABA . Pyramidal neurons had AMPA-like synapses , and interneurons had GABA-like synapses . Moreover , each neuron received excitatory external drive from ( 1 ) a long range cortico-cortical input activating AMPA synapses identical to those of the recurrent connections and ( 2 ) a thalamic input activating AMPA synapses with timescales and strengths resembling those of thalamocortical synapses . Synaptic parameters such as rise time , decay time , and amplitude depended on the type of synapse and the category of the post-synaptic neuron . All simulation parameters were in the range of the values reported in the literature [68–70] and are listed in Table 2 . We verified that modifying these values did not affect the results qualitatively [15 , 46] . The default network was composed of 4000 pyramidal neurons and 1000 interneurons ( Fig 1A ) . The LIF network connectivity was random and sparse , with a directed connection probability of 0 . 2 between any pair of cells . This resulted in an inhomogeneous connectivity with an average of 1000 pre- and post- synaptic connections for each cell . Each neuron received inhibitory and excitatory inputs from the neurons in the network , and also cortico-cortical and thalamic excitatory drives as described above . The long-range cortico-cortical drive represented the ongoing activity and the global contributions from other areas of cortex . Since ongoing cortical activity has most power for slow frequencies , this external drive was generated by an Ornstein-Uhlenbeck process with a low pass cut-off frequency of 10 Hz and a 0 . 25 mV standard deviation . Thalamic inputs were time-invariant in this set of simulations . Synapses carrying both types of external inputs were activated by random Poisson spike trains , with time-varying rates identical for all neurons . Details can be found in Table 1 and 2 . Simulations were computed with time steps of 0 . 05 ms and lasted 10 . 1 seconds , with the first 100 ms removed to limit the analysis to the network steady state . Current based and conductance based LIF model source codes are identical to those used in [47] and are already available on the ModelDB sharing repository ( http://senselab . med . yale . edu/ModelDB/ShowModel . asp ? model=152539 ) with accession number 152539 . In order to compute the transmembrane currents that lead to an LFP signal , we constructed 3D morphological neuron models that captured the main morphological features of the cortical network described by point neurons in the LIF model . The algorithm used to construct the model morphologies was based on the fact that dendrites connect to their presynaptic partners in a manner minimizing their total length and conduction times from all synapses to the soma [71] . In such a framework , pyramidal cell dendrites can be seen as tree structures connecting as directly as possible to axons that are distributed in two distinct layers [51] . The generation of synthetic trees and subsequent analysis were performed using the TREES toolbox [71 , 72] [http://www . treestoolbox . org] . Two cylinders ( 250 μm radius and 250 μm height each ) were therefore stacked to form a cylindrical column ( Fig 1B ) . Somata of all cells were homogeneously distributed in the lower cylinder for both cell types . Axons were distributed isotropically within planes perpendicular to the cortical depth at random depth values . Pyramidal cells were connected first to the axons in the upper cylinder and then to the axons of the lower cylinder , this resulted in characteristic apical and basal dendritic trees . Stellate cells were only connected to the axons in the lower cylinder . Using 160 axons in each layer and a maximal reach distance of 150 μm for any dendrite to an input axon , resulted in realistic membrane surfaces , cable lengths and branch point number distributions ( see S1 Fig ) . Diameter taper was selected to equalize synaptic democracy [73] and yielded good fits to the real counterparts with similar parameters for interneurons and pyramidal cells . The resulting pyramidal cell somatic input resistance was about 200 MΩ with specific membrane resistances Rm = 20000 Ωcm2 and axial resistances Ra = 150 Ωcm . The stellate cell input resistance was 175 MΩ with specific membrane resistances Rm = 10000 Ωcm2 and axial resistances Ra = 150 Ωcm . In order to spatially embed the simplified LIF network model , 4000 such pyramidal cells and 1000 interneurons were generated to populate the simplified columnar architecture . The resulting morphologies were then exported to NEURON [74 , 75] using the TREES toolbox functions . In order to continuously alter the “pyramidalness” of cortical neurons as in Fig 8 we simply modulated the distance between the two cylinders corresponding to the two layers . With a distance of 0 μm , a perfect overlap of both cylinders , the resulting shape was symmetric as for the stellate cell . As the distance was increased between the two cylinders , the shape of the cortical cell traversed the shape of layer 2/3 pyramidal cells ( distance of 250 μm ) , layer 4 pyramidal cells ( distance of 350 μm ) and layer 5 pyramidal cells ( distance larger than 500 μm ) . The corresponding validation of morphological features compared with real dendrite reconstructions as can be observed in S1 Fig . As a control for use of algorithmically constructed morphologies we derived an alternative model using multiple copies of real reconstructions distributed within the columnar arrangement . We used reconstructions from NeuroMorpho . org [76] , made available by the group of Markram [76] , of both stellate cells and layer 2/3 pyramidal cells in young rat somatosensory cortex . Since only 4 stellate cells and 36 layer 2/3 pyramidal cell morphologies were available , we reached the number of 1000 interneurons and 4000 pyramidal neurons by randomly selecting copies of the smaller sample and distributing them within the simplified columnar geometry . Cell body locations were chosen to preserve a fairly homogeneous distribution of membrane throughout the cylinders . This alternative model was then injected with the same synaptic current stimuli as the original model based on algorithmically developed morphologies , and yielded similar results ( compare Fig 6 and S4 Fig ) . Spike trains generated by the LIF network were used as input in the 3D network model used for LFP generation . Each multi-compartmental neuron model in the 3D network was associated with a given point neuron in the LIF network . To make sure the total synaptic currents in each cell were identical in the two simulation environments , we used the connectivity structure of the LIF network to determine the presynaptic LIF neurons for each postsynaptic multi-compartmental neuron in the 3D network . We triggered the synaptic currents in the multi-compartmental neurons of the 3D network at the precise times given by the spike trains generated by the presynaptic cells during LIF network simulations . Note that we did not take into account synaptic latency time . In the 3D network we associated with each presynaptic cell a single specific synapse in the postsynaptic cell . Synaptic dynamics in the 3D network was identical to the one in the LIF network ( Eqs 4 and 5 ) . In addition we recreated the external inputs ( “Thalamic” and “Cortical” , see Fig 1A ) used in the LIF network simulations and injected the same patterns of external spike trains in specific AMPA synapses in the 3D network neurons . Since the LIF neuron model used in the LIF network simulations lacked spatial structure , we needed to make additional assumptions regarding the synapse placement when simulating the multi-compartmental neurons in the 3D network . The cylinders that were used to create the morphologies of the multi-compartmental models ( see above ) were also used to broadly define the synaptic regions . Our default setting was to place GABA synapses only in the lower cylinder , while AMPA synapses were placed in both cylinders . We tested also other scenarios in the “Dependency of the LFP signal on the distribution of synapses” subsection of Results ( Fig 9 ) . We randomly chose the detailed spatial position on the dendritic structure for each synapse , with the probability for a section to be selected being proportional to its membrane area , such that the resulting synaptic density was homogeneous within the selected cylinder We calculated the model LFP signal from the transmembrane currents in the multi-compartmental neuron populations based on volume conduction theory and the line-source approximation implemented in the Python package LFPy ( http://lfpy . github . io/ ) [34] . We first simulated transmembrane currents resulting from synaptic activity using the NEURON simulation environment [74 , 75] after which extracellular potentials were calculated as a weighted sum of those transmembrane currents [31 , 32 , 34] . The extracellular potentials were computed for 32 equispaced vertically aligned points in space ( simulating a laminar multielectrode ) , set at 25 μm intervals along the central vertical axis of the 3D network cylinder ( Fig 1B ) . For the analysis illustrated in the subsection “Spatial distribution of simulated LFP signal” the recording locations were set at different distances from the vertical axis of the 3D network cylinder . To directly match the LIF network simulations , morphological neurons used current synapses in the reference case , except in the simulation discussed in the subsection “Difference between current-based and conductance-based synapses for the LFP signal” were conductance synapses were adopted ( Table 3E ) . The calculations of transmembrane currents in the morphological model were performed using passive neuron models with the parameters listed above ( Table 4 ) . Following volume conductor theory , the model neurons were assumed to be surrounded by an infinitely sized extracellular medium with conductivity assumed to be real , scalar ( the same in all directions ) and homogeneous ( the same everywhere ) with σ = 0 . 3 S/m [77] . For further discussion on these assumptions see [32] . The Python codes we used to generate LFP from artificial morphologies injected with LIF spike dynamics are available on the LFPy official site ( http://lfpy . github . io/ ) . We tested several simple models to match the LFP simulation based on the different variables describing the activity in the LIF network: firing rate , membrane potential , AMPA and GABA synaptic currents . We considered variables computed over the set of all pyramidal neurons , of all interneurons or both populations . We considered proxies based on these variables and on the simple sum or the sum of absolute values of synaptic currents as in [15 , 46] . Then we considered linear combinations of synaptic currents with different time delays . We tested the accuracy of the proxy in describing the time evolution of the LFP given by the morphological model by using the mean of squared values of the correlation coefficient R ( which is equivalent to the fraction of variance explained ) . The quality of the proxy was tested separately for each depth . We computed the cross-correlation between the simulated LFP signal and the corresponding proxy and we determined the delay as the lag of the cross-correlation peak ( see Fig 4 ) . For this delay we determined the best linear fit using the Matlab function polyfit for single regressors and the Matlab function regress for regressor combinations . We estimated the quality of the proxy as the squared correlation coefficient between the best fit and the LFP . The proxy for each depth is defined by the optimal delays and the coefficients of the different components for regressor combinations . To compare the performance of the different proxies taking into account the different number of free parameters between WS , RWS and all the other proxies , we used the Bayesian Information Criterion ( BIC , [54 , 78] ) BIC=−2l+Klogn ( 8 ) where l is the optimized loglikelihood function , K the number of estimable parameters and n the sample size . Under the assumption of Gaussian noise , −2l can be approximated as constant+nlogRSSn [79] where RSS is the sum of the residual squares , so the BIC criterion becomes BIC=nlogRSSn+Klogn ( 9 ) which is the criterion we adopted in the manuscript .
Leaky integrate-and-fire ( LIF ) networks are often used to model neural network activity . The spike trains they produce , however , cannot be directly compared to the local field potentials ( LFPs ) that are measured by low-pass filtering the potential recorded from extracellular electrodes . This is because LFPs are generated by neurons with spatial extensions , while LIF networks typically consist of point neurons . In order to still be able to approximately predict LFPs from LIF network simulations , we here explore simple proxies for computing LFPs based on standard output from LIF network simulations . Predictions from the various LFP proxies were compared with “ground-truth” LFPs computed by means of well-established volume conduction theory where synaptic currents corresponding to the LIF network simulation were injected into populations of multi-compartmental neurons with realistic morphologies . We found that a simple weighted sum of the LIF synaptic currents with a single universally applicable set of weights excellently capture the time course of the LFP signal when the LFP predominantly is generated by a single population of pyramidal cells . Our study therefore provides a simple formula by which the LFP signal can be estimated directly from the LIF network activity , providing a missing quantitative link between simple neural models and LFP measures in vivo .
[ "Abstract", "Introduction", "Results", "Discussion", "Methods" ]
[]
2015
Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models
Flagellin is a wide-spread bacterial virulence factor sensed by the membrane-bound Toll-like receptor 5 ( TLR5 ) and by the intracellular NAIP5/NLRC4 inflammasome receptor . TLR5 recognizes a conserved region within the D1 domain of flagellin , crucial for the interaction between subunits in the flagellum and for bacterial motility . While it is known that a deletion of the D0 domain of flagellin , which lines the interior of flagella , also completely abrogates activation of TLR5 , its functional role remains unknown . Using a protein fusion strategy , we propose a role for the D0 domain in the stabilization of an active dimeric signaling complex of flagellin-TLR5 at a 2:2 stoichiometric ratio . Alanine-scanning mutagenesis of flagellin revealed a previously unidentified region of flagellin , the C-terminal D0 domain , to play a crucial role in TLR5 activation . Interestingly , we show that TLR5 recognizes the same hydrophobic motif of the D0 domain of flagellin as the intracellular NAIP5/NLRC4 inflammasome receptor . Further , we show that residues within the D0 domain play a previously unrecognized role in the evasion of TLR5 recognition by Helicobacter pylori . These findings demonstrate that TLR5 is able to simultaneously sense several spatially separated sites of flagellin that are essential for its functionality , hindering bacterial evasion of immune recognition . Our findings significantly contribute to the understanding of the mechanism of TLR5 activation , which plays an important role in host defense against several pathogens , but also in several diseases , such as Crohn’s disease , cystic fibrosis and rheumatoid arthritis . Toll-like receptors ( TLRs ) belong to a family of germ-line encoded innate immune receptors able to sense pathogen-associated molecular patterns ( PAMPs ) [1] . Upon ligand binding , TLRs dimerize , recruiting adaptor molecules that bind to the intracellular TIR domain dimer and induce downstream signaling , resulting in the synthesis of pro-inflammatory cytokines and other immune response effectors [2 , 3] . Despite a conserved global fold of the structures of TLR receptors , the diversity of ligands they are able to recognize is very broad , ranging from small molecules such as nucleoside analogues to larger molecules such as nucleic acids and proteins . The ability to respond to such a wide array of agonists lies in the distinct recognition mode specific for each TLR and its ligands [4] . Recognition of the characteristic molecular features of microbial ligands ( PAMPs ) that are essential for the microbial survival and virulence makes it difficult for the pathogen to modify the structure of PAMP in order to circumvent immune recognition without losing its functionality . Toll-like receptor 5 ( TLR5 ) recognizes flagellin , the main structural protein of bacterial flagella , which exhibits a remarkable level of conservation among bacterial species , thus representing an attractive target for innate immune recognition [5] . Flagellin is composed of multiple structural domains , D0-D3 . Flagellin monomers are stacked into a helical filament with the conserved D0 and D1 domains facing inward into the filament core channel , through which flagellin molecules are transported during flagella formation , while the variable domains D2 and D3 protrude outward from the core and are solvent exposed [6] . Functional flagella represent a virulence factor for several important human pathogens [7–9] . Flagellins of β- and γ-proteobacteria , such as Serratia marcescens or Salmonella typhimurium , are efficiently detected in their monomeric form by TLR5 at pico-molar concentrations , while some bacterial species , such as the Epsilonproteobacteria gastric pathogen Helicobacter pylori or the food-borne pathogen Campylobacter jejuni , have evolved their flagellin to evade TLR5 recognition while retaining bacterial motility . Several amino acid changes that contribute to TLR5 evasion in H . pylori FlaA flagellin have been identified within the D1 domain [10] . This evolutionary adaptation involves large restructuring of packing of flagellin monomers into filaments , which comprise 7 molecules of the FlaA of H . pylori per turn , in comparison to the 11-fold symmetry in flagellin FliC of S . typhimurium [11] . Mutational and structural studies have identified conserved regions on the TLR5 ectodomain that interact with amino acid residues within the conserved D1 domain of flagellin [12–14] . The crystal structure of the N-terminal fragment of the zebrafish TLR5-N14VLR comprising approximately two thirds of the TLR5 ectodomain in complex with a fragment of Salmonella flagellin , lacking the conserved D0 domain , provides a detailed characterization of this interaction . The structure reveals a primary binding interface for α-helices of the D1 domain of flagellin on the ascending lateral surface of TLR5 , stretching from leucine-rich repeat LRRNT to LRR10 , leading to formation of a 1:1 TLR5-flagellin complex . A secondary but relatively small binding interface mediates the interaction of the αND1b helix and the subsequent β-hairpin region of flagellin with the convex side of LRR12/13 on the opposite TLR5 receptor and is thus proposed to guide the dimerization of the complex in a stoichiometry of 2:2 , the physiological relevance of which has also been confirmed in human cells [14 , 15] . However , the identified interactions do not appear to be sufficient for the formation of an active signaling receptor dimer , since the truncated form of flagellin lacking the D0 domain is insufficient for TLR5 activation [12 , 16] . Moreover , the 2:2 complex observed in the crystal was not detected in solution , despite binding of flagellin to TLR5 in a 1:1 stoichiometric ratio [14] . A 2:2 complex required for receptor activation was also not observed in a subsequent report on the crystal structure of B . subtilis flagellin in complex with a fragment of the TLR5 ectodomain [17] . This suggests , in agreement with previous studies , that the truncated fragment of the receptor lacks part of the binding site for flagellin [18] . The D0 domain is essential for functional flagella formation and signaling , yet its deletion only slightly impairs binding to the TLR5 monomer; therefore , the mechanism of its functional role in TLR5 signaling remains unknown [14 , 16 , 19] . The motivation to clarify the mechanism of TLR5 signaling and , on the ligand side , to assess the contribution of the D0 region to the evasion of host sensing led us to analyze the role of the D0 domain in receptor activation . We propose a role for the D0 domain in crosslinking the two TLR5 receptor monomers and hence stabilizing the functional signaling complex . While flagellin lacking the D0 domain was incompetent in triggering TLR5 activation , tethering two inactive flagellin D0 deletion variants into a covalent dimer restored activation , suggesting a role for the D0 domain in receptor dimerization . A structure-guided mutagenesis study identified amino acid residues at the C-terminal segment of the D0 domain involved in receptor activation . Further , we pinpointed amino acid residues within the D0 domain that enabled evasion of immune recognition by the H . pylori flagellin . Together , this shows that the multipartite recognition of flagellin by TLR5 hinders an easy evasion of immune recognition by single point mutations through targeting conserved segments of flagellin , which are essential for flagellar self-assembly . To assess the role of the D0 domain of flagellin in TLR5 activation , we used a synthetic biology strategy by constructing a chimeric protein , composed of the D0 and D1 domains of S . typhimurium flagellin fliC , which we named short flagellin ( SF ) , fused via a flexible peptide linker to the N-terminus of human TLR5 ( SF-TLR5 ) . This chimeric protein , combining selected ligand domains and the full-length receptor in a single molecule , exhibited constitutive activity when expressed in HEK293 cells , while a fusion protein lacking the D0 domain ( SFΔD0-TLR5 ) was inactive [15] . Most post-translational modifications of flagellin are located in the variable D2 and D3 domains and it has been shown previously that bacteria-specific post-translational modification of flagellin is not required for TLR5-based recognition [12 , 20] . The D0 domain is composed of two discontinuous epitopes spanning the N- and C-terminal regions ( hereafter referred to as ND0 and CD0 ) , comprising amino acid residues 1–42 and 455–494 of SaTy , respectively ( Fig 1A ) . We aimed to assess the contribution of distinct regions of the D0 domain to TLR5 activation . HEK293 cells were transiently transfected with plasmids for the chimeric proteins and activation of NF-kB reporter was determined through a dual luciferase reporter assay ( Fig 1 ) . SF-TLR5 was used as a positive and SFΔD0-TLR5 as a negative control . A fusion protein variant comprising short flagellin lacking the ND0 domain ( SFΔND0-TLR5 ) retained the constitutive activity of the positive control , SF-TLR5 ( Fig 1B ) . On the other hand , deletion of the C-terminal D0 domain ( SFΔCD0-TLR5 ) completely abrogated NF-κB activation , as did the negative control , a chimeric receptor lacking the complete D0 domain ( SFΔD0-TLR5 ) ( Fig 1C ) . One explanation for this effect might be that the truncation of the CD0 domain in the chimeric construct shortens the distance between flagellin and TLR5 and could thus lead to a steric hindrance in binding . To rule out this possibility and confirm that the truncation of the C-terminal flagellin segment on its own is the cause of the impaired signaling , we tested constructs with two longer linker lengths , 27 and 57 aa ( Fig 1A ) , which resulted in the same level of activation ( Fig 1C ) . To rule out inactivation of the truncated constructs due to protein misfolding , a cotransfection assay with wtTLR5 was performed . The results showed a decrease in wild-type TLR5 activation by flagellin upon addition of increasing levels of the inactive SFΔD0-TLR5 , suggesting an interaction between wt-TLR5 and TLR5-SFΔD0 , which is inactive but can still bind TLR5 and can therefore inhibit TLR5 activation ( Fig 1D ) . Western blot analysis confirmed that the expression level of wtTLR5 remains constant , even upon expression of increasing levels of the fusion receptor SFΔD0-TLR5 ( S1B Fig ) . The decrease in signaling is therefore not due to a limited cell capacity for overexpression of recombinant proteins . Further , we prepared deletion constructs lacking the C-terminal 10 ( SFΔC10D0-TLR5 ) or 20 ( SFΔC20D0-TLR5 ) amino acids of the CD0 domain ( Fig 1A ) . The NF-κB luciferase reporter assay showed a decrease in signaling proportional to the size of the deletion , although complete abrogation of signaling was observed only upon deletion of the complete C-terminal D0 domain ( Fig 1E ) . All proteins where detected in cell lysates at comparable levels , confirming that the decrease in activation is not a consequence of altered protein expression ( S1A Fig ) . These results underline a prominent role for the conserved C-terminal D0 domain of flagellin in TLR5 activation . Based on the alignment of flagellins of different bacterial species , amino acid residues were selected for alanine mutagenesis . The selection criteria included amino acid residues of the Salmonella flagellin FliC , which differ from their H . pylori FlaA counterparts , but also residues that are conserved among the different clades ( Fig 2A ) . Alanine point mutations were introduced , with the exception of a substitution of the terminal arginine R494 to glutamic acid since an alanine mutant at this site was highly prone to proteolysis in bacterial overexpression . In addition to single alanine point mutations in the CD0 domain , a substitution of the hydrophobic motif in the region 489 to 493 ( VLSLL ) by five alanine residues was introduced ( VLSLL_A ) ( Fig 2A ) . Recombinant flagellins were produced and isolated via affinity chromatography ( S2A Fig ) and tested for TLR5 activation potential in two reporter cell lines: HEK293 cells transiently expressing human TLR5 ( Fig 2B and 2C ) and the human epithelial cell line A549 , which endogenously expresses TLR5 ( S2B–S2E Fig ) . Mutations D457A and Y458A in the conserved spoke region showed a strong negative effect on TLR5 activation . A mutation in the central region of CD0 , R467A , had a profound effect on activation , while other mutations in the central region had a low or no apparent effect on the activation of the NF-κB signaling pathway . Substitution of the hydrophobic amino acid residues at the very tip of the protein VLSLL_A completely abrogated TLR5 signaling , while a single point mutation of serine ( S491A ) within this motif had no effect , thus attributing the effect of protein VLSLL_A entirely to the hydrophobic residues . Mutation of the polar residue N488A , located at the tip of the CD0 domain preceding the hydrophobic motif VLSLL also significantly impaired signaling . Additionally , regions 460–463 ( TEVS ) and 472–474 ( QQA ) within the CD0 domain , which differ between S . typhimurium and H . pylori flagellin ( SaTy and HePy , respectively ) , were mutated from SaTy to the corresponding residues of HePy ( TEVS_EESA and QQA_VGS ) . The isolated recombinant proteins were tested for TLR5 activation ( S2F Fig ) . However , these substitutions showed no effect on TLR5 activation , suggesting that the residues in the spoke region and at the C-terminus are crucial for TLR5 activation . The mutant protein VLSLL_A was also tested for activation of the intracellular NAIP5/NLRC4 inflammasome . Wild type or NLRP3-deficient macrophages were primed with LPS and stimulated with wt or mutant flagellin and IL-1β secretion was measured . Mutation of the terminal hydrophobic residues resulted in decreased NAIP5/NLRC4 inflammasome activation , in agreement with a previous report ( S2H and S2I Fig ) [21] . Co-immunoprecipitation studies showed a comparable binding intensity of recombinant mutated flagellins to the TLR5 ectodomain , regardless of their activation potential , which is in agreement with previous studies where a deletion of the D0 domain completely abrogated signaling but hardly affected the binding efficiency of a truncated flagellin molecule to TLR5 [14] ( Fig 2D ) . These results suggest that the role of the D0 domain in TLR5 activation is largely defined by amino acid residues in the conserved C-terminal spoke region and in the C-terminal hydrophobic tip of flagellin . However , these point mutations do not significantly affect the binding of flagellin to hTLR5 , as the primary binding site is located within the D1 region , as shown by previous structural studies [14] . In innate immune recognition , a correlation is often observed between function of the PAMP or its structural moiety for the microbe and recognition by TLRs , as structural or functional restrictions often hinder modifications of PAMPs that would enable immune evasion . Previous reports suggested a correlation between TLR5 activation and effects on the motility in the conserved D1 region of flagellin [10 , 22] . Amino acids in the C-terminal region of flagellin are also evolutionarily conserved among bacterial species since they tile the inner channel of the flagellum and participate in packing of neighboring flagellin chains . To assess whether there is a correlation between TLR5 recognition and motility for this region of flagellin , we tested mutated flagellins for their effect on functional flagella formation using a swarming motility assay ( Fig 3 ) . The effect on bacterial motility could be grouped into three categories: mutations exerting low motility ( left panel ) , mutations with a moderate effect on motility ( middle panel ) , and mutations which increased bacterial motility with respect to wild-type flagellin ( right panel ) . Among the mutations with a significant effect on TLR5 activation , Y458A in the spoke region and VLSLL_A at the C-terminus had the most profound effect on motility while mutation R467A had a moderate effect . These results show that , while the majority of mutations that impair TLR5 activation also have a negative effect on motility , there is no direct correlation between TLR5 stimulation and function , at least not at the amino acid level . For the formation of an active signaling complex , two flagellin molecules must bind to two TLR5 ectodomains [15] , forming an active complex in which a dimer of the intracellular TIR domains initiates recruitment of the signaling adapter MyD88 . Flagellin binds to TLR5 via a two-partite primary binding site and to the opposing TLR5 through a secondary site , both encompassed in the D1 domain of flagellin [14] . Superposition of full-length flagellin from the assembled flagellum ( PDB code 1UCU ) to the truncated FliC-ΔD0 from the crystal structure TLR5-N14VLR/FliC-ΔD0 demonstrates that in the extended form , the D0 domain of flagellin would clash with the cell membrane ( S3 Fig ) . We reasoned that , upon binding of flagellin to TLR5 , the D0 domain must reorient itself relative to the conformation in the assembled flagella . Taking into account this information and the necessity of TLR5 ectodomain dimerization for activation but not for flagellin binding , we hypothesized that the D0 domain might have a role in dimer formation by binding to the opposite TLR5 ectodomains and stabilizing an active complex by bringing the two 1:1 complexes of flagellin:TLR5 into sufficient proximity for signal transduction mediation by the dimerized cytosolic TIR domains . To test this hypothesis , we prepared a recombinant truncated flagellin molecule lacking the D0 and variable D2 and D3 domains ( SFΔD0 ) and a recombinant protein in which the two SFΔD0 domains are tethered into a single polypeptide chain by a 27 amino acid peptide linker ( dimSFΔD0 ) ( Fig 4A ) . SDS PAGE ( Fig 4B ) and particle size analysis determined by dynamic light scattering ( DLS ) confirmed the expected size of dimSFΔD0 ( 6 . 0 nm ) as being approximately twice the size of the monomeric SFΔD0 ( 2 . 3–2 . 5 nm ) and comparable to the size of the full-length SaTy flagellin ( 6 . 1 nm ) . The monomeric SFΔD0 was unable to stimulate TLR5 ( Fig 4C and 4D A549 cells , S4A and S4B Fig hTLR5-transfected HEK293 cells ) in agreement with previous findings [14] . If the role of D0 is indeed to crosslink the two TLR5 ectodomains into a functional signaling dimer , tethering of the truncated flagellin fragment should improve activation . Indeed , in contrast to SFΔD0 , dimSFΔD0 was active at concentrations as low as 50 ng/ml ( Fig 4A and 4D ) . Tethering the two inactive short flagellins therefore substantially restores activation , albeit not to the full extent of wild type flagellin . We propose that the linker to a certain extent substitutes the protein-protein interactions mediated by the D0 domain in the full-length flagellin-TLR5 heterodimeric complex . Together , our results suggest a role for the D0 domain in crosslinking two 1:1 TLR5:flagellin complexes into an active 2:2 signaling complex . Despite a high level of sequence conservation in the D1 and D0 domains of flagellin , the quaternary filament structures differ between the two proteobacteria clades [6 , 11] . H . pylori belongs to the clade of ε-proteobacteria that form a distinct flagellar filament assembly composed of 7 rather than of 11 protofilaments in the flagellum and are able to evade immune recognition by TLR5 . A study by Andersen-Nissen et al . ( 2005 ) identified mutations in the primary binding site located within the D1 domain that could contribute to the evasion of immune recognition at the cost of impaired motility and compensatory mutations , which restored functional flagella formation and mobility . However , the D0 domain of flagellin also plays a crucial role in flagellar filament assembly by forming contacts with other monomers within the inner channel . To assess the role of the D0 region in the evasion of TLR5 detection by H . pylori , chimeric flagellins were constructed by exchanging the D0 domain of SaTy with the C-terminal D0 domain of HePy ( SaTy-CD0 ( HePy ) ) or both the C- and N-terminal D0 domains of HePy ( SaTy-D0 ( HePy ) ) ( Fig 5A ) . All chimeric flagellin variants were produced in a bacterial expression system and purified via affinity chromatography ( S5A Fig ) . These chimeric proteins comprised both the primary and secondary binding sites of SaTy . Therefore , any difference in the ability of these variants to activate TLR5 depends on the differences between the SaTy and HePy D0 domains . Circular dichroism analysis of chimeric flagellins demonstrated comparable secondary structure content to that of the wild type flagellin , indicating no deleterious effects on protein folding ( Fig 5B ) . Both chimeric flagellins , SaTy-D0 ( HePy ) and SaTy-CD0 ( HePy ) were unable to activate TLR5 either at the endogenous level or ectopic TLR5 expression ( Fig 5C human lung epithelial cell line A549 , S5B Fig hTLR5-transfected HEK293 cells ) , similar to a protein completely lacking the D0 domain ( SFΔD0 ) , demonstrating the crucial role of the CD0 domain of flagellin in TLR5 activation . Chimeric flagellin of H . pylori with the C- and N-terminal D0 domains of SaTy also failed to activate TLR5 ( Fig 5C , S5C Fig ) . The chimeric proteins SaTy-CD0 ( HePy ) and SaTy-D0 ( HePy ) had a similar secondary structure to SaTy , as demonstrated by the analysis of the circular dichroism spectra of the isolated proteins . This indicates that the evolutionary adaptations that allowed for the immune evasion of H . pylori are distributed across the whole length of flagellin and are not restricted to the primary TLR5 recognition surface located within the D1 region . The amino acid sequences of HePy and SaTy flagellin are highly conserved in the D0 region , pointing to a high level of evolutionary constraints in this area . However , several residues represent more pronounced differences in charge or hydrophobicity . Therefore , we tested a combination of five substitutions from HePy to SaTy counterparts to identify the role of these residues in the evasion of TLR5 activation ( Fig 5A ) . Isolated chimeric protein SaTy-CD0 ( HePy ) mut with the selected counterpart mutations showed a significant recovery of the TLR5 activation potential in comparison to the chimeric protein SaTy-CD0 ( HePy ) , suggesting that these particular amino acid differences contribute to immune evasion by H . pylori ( Fig 5C , S5D Fig ) . A crucial step in the detection of PAMPS and active complex formation of TLRs is receptor ectodomain dimerization . Despite the important insight into the mechanism of ligand binding by TLR5 from the crystal structure of the complex of the ligand and receptor fragments , important aspects of TLR5 activation by flagellin remain unknown . A distributed binding site on the concave and lateral surfaces of TLR5 , extending from LRRNT to LRR10 , directs the primary binding of flagellin , enabling formation of a TLR5:flagellin 1:1 complex . A secondary binding site between the D1 domain of flagellin and LRR12-13 of the opposing TLR5 contributes to the formation of a 2:2 complex . These observed interactions are however not sufficient for receptor activation in vivo , as the truncated flagellin lacking the D0 domain is not able to trigger signaling . Furthermore , a subsequent study reported weak binding of flagellin to the ectodomain region beyond LRR17 , not included in the crystal structure [23] . The unresolved issues concerning the TLR5 activation mechanism motivated us to investigate the role of the D0 domain in TLR5 activation in greater detail . Results of a flagellin subdomain deletion revealed a key role of the C-terminal segment of the D0 domain in TLR5 activation . Further , a detailed alanine-scanning mutagenesis of this region revealed the contribution of several amino acid residues to TLR5 activation . Substitutions of two amino acid residues in the spoke region significantly affected receptor activation . The most conserved residues across all flagellins are those in the spoke region . The spoke region is crucial in filament formation for maintaining the integrity of the inner and outer tubes of the filament , and it forms inter-subunit interactions that enable tight packing of flagellin monomers [5] . A pronounced role in TLR5 activation was also demonstrated for amino acids at the very tip of flagellin , including polar asparagine at position 488 and the hydrophobic motif VLSLL from positions 489 to 493 , excluding Ser491 . While this region has not been previously reported to have a role in TLR5 activation , the same terminal hydrophobic motif is also crucial for recognition by the intracellular NAIP5/NLRC4 inflammasome [21] . Our results therefore suggest a dual mechanism of sensing the same region of bacterial flagellin through two distinct receptors of innate immunity . Although the molecular mechanism of flagellin recognition by NAIP5 is not known and is likely to differ from the mechanism of recognition by TLR5 , selection of the same segment as the target for the innate immune receptors is likely due to the functional importance of this region for the assembly of functional flagella . In the crystal structure study , Yoon et al . demonstrated that the contribution of the D0 domain to the formation of the 1:1 complex of flagellin:TLR5 is minimal [14] . Therefore , unless there is a third unknown co-factor involved in the TLR5-flagellin signaling event , the contribution of the D0 domain is most likely pertained to the formation of the 2:2 complex , required for activation . In line with these findings , we showed that , while point mutations within the C-terminal D0 domain strongly decreased signaling , binding of flagellin to TLR5 was not affected . Superposition of full-length flagellin to the TLR5 dimer based on the crystal structure TLR5-N14VLR/FliC-ΔD0 demonstrates that the structure of TLR5-bound flagellin must differ from the structure of flagellin in filaments . The spoke region connecting D0 and D1 is highly conserved among bacterial species and represents a flexible subunit of the flagellin molecule between domains D0 and D1 , which are mostly α-helical in flagellar filaments , whereas the D0 domain is disordered in monomeric flagellin [6 , 24] . We propose that the flexible spoke region connecting the D0 and D1 domains functions as a hinge , enabling the reorientation of the D0 domain toward the opposing TLR5 ectodomain and thus stabilizing the 2:2 complex . This is supported by the recovered activity of a completely inactive form of flagellin without the D0 domain ( SFΔD0 ) by forced dimerization in a covalently tethered SFΔD0 dimer . One might argue that increased activation of the dimeric construct could be simply due to increased local concentration of the ligand , although we believe this to be less likely , since the monomeric form of flagellin lacking the D0 domain is essentially inactive , even in a higher concentration range . Our results therefore identify a previously unrecognized segment of flagellin to play an essential role in TLR5 activation and suggest a role for the D0 domain in the process of receptor dimerization most likely through binding to the opposing ectodomain in the active complex . We note that these results provide indirect evidence to the mechanism of TLR5 dimerization and that direct proof of this concept would have to be supported by a structural study of the full length form of the ligand-receptor complex . However , structural studies of the D0 domain in the monomeric form of flagellin have , at least do date , been unsuccessful , most likely due to the disordered structural conformation of the terminal regions of flagellin [6 , 14 , 17 , 24] . Inter- and intra-subunit interactions of the D1 and D0 domains enable the tight packing of flagellin subunits into functional filaments [6] . The high level of sequence conservation in these regions illustrates their functional importance , and single point mutations can disrupt the proper quaternary structure and therefore bacterial motility . Indeed , bacterial motility was significantly reduced for several flagellin mutants . Inter-subunit connections between the D0 domain in the filament core are mostly hydrophobic [6 , 25] , and therefore it is not surprising that of the mutants that affected TLR5 activation , the two hydrophobic mutations , Y458A and VLSLL_A , located in the spoke region and at the C-terminal tip , profoundly impaired motility , exhibiting a link between structure and function as an ideal target for immune recognition . These residues are directly involved in packing interactions with neighboring flagellin molecules in the filament . Main-chain and side-chain atoms from residues at the C-terminus of flagellin , including Q484 , N488 , S491 , and R494 , constitute the hydrophilic surface of the inner channel , which is important for the transport of monomeric flagellin through the channel in the process of filament formation [6 , 26] . While mutations at positions S491 and R494 weakly impaired motility , mutations at positions N488 and Q484 even increased motility with respect to wt flagellin . Retained and even increased bacterial motility might be explained by the position of residues N488 and Q484 , which are oriented towards the center of the hydrophilic channel , where they do not participate in filament packing . Mutation of asparagine into alanine is expected to increase the size of the channel , without significantly affecting its polarity due to the small size of the alanine side chain , presumably facilitating transport as a result of increased channel size . The distribution of the TLR5 binding sites across the length of the flagellin molecule includes several conserved segments of flagellin within the D1 and D0 domains required for flagellin self-assembly [12] , thereby rendering the evasion of immune recognition rather difficult , requiring multiple coordinated mutations . The extent of the required change is illustrated by radically different packing of flagellar filaments in ε-proteobacteria , such as H . pylori . In addition to the previously recognized contribution of the D1 domain , we demonstrated a role of the D0 domain in the evasion of TLR5 recognition based on the exchange of the D0 domain of a potent TLR5 activator , S . typhimurium flagellin ( FliC ) , with the D0 domain of the H . pylori flagellin ( FlaA ) , which is unable to trigger TLR5-based immune activation . Further , specific amino acid residues have been identified , which had to be altered in the evolution of H . pylori to enable this evasion . We may speculate that the complex rearrangement of flagellin sequences that modify the supramolecular structure of flagella and evade TLR5 recognition may be easier in H . pylori due to its ability to recombine the genetic material from several strains within the same organism and therefore simultaneously combine multiple point mutations within the same molecule [27] . In conclusion , we suggest a functional role of the D0 domain of flagellin in TLR5 activation through the promotion of receptor dimerization . We propose a mechanism for the formation of a fully active TLR5:flagellin complex , in which the contribution of at least three distinct sites on flagellin is required; the primary binding site within the D1 domain that guides the formation of the flagellin:TLR5 heterodimer , and the secondary binding site that promotes interaction between flagellin and the opposite TLR5 ectodomain [14] , which needs to be supported by an additional third interaction between the D0 domain of flagellin and the opposing TLR5 ectodomain . The multiple interaction surfaces identified in this and previous studies [12–14] contribute to the high affinity of binding and underlay the evolutionary robustness of TLR5 recognition . The importance of the D0 domain in the self-assembly of flagella makes it an excellent choice for a recognition target and it is not surprising that two completely different types of receptors of the innate immune system target the same region of the molecule . In fact , the membrane TLR4 and cytosolic caspase 11 receptors also recognize a very similar structural pattern of the LPS molecule [28] , demonstrating the convergent evolution of the innate immune system to the functionally most relevant microbial targets . Human embryonic kidney cell lines HEK293 ( ATCC CRL-157 ) and HEK293T ( ATCC CRL-3216 ) and A549-Dual adherent epithelial cells ( Invivogen ) were cultured in complete media ( DMEM; 1 g/l glucose , 2 mM L-glutamine , 10% heat-inactivated FBS ( Gibco ) ) in 5% CO2 at 37°C . The human A549 lung carcinoma cell line expresses a secreted embryonic alkaline phosphatase ( SEAP ) reporter under the control of the IFN-β minimal promoter fused to five NF-κB binding sites , and a secreted luciferase under the control of an ISG54 minimal promoter in conjunction with five IFN-stimulated response elements ( Invivogen ) . The plasmids used include pUNO-hTLR5 encoding human TLR5 ( InvivoGen ) and pcDNA3 ( Invitrogen ) . S . typhimurium flagellin , chimeric flagellins , and flagellin point mutants were cloned into the pET19b expression vector ( Novagen ) . For the bacterial motility assay , flagellin mutants were cloned into the pRP4 plasmid expressing wild-type flagellin ( courtesy of E . Miao , Institute for Systems Biology , Seattle ) . Further , chimeric proteins SFΔD0 , dimSFΔD0 , and chimeras of SaTy and HePy flagellin were cloned into the pET19b expression vector ( Novagen ) . Fusions of short flagellins with TLR5 , SF-TLR5 , SFΔD0-TLR5 [15] , SFΔCD0-TLR5 , SFΔCD0-l57-TLR5 , SFΔND0-TLR5 , SFΔC10D0-TLR5 , and SFΔC20D0-TLR5 were cloned into the pFLAG-CMV3 expression vector ( Sigma-Aldrich ) . The chimeric constructs prepared for this study are described in detail in S1 Table . Escherichia coli BL21 ( DE3 ) pLysS cells transformed with the pET19b plasmid expressing wild-type or mutant flagellin were cultivated at 37°C in Luria-Bertani ( LB ) medium , containing 50 μg/ml ampicillin . Overnight cultures were transferred to fresh media , grown to an optical density of ~0 . 8 at 600 nm , and supplemented with 1 mM Isopropyl β-D-thiogalactoside ( IPTG ) . Cells were grown at 37°C for 4 hours , harvested , and lysed in buffer ( 10 mM TRIS pH 7 . 5 , 1 mM EDTA , 0 . 1% DOC ) containing a protease inhibitor cocktail ( Sigma P8849 ) , followed by sonication ( pulse 1 s on , 2 s off , 10–15 min ) and centrifugation ( 12000 rpm for 30 min ) . N-terminally His10-tagged recombinant proteins were purified on Ni-NTA affinity agarose ( Qiagen ) and dialyzed against 20 mM HEPES buffer . Flagellin point mutants prone to protease degradation ( D455A , D457A , Y458A , and T476A ) were additionally purified via a Strep-tag on the C-terminus on a Strep-Tactin Sepharose column ( Iba ) , according to the manufacturer’s guidelines . Protein concentration was determined with the BCA assay ( Pierce ) , and purity was confirmed using SDS-PAGE and immunoblotting . For the dual-luciferase assays , HEK293 cells were seeded in 96-well plates ( Corning ) at 2–3 × 104 cells per well ( 0 . 1 ml ) . The next day , the cells were transiently transfected with plasmids expressing wtTLR5 or chimeric constructs , pELAM-1 ( C . Kirschning , University of Duisburg-Essen , Germany ) expressing NF-κB-dependent firefly luciferase ( 50 ng per well ) , and phRL-TK ( Promega ) constitutively expressing Renilla luciferase ( 5 ng per well ) using the jetPEI transfection reagent ( Polyplus Transfection ) . The total amount of DNA for each transfection was kept constant by adding appropriate amounts of control plasmid pcDNA3 ( Invitrogen ) . After 24 h , the cells were either lysed or the medium was changed and the cells were stimulated with purified recombinant flagellin ( 10 μl ) for an additional 18 h before lysis . The cells were lysed in Passive Lysis 5x Buffer ( Promega ) and analyzed for reporter gene activities using a dual-luciferase reporter assay . Each experiment was repeated at least three times , and each measurement was performed in at least four biological parallels . A student’s unpaired two-tailed t-test was used for statistical comparison . A549 epithelial cells express endogenous TLR5 and an NF-κB-inducible SEAP reporter and do not express the intercellular reporter for flagellin , NLRC4 [29] . Cells were seeded in 96-well plates ( Corning ) at a density of 5 × 104 cells per well ( 0 . 1 ml ) . Immediately after seeding , the A549 cells were stimulated with flagellin . After 10 h , the supernatants were collected , heated for 1 h at 65°C , and the NF-κB-dependent SEAP activity was determined using Quanti Blue reagent according to the manufacturer’s instructions ( Invivogen ) . Each experiment was repeated at least three times , and each measurement was performed in at least five biological parallels . A student’s unpaired two-tailed t-test was used for statistical comparison . HEK293T cells were seeded in a 6-well plate ( Techno Plastic Products ) at a density of 5–7 × 105 cells per well . The next day , the cells were transiently transfected with 2 μg of plasmids expressing TLR5 or TLR5 fusion constructs using the Lipofectamine transfection reagent ( Thermo Fisher Scientific ) . In the case of transfection of varied amounts of plasmids , the overall amount of transfected DNA was equalized using control plasmid pcDNA3 . Forty-eight h post transfection , the cells were lysed in lysis buffer ( 50 mM Tris-HCl ( pH 8 ) , 1 mM EDTA , 1 mM EGTA , 137 mM NaCl , 1% Triton X-100 , 1% sodium deoxycholate ( DOC ) , 10% glycerol , 1 mM Na3VO4 , and 50 mM NaF ) containing a cocktail of protease inhibitors ( Roche ) . Cell debris was removed by centrifugation at 13200 rpm for 15 min . The total protein concentration in the supernatant was determined using the BCA assay . Further , proteins from the supernatant were separated by SDS-PAGE and transferred to a Hybond ECL nitrocellulose membrane ( GE Healthcare ) . The membrane was washed ( 1 × PBS buffer ) and incubated in blocking buffer ( 1 × PBS , 0 . 1% Tween 20 , 0 . 2% I-Block ( Tropix ) ) overnight at 4°C . The membranes were incubated with primary antibodies diluted in blocking buffer for 90 min , washed ( 1 × PBS , 0 . 1% Tween 20 ) , and incubated with secondary antibodies for 45 min at room temperature . Secondary antibodies were detected with the ECL Western blotting detection reagent ( GE Healthcare ) , according to the manufacturer’s protocol . The primary antibodies were rabbit anti-FLAG ( F7425 , Sigma ) , rabbit anti-AU1 ( ab3401 , Abcam; ) and mouse β-aktin ( Cell Signaling techn . , 3700; ) , all diluted 1:1000 . Secondary antibodies were horseradish peroxidase-conjugated goat anti-rabbit IgG ( ab6721 , Abcam ) and HRP-conjugated goat anti-mouse IgG ( Santa Cruz , sc-2005 ) , both diluted 1:4000 . Further , the purity of the recombinant flagellins isolated from E . coli was analyzed by SDS-PAGE and western blot analysis using mouse Tetra-His antibodies diluted 1:2000 ( 34670 , Qiagen ) and horseradish peroxidase-conjugated goat anti-mouse IgG diluted 1:4000 ( sc-2005 , Santa Cruz ) as secondary antibodies . For the co-immunoprecipitation studies , a soluble hTLR5 ectodomain fused to the Fc region of human IgG1 ( Invivogen ) was bound to protein A-coupled Dynabeads ( Thermo Fisher Scientific ) , according to the manufacturer’s protocol . A total of 10 μg of hTLR5-Fc diluted in MQ water or just MQ water as a control was incubated with 40 μl of beads for 1 h at room temperature and washed 3 times with wash buffer ( 1 × PBS , 0 . 02% Tween 20 , pH 7 . 5 ) . Twenty μg of wt or mutant flagellin was added per sample , incubated for 1 h at room temperature , and washed 3 times with wash buffer . The samples were eluted in 0 . 1% SDS at 95°C for 5 min . The flagellin in the samples was detected with Western blot analysis using anti-His antibodies . The impact of flagellin mutations on bacterial motility was tested using an immobile bacterial strain , S . typhimurium FliC FljB ATCC 14028s ( courtesy of E . Miao , Institute for Systems Biology , Seattle ) , transformed with a pRP4 plasmid expressing wild-type or mutant flagellin . The bacterial cells were transformed using electroporation at 2 . 5 kV , 200 Ω , and 25 μF in 10% glycerol . A single colony of transformed bacteria grown on LB agar plates containing 50 μg/ml ampicillin was selected and transferred to the motility test plates ( LB media containing 0 . 3% agar , 1 mM IPTG , and 50 μg/ml ampicillin ) . The cultures were incubated overnight in an upright position at room temperature . Each plate was inoculated with a single colony of S . typhimurium expressing mutated flagellin and bacteria expressing wild-type SaTy as a control . The motility of the strains expressing the mutated flagellin was compared to the motility of the control strain transformed with wild-type SaTy . Immortalized wild type BMDMs from C57BL/6 mice and NLRP3-deficient mice ( both gift of K . A . Fitzgerald; University of Massachusetts Medical school , Worcester , MA , USA ) were cultured in DMEM supplemented with 10% FBS . NAIP5/NLRC4 activation assays were performed in serum-free DMEM . Cells were seeded at 1 . 5 x 105 cells per well on 96 well plates and primed with ultra-pure LPS ( 100 ng/mL ) for 6 hours for the stimulation of pro-IL-1β expression . Further , the growth medium was removed and wild type or mutant flagellin ( 3 μg/ml ) mixed with DOTAP ( 1:5 ) in DMEM was added for 4 h . The concentration of secreted IL-1β was measured by ELISA ( e-Bioscience ) according to manufacturer’s instructions . DLS data of the soluble flagellin species were acquired using a Zetasizer Nanoseries instrument ( Malvern ) . The samples were centrifuged at 13000 rpm for 30 min to remove protein aggregates . Measurements were made at 20°C using automated settings , and 3 independent acquisitions of 10 measurements each were analyzed using the associated DTS nanoparticle-sizing software . CD measurements were used to determine the secondary structure of soluble flagellins . The CD spectra were taken between 195 and 280 nm on a Chirascan CD spectrometer ( Applied Photophysics ) fused with nitrogen gas and equipped with a temperature controlled cuvette holder . A cell path length of 1 mm was used with concentrations of flagellins in the range of 0 . 1–0 . 5 mg/ml . All samples were dissolved in demi-water , and the results are the average of 3 spectra measured at 20°C . Flagellin amino acid sequences of S . typhimurium ( SaTy , UniProt id . P06179 ) , S . marcescens ( SeMa , UniProt id . P13713 ) , S . dublin ( SaDu , UniProt id . Q06971 ) , H . pylori ( HePy , UniProt id . P0A0S1 ) , and C . jejuni ( CaJe , UniProt id . P22252 ) were aligned using ClustalW ( http://embnet . vital-it . ch/software/ClustalW . html ) . UCSF Chimera 1 . 6 . 2 software was used to generate the structural figures and to determine the distances among atoms ( http://www . cgl . ucsf . edu/chimera/ ) [30] . Graphs were prepared with Origin 8 . 1 software ( http://www . originlab . com/ ) , and GraphPad Prism 5 ( http://www . graphpad . com/ ) was used for statistics . A students’ unpaired two-tailed t-test was used for statistical comparison of the data .
Receptors of the innate immune system typically recognize conserved microbial patterns , crucial for pathogen fitness and survival . Flagellin , the main structural protein of bacterial flagella , is recognized by two receptors of the innate immune system , the intracellular inflammasome receptor NAIP5/NLRC4 and the membrane-bound Toll-like receptor 5 . Ligand-induced dimerization is a crucial step in Toll-like receptor 5 activation . A crystal structure of segments of the ligand-bound receptor revealed binding interfaces on the ligand and receptor , but failed to fully clarify the activation mechanism , since the D0 domain of flagellin , which is crucial for receptor activation , is missing in the structure . We propose a role for the D0 domain in receptor dimerization and pinpoint specific amino-acid residues within the D0 domain , which contribute to Toll-like receptor 5 activation . We show that Toll-like receptor 5 recognizes the same protein motif detected by the intracellular NAIP5/NLRC4 receptor . Our work represents an important advance in the understanding of the mechanism of activation of Toll-like receptor 5 .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]
[ "bacteriology", "medicine", "and", "health", "sciences", "crystal", "structure", "pathology", "and", "laboratory", "medicine", "pathogens", "immunology", "condensed", "matter", "physics", "microbiology", "immune", "receptor", "signaling", "bacterial", "diseases", "mutation", "helicobacter", "membrane", "receptor", "signaling", "enterobacteriaceae", "crystallography", "bacteria", "bacterial", "pathogens", "salmonella", "typhimurium", "immune", "system", "proteins", "infectious", "diseases", "solid", "state", "physics", "microbial", "physiology", "proteins", "medical", "microbiology", "microbial", "pathogens", "salmonella", "pathogen", "motility", "physics", "toll-like", "receptors", "biochemistry", "bacterial", "physiology", "signal", "transduction", "point", "mutation", "cell", "biology", "helicobacter", "pylori", "virulence", "factors", "flagellin", "genetics", "biology", "and", "life", "sciences", "immune", "receptors", "physical", "sciences", "cell", "signaling", "organisms" ]
2017
The role of the C-terminal D0 domain of flagellin in activation of Toll like receptor 5
Shoot organ primordia are initiated from the shoot apical meristem and develop into leaves during the vegetative stage , and into flowers during the reproductive phase . Between the meristem and the newly formed organ primordia , a boundary with specialized cells is formed that separates meristematic activity from determinate organ growth . Despite interactions that have been found between boundary regulators with genes controlling meristem maintenance or primordial development , most boundary studies were performed during embryogenesis or vegetative growth , hence little is known about whether and how boundaries communicate with meristem and organ primordia during the reproductive stage . We combined genetic , molecular and biochemical tools to explore interactions between the boundary gene HANABA TARANU ( HAN ) and two meristem regulators BREVIPEDICELLUS ( BP ) and PINHEAD ( PNH ) , and three primordia-specific genes PETAL LOSS ( PTL ) , JAGGED ( JAG ) and BLADE-ON-PETIOLE ( BOP ) during flower development . We demonstrated the key role of HAN in determining petal number , as part of a set of complex genetic interactions . HAN and PNH transcriptionally promote each other , and biochemically interact to regulate meristem organization . HAN physically interacts with JAG , and directly stimulates the expression of JAG and BOP2 to regulate floral organ development . Further , HAN directly binds to the promoter and intron of CYTOKININ OXIDASE 3 ( CKX3 ) to modulate cytokinin homeostasis in the boundary . Our data suggest that boundary-expressing HAN communicates with the meristem through the PNH , regulates floral organ development via JAG and BOP2 , and maintains boundary morphology through CKX3 during flower development in Arabidopsis . Leaves and flowers originate from the shoot apical meristem ( SAM ) , which contains pluripotent stem cells and resides at the tip of each stem . Primordia are initiated from the peripheral zone of the SAM in a predictable pattern , and develop into leaves during the vegetative stage , and into flowers during the reproductive phase . Each flower consists of four concentric whorls of organ types: the protective sepals , the showy petals , the male stamens , and the female carpels [1] . Between the meristem and newly formed leaf or flower primordia , a boundary forms with specialized cells that separate meristematic activity from determinate organ growth [2] . Cells in the boundary have reduced rates of cell division , concave surfaces , elongated shapes , and exhibit low auxin concentration compared to the adjacent cells in meristems or primordia [3–6] . There are two types of boundaries in the developing shoot apices . M-O ( meristem-organ ) boundaries separate leaf and flower primordia from the SAM , whereas O-O ( organ-organ ) boundaries develop between individual floral organs and create space between them [2 , 7] . Based on boundary-specific expression patterns and mutant defects in boundary formation , organ separation , SAM initiation and maintenance , branching , or floral organ patterning , several transcription factors have been identified as important boundary regulators , including CUP-SHAPED COTYLEDONS 1 , 2 and 3 ( CUC1 , CUC2 , CUC3 ) , LATERAL SUPPRESSOR ( LAS ) , LATERAL ORGAN BOUNDARIES ( LOB ) , JAGGED LATERAL ORGANS ( JLO ) , LATERAL ORGAN FUSION ( LOF ) , HANABA TARANU ( HAN ) , SUPERMAN ( SUP ) and RABBIT EARS ( RBE ) [5 , 8–21] . Interactions have been found between boundary regulators and genes controlling meristem maintenance or primordia development . For example , CUC genes promote SAM formation via the activation of meristem marker SHOOT MERISTEMLESS ( STM ) , and in return , STM represses CUC expression in the meristem [9 , 22] . CUC genes are also inhibited by primordia marker ASYMMETRIC LEAVES 1 ( AS1 ) and AS2 in the organ primordia [23–25] . However , as most boundary studies were performed during embryogenesis or vegetative growth , little is known about how boundary regulators communicate with meristem and organ primordia during the reproductive stage . The boundary regulator HAN encodes a GATA-3 type transcription factor with a single zinc finger domain and plays a role in Arabidopsis flower development . HAN is expressed at the boundaries between meristem and floral organ primordia and at the boundaries of floral organs [13] . Mutation of HAN leads to fused sepals , and reduced numbers of petals and stamens [13] . The meristem regulator KNAT1 /BREVIPEDICELLUS ( BP ) encodes a KNOTTED1-LIKE HOMEOBOX ( KNOX ) class I homeobox gene that is required for inflorescence architecture . Disruption of BP function results in short internodes and pedicels , and downward-oriented siliques [26 , 27] . Similarly , ARGONAUTE 10/PINHEAD ( PNH ) , a founding member of the ARGONAUTE family , is a regulator of meristem maintenance that acts by sequestering miR166/165 , preventing its incorporation into an ARGONAUTE 1 complex [28–31] . In pnh mutants , phenotypes are pleiotropic including an SAM occupied by pin-like structures , increased numbers of floral organs , and disrupted embryo and ovule development [32] . In primordia , indeterminate meristematic activities are repressed and primordia-specific genes are induced to ensure proper determinate organ development [2 , 23 , 24] . PETAL LOSS ( PTL ) , JAGGED ( JAG ) and BLADE-ON-PETIOLE ( BOP ) belong to the class of primordia-specific genes that regulates flower organ development [32] . PTL is expressed in the margins of developing sepals , petals and stamens , and ensures normal petal initiation by maintaining auxin homeostasis [33 , 34] . Loss of function of PTL leads to reduced numbers of petals and disrupted petal orientation [11 , 35 , 36] . JAG , a putative C2H2 zinc finger transcription factor , expresses in the initiating primordia but not the meristem , and regulates lateral organ development in Arabidopsis [37] . A JAG knockout mutant displays serrated sepals and narrow petals [37 , 38] . JAG controls cell proliferation during organ growth by maintaining tissues in an actively dividing state [37] , and acts redundantly with NUBBIN , a JAGGED-like gene , to control the shape and size of lateral organs [39] . BOP1/2 specify BTB/POZ domain proteins and express in the base of flower primordia . They function redundantly to control flower and leaf development [17 , 40–42] . Loss of function of BOP1 and BOP2 results in increased petal numbers , lack of floral organ abscission and leafy petioles [42–44] . Whether and how boundary genes interact with meristem-related regulators and primordia-specific genes during flower development remains largely unknown . In this study , we combined genetic , molecular and biochemical tools to explore interactions between the boundary gene HAN and two meristem regulators ( BP and PNH ) , and three primordia-specific genes ( PTL , JAG and BOP1/2 ) that function in flower development . We found that HAN plays a central role among these seven regulators in the control of petal development . At the transcriptional level , HAN promotes PNH transcription and represses BP expression , BP represses PNH while PNH positively feeds back on the expression of HAN . At the protein level , HAN physically interacts with PNH and PNH interacts with BP to regulate meristem organization . HAN also interacts with JAG , and directly promotes the expression of JAG and BOP2 to regulate floral organ development . Further , HAN directly stimulates CYTOKININ OXIDASE 3 ( CKX3 ) expression to modulate cytokinin levels in the boundary . Therefore , our data suggest a new link by which HAN communicates with the meristem through PNH , regulates primordia development via JAG and BOP2 , and maintains boundary morphology through CKX3-mediated cytokinin homeostasis during flower development in Arabidopsis . Mutation in HAN results in reduced numbers of petals and stamens , and fused sepals [13] . In contrast to the wild-type flower with four sepals and four petals , the han-2 mutant has an average of only 3 . 4 sepals and 2 . 6 petals in the Ler or Col background ( Fig 1A–1C , Table 1 ) . In order to explore the potential genetic interactions between HAN , meristem regulators , and primordia-specific genes during flower development , we generated double or triple mutant combinations of han-2 with bp-1 , pnh-2 , ptl-1 , jag-3 and bop1 bop2 ( Fig 1 and S1 Fig ) . Firstly , we explored the genetic interaction of HAN with meristem regulator BP . The bp-1 mutant shows a normal number of floral organs , with downward-pointing flowers and a compact inflorescence ( Fig 1D and S1B Fig ) [26] . The number of petals and sepals was reduced in a han-2 bp-1 double mutant , with an average of 1 . 7±0 . 1 ( n = 120 ) petals ( Fig 1E and S1C Fig , Table 1 ) . The phenotype of fused sepals is similar to han-2 . Then , we examined the genetic interaction of HAN with PNH , whose mutations result in increased numbers of petals . The average petal number was 4 . 3±0 . 1 ( n = 40 ) in the pnh-2 mutant ( Fig 1F , Table 1 ) . han-2 pnh-2 double mutants have fewer petals than the han-2 single mutant ( Fig 1G and S1D–S1G Fig , Table 1 ) . Therefore , mutation of meristem regulators BP and PNH enhanced the petal loss phenotype of han-2 . Given that both BP and PNH are meristem regulators [24 , 45] , we next explored the phenotypes of meristem organization upon induction of han-2 into bp-1 or pnh-2 mutant background ( Fig 2 ) . In inflorescence meristems ( IM ) and floral meristems ( FM ) , no obvious changes were observed in the meristem organization of the single mutant han-2 and bp-1 , or double mutant han-2 bp-1 as compared to the wild-type ( Fig 2A–2D , 2G–2J , and 2M ) . However , mutation of HAN greatly enhanced the smaller and taller IM and FM phenotype in the pnh-2 ( Fig 2E and 2F , 2K and 2L , and 2M ) , suggesting that HAN and PNH coordinatively regulate meristem organization in Arabidopsis . Next , we examined the genetic interactions of HAN with the primordial- specific genes PTL , JAG and BOP1/2 during flower development . The single mutant ptl-1 displays defective flowers with reduced petal numbers and disrupted petal orientation ( Fig 1H and S1J Fig ) [11 , 35] , and introduction of han-2 into the ptl-1 mutant further decreased petal numbers ( Fig 1I and S1H–S1K Fig ) , resulting in an average of 0 . 9±0 . 2 petals ( n = 60 ) in each han-2 ptl-1 double mutant flower ( Table 1 ) . JAG is required for lateral organ morphology , and loss of function of JAG results in flowers with narrow floral organs , jagged organ margins and slightly reduced numbers of petals ( Fig 1J and S1L Fig , Table 1 ) [37 , 38] . Loss of function of both HAN and JAG genes led to reduced petal numbers ( Fig 1K and S1L and S1M Fig ) . The average number of petals in a han-2 jag-3 double mutant was 1 . 8±0 . 2 ( n = 40 ) , a more severe phenotype than that in the han-2 single mutants ( Table 1 ) . Similarly , the sepals were more serrated and the petals were narrower in han-2 jag-3 than in a jag-3 single mutant ( Fig 1L and S1M Fig ) , suggesting that HAN and JAG have a synergistic effect on regulation of petal number , and sepal and petal morphology . In the han-2 bop1 bop2 triple mutant , on the other hand , the number of petals was largely rescued to normal ( 3 . 6±0 . 1 ) compared to 5 . 2±0 . 1 in bop1 bop2 mutants ( Fig 1M and 1N and S1N and S1O Fig , Table 1 ) . However , there were two developmental phenotypes in the han-2 bop1 bop2 triple mutants similar to the phenotype of bop1 bop2 mutants: 1 ) petaloid tissue replacing the sepal ( Fig 1M and 1N ) ; and 2 ) floral organs never fall off due to lack of an abscission zone ( S2A and S2B Fig ) . However , the ectopic leaf tissues on the petioles observed in bop1 bop2 double mutants were mostly rescued upon introduction of han-2 ( S2C Fig ) . The mutant phenotypes suggested that HAN , together with BP , PNH , PTL , JAG and BOP1/2 regulates flower development via complex genetic interactions . To explore this potential regulatory network at the transcriptional level , gene expression was quantified by real time qRT-PCR in the mutant lines ( Fig 3A and 3B and S3 Fig ) , and temporal and spatial expression patterns of these regulators were further analyzed by in situ hybridization ( Fig 3C–3R and S4 Fig ) . HAN transcripts localize to the boundaries between the meristem and developing organ primordia , the junctional domain between the SAM and the stem , and the boundaries between different floral whorls [13] . qRT-PCR showed that the expression level of HAN was significantly reduced in the pnh-2 , ptl-1 and bop1 bop2 mutant inflorescences , especially in the pnh-2 mutant , where transcript accumulation of HAN was decreased to 17% of the wild-type level , while there was no significant change in the jag-3 or bp-1 mutant plants ( Fig 3A ) , suggesting that PNH , PTL and BOP1/2 promote HAN expression . Consistently , in situ hybridization showed that the HAN signal was dramatically decreased and diffused in the pnh-2 mutant ( Fig 3C and 3D and S4A and S4B Fig ) . However , no obvious difference was detected for the signal of HAN in the bop1 bop2 , ptl-1 , jag-3 or bp-1 mutant as compared to that in wild-type ( WT ) ( S4C–S4H Fig ) , probably due to such levels of reduction in the bop1 bop2 ( -1 . 7 fold ) and ptl-1 ( -1 . 8 fold ) are visibly undetectable by in situ hybridization . The meristem regulator BP is expressed in the cortex of developing pedicels within the base of floral primordia in WT plants ( arrows in Fig 3E ) . In the han-2 mutant , BP signal appeared to expand into the initiating sepal primordia ( triangle in Fig 3F ) , or expand to the abaxial of the sepal primordia in stage 5 ( S4I and S4J Fig ) . qRT-PCR verified that the expression of BP was upregulated 2-fold in the han-1 inflorescence ( Fig 3B ) , suggesting that the boundary gene HAN may inhibit the expression of BP from expanding into organ primordia . As for the other meristem regulator PNH , qRT-PCR showed that PNH transcription was reduced nearly 3-fold in han-1 , but increased 4-fold in bp-1 ( Fig 3B and S3B Fig ) . Consistently , the mRNA signal of PNH was concentrated in the adaxial side of sepal primordia ( asterisk in Fig 3G ) , the floral meristem ( FM ) ( Fig 3G and asterisk in Fig 3I ) , and the provascular tissue ( arrow in Fig 3I ) [28] . In the han-1 mutant , PNH signal was decreased , especially at the adaxial side of sepal primordia at stage 2 ( Fig 3H ) , and the center of FM at stage 5 ( Fig 3J ) . In the bp-1 mutant , PNH signal was greatly enhanced ( Fig 3K and 3L ) , supporting the conclusion that HAN promotes while BP inhibits PNH expression during flower development . Therefore , the boundary-expressing HAN and the two meristem regulators PNH and BP form a regulatory feedback loop , in which HAN promotes PNH and represses BP transcription , and BP represses PNH while PNH acts positively on HAN expression . The organ primordia-expressed gene PTL appears to be unaffected in the han mutant as detected by qRT-PCR ( Fig 3B ) and in situ hybridization ( S4K and S4L Fig ) , and it is expressed in the margins of developing sepals and in the boundary between sepals and sepal primordia as previously reported ( S4K and S4L Fig ) [11] . In the five tested mutant lines ( han-1 , bp-1 , pnh-2 , ptl-1 and bop1 bop2 ) , JAG expression was significantly downregulated , with the lowest expression in bp-1 ( Fig 3B and S3D Fig ) , implying that JAG may be a downstream gene in the regulatory network . Consistently , about 25% of the han-1 mutant flowers almost abolished the JAG signal as compared to the enriched mRNA level in the emerging sepal primordia and stamen primordia in WT flowers ( asterisks in Fig 3M–3P ) [38] , supporting the idea that HAN can stimulate JAG expression in organ primordia . Previous studies showed that BOP1 and BOP2 function redundantly and exhibit similar expression patterns [41 , 46] . We found the expression of BOP1 displayed slight changes in all five mutant lines ( han-1 , bp-1 , pnh-2 , ptl-1 and jag-3 ) ( Fig 3B and S3E Fig ) , while the expression of BOP2 was significantly repressed in the han-1 or jag-3 mutant , and significantly enhanced in the bp-1 mutant ( Fig 3B and S3F Fig ) . Both BOP1 and BOP2 were expressed at the boundary between FM and sepal primordia , and base of sepals and other floral organs as previously reported ( Fig 3Q and 3R and S4M–S4P Fig ) [41 , 47] . In han mutant flowers , the BOP2 signal appeared low ( Fig 3R and S4P Fig ) , but the BOP1 signal remained unchanged ( S4M and S4N Fig ) , suggesting that transcription of BOP1 and BOP2 may be under different regulatory control during flower development . Based on the genetic and transcriptional data , yeast two-hybrid assays were used to investigate possible protein interactions between HAN with the two meristem regulators ( BP and PNH ) and three primordia-expressed regulators ( PTL , JAG , BOP1/2 ) ( Fig 4A ) . Considering the possible toxicity of full length PNH to the yeast cells , a series of deletion constructs of PNH was generated to test its interaction with other proteins ( Fig 4B and S5 Fig ) . The PNH protein can be divided into three regions: the N-terminus ( part I ) , the PAZ domain ( part II ) and the MID and KIWI domains ( part III ) ( S5A Fig ) [48] . Among all the deletion constructs of PNH , the construct ( PNHΔ1 ) without the MID and KIWI domains had the strongest interaction with HAN and BP ( Fig 4B and S5B Fig ) . However , HAN showed no physical interactions with BP directly ( S5B Fig ) , suggesting that HAN communicates with the meristem through effects on PNH , and PNH interacts with BP . In addition , yeast cells co-expressing full length HAN and JAG can grow on selective medium , indicating that HAN physically interacts with JAG ( Fig 4B ) , but there is no interaction observed between HAN and PTL , HAN and BOP1/2 , no interactions detected between JAG and PTL ( Fig 4A and S5B Fig ) . To verify the interactions between HAN , BP , PNH and JAG in planta , bimolecular fluorescence complementation ( BiFC ) assays were performed in the abaxial side of tobacco leaves . The results indicated interaction of HAN with PNH , HAN with JAG , and PNH with BP , confirming that HAN physically interacts with the meristem regulator PNH and primordial regulator JAG , and PNH interacts with the other meristem regulator BP in the nucleus ( Fig 4C ) . Consistently , the BiFC assay showed no interaction between HAN and BP , or HAN and BOP1/2 in planta ( Fig 4C and S6 Fig ) . Our previous research by time-course microarray indicated that transient induction of HAN by dexamethasone ( DEX ) treatment in the p35S:HAN-GR line led to downregulation of HAN through autoregulation , and specifically repressing a cytokinin degradation gene CYTOKININ OXIDASE 3 ( CKX3 ) among the CKX family [49] . To further characterize the regulation of CKX3 by HAN , we examined the expression level of CKX3 in the han-1 null allele by qRT-PCR as well as by in situ hybridization ( Fig 5A–5C ) . CKX3 mRNA abundance was reduced more than 6-fold in the han-1 inflorescence ( Fig 5A ) . In situ hybridization showed that CKX3 mRNA is located in the center of the FM and in the boundary between the long stamen primordia and the gynoecial primordium in WT flowers , as previously reported ( Fig 5B ) [50] . In the han-1 mutant , CKX3 signal was decreased and diffused , appearing throughout the FM ( Fig 5C ) . Given that the expression domain of HAN and CKX3 are overlapping ( Figs 3C , 5B , S4E and S4G ) [13] , and that transient overexpression of HAN mimics loss of HAN function through self-repression [49] , HAN may function through stimulating the expression of CKX3 to maintain a low cytokinin level and thus reduced cell division in the boundary . Next , the content of the cytokinin trans-zeatin riboside ( ZR ) in the inflorescence was measured . As expected , the ZR levels increased in homozygotes for the han-2 weak allele and were even higher in han-1 null mutants as compared to WT ( Fig 5D ) . We also measured the levels of gibberellins ( GA ) and auxin ( IAA ) in the han mutant inflorescence and found a significant decrease in the GA content and a small increase in the IAA level ( Fig 5E and 5F ) . To test whether cytokinin regulates boundary function , plants were treated with 50μM N6-benzylaminopurine ( BAP ) for three days , and boundary formation was observed every 24 hours by following expression of the boundary-specific reporter pLAS::LAS-GFP [51] . 50μM BAP treatment resulted in enlargement of the SAM and increased numbers of floral organs as previously reported [52] . As compared to mock-treated plants ( Fig 5G–5I ) , meristem-to-sepal boundaries ( marked by pLAS::LAS-GFP in purple ) displayed improper placement , enlarged domains , and increased numbers of boundaries , which preceded and predicted the increased numbers of sepals ( Fig 5J–5L ) . For example , as shown in Fig 5K , the formation of five boundaries predicts the development of five sepals with unequal sizes , which is often the case in cytokinin-treated lines [52] . To explore whether HAN directly regulates the transcription of BP , PNH , PTL , JAG , BOP1/ 2 and CKX3 , qRT-PCR was performed in the inflorescence after 4h treated with DEX and cycloheximide in 35S:HAN-GR plants . As shown in Fig 6A , the expression of CKX3 , JAG , BOP1 , BOP2 and BP was significantly reduced compared to the mock-treated plants [49] . Thus , a chromatin immunoprecipitation assay ( ChIP ) was performed , followed by quantitative PCR analysis ( ChIP-PCR ) , with anti-HAN antibodies , to verify the direct bindings . The specificity of anti-HAN antibodies has been previously tested [49] . The various amplicons used for the ChIP-PCR assay are shown in Fig 6B , which contained the enriched regions of DNA sequences WGATAR ( W = A or T and R = A or G ) in the promoters and genic regions of CKX3 , JAG , BOP1 , BOP2 or BP . The promoter region −977 to −735 bp of HAN was used as a positive control and an amplicon derived from the UBQ10 promoter was used as a negative control [49] . Amplicons CKX3p4 and CKX3i3 were significantly enriched when normalized to the negative control ( Fig 6C ) . CKX3p4 spans the promoter region from -1677 to -1511 bp of CKX3 , with the recognition motif WGATAR at -1619 ~ -1614bp . CKX3i3 locates in the first intron region from 1539 to 1693 bp of CKX3 , with the recognition motif at 1569~1574 bp and the ChIP/Input ratio increased over 5-fold compared to the positive control HAN ( Fig 6C ) . Similarly , amplicon JAGp9 , which spans the promoter region from -282 to -96 bp of JAG , with two recognition motifs at -175~ -170 bp and -166 ~ -161 bp , respectively , was significantly enriched ( Fig 6D ) . BOP1e1 and BOP2e1 , which span the first exon from 330 to 476 bp of BOP1 ( recognition motif at 363~368 bp ) , and the second exon region from 1862 to 2012 bp of BOP2 ( recognition motif at 1939~1944bp ) , respectively , were also significantly enriched , with the ChIP/Input ratio increased 1 . 5 and 7 . 7 fold , respectively , as compared to the positive control HAN ( Fig 6E and 6F ) . By contrast , all of the other tested amplicons from CKX3 , JAG or BOP1/2 were not enriched compared to the UBQ10 amplicon , suggesting that the ChIP-PCR assay was amplicon-specific . Further , no amplicons in the promoters and genic regions of BP , PNH and PTL were found to be significantly enriched ( S7 Fig ) , indicating that HAN did not directly bind to BP , PNH and PTL . Given that the expression of CKX3 , JAG and BOP2 were greatly reduced in both han-1 and DEX-treated 35S:HAN-GR plants , we verified the HAN autoregulation by western blotting and the binding of HAN and CKX3 using ChIP-PCR between the DEX- and mock-treated 35S:HAN-GR plants . Our data showed that HAN protein was greatly reduced in the 35S::HAN-GR line upon DEX treatment , supporting the self-regulation of HAN ( Fig 6G ) . Consistently , binding on CKX3 and on HAN itself was significantly reduced upon DEX treatment ( Fig 6H ) . Proper boundary formation is required for meristem maintenance , organ separation , floral organ patterning , and axial meristem initiation [10 , 11 , 14 , 16 , 19 , 20 , 22 , 53] . Previous studies have shown that boundary-expressing CUC genes induce the expression of the meristematic marker STM , while STM represses CUC expression in the meristem , forming a negative feedback loop during embryogenesis [9 , 22] . Here we found that the boundary-expressing gene HAN interacts with meristem regulators PNH genetically , transcriptionally and biochemically . Double mutant han-2 pnh-2 displayed synergistic effect on petal reduction and meristem organization ( Figs 1 and 2 ) . At the transcriptional level , HAN and PNH promote each other , while HAN represses BP , and BP represses PNH ( Fig 3 ) . At the protein level , HAN interacts with PNH and PNH interacts with BP ( Fig 4 ) . Therefore , HAN may communicate with the meristem through a direct interaction with PNH and indirectly with BP to ensure proper meristem organization and flower development ( Fig 7B ) . The expression of HAN and PNH overlap in the boundary regions and the bottom of the meristem in the stage 2 flowers ( Fig 3C and 3G ) [13] , the interaction between HAN and PNH may occur in these overlapping regions to maintain proper meristem organization during continuous organogenesis ( Fig 7B ) . On the other side , the boundary gene HAN communicates with floral organ primordia through JAG and BOP2 . Genetically , HAN coordinatively regulates flower organ development with JAG and BOP1/2 ( Fig 1 and S1 Fig ) . qRT-PCR analysis and in situ hybridization showed that HAN promotes the expression of JAG and BOP2 in the organ primordia ( Fig 3 ) . Biochemical analysis showed that HAN physically interacts with JAG ( Fig 4 ) , and a ChIP-PCR assay indicated that HAN directly binds to the promoter of JAG and exon of BOP2 ( Fig 6 ) . Therefore , HAN directly stimulate the transcription of JAG and BOP2 , and interact with JAG at the protein level ( Fig 7B ) . Given that transcripts of HAN , JAG and BOP2 overlap in the boundary regions in the stage 2 flower ( Fig 3C , 3M and 3Q ) , HAN may directly stimulate the transcription of JAG and BOP2 in the boundary region to promote organ primordia development . Consistent with this notion , the serrated sepals in the jag mutant were also observed in a han-1 mutant , which could be due to reduced JAG expression , or to elimination of its protein partner , in the han-1 mutant [38] . Previous finding showed that JAG can directly bind to the promoter of BOP2 [54] . Our data showed that BOP2 was significantly reduced in the jag-3 mutant , indicating that JAG directly stimulates the transcription of BOP2 in the inflorescence . Given that HAN directly binds to the exon of BOP2 , HAN promotes the expression of BOP2 directly or indirectly through JAG . Despite HAN also directly binds to the exon of BOP1 ( Fig 6E ) , transcription of BOP1 was not changed in the han-1 mutant , suggesting that additional factors may antagonize the effect of HAN on BOP1 . In the bop1 bop2 mutant , JAG expression in the inflorescence is down-regulated , in contrast to the upregulation of JAG expression in leaves or in the vegetative shoot apex [40] , indicating that different interaction modules of JAG and BOP1/2 exist during leaf and flower development . Previous data showed that JAG directly binds to the promoter of PTL to control petal growth and shape [55] , and we found that JAG repressed the expression of PTL ( S3C Fig ) . Thus , the role of HAN in control of petal number and petal morphology as revealed by the double mutant analysis ( Fig 1 ) can be explained by the direct interaction with JAG and BOP2 in the petal boundaries , and thus indirect interaction with PTL during flower development in Arabidopsis . Notably , ChIP-Seq showed that JAG directly targets HAN as well [54] . However , the expression of HAN showed no significant change in the jag-3 mutant ( Fig 3A ) , probably due to autoregulation of HAN [49] . Our qRT-PCR , in situ hybridization and ChIP-PCR data showed that HAN directly binds to the cytokinin degradation gene CKX3 and promotes CKX3 expression ( Figs 5 and 6 ) . In the han-1 mutant , the CKX3 signal intensity was reduced and diffused throughout the FM ( Fig 5C ) , resulting in elevated cytokinin content ( Fig 5D ) . Exogenous cytokinin treatment disrupts boundary formation and results in increased floral organ numbers ( Fig 5G–5L ) . Therefore , HAN may maintain the proper boundary function by directly activating CKX3 , thus reducing cytokinin content and suppressing cell division in the boundary ( Fig 7B ) . Han et al . [56] showed that pAP1::IPT8 ( which encodes a rate-limiting enzyme in cytokinin biosynthesis ) lines displayed loss of petals [56] , while loss of function of both CKX3 and CKX5 results in a slight increase in the number of sepals and petals[50] , rather than the reduced floral organ number observed in the han-1 mutant , suggesting that the distribution of cytokinin rather than the content of cytokinin in the flower is more essential for regulation of petal number , and that HAN regulates flower development via a complex interaction network , with the CKX3-mediated cytokinin pathway only as one branch . In addition , the signal intensity of the auxin response marker DR5 was previously shown to be greatly reduced in the han-2 mutant [49] , while the IAA level was up-regulated in the han mutant ( Fig 5F ) , suggesting that HAN represses auxin biosynthesis and promotes auxin signaling in the inflorescence . Consistent with the antagonistic interaction between auxin and GA [57] , the GA level was significantly decreased in the han mutant inflorescence ( Fig 5E ) . Recently , HAN was shown to repress itself and three GATA3 family genes , HAN-LIKE 2 ( HANL2 ) , GATA , NITRATE-INDUCIBLE , CARBON-METABOLISM-INVOLVED ( GNC ) , and GNC-LIKE ( GNL ) [49] . GNC and GNL are direct downstream targets of AUXIN RRESPONSE FACTOR2 ( ARF2 ) that mediates auxin response , and GNC and GNL are also downstream targets of the GA signaling pathway involving DELLAs and PIFs in Arabidopsis [58] . Therefore , HAN may regulate flower development through the CKX3-mediated cytokinin homeostasis , auxin and GA biosynthesis , and GNC/GNL-mediated auxin and gibberellin responses . The Arabidopsis thaliana Landsberg erecta ( Ler ) and Columbia ( Col ) ecotypes , the mutant alleles han-1 ( Ler ) , han-2 ( Ler ) , pnh-2 ( Ler ) , jag-3 ( Ler ) , bp-1 ( Ler ) , han-2 ( Col ) and ptl-1 ( Col ) were described previously [11 , 13 , 26 , 38 , 59] and obtained from the Meyerowitz lab stock collection . The reporter line pLAS::LAS-GFP was kindly provided by Dr . Yuval Eshed [51] . The bop1-4 bop2-11 double mutant plants ( Col ) were kindly provided by Jennifer C . Fletcher . Double or triple mutant combinations with han-2 were generated by crossing using the same ecotype background , and identified by genotyping using the primers listed in S1 Table . For han-2 genotyping , a 852-bp fragment was amplified and digested by TseI , which recognizes the mutant site . For jag-3 genotyping , PCR products from the mutant were cleaved by TseI . For pnh-2 genotyping , a 111-bp product was amplified by PCR , and EcoRIcleaves only the wild-type product . For ptl-1 genotyping , a 726-bp fragment was amplified and digested by CfrI , which digests only the wild-type product . Genotypings for bop1-4 bop2-11 and bp-1 were performed as described previously [44] . Plants were grown in soil at 22°C under conditions of 16h light/8h dark . Total RNA was isolated from 3–5 inflorescence samples using RNaEXTM Total RNA Isolation Solution ( Generay , China ) . cDNA was synthesized from 4μg total RNA using reverse transcriptase ( Aidlab , China ) and qRT-PCR analyses were performed on an ABI PRISM 7500 Real-Time PCR System ( Applied Biosystems , USA ) . Each qRT-PCR experiment was performed in three biological replicates and three technical replicates . The ACTIN2 gene was used as an internal reference to normalize the expression data . Fold change was calculated using the 2-ΔΔCt method [60] and the standard deviation was calculated between three biological replicates , using the average of the three technical replicates for each biological sample . The gene-specific primers are listed in S1 Table . To examine auxin , cytokinin and gibberellin levels in the han-1 and han-2 mutant plants , about 0 . 1g of inflorescence ( about 20–35 inflorescence ) was harvested from han-1 , han-2 or Ler plants grown under the same conditions and immediately frozen in liquid nitrogen until further use . Sample extraction and hormone measurements were performed using enzyme-linked immunosorbent assays as previously reported [61] . Standard IAA , GA and trans-zeatin riboside ( ZR ) ( Sigma , USA ) was used for calibration . Arabidopsis inflorescences were fixed in 3 . 7% formol-acetic-alcohol ( FAA ) ( 3 . 7% formaldehyde , 5% glacial acetic acid , and 50% ethanol ) and stored at 4°C until use . Probe synthesis was performed on cDNA using gene-specific primers including SP6 and T7 RNA polymerase binding sites . Probes for HAN , PNH , BOP1/2 , PTL , and CKX3 were made using the same sequences as previously reported [13 , 40 , 44 , 50 , 59 , 62] , and probes for JAG and BP were synthesized with the specific coding sequence fragments as templates . Sample fixation , sectioning and in situ hybridization was performed as previously described [49] . The primers for probe synthesis are listed in S1 Table . Transient overexpression of HAN was achieved through 10μM DEX treatment on p35S::HAN-GR inflorescence apices . 10μM cycloheximide was used with 10μM DEX for 4h treatment . DEX solution was applied by pipette every 24h . Cytokinin treatment was performed using 50μm N6-benzylaminopurine , and applied by pipette every 24h . Each treatment was repeated at least three times with corresponding mock-treated controls . Plants were grown and inflorescence meristems were prepared for live imaging as previously described [6] . All imaging was done using a Zeiss 510 Meta laser scanning confocal microscope with a 40x water dipping objective using the Z-stacks mode . For the pLAS::LAS-GFP reporter line , 20 samples were imaged to confirm the observed patterns were representative , and similar sets of lasers and filters were used to image the reporter as previously described [6 , 63] . Full-length coding sequences for HAN , JAG , PNH , BP , IND , SPT , PTL , BOP1 , BOP2 or a series of truncated PNH fragments were cloned into pGBKT7 ( bait vector ) or pGADT7 ( prey vector ) . All constructs were confirmed by sequencing before transformation into yeast strain AH109 . The bait and prey vectors were transformed according to the manufacturer’s instructions of MatchmakerTM GAL4 Two-Hybrid System 3 & Libraries ( Clontech ) . Protein interactions were assayed on selective medium lacking Leu , Trp , His and Ade or supplemented with 2 . 5 mM 3-Amino-1 , 2 , 4-triazole ( 3-AT ) . The gene primers used for yeast two hybrid experiments are listed in S1 Table . Full-length coding sequences for HAN , JAG , PNH , BP , IND , SPT , BOP1 and BOP2 ( without stop codons ) were amplified by PCR using gene-specific primers , and cloned into the vectors pSPYNE-35S or pSPYCE-35S containing each half of YFP ( N- or C- terminus ) to generate the fusion proteins ( such as HAN-YFP N-terminus ) in frame as previously described [64] . All constructs were verified by sequencing before transformation into Agrobacterium tumefaciens strain GV3101 . The two plasmids for testing specific interaction were co-transformed into the abaxial sides of 4-7-week old Nicotiana benthamiana leaves as previously described [65] . After 48h co-infiltration , the tobacco leaves were imaged using a Zeiss LSM 510 Meta confocal laser scanning microscope . YFP signals and DIC of tobacco cells were taken at the same time from different detection channels . The gene primers used for BiFC are listed in S1 Table . ChIP-PCR was performed as described by Gendrel et al . [66] with slight modifications . Briefly , about 2g of inflorescence tissue from wild-type Ler or DEX-treated p35S::HAN-GR line for three days were harvested and fixed in 37ml 1% formaldehyde and cross-linked for 15 min with vacuum infiltration at room temperature , followed by addition of glycine with vacuum infiltration for 5 min to terminate the cross-linking reaction . Nuclei were isolated and lysed , and chromatin was sonicated to an average size of 500 bp . The sonicated chromatin served as input and stored at -20°C until use . Immunoprecipitation reactions were performed using anti-HAN antibody [49] and no antibody as a negative control . The complex of chromatin-antibody was captured with protein G agarose beads ( Millipore ) followed by precipitated DNA purification and elution , and DNA deposited with glycogen carrier ( Thermo ) served as a template for qRT-PCR . The enrichment regions of DNA sequences WGATAR ( W = A or T and R = A or G ) in the promoters or genic regions were chosen to perform qRT-PCR [67–69] . Two biological repeats and three technical replicates were performed for each gene . HAN and UBQ10 were used for positive and negative controls , respectively [49] . The ChIP/Input ratio was calculated by the equation 2 ( Ct ( MOCK ) -Ct ( HAN-ChIP ) ) /2 ( Ct ( MOCK ) -Ct ( INPUT ) ) . The primer pairs used in ChIP-PCR were listed in S1 Table . Inflorescence of Ler , han-2 , bp-1 , han-2 bp-1 , pnh-2 , han-2 pnh-2 from 40-day-old plants , and stage 7–9 fruit samples of han-2 , bop1bop2 and han-2bop1bop2 were prepared for SEM . After removing the flowers or floral organs , samples were fixed in FAA overnight . The samples were then critical-point dried in liquid CO2 , sputter coated with gold and palladium for 60s , and examined at an acceleration voltage of 2kV using a scanning electron microscope ( Hitachi Model S-4700 , Japan ) . Inflorescence tissues from mock or DEX-treated p35S::HAN-GR line for three days were harvested in liquid nitrogen . The plant total protein extraction kit ( Sigma-Aldrich ) was used for protein extraction . Western blotting was performed as previously described [49] . Sequence data from this article can be found in the Arabidopsis Genome Initiative or GenBank/EMBL databases under the following accession numbers: HAN ( AT3G50870 ) , PNH ( AT5G43810 ) , BOP1 ( AT3G57130 ) , BOP2 ( AT2G41370 ) , JAG ( AT1G68480 ) , PTL ( AT5G03680 ) , KNAT1/BP ( AT4G08150 ) , CKX3 ( AT5G56970 ) , IND ( AT4G00120 ) , SPT ( AT4G36930 ) .
The shoot apical meristem is the stem cell pool in plants that gives rise to all above-ground organs including leaves , flowers and fruits . Between the meristem and the newly formed organ primordia , a boundary with specialized cells is formed to separate them . Boundary genes are specifically expressed in boundaries and function in boundary formation and maintenance . Previous studies showed that boundary genes interact with meristem regulators and primordia genes during embryogenesis or leaf development . But whether and how boundaries communicate with meristem and organ primordia during flower development remains largely unknown . Here we combined genetic , molecular and biochemical tools to explore interactions between the boundary gene HANABA TARANU ( HAN ) and two meristem regulators BREVIPEDICELLUS ( BP ) and PINHEAD ( PNH ) , and three primordia-specific genes PETAL LOSS ( PTL ) , JAGGED ( JAG ) and BLADE-ON-PETIOLE ( BOP ) during flower development . We showed that boundary-expressing HAN communicates with the meristem through PNH , regulates floral organ development via JAG and BOP2 , and maintains boundary morphology through CYTOKININ OXIDASE 3 ( CKX3 ) -mediated cytokinin homeostasis . Thus , our findings shed light on the “bridge” role of boundaries between meristem and organ primordia during flower development in Arabidopsis .
[ "Abstract", "Introduction", "Results", "Discussion", "Methods" ]
[]
2015
HANABA TARANU (HAN) Bridges Meristem and Organ Primordia Boundaries through PINHEAD, JAGGED, BLADE-ON-PETIOLE2 and CYTOKININ OXIDASE 3 during Flower Development in Arabidopsis
Lipophorin , the main Drosophila lipoprotein , circulates in the hemolymph transporting lipids between organs following routes that must adapt to changing physiological requirements . Lipophorin receptors expressed in developmentally dynamic patterns in tissues such as imaginal discs , oenocytes and ovaries control the timing and tissular distribution of lipid uptake . Using an affinity purification strategy , we identified a novel ligand for the lipophorin receptors , the circulating lipoprotein Lipid Transfer Particle ( LTP ) . We show that specific isoforms of the lipophorin receptors mediate the extracellular accumulation of LTP in imaginal discs and ovaries . The interaction requires the LA-1 module in the lipophorin receptors and is strengthened by a contiguous region of 16 conserved amino acids . Lipophorin receptor variants that do not interact with LTP cannot mediate lipid uptake , revealing an essential role of LTP in the process . In addition , we show that lipophorin associates with the lipophorin receptors and with the extracellular matrix through weak interactions . However , during lipophorin receptor-mediated lipid uptake , LTP is required for a transient stabilization of lipophorin in the basolateral plasma membrane of imaginal disc cells . Together , our data suggests a molecular mechanism by which the lipophorin receptors tether LTP to the plasma membrane in lipid acceptor tissues . LTP would interact with lipophorin particles adsorbed to the extracellular matrix and with the plasma membrane , catalyzing the exchange of lipids between them . Lipids are continuously trafficked between tissues , from sites of absorption and synthesis to the organs that will utilize them . These transport routes must adapt to the changing metabolic status and developmental stage of the animal . Thus , during the feeding period of Drosophila larvae , a main pathway of lipid transport originates at the gut and delivers lipids to the fat body for storage . Imaginal discs also accumulate considerable amounts of neutral lipids during this stage . In contrast , under starvation and during the non-feeding pupal stage , lipids are mobilized from the fat body to support organismal growth and metabolism . Other main transport routes carry neutral lipids derived from the fat body to the muscles during flight and , in adult females , large amounts of lipids are also transferred to vitellogenic oocytes as an essential energy reserve [1–4] . How these routes are regulated and how lipids are targeted to particular tissues at specific developmental times is not well understood . In insects , lipids are transported in hemolymph as lipoprotein particles , the most abundant being lipophorin , which carries about 95% of all hemolymph lipids in Drosophila [1 , 4] . Each particle contains a single copy of Apolipophorin I and Apolipophorin II , derived from the cleavage of a common precursor with homology to mammalian ApoB [5] and multiple lipid species , predominantly diacylglycerol ( DAG ) and phospholipids . Circulating lipophorin comes in contact with all tissues and cells , allowing for the potential exchange of lipids . Unfortunately , the mechanisms that mediate and regulate this exchange are only partially understood . Classic experiments demonstrated that lipophorin operates by a shuttle mechanism . Apolipophorin has a long half-life , calculated to exceed one day in some species [6] , and each particle participates in multiple cycles of lipid loading and unloading in tissues without apparent degradation of the Apolipophorin moiety [7] . Biochemical and kinetic studies indicated that the interaction of lipophorin with cells is mediated through specific receptors [8–11] . At the molecular level , the best characterized are the lipophorin receptors of the Low Density Lipoprotein Receptor ( LDLR ) family , which were initially identified by their capacity to induce lipophorin endocytosis when overexpressed in a cell culture system [12] . In Drosophila , lipophorin receptor 1 and 2 ( lpr1 and lpr2 ) are required for the uptake of neutral lipids in imaginal discs , oocytes and oenocytes [13 , 14] . However , lpr1 , lpr2 double mutant animals are viable and do not display significant changes in total neutral lipid content , suggesting that the major routes for lipid transport are not grossly disrupted . Drosophila lipophorin receptors promote lipid uptake by an endocytosis independent mechanism still poorly characterized [13] . Interestingly , these genes generate multiple , functionally diverse isoforms . Those containing a specific LDLR class A ( LA ) domain mediate neutral lipids uptake whereas the involvement of the remaining isoforms in lipid metabolism is unclear [13] . In insects , the exchange of lipids between lipophorin and tissues was shown to be facilitated by a circulating , low abundance , high density lipoprotein named Lipid Transfer Particle ( LTP ) . Early in vitro studies showed that LTP had a surprising catalytic activity . It promoted the exchange of lipids , mostly DAG , between lipophorin particles of different densities and even between human LDL and insect lipophorins [15] . Additional experiments demonstrated that LTP also promoted the transfer of lipids between explanted tissues and purified lipophorin in vitro . In particular , transfer of lipids from the midgut to lipophorin and from lipophorin to the fat body and to ovaries was shown to be blocked by an anti-LTP antibody and resumed by the addition of purified LTP [16–19] . More recently , the genes coding for apoLTP in Drosophila and in Bombyx mori were identified and novel mutations isolated [4 , 20] . One of the most prominent phenotypes of apoLTP loss of function in Drosophila is the accumulation of neutral lipids in the gut , a phenotype similar to apolipophorin silencing [21] that demonstrates the essential role of LTP for loading lipophorin with lipids in enterocytes [4] . Here , we examine the molecular mechanisms that mediate the transfer of neutral lipids from lipophorin to imaginal discs and to ovaries . We identified LTP as a novel lipophorin receptor ligand . Our results indicate that recruitment of LTP to cell membranes mediated by the lipophorin receptors is a key event that initiates the transfer of neutral lipids to cells . To improve our understanding of the molecular mechanisms involved in lipophorin receptor-mediated lipid uptake , we decided to search for lipophorin receptor interacting proteins that could potentially participate in the process . To this end , we used an affinity purification strategy . We selected isoforms Lpr2E and Lpr2F as baits . Lpr2E mediates lipid uptake in imaginal discs and ovaries whereas Lpr2F , despite being 95 . 5% identical to Lpr2E , is inactive in this regard and was used as a negative control [13] . Both isoforms were tagged with TAP at the C-termini to facilitate purification [22] , overexpressed in ovaries , a tissue of high lipid uptake activity , and affinity purified from ovary extracts . Proteins that differentially co-purified with Lpr2E compared to the control Lpr2F were identified by mass spectrometry ( S1 Fig ) . To increase the probability to find interactors , we performed a second affinity purification experiment using Lpr2E and Lpr2F extracellular domains as baits instead of full-length proteins . In this experiment , we directed expression of the secretable baits to the fat body and purified the proteins from total larval extracts . Notably , in both experiments we identified the circulating lipoprotein LTP as a main Lpr2E interactor . In contrast , LTP was not isolated when Lpr2F was used as bait , either in the full-length or in the secretable form ( S1 Fig ) . To validate the previous results , we examined the physical interaction between LTP and the lipophorin receptor isoforms by co-IP . HA tagged Lpr2E and Lpr2F isoforms were expressed and purified from Drosophila S2 cells and incubated in vitro with diluted hemolymph from wild type larvae . Hemolymph LTP strongly bound to Lpr2E , generating a robust signal , whereas it did not co-immunoprecipitate with Lpr2F ( Fig 1A ) . Thus , these results confirm that LTP is a ligand of lipid uptake-promoting Lpr2E isoform but not of lipid uptake-inactive Lpr2F isoform and suggest that LTP might be a factor specifically involved in the transfer of lipids from lipophorin to tissues . Surprisingly , under the same conditions we were unable to detect an interaction between the lipophorin receptors and lipophorin ( Fig 1A ) , even though lipophorin is more abundant than LTP in hemolymph . Thus , the affinity of lipophorin for the lipophorin receptors is weaker than that of LTP . Moreover , this result indicates that LTP binding to Lpr2E does not require lipophorin . Our in vitro data indicated Lpr2E binds with high affinity to LTP . To examine the functional relevance of this interaction , we first tested whether Lpr2E can promote LTP endocytosis or affect LTP distribution in vivo in imaginal discs , a tissue where lipophorin receptors activity was well characterized [13] . Interestingly , we found UAS-lpr2E overexpression induced a strong accumulation of LTP in basolateral cell membranes as well as formation of intracellular particles suggestive of LTP endocytosis ( Fig 1B and 1C ) . These particles showed partial colocalization with the early endosome marker Rab5 and the lysosomal marker Lamp1 , indicating that they represent different stages of LTP endocytosis ( Fig 1F and 1G ) . The accumulation of LTP at the basolateral domain was mostly extracellular , since it could be detected with an immunostaining protocol performed without cell permeabilization ( Fig 1D and 1E ) . Of notice , no such LTP accumulation or endocytosis was detected after Lpr2F overexpression ( Fig 1H ) . Thus , isoform Lpr2E interacts in vivo with LTP and can promote LTP extracellular accumulation and endocytosis when overexpressed . The interaction is isoform-specific in vivo , as was already suggested by our in vitro data . In contrast , similar experiments indicated that overexpression of UAS-lpr2E or of UAS-lpr2F , both induced the endocytosis of lipophorin in imaginal discs , as shown by the formation of lipophorin intracellular vesicles that partially co-localized with endosome markers ( [23] and S2 Fig ) . Thus , lipophorin receptor isoforms can induce lipophorin endocytosis irrespectively of their capacity to mediate lipid uptake . The lipophorin receptors are expressed in adult ovaries and in larval imaginal discs where they are required for neutral lipid uptake [13] . Thus , we examined whether LTP distribution in these tissues was altered in lipophorin receptor mutants . We found that in wild type ovaries , LTP accumulated in nurse cells plasma membranes starting at stage 9 follicles and strongly increasing at stage 10 ( Fig 2A ) , essentially coinciding with lpr2 expression pattern [13] . Interestingly , this accumulation mostly disappeared when lpr2 was eliminated in Df ( 3R ) lpr2 or in Df ( 3R ) lpr1/2 females , which removes lpr1 and lpr2 genes ( Fig 2B and 2C ) . In both mutants , occasional patches of LTP remained , mostly in crevices between nurse cells . Thus , lpr2 is essential for LTP accumulation in nurse cells plasma membrane . In wild type wing imaginal discs LTP is mostly found in the wing pouch region , accumulating in basolateral cell membranes as well as in a few apical vesicles ( Fig 2D and inset ) . The wing pouch area expresses lpr1 and lpr2 and thus , these receptors could potentially mediate the observed LTP distribution . Accordingly , no LTP is detectable in imaginal discs from lpr1 , lpr2 double mutant larvae ( Fig 2E ) . Taken together , our data demonstrates that both , in ovarian follicles and in imaginal discs cells , the lipophorin receptors are required for the accumulation of LTP at the cell surface . In addition , they mediate LTP endocytosis in imaginal discs . We detected LTP in additional larval tissues . In particular , we saw a strong signal in gastric caeca and in discrete regions of the midgut ( S3 Fig and [4] ) , the ring gland , the oenocytes , the salivary gland imaginal rings and a weaker staining in the fat body ( S3 Fig ) . LTP distribution in these tissues did not change or slightly decreased in Df ( 3R ) lpr1/2 larvae ( S3 Fig ) , suggesting the existence of other , still unidentified , LTP receptors . The specific interaction between LTP and the lipophorin receptor isoforms that mediate lipid uptake suggests that LTP is involved in this process . To examine this possibility , we generated a novel mutation in apoLTP by the imprecise excision of the artificial transposon P{wHy}DG06206 , inserted close to apoLTP promoter ( S4 Fig ) . The excision removed a 4 . 8 Kb fragment which included the apoLTP promoter and the first non-coding exon , without affecting neighboring genes ( S4 Fig ) . Accordingly , the novel mutation was named apoLTP[excDG06206] . In homozygous animals , embryogenesis was not affected . However , larvae remained small after hatching and eventually died after a prolonged first instar ( S4 Fig , see also [4] ) . We observed a strong accumulation of neutral lipids in the gut of mutant larvae ( S4 Fig ) , a phenotype also described by Palm et al . that reflects an essential role of LTP in the transfer of lipids from the gut to lipophorin [4] . The phenotype was caused by apoLTP loss of function since it could be completely rescued by a genomic BAC containing an apoLTP transgene . To examine apoLTP loss of function phenotype in adults , we silenced it in the fat body , the only tissue where the gene is expressed [4] , by the temporally controlled expression of a UAS-apoLTP-RNAi transgene using the Gal80ts technique [24] and the driver Cg-gal4 , which is expressed in larval and adult fat body ( [25] and S5 Fig ) . The most obvious phenotype was a pronounced reduction in female fertility two days after the activation of UAS-apoLTP-RNAi . In these animals , a fraction of the follicles degenerated . However , those that reached vitellogenic stages displayed strongly diminished levels of intracellular neutral lipids in nurse cells and oocytes ( Fig 3A and 3B ) . Thus , LTP is required for the accumulation of neutral lipids by vitellogenic follicles . Silencing apolipophorin for six days using an equivalent approach resulted in a similar blockage of lipid uptake in ovarian follicles ( Fig 3C ) . Thus , both lipophorin and LTP are required for the acquisition of neutral lipids during vitellogenesis . To examine LTP requirement in imaginal discs , we silenced apoLTP in the fat body as before for four days . Since this treatment delays larval growth , we staged the animals by selecting white pupa , a developmental period that last for about one hour . apoLTP silenced pupa had imaginal discs of wild type size but notably reduced levels of neutral lipids compared to controls ( S6 Fig and [4] ) , indicating that LTP is also required for lipid accumulation in imaginal discs . Several hypotheses can account for the observed reduction in lipid droplets in ovaries and imaginal discs . LTP-lipopophorin receptor complexes could be required locally in these tissues for lipid uptake . Alternatively , the phenotypes could be indirectly caused by the blockage of lipophorin loading with lipids at the gut and the resulting decrease in lipid content of circulating lipophorin [4] . Finally , a combination of the two effects is also possible . However , the fact that LTP accumulates in nurse cells and imaginal disc cells plasma membranes and that only lipophorin receptor isoforms that mediate lipid uptake do induce this recruitment ( Figs 1 and 2 ) support a direct involvement of LTP in the transfer of lipids from circulating lipophorin to nurse cells and imaginal disc cells . The observation that isoform Lpr2E physically interacted with LTP but isoform Lpr2F did not indicated that protein domains specifically present in Lpr2E were required for binding . To identify them , we assayed chimeric receptors generated by domain swapping between Lpr2E and Lpr2F ( S7 Fig ) . It was previously shown that a 232 amino acids N-terminal region of Lpr2E was essential for neutral lipid uptake and for lipophorin extracellular stabilization ( S7 Fig and [13] ) . Thus , we first tested whether the same N-terminal region played a role in LTP binding . To this end , we overexpressed the transgene UAS-Lpr2F+LA1+NCN coding for a chimera containing Lpr2E 232 amino acids N-terminal region fused to Lpr2F , in the posterior compartment of wing imaginal discs . This chimera induced LTP accumulation in basolateral plasma membranes as well as in intracellular vesicles , a phenotype identical to that of Lpr2E ( compare Fig 4E and 4A ) . Thus , the 232 amino acids N-terminal domain of Lpr2E is required for the interaction with LTP . This region includes a specific LDLR A domain ( LA-1 ) preceded by a stretch of 16 amino acids that is conserved between lipophorin receptors of several high dipteran species and that we call "extension domain" ( ED ) , since it appears to extend the LA-1 domain ( S7 Fig , underlined in red ) . In addition , Lpr2E and Lpr2F contain specific signal peptides . To examine which domains of the N-terminal region are involved in LTP binding , we tested additional chimeras in the same assay . Addition of LA-1 to Lpr2F ( UAS-lpr2F+LA1 ) did not change the activity of the protein ( Fig 4F , compare with Fig 4B ) . However , further addition of the 16 amino acids ED domain generating the chimera UAS-lpr2F+LA1+ED , provided a strong capacity to bind LTP ( Fig 4I ) . In contrast , a similar chimera lacking LA-1 ( UAS-Lpr2F+NCN ) was unable to mediate LTP accumulation ( Fig 4J ) . In conclusion , an LA-1 domain preceded by a stretch of 16 conserved amino acids ( ED ) is essential for a robust interaction between the lipophorin receptors and LTP . A similar set of experiments was performed under conditions in which endocytosis was blocked for three hours before dissection using a temperature sensitive shibire ( shi ) allele , Drosophila Dynamin homolog . Under these conditions , receptors as well as their ligands accumulate at the cell surface improving their detection by immunostaining [26] . Consistent with our previous results , after blocking endocytosis we observed strong LTP extracellular accumulation in basolateral membranes for all isoforms and chimeras containing LA-1+ED domains ( Fig 4C , 4G and 4K ) and no effect on LTP distribution for Lpr2F or the chimera lacking LA-1 ( Fig 4D and 4L ) . Interestingly , the chimera that contained LA-1 but not the ED ( UAS-lpr2F+LA1 ) did promote a moderate LTP accumulation that was undetectable under normal conditions ( Fig 4H ) . These results suggest that the LA-1 domain provides some capacity to bind LTP but the interaction is potentiated by the 16 conserved amino acids that precede it . In vitro co-IP experiments examining the affinity between Lpr2F-Lpr2E chimeras and LTP gave results that were consistent with the previous in vivo data . We could not detect an interaction above background with Lpr2F ( Fig 4M ) . Addition of the LA-1 module to Lpr2F conferred a very weak affinity . However , when the ED was also included , the interaction with LTP was robust and similar to that of Lpr2E ( Fig 4M ) . Thus , ED synergizes with LA-1 to bind LTP . In Drosophila , the extended LA-1 domain is present in five lipophorin receptor isoforms in addition to Lpr2E [13] . We tested two of them , Lpr1H and Lpr1J , for their capacity to interact with LTP and in both cases we saw LTP accumulation after overexpression in imaginal discs ( S8 Fig ) . In contrast , no LTP stabilization was observed with isoform Lpr1M , which does not contain the extended LA-1 module ( S8 Fig ) . Taken together , our results indicate that only Lpr1 and Lpr2 isoforms or chimeras containing the LA-1 module can bind LTP . Since the LA-1 module is also essential for the lipophorin receptors to mediate lipid uptake [13] , our results strongly support a direct role of LTP-lipophorin receptor complexes in the cellular acquisition of lipids . Our results suggest that LTP recruitment to the plasma membrane mediated by a subset of lipophorin receptor isoforms is an essential component of the lipid uptake mechanism . Expression of Lpr2E but not of Lpr2F , also promotes a stabilization of lipophorin in the extracellular matrix of imaginal disc cells that is visible by an immunostaining protocol performed without permeabilization of cell membranes . This lipophorin stabilization might be related to the lipid transfer process [13] . Thus , we decided to examine whether LTP recruitment to cells was required for lipophorin stabilization or whether they were independent phenomena . To silence apoLTP and test for lipophorin stabilization in imaginal discs , we expressed a UAS-apoLTPi and a UAS-Lpr2E transgenes simultaneously in the fat body ( Cg-gal4 ) and in imaginal discs ( hh-gal4 ) . Expression was temporally controlled by the Gal80ts technique [24] . We reasoned that UAS-apoLTPi expression would silence apoLTP in the fat body and have no effect in imaginal discs , where apoLTP is not expressed . To exclude the possibility that UAS-Lpr2E expression in the fat body would affect lipophorin secretion , we examined lipophorin levels in the hemolymph of these larvae two days after the activation of the transgenes . We did not see a difference with the wild type , even though LTP was undetectable ( Fig 5C ) , validating the use of these animals . As previously reported , UAS-lpr2E overexpression in otherwise wild type imaginal discs induced the stabilization of lipophorin in basolateral cell membranes ( Fig 5A ) . However , this accumulation was strongly diminished in the apoLTP silenced animals described before ( Fig 5B ) , in which LTP is undetectable in imaginal discs and fat body ( S9 Fig ) . Thus , Lpr2E-mediated extracellular stabilization of lipophorin requires circulating LTP and probably , its recruitment to the plasma membrane . If this conclusion were correct , we would expect that the capacity of Lpr2E-Lpr2F chimeras to bind LTP would parallel their ability to promote lipophorin extracellular stabilization . To examine this prediction , we used the assay described in the previous section , the expression of Lpr2E , Lpr2F and their chimeras under conditions of blocked endocytosis . Overexpression of Lpr2E induced an accumulation of lipophorin mostly in basolateral membranes ( Fig 6C ) . In contrast , Lpr2F expression had a very limited effect ( Fig 6B ) , even though both isoforms were detected at similar levels at the plasma membrane under these conditions , as shown by extracellular immunostaining ( Fig 6A ) . We then tested chimeras UAS-Lpr2F+LA1 , UAS-Lpr2F+LA1+ED , UAS-Lpr2F+NCN and UAS-Lpr2F+LA1+NCN ( Fig 6D–6G , see S7 Fig for a description of these chimeras ) . Adding module LA-1 to Lpr2F only slightly increased lipophorin accumulation ( Fig 6D ) . However , addition of LA-1 plus ED or addition of the complete N-terminal region from Lpr2E , converted Lpr2F into a chimeric receptor with the same capacity as Lpr2E to mediate lipophorin accumulation ( Fig 6E and 6G ) . The LA-1 domain was essential for this increased accumulation , since the chimera UAS-Lpr2F+NCN that lacks LA-1 had almost no effect ( Fig 6F ) . Thus , the extended LA-1 domain is required both , for LTP binding ( Fig 4 ) and for robust lipophorin accumulation in the plasma membrane ( Fig 6 ) . Together with our previous results , this strongly suggests that lipophorin receptor-mediated LTP recruitment to the plasma membrane helps stabilize lipophorin . The molecular mechanisms involved are unclear . Lipophorin receptors , LTP and lipophorin could bind cooperatively . However , in co-IP experiments Lpr2E readily pulled down LTP from hemolymph but lipophorin could not be detected above background ( Fig 1A ) , suggesting a trimeric complex does not form in vitro . Alternatively , lipophorin stabilization could represent a functional intermediate during the process of lipid transfer to cells , maybe by direct contact between lipophorin particles and LTP . In this direction , we observed that Lpr2E-mediated lipophorin accumulation in cell membranes was transient . A 30 minutes wash of unfixed imaginal discs in ice-cold cell culture media strongly reduced lipophorin signal ( Fig 7A and 7B ) . In contrast , LTP staining was not affected , even after a 60 minutes wash ( Fig 7D–7F ) , further suggesting that a stable Lpr2E-LTP-lipophorin complex does not form . Rather , transient interactions between lipophorin and LTP could be limited to the duration of the lipid transfer process . Additional experiments would be required to reach a definitive conclusion . During development and growth , tissues exhibit changing requirements for an external supply of lipids . For instance , oocyte maturation involves a massive uptake of neutral lipids from hemolymph . Development of Drosophila imaginal discs is also accompanied by an increase in intracellular lipid droplets , which is most striking in the wing pouch area of the wing discs . This accumulation is mediated , at least in part , by the expression of lipophorin receptors of the LDLR family in the area [13] . However , the molecular mechanisms involved are still unclear . It was shown that blocking endocytosis did not inhibit neutral lipid uptake [13] , ruling out a mechanism similar to the uptake of cholesterol by human LDLR [27] . This conclusion is consistent with biochemical studies indicating that in insects , lipophorin functions via a reusable shuttle mechanism [28 , 29] . An alternative model , inspired by mammalian chylomicron and VLDL metabolism [30] , posits that lipophorin binding to the lipophorin receptors bring these particles near the plasma membrane where putative lipases and lipid transporters associated to the plasma membrane or to the extracellular matrix make lipophorin lipids available to cells [2 , 29] . However , lipophorin is detected at high levels in the extracellular matrix of most cells by immunohistochemistry , independently of lipophorin receptors expression . This localization is mediated , at least in part , by the interaction of lipophorin with heparan sufate proteoglycans ( HSPG ) [31] . Thus , recruitment of lipophorin to the cell surface is not sufficient for lipid uptake . The results we present here support a different model in which the central event that initiates the transfer of lipids to imaginal disc cells and oocytes is the recruitment of the lipoprotein LTP to the plasma membrane mediated by a subset of lipophorin receptor isoforms . Three lines of evidence support this conclusion . First , lipophorin receptor isoforms that mediate lipid uptake also promote the recruitment of LTP to the plasma membrane ( Figs 1 , 2 , S8 and [13] ) . Second , deletion of the LA-1 domain in the lipophorin receptors disrupts LTP binding and also impairs lipid uptake ( Fig 4 and [13] ) , strongly suggesting that LTP binding is required for lipophorin receptor-mediated neutral lipid uptake . Third , lipophorin receptors induce a stabilization of lipophorin in the plasma membrane associated to the lipid uptake process . We showed that LTP is required for this stabilization ( Figs 5 and S9 ) . Accordingly , only the lipophorin receptor isoforms and chimeras that bind LTP and mediate lipid uptake are also able to induce lipophorin stabilization ( Fig 6 ) . Finally , we show that animals with low levels of circulating LTP display a severe reduction in the lipid content of ovaries and imaginal disc , a phenotype that is consistent with a local requirement of LTP for lipid uptake ( Figs 3 and S6 ) . However , since LTP is also required for the loading of lipophorin with lipids in the gut ( S4 Fig and [4] ) , the decreased lipid content of lipophorin in the hemolymph of these animals could also contribute to the previous phenotype . The model we propose is consistent with the biochemical activity described for LTP in insects . In particular , experiments in which Bombyx mori ovarioles were cultured in medium containing radiolabeled lipophorin indicated a transfer of DAG from lipophorin to ovarioles . This transfer was inhibited by anti-LTP antibodies and restored by the addition of purified LTP , demonstrating an essential and local role of LTP in lipid uptake [18] . At the mechanistic level , LTP was suggested to use a carrier mechanism . LTP would acquire and store a limited amount of lipids from a donor lipophorin particle or cell and subsequently transfer them to a receptor . This process would not require the formation of a ternary complex between donor , acceptor and LTP [32] . Interestingly , electron microscopy studies showed that LTP particles displayed a remarkable shape with two well differentiated regions , a spherical , lipid containing head and a tail region that appeared to include a flexible hinge . It was suggested that this flexible tail would allow LTP to alternate between two conformations , contacting with lipid donor and acceptor during the lipid transfer process [15 , 33] . These observations prompt us to speculate that during lipid uptake , LTP recruited to the plasma membrane by the lipophorin receptors might alternately contact lipophorin particles and the plasma membrane , transferring an amount of DAG and possibly other lipids in each cycle . The interaction between LTP and lipophorin might transiently stabilize lipophorin in the extracellular matrix , as suggested by our results ( Figs 5–7 and S9 ) . Binding of the lipophorin receptors to LTP involves the LA-1 and ED domains ( Fig 4 ) . It is tempting to speculate that they would bind to LTP tail , which would leave the head region free to interact with lipophorin and the plasma membrane . An important question still completely unsolved is how lipids are incorporated into the cell . Lipids could be directly added to the lipid bilayer by LTP or alternatively , transmembrane lipid transporters might be required . A surprising finding from our study is that all lipophorin receptor isoforms display low affinity for lipophorin , since we could not detect an interaction between Lpr2E or Lpr2F and lipophorin by co-IP , even though LTP interaction with Lpr2E was readily detected ( Fig 1A ) . This is also supported by our in vivo results . Lpr2F overexpression promotes a very weak accumulation of lipophorin in imaginal discs under conditions of blocked endocytosis ( Fig 6B ) . Isoform Lpr2E induces a much higher accumulation ( Fig 6C ) but this effect is indirect , since it requires LTP ( Fig 5 ) . More generally , we observed that lipophorin accumulation in the extracellular matrix of imaginal discs was very labile since short incubations of the unfixed tissue with ice cold buffer strongly reduced lipophorin staining ( Fig 7 ) . These observations suggest that there is a pool of lipophorin weakly associated to the extracellular matrix through low affinity interactions with HSPG [31] , the lipophorin receptors or other still unidentified receptors . Entrapment of lipophorin in extracellular spaces close to the plasma membrane was described in other insects by electron microscopy [34] . This lipophorin pool would be in a dynamic equilibrium with hemolyph lipophorin , allowing lipid-depleted particles generated after the transfer of their lipid cargo to cells to quickly be replaced by lipid-rich lipophorin from the hemolymph . A high affinity binding of lipophorin to its receptors would impair such exchange . A pending issue in our understanding of insect lipid metabolism is the role of lipophorin endocytosis [35–37] . As mentioned above , there is compelling evidence in Drosophila and in other insects indicating that endocytosis is not required for neutral lipid uptake . Blocking endocytosis with a rab52 allele did not hamper lipid uptake in Drosophila ovaries [13] . Similarly , we blocked endocytosis for 8 hours in clones of shits homozygous cells in imaginal discs and did not observe changes in the lipid droplets accumulation pattern ( S2 Fig ) , suggesting that also in imaginal discs , endocytosis is not required for lipid uptake . Moreover , it was shown that chemical inhibition of the endocytic pathway did not interfere with LTP-mediated lipid exchange between lipophorin and the fat body in locust [19 , 35] . However , other data indicates that lipophorin particles are endocytosed in certain tissues . For example , locust fat body explants internalized fluorescently labeled lipophorin in vitro , an activity that was maximal in young adults . The internalized particles did not accumulate in the fat body and were suggested to be resecreted [35 , 37] . In addition , lipophorin receptors induced lipophorin endocytosis in transfected mammalian cells [12 , 36] or insect cells [37] . Also , overexpression of the lipophorin receptors induced lipophorin endocytosis in imaginal discs [23 , 38] . Interestingly , we observed that all tested lipophorin receptor isoforms induced lipophorin endocytosis in imaginal discs irrespectively of their capacity to mediate the acquisition of neutral lipids ( S2 Fig ) . Thus , available data clearly indicates lipophorin receptors endocytic activity and their capacity to mediate cellular acquisition of neutral lipids are independent of each other . However , it is still possible that endocytosis is important for the acquisition of minor lipid species present in lipophorin [35] . LTP plays additional roles besides mediating lipophorin receptor-dependent lipid transfer from lipophorin to tissues . In particular , LTP is critical for the loading of lipophorin with lipids in the midgut ( S4 Fig and [4 , 16 , 17] ) . However , in this case the molecular mechanisms involved appear to be different . First , in the midgut LTP is mostly found in the cytoplasm of enterocytes and not in the cell surface [4] . Second , the lipophorin receptors are not essential for LTP activity in the midgut since lpr1- , lpr2- animals do not display the massive increase in gut lipids characteristic of ApoLTP mutants . The receptors that mediate LTP endocytosis in the midgut are not known . We have shown that Lpr2E is able to endocytose LTP in imaginal disc ( Fig 1C and 1F and 1G ) . Since the lipophorin receptors are also expressed in the midgut [39] , they could contribute to LTP endocytosis , even though other redundant receptors must necessarily exist . LTP was also shown to mediate the bidirectional transfer of lipids between the larval fat body and lipophorin in Manduca sexta [19] . In this direction , we observed that Lpr2E overexpression in the fat body increases LTP in the plasma membrane and also induces an LTP-dependent accumulation of lipophorin ( S9 Fig ) , pointing to a potential role of these receptors in LTP activity in the fat body . However , in lipophorin receptor mutants LTP distribution in the fat body does not significantly change ( S3 Fig ) , suggesting that other LTP receptors must exist . An important question in lipid metabolism concerns the selectivity of lipid transfer between lipophorin and tissues . Our results suggest that expression of the lipophorin receptors promotes the transfer of DAG to cells , where it accumulates as TAG in lipid droplets , a process that requires LTP . However , uptake of other lipid species could also be facilitated by LTP . In this direction , LTP was shown to catalyze the transfer of DAG , phospholipids , hydrocarbons and cholesteryl esters between lipoproteins in vitro , but not of cholesterol , which can be exchanged through the aqueous phase [40] . However , the rates of facilitated transfer were found to be variable and dependent on the specific nature of the donor and acceptor particles , making it difficult to extrapolate to LTP specificity in vivo . In this direction , it was reported that apoLTP silencing in Drosophila induced changes in the composition of lipophorin DAG and sterols , suggesting LTP participates in the loading of these lipid species into lipophorin [4] . Thus , the lipophorin receptors , by recruiting LTP , may promote the uptake of most lipid species present in lipophorin . On the other hand , proteins that mediate the uptake of lipids with a high degree of specificity have also been described . In Bombyx mori , the CD36 proteins SCRB15 and Cameo2 mediate the selective uptake of β-carotene and lutein respectively into the silk gland despite both carotenoids being similarly transported in lipophorin particles [41] . Unfortunately , the molecular basis of this selectivity or the participation of the lipophorin receptors or LTP in the process is unknown . The following alleles and transgenes were used: shits [42] , Df ( 3R ) lpr2 and Df ( 3R ) lpr1/2 [13] , UAS-Rab5-GFP [43] , UAS-Lamp-GFP [44] , UAS-apoLpp-RNAi ( stock 106311 from VDRC ) , hh-Gal4 [45] , Cg-Gal4 [25] , FB-Gal4 [46] , V32-Gal4 ( a gift from Daniel St Johnston ) and tub-Gal80ts [24] . To generate the apoLTP[excDG06206] mutation , we induced the imprecise excision of transposon P{wHy}DG06206 following the protocol described in [47] . The extent of the deletion was mapped by inverse PCR and sequencing of the resulting fragments . Clones of cells homozygous for the shits allele in imaginal discs were induced by heat shocking shits , FRT9-2/Ubi-GFP , FRT9-2;hs-flp/+ larvae for one hour at 37°C , 48–72 hours AEL . After heat shock , larvae were cultured at 18°C and switched to the restrictive temperature ( 33°C ) for 8 hours before dissection . Blocking endocytosis for 10 hours or more induced severe morphogenetic phenotypes in imaginal discs . To overexpress Lpr2E in the posterior compartment of wing imaginal discs in animals with reduced levels of LTP , the following genotype was used: Cg-gal4 , tub-gal80ts/UAS-lpr2E;hh-gal4/UAS-apoLTP-RNAi . The cross was maintained at 18°C and mid third instar larvae were transferred to 29°C for two days to activate the UAS transgenes . Control larvae shown in Fig 6 did not carry the Cg-gal4 , tub-gal80ts chromosome . Additional controls were performed with larvae of genotype: Cg-gal4 , tub-gal80ts/UAS-lpr2E;hh-gal4/+ , similarly obtaining a robust lipophorin stabilization in the posterior compartment . To examine protein distribution under conditions of blocked endocytosis , male larvae of the following genotype: shits/Y;UAS-lpr2X/+;hh-Gal4/+ , where UAS-lpr2X stands for the different chimeras tested , were placed inside a glass tube and submerged in water at 33°C for 2 . 5 or 3 hours . Afterwards , they were immediately transferred to ice and dissected at 4°C . To generate UAS-lpr2E_ecto_TAP , a lpr2E cDNA fragment coding from Met1 to Glu985 was flanked by Kpn I and Xba I restriction sites and cloned in frame into pUAST-CTAP [48] . UAS-lpr2F_ecto_TAP was similarly generated using a fragment comprising from Lpr2F Met1 to Glu782 . In both constructs , the transmembrane and intracellular domains were deleted , generating secretable proteins fused to a C-terminal TAP tag . To generate plasmids for expression of full length Lpr2E and Lpr2F proteins fused to a C-terminal TAP tag in the germ line , we first cloned the tag CTAP flanked by engineered Xba I and Spe I sites into the Xba I site of pUASp [49] , creating pUASp_TAP . Full-length lpr2E or lpr2F DNA fragments excluding the stop codons and with engineered Kpn I and Xba I flanking sites were cloned in frame into pUASp_TAP , generating UASp-lpr2E_TAP and UASp-lpr2F_TAP . Transgenic flies were generated with all four plasmids . The following transgenes to overexpress Lpr2E , Lpr2F and chimeras as a fusion to a C-terminal 3xHA tag were described in [13]: UAS-lpr2E , UAS-lpr2F , UAS-lpr2F+LA1+NCN , UAS-lpr2F+LA1 , UAS-lpr2F+NCN . To generate UAS-lpr2+LA1+ED , first a DNA fragment coding for ED+LA1 domains of Lpr2E ( from Leu170 to Thr232 ) was flanked by Not I sites ( each coding for 3 Ala ) and cloned into pAc-lpr2F-NotI [13] and transferred to pUASTattb [50] . To introduce a Myc tag into Lpr2E and Lpr2F extracellular domains , NotI sites were first engineered after Lpr2E Glu920 or after Lpr2F Glu717 , located between the EGF-C module and the O-glycosylation region . Not I flanked fragments containing six copies of a Myc tag were generated by PCR using pCS2+NLS MT vector as template [51] and cloned into the engineered Not I sites . These cDNAs , also containing a C-terminal 3xHA tag , were transferred to pUASTattB to generate UAS-lpr2E-Myc and UAS-lpr2F-Myc . To silence ApoLTP by RNA interference ( RNAi ) , an 874 base pair genomic fragment corresponding to part of ApoLTP 7th exon was cloned as an inverted tandem repeat containing an intervening 81 base pair region into pBluescript ( Stratagene ) . The tandem repeat was transferred to pUAST and transgenic flies were obtained ( UAS-ApoLTPi ) . To generate an ApoLTP-myc genomic rescue transgene , we started from the attB-P[acman]-CmR-BW based genomic clone CH321-38C23 [52] , which contains a 84241 base pair fragment that includes ApoLTP . A 6XMyc tag was inserted in ApoLTP C-terminus ( after Ser4333 ) and a V5 tag was inserted after Arg23 , two amino acids after the predicted signal peptide cleavage site , by recombineering [53] . We could not obtain transgenic flies with this modified BAC clone , possibly because of its length . Thus , we deleted sequences downstream of ApoLTP by recombineering , generating a BAC clone containing a 43241 base pair insert which includes 25645 base pairs upstream and 5087 base pairs downstream of ApoLTP CDS . With this shorter BAC , we generated transgenic flies at the CBMSO transgenesis facility . This genomic clone , even though it rescues a null ApoLTP mutant , is expressed at lower levels than the endogenous gene . For unknown reasons , we cannot detect the protein using α-V5 . UAS-lpr2E_ecto_TAP and UAS-lpr2F_ecto_TAP were expressed in the larval fat body using the driver FB-gal4 . Notice that UAS-lpr2E_ecto_TAP overexpression delayed growth but eventually larvae reached normal size . Wandering 3th instar larvae were collected , washed and frozen in liquid nitrogen . 6 g per genotype were used in the experiment . Larvae were powdered in a mortar and pestle in liquid nitrogen and the powder was added to 50 ml of ice cold extraction buffer ( 20 mM K-HEPES pH 7 . 9 , 50 mM KCl , 100 mM NaCl , 2 mM DTT , 0 . 5 mM CaCl2 , 0 . 5 mM PMSF , 1x protease inhibitor cocktail from Roche ) . The extract was centrifuged at 4000 rpm in a falcon tube for 5 minutes and the supernatant filtered successively through a 2 . 7 μm and 0 . 7 μm syringe filters fitted with glass microfiber pre-filters to reduce clogging . For the affinity purification step , we essentially followed the protocol from Puig et al . [22] with some modifications . In particular , the cleared lysate was incubated for 2 hours with 500 μl of IgG-sepharose matrix ( GE ) previously equilibrated with extraction buffer . Beads were then washed five times in IPP150-Ca buffer ( 10 mM Tris-HCl pH8 . 0 , 150 mM NaCl , 0 . 5 mM CaCl2 , 0 . 1% triton X100 ) and resuspended in CBB buffer ( 10 mM Tris-HCl pH 8 , 150 mM NaCl , 0 . 1% triton X100 , 2 mM CaCl2 , 10 mM β-mercaptoethanol , 1 mM magnesium acetate , 1 mM imidazole ) . 30 μl of TEV protease ( 10u/μl ) were added and the reaction allowed to proceed over night at 4°C . The supernatant and two additional 500 μl washes in CBB buffer were pooled and incubated with 500 μl of calmodulin-sepharose matrix ( GE ) for 4 hours at 4°C . Beads were then washed six times for a total time of 30 minutes in CBB buffer and eluted in 500 μl of CEB buffer ( 10mM Tris-HCl pH8 . 0 , 10 mM β-mercaptoethanol , 150 mM NaCl , 1 mM magnesium acetate , 1 mM imidazole , 0 . 1% triton X-100 , 20 mM EGTA ) for 20 minutes at 4°C . Proteins were precipitated from the supernatant using the 2-D Clean-Up kit ( GE ) , resuspended in Laemmli buffer and separated by PAGE . Proteins were stained with colloidal Coomassie and bands differentially present in the samples were excised and identified by peptide mass fingerprinting and peptide fragmentation at the "Parque Científico de Madrid" facility . UASp-lpr2E_TAP and UASp-lpr2F_TAP were expressed in the germ line driven by V32-gal4 . 250 ovaries from 4–5 days old females fed with yeast paste were dissected and homogenated in a Tenbroeck tissue grinder in 500 μl ice cold lysis buffer ( 10mM Tris-HCl pH 7 . 6 , 150 mM NaCl , 0 . 5 mM CaCl2 , 0 . 1% Triton X-100 , 0 . 5 mM PMSF and 1x Roche protease inhibitor cocktail ) . The homogenate was kept on ice for 10 minutes and centrifuged at 16 , 000g for 15 minutes . Cleared lysates from 4 , 000 ovaries were pooled and filtered through a 0 , 22 μm syringe filter , obtaining a total of 5 . 5 ml of extract . Affinity purification of Lpr2E_TAP and Lpr2F_TAP was performed in a single step using IgG coated dynabeads . Conjugation of dynabeads M-270 Epoxy ( Invitrogen ) with rabbit IgG was performed as described [54] . 5 . 5 ml of ovary extract was incubated with 30 mg IgG-coated dynabeads for 2 hours at 4°C with continuous shaking . The magnetic beads were then washed five times in cold lysis buffer for a total of 30 minutes and bound proteins eluted by cleavage of the TAP tag with TEV protease ( 150 u ) in 200μl of lysis buffer supplemented with 1 mM DTT during 3 hours at 4°C . Proteins were precipitated from the supernatant , separated and processed for mass spectrometry analysis as before . To express Lpr2E-HA and Lpr2F-HA in S2 cells , UAS-lpr2E or UAS-lpr2F [13] plasmids were co-transfected with pAC-Gal4 [55] . About 4 . 5 million cells were lysated in 300 μl lysis buffer ( 150mM NaCl , 20 mM Tris HCl pH 7 . 8 , 0 . 5% triton X-100 , 1 mM DTT , 0 . 5 mM PMSF , 1x Roche protease inhibitor cocktail ) by one cycle of freezing and thawing . The lysate was cleared by centrifugation at 16 , 000 g for 5 minutes and added to protein G dynabeads ( Invitrogen ) conjugated to mouse anti-HA ( Santa Cruz Biotechnology ) following manufacturer instructions . Beads were incubated with the lysate for 10 minutes at room temperature , washed twice with lysis buffer and incubated with 100 μl of diluted hemolymph for one hour at 4°C with shaking . After three washes in washing buffer ( 150 mM NaCl , 20 mM Tris-HCl pH 7 . 8 , 0 . 1% triton X-100 ) , proteins were eluted from dynabeads by boiling in Laemmli buffer for 4 minutes . Diluted hemolymph was prepared as follows: 50 wild type wandering larvae were washed , dried and placed in 350 μl of ice cold hemolymph buffer ( 150 mM NaCl , 20 mM Tris-HCl pH7 . 8 , 1 mM DTT , 0 . 5 mM PMSF , 1x Roche protease inhibitor cocktail ) . Under the dissecting microscope , larvae were pierced with a pair of forceps so that hemolymph bled out into the buffer . The diluted hemolymph was collected and centrifuged for 5 minutes at 5 , 000 rpm to remove cells and tissue debris . Diluted hemolymph was used immediately after being prepared . The following antibodies were used in this work: rabbit α-ApoLTPI and α-ApoLTP II [4] , rabbit α-LpFL and rabbit α-LpII [5] , rat α-HA ( Roche ) , mouse α-HA ( Santa Cruz Biotechnology ) and mouse α-Myc ( DSHB ) . Immunostaining of imaginal discs and ovaries as well as immunostaining of extracellular proteins was performed as described [13] . To examine the stability of lipophorin association to Lpr2E in vivo ( Fig 7 ) , wing imaginal discs were dissected in Sf-900 II SFM culture media ( gibco ) at 4°C and incubated in the same media for 30 minutes or 60 minutes , also at 4°C . They were subsequently fixed and processed following standard protocols . Lipids were visualized by Nile red or by oil red O stains . For Nile red , fixed imaginal discs or ovaries were incubated with 0 . 002% Nile red dye diluted in PBS and 0 . 3% triton X-100 for 60 minutes and washed for 10 minutes in the same buffer without the dye . For oil red O stain , fixed imaginal discs were incubated in a 0 . 5% solution of oil red O in propylene glycol at 60°C for one hour and then washed twice in 85% propylene glycol and three times in PBS , essentially as described in [56] . To quantitatively compare LTP and lipophorin levels in hemolymph , we placed two washed and dry larvae on a piece of parafilm on ice and pierced them with a pair of forceps . Hemolymph was collected by capillarity filling 0 . 5 μl glass micropipettes ( Drummond ) and immediately transferring the contents to 20 μl Laemmli buffer for western blot analysis .
In multicellular animals , nutrients and metabolites required for cell growth are distributed throughout the body by the blood circulation or in insects , by hemolymph . The uptake of these molecules by cells is tightly controlled to ensure the necessary coordination between cellular requirements and organismal homeostasis . Here we examine the mechanisms that mediate the cellular uptake of lipids in Drosophila melanogaster , a model organisms increasingly used in studies of metabolic homeostasis and its intersection with growth , aging and disease . In Drosophila , the majority of hemolymph lipids are carried in a lipoprotein particle named lipophorin . Lipid uptake in organs such as the ovaries or the imaginal discs is initiated by the expression of receptors of the LDLR family in the cell membrane . We show that these receptors bind with high affinity to a circulating lipoprotein named LTP , recruiting it to the cell surface . Surprisingly , LTP is not a major lipid carrier but instead catalyzes the transfer of lipids from lipophorin to cells . Our results improve our understanding of a central aspect of lipid metabolism in Drosophila and illustrate that although homologous proteins of the LDLR family play central roles in lipid uptake across phyla , the specific molecular mechanisms involved are diverse .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[]
2015
Drosophila Lipophorin Receptors Recruit the Lipoprotein LTP to the Plasma Membrane to Mediate Lipid Uptake
The molecular mechanisms controlling the subunit composition of glutamate receptors are crucial for the formation of neural circuits and for the long-term plasticity underlying learning and memory . Here we use the Drosophila neuromuscular junction ( NMJ ) to examine how specific receptor subtypes are recruited and stabilized at synaptic locations . In flies , clustering of ionotropic glutamate receptors ( iGluRs ) requires Neto ( Neuropillin and Tolloid-like ) , a highly conserved auxiliary subunit that is essential for NMJ assembly and development . Drosophila neto encodes two isoforms , Neto-α and Neto-β , with common extracellular parts and distinct cytoplasmic domains . Mutations that specifically eliminate Neto-β or its intracellular domain were generated . When Neto-β is missing or is truncated , the larval NMJs show profound changes in the subtype composition of iGluRs due to reduced synaptic accumulation of the GluRIIA subunit . Furthermore , neto-β mutant NMJs fail to accumulate p21-activated kinase ( PAK ) , a critical postsynaptic component implicated in the synaptic stabilization of GluRIIA . Muscle expression of either Neto-α or Neto-β rescued the synaptic transmission at neto null NMJs , indicating that Neto conserved domains mediate iGluRs clustering . However , only Neto-β restored PAK synaptic accumulation at neto null NMJs . Thus , Neto engages in intracellular interactions that regulate the iGluR subtype composition by preferentially recruiting and/or stabilizing selective receptor subtypes . Ionotropic glutamate receptors ( iGluRs ) play major roles in excitatory transmission in the vertebrate brain and at the insect neuromuscular junction ( NMJ ) . The synapse properties are primarily shaped by the subunit composition of the receptors , which could be further modified by RNA editing and alternative splicing [1] . Changes in the subunit composition of postsynaptic iGluRs , in particular the AMPA-type receptors , have a tremendous impact on the development and plasticity of glutamatergic synapses [2 , 3] . Mechanisms controlling the recruitment of selective receptor subtypes exist and they integrate signals from multiple signaling pathways and regulate synaptic trafficking via posttranslational modifications within the iGluRs intracellular domains [4 , 5] . In addition , several auxiliary subunits , which primarily modulate the channel properties , have been implicated in the subcellular distribution of receptors [6] . Whether and how the auxiliary subunits modulate the subunit composition of iGluRs remains unclear . The Drosophila NMJ is a glutamatergic synapse similar to mammalian central synapses . In flies , the iGluRs are heterotetrameric complexes composed of three shared subunits , GluRIIC , GluRIID and GluRIIE , and either GluRIIA ( type-A receptors ) or GluRIIB ( type-B ) [7–11] . The function of the fly NMJ also requires Neto ( Neuropillin and Tolloid-like ) , an obligatory auxiliary subunit of the iGluR complexes [12] . In the absence of Neto , or any of the shared iGluR subunits ( or GluRIIA and GluRIIB together ) the receptors fail to cluster at synaptic locations and the animals die as paralyzed embryos unable to develop into larval stages [12 , 13] . Genetic manipulation of Neto and iGluR levels indicated that Neto and the shared iGluR subunits are limiting for the synaptic localization of receptors and that GluRIIA and GluRIIB compete with each other for synaptic localization [9 , 14] . The type-A and type-B receptors differ in their single-channel properties , synaptic currents , regulation by second messenger , and sub-synaptic distribution [13] . The type-B channel desensitizes ten times faster than the type-A [8] . Also , the postsynaptic response to the fusion of single synaptic vesicles ( the quantal size ) is much reduced when only the type-B receptors are present; in fact , the dose of synaptic GluRIIA versus GluRIIB is a key determinant of quantal size . The interplay between the type-A and type-B synaptic receptors modulates synapse strength and plasticity . The synaptic accumulation of type-A and type-B receptors at the fly NMJ is differentially regulated . Two hybrid and genetics screens identified Coracle , the Drosophila homologue of the mammalian cytoskeletal protein 4 . 1 , as a direct binding partner for the GluRIIA cytoplasmic domain [15] . Disruptions of the actin cytoskeleton or mutations in coracle caused selective reduction in GluRIIA synaptic accumulation , suggesting that Coracle anchors the GluRIIA to the actin cytoskeleton . Mutations in the p21-activated kinase ( PAK ) also disrupt the localization and function of the GluRIIA . PAK co-localizes with the iGluR complexes at postsynaptic densities ( PSDs ) and , in conjunction with the guanine nucleotide exchange factor Pix and the adaptor protein Dreadlocks , promotes the synaptic accumulation of GluRIIA [16 , 17] . The synaptic accumulation of GluRIIB appears reduced in mutants of the PDZ ( PSD-95/Dlg/Zona occludens-1 ) domain-containing scaffolding protein Discs large ( Dlg ) [18] . However , there is no evidence for a direct interaction between GluRIIB and Dlg , and except for GluRIIC [9] , the iGluR subunits of the Drosophila NMJ lack a PDZ binding domain , suggesting that Dlg influences the synaptic abundance of GluRIIB indirectly . Also , Dlg localizes perisynaptically , at subsynaptic reticulum ( SSR ) [19] . In contrast , mammalian PSD-95 is a major component of the PSD , and it directly binds and modulates the synaptic targeting of iGluRs [20] and some of their interacting partners , such as the 4 . 1N protein [21] . The PSD-95-mediated interactions can be further regulated by post-translational modifications such as palmitoylation and phosphorylation [5] . Here we show that Neto is key in regulating the subtype composition of iGluRs at the Drosophila NMJ . We characterized a novel Neto isoform , Neto-β , which appears to be the predominant isoform at the fly NMJ . The two Drosophila isoforms , Neto-α and Neto-β , share the highly conserved extracellular and transmembrane domains , the hallmark of the Neto family of proteins [6] , but have distinct cytoplasmic domains , rich in protein interaction motifs . We generated neto-β isoform specific mutants and found that the synaptic accumulation of type-A receptors is selectively impaired at neto-β mutant NMJs . Furthermore , Neto-β-deficient synapses fail to recruit PAK , a critical PSD signaling molecule required for the synaptic stabilization of type-A receptors and for the recruitment of other postsynaptic components . Muscle overexpression of either Neto-α or Neto-β isoforms could rescue the lethality and NMJ function of neto null mutants , indicating that the shared domains of Neto function to mediate iGluRs clustering . However , only Neto-β could restore the recruitment of PAK at neto null synapses . Our data demonstrate that Neto-mediated cytoplasmic interactions control the subtype composition of iGluRs and shape postsynaptic composition . Drosophila neto codes for two transcripts , cDNA references GH11189 and RE42119 . They share the first 10 exons , encoding the extracellular and transmembrane part of Neto , but have alternative exons that generate different intracellular domains ( Fig 1A ) . Exon 11 encodes the cytoplasmic domain of Neto-α , a 206-residue acidic domain ( pI 3 . 88 ) . Exons 12–14 encode the 351-residue Neto-β intracellular domain ( pI 8 . 90 ) . The two domains are predicted to contain multiple phosphorylation and protein interaction motifs ( Neto-β shown in S1 Fig ) , but they show no homology with other Neto proteins . In fact , unlike the highly conserved extracellular and transmembrane domains of Neto proteins , the intracellular parts are highly divergent or even missing , such as in the C . elegans Neto ( Fig 1A ) [22–24] . Previous RNA-Seq analyses indicated that neto-α and neto-β transcripts are expressed throughout development , including larval stages . Using RT-PCR and qPCR analyses we also detected both transcripts in the third instar larval carcasses and estimated that the neto-α transcript was ~4 fold less abundant than neto-β . To examine if Neto-β is present at the NMJs , we generated Neto-β isoform specific antibodies ( details in Materials and Methods and S2 Fig ) and compared them with previously described Neto antibodies , raised against the extracellular CUB1 domain [12] . The Neto-β positive signals co-localized with the Neto-ex puncta at control and at neto-β-rescued NMJs , but not at neto null NMJs rescued with neto-α transgenes ( Fig 1B ) . Importantly , muscle overexpression of either isoform could rescue the embryonic lethality and paralysis of neto null mutants . This suggests that the shared part of Neto , including the CUB1 , CUB2 , LDLa and the transmembrane pass , contains all the components essential for iGluRs clustering and NMJ development . Indeed , muscle expression of a Neto variant in which the intracellular domains were replaced by eGFP rescued the neto null paralyzed embryos to fertile adults ( Fig 1B ) . The caveat of these rescue experiments is that Neto overexpression highly exceeds the endogenous levels and may obscure the contribution of individual isoforms . The function of the extracellular and transmembrane domains of Drosophila Neto will be examined elsewhere . Here we will focus on characterizing the new isoform , Neto-β , and its role during NMJ development . We have previously demonstrated an essential role for Neto in the formation of iGluR clusters during synapse assembly and development . Likewise , Neto-β started to accumulate at the NMJ synapses at the onset of synaptogenesis ( Fig 1C and 1D ) . In late embryo fillets , Neto-β antibodies labeled distinct synaptic puncta , which were also Neto-ex positive and co-localized with the iGluR synaptic signals ( GluRIIA shown in Fig 1D ) . Neto-β signals remained at junctional locations throughout larval development and appeared to represent a significant fraction of the total Neto at the Drosophila NMJ ( S2 Fig ) . To examine the role of Neto-β in synapse assembly and development we generated isoform specific mutants by imprecise excision of a transposable element Mi ( ET1 ) Neto[MB07125] [25] . Several lines including precise excisions and small deletions were isolated and characterized by PCR amplification and sequencing ( Fig 2A and Materials and Methods ) . The neto203 allele is partly missing neto-β specific exons; neto204 lacks the entire neto-β specific coding sequence and is likely a neto-β genetic null . No neto-β transcript was detectable in neto204 animals , but in neto203 larvae we found a truncated transcript , predicted to encode a short Neto-β variant ( S1 Fig ) . For clarity we will refer to neto203 as netoβshort , neto204 as netoβnull , neto36 as netonull , and neto109 as netohypo [12] . A diagram of the neto-β alleles and predicted Neto-β proteins is shown in Fig 2A and 2B . The predicted Neto-β variants were detected by Western analysis in larval muscle extracts ( Fig 2C ) : a full-length Neto-β was detected in control , and a band of 65 kD , corresponding to the truncated Neto-β variant , was detected in netoβshort larval muscle . Interestingly , no band corresponding to Neto-α was detected suggesting that the endogenous Neto-α levels are very low , even in the absence of Neto-β . The Neto-β variants ( full length and short ) appeared expressed at equivalent levels relative to Tubulin . However , the synaptic Neto levels were decreased by 34% at netoβshort NMJs , suggesting that the cytoplasmic domain influences Neto distribution ( Fig 2C and S3 Fig ) . The Neto levels were decreased by 70% at netoβnull NMJs , consistent with the estimated 4-fold difference in the transcript levels . Further purification of Neto-β polyclonal sera against the two antigenic peptides ( β1 and β2 ) produced two pools of antibodies ( Fig 2B and S1 Fig ) . As expected , only Neto-β2 antibody labeled the netoβshort synapses , whereas no Neto-β1 signals were found at netoβshort or netoβnull NMJs ( Fig 2D and 2E ) . In contrast , clear Neto-β1 synaptic signals were detected at netohypo NMJs . The netohypo allele lacks the exon containing the translation initiation codon [12] . This lesion should affect both Neto isoforms . We next examined the viability and behavior of neto-β mutants . Under optimal culturing conditions , 75% of netoβnull embryos and 90% of netoβshort developed into larval stages compared with control ( n>200 ) . In both cases , the third instar larvae were sluggish and exhibited a significant delay in rolling back when placed with dorsal side down . The netoβshort adult flies have no apparent defects , but the netoβnull homozygous have impaired climbing ability and are outcompeted by their heterozygous siblings . Further reduction to one copy of neto-α , such as in netoβnull/netonull trans allelic combinations , induced 100% lethality in more than 600 progenies examined . This lethality was rescued by a duplication covering the neto gene , Dp ( 1:3 ) DC270 [26] , indicating that the molecular lesion is confined to the neto locus . In contrast , netoβshort/netonull were viable indicating that one copy of neto-α and neto-βshort could support adult viability . To test the functionality of NMJ synapses lacking an intact Neto-β isoform we recorded spontaneous miniature potentials ( minis or mEJPs ) and evoked excitatory junctional potentials ( EJPs ) from muscle 6 of third instar larvae . The mean frequency of mEJPs was decreased in both neto-β alleles compared to control ( Fig 3A–3C and S1 Table ) . Mini amplitude or quantal size , the postsynaptic response to the fusion of a single vesicle , was also reduced in both neto-β alleles ( to 62% of control in netoβshort , and respectively 43% in netoβnull ) . We found no significant change in the resting potential and input resistance in the neto-β mutant larvae . The reduced mEJP frequency and amplitude at neto-β mutant NMJs were similar but less severe than at Neto- or iGluR-deprived NMJs [9 , 12] . But while Neto-deprived NMJs have severely reduced EJPs , the amplitude of EJPs was reduced by 12–14% compared to the control in netoβshort mutant larvae and was relatively normal at netoβnull NMJs ( Fig 3D–3F and S1 Table ) . This suggests that neto-β mutant NMJs must compensate for the decreased postsynaptic sensitivity by a compensatory increase in quantal content , the number of vesicles released in response to each action potential . We found that quantal content , estimated as ratio of average EJP amplitude to the mEJP amplitude , was increased 1 . 4 fold in netoβshort larvae and 2 . 7 fold in netoβnull compared with control ( Fig 3F ) . In contrast , the netohypo or netoRNAi larvae show no presynaptic compensatory response to reduced quantal size [12 , 27] . These studies show that the loss of Neto-β significantly alters the number , type and/or density of postsynaptic iGluRs and elicits a homeostatic compensatory increase of presynaptic release . These changes resemble the GluRIIA loss-of-function phenotypes , and differ from Neto- or iGluR-deprived NMJs [7–12] . Thus , the defects observed in neto-β mutants do not seem to originate from a general decrease in Neto levels and suggest isoform specific Neto activities at the NMJ . NMJ morphological analyses further emphasized these specific defects . We have previously shown that Neto-deprived NMJs have longer and fewer branches , with significantly reduced number of boutons [27] . In contrast , at similarly reduced Neto levels , the netoβnull NMJs were reduced in length and had shorter branches ( Fig 3G and 3H ) . Compared with controls , neto-β mutant NMJs had fewer but larger type Ib boutons ( Fig 3I and 3J ) . In particular , the distal boutons had an estimated 2 . 4 fold volume increase ( netoβshort: 18 . 9 μm3 ± 2 . 6 , n = 29 , netoβnull: 20 . 8 μm3 ± 2 . 3 , n = 45 , and control: 7 . 8 μm3 ± 1 . 3 , n = 29 ) . Together these data indicate that perturbations of Neto-β have profound effects on the NMJ morphology and function that are different from a reduction in Neto levels . At the Drosophila NMJ , Neto activities are essential for trafficking and clustering of iGluRs at synaptic sites . To assess the contribution of Neto-β to these functions we examined the iGluRs distribution at neto-β mutant NMJs . For all the quantitative analyses below control/neto-β mutant sets ( 5–10 larvae/genotype ) were processed together and imaged under the same conditions to capture and compare the synaptic signals . At the fly NMJ synapses , the sites of neurotransmitter release are marked by presynaptic specializations called T-bars , where Bruchpilot ( Brp ) , the fly homolog of the vertebrate active zone protein ELKS , accumulates [28] . Opposite to the T-bars , the iGluRs are concentrated and stabilized at synaptic sites by a myriad of PSD-associated proteins . We found that neto-β mutant boutons contained more synaptic contacts , as visualized by Brp/GluRIIC juxtaposing puncta , and their density was increased at proximal boutons but not at the distal ones ( Fig 4A and 4B ) . This increase in synaptic density is consistent with the compensatory response observed at neto-β mutant NMJs ( Fig 3F ) . The intensity of Brp signals was largely constant , but the GluRIIC levels were diminished to 78% from control at netoβshort NMJs ( n = 10 ) and to 39% at netoβnull NMJs ( n = 10 ) ( Fig 4A and 4C ) . The reduction of GluRIIC synaptic signals at neto-β mutant NMJs occurred without a change in the GluRIIC net protein indicating a defect in the synaptic localization of the receptors ( Fig 4D ) . Importantly , the GluRIIC synaptic signals paralleled the levels of synaptic Neto observed at these NMJs ( Fig 2D and 2E , S3A Fig ) , as Neto is limiting for receptors localization . Drosophila NMJ utilizes two types of iGluRs , type-A and type-B , that differ in their subunits composition and properties [13] . The single channel current amplitude of both receptor types is identical , but the type-B channel desensitizes nearly ten times faster , thus the relative ratio of synaptic type-A/type-B receptors is a key determinant of quantal size [8] . Also , reduced levels of postsynaptic type-A receptors trigger a robust presynaptic compensatory response [7] . The physiological similarities between the GluRIIA and neto-β mutant NMJs suggest that Neto-β may primarily influence the synaptic accumulation of type-A receptors . We tested this possibility by examining the relative intensity of GluRIIA and GluRIIB signals at neto-β mutant NMJs . The netoβshort larvae had severely reduced GluRIIA synaptic levels ( to 26% from control , n = 13 ) , accompanied by no significant change in the GluRIIB levels ( Fig 4C and 4E ) . The netoβnull NMJs showed an even stronger reduction in the GluRIIA synaptic levels ( to 18 . 5% from control , n = 14 ) and also decreased GluRIIB levels ( Fig 4C and 4E ) . In contrast , the netohypo NMJs showed uniformly reduced levels for all iGluR subunits examined ( S3 Fig ) . The drastic decrease of GluRIIA synaptic signals at neto-β mutant NMJs exceeded the reduction of GluRIIC and Neto synaptic levels observed at these NMJs , indicating a key role for the Neto-β isoform in regulating the GluRIIA synaptic abundance . How does Neto-β regulate the selective accumulation of type-A receptors at synaptic sites ? Neto-β may preferentially recruit the type-A receptors and/or influence their synaptic stabilization . If Neto isoforms promote the synaptic accumulation of selective iGluRs , and Neto-β influences the type-A receptors , the inference is that Neto-α should preferentially impact the distribution of type-B receptors . In support for this possibility , in RNAi knockdown experiments we found that Neto-α-deprived NMJs showed a dramatic loss of GluRIIB synaptic signals ( by 68% ) and a modest , variable increase of synaptic GluRIIA ( by 18% ) ( Fig 4F and 4G ) . The efficiency of the RNAi-mediated knockdown was verified by Western blot analysis of muscle extracts from netonull larvae rescued with a V5-tagged neto-α transgene ( Fig 4H and [12] ) . Lack of Neto-α-specific reagents precluded us from expanding these experiments . Nonetheless , these results raise the possibility that Neto isoforms utilize their distinct cytoplasmic domains to modulate the synaptic abundance of specific receptor subtypes . In the case of Neto-β , the last 260 C-terminal cytoplasmic residues appear to be critical for the synaptic accumulation of type-A receptors . In Drosophila , several postsynaptic and perisynaptic proteins influencing the synaptic abundance of type-A and type-B receptors have been reported [15 , 16 , 18] . For example , PAK , a PSD component that co-localizes with the GluRIIA subunit , was shown to promote accumulation of type-A receptors at synaptic sites [16 , 17 , 29] . PAK also influences the size of the SSR , a stack of membrane folds that underlines the postsynaptic cell membrane of the type Ib boutons [16 , 17] . We found that PAK signals were severely reduced at neto-β mutant NMJs ( Fig 5A ) . PAK synaptic levels dropped to 4% from control in netoβshort larvae ( n = 11 ) and to 19% in netoβnull ( n = 12 ) . This drastic decrease exceeded the reduction in PAK signals observed at netohypo NMJs , suggesting that the low level of Neto-β at netohypo NMJs could partly mediate PAK stabilization at PSDs ( Fig 2E and [12] ) . A similar , albeit less pronounced effect was observed for Dlg ( Fig 5B–5D ) . The Dlg junctional signals were reduced to 48% from control in netoβshort third instar larvae ( n = 14 ) and to 27% from control in netoβnull ( n = 11 ) . Importantly , the levels of PAK and Dlg net proteins were not changed in extracts from neto-β larval muscles ( Fig 5E ) . Also , the accumulation of PAK and Dlg at junctional locations was completely restored in neto-β mutants by a duplication covering the neto locus ( S4 Fig ) , indicating that the observed phenotypes are neto specific . In contrast , cysteine string protein ( CSP ) , which labels clusters of synaptic vesicles in the vicinity of presynaptic membranes [30] , and α-Spectrin , which labels the presynaptic axon and surrounds the bouton postsynaptically [31] appeared normal at neto-β mutant NMJs ( S5 Fig ) . Under electron microscopy , control synapses are marked by presynaptic specializations called T-bars and electron-dense membranes , corresponding to PSDs ( Fig 6A–6C ) . At neto-β mutant boutons , serial sections revealed that the PSDs were significantly reduced in size but increased in number ( Fig 6B–6D and Fig 4 ) . The diminished PSD length ( up to 2 fold in netoβnull mutants ) matched the observed reduction of postsynaptic receptor fields ( Fig 4 ) . A thick stack of SSR membranes surrounds the control type Ib boutons . In contrast , the enlarged neto-β mutant boutons had diminished or even absent SSR structures , consistent with the reduced Dlg levels observed ( Fig 6E–6H ) . In addition , neto-β mutants appeared to have misshaped T-bar structures , including double T-bar structures ( Fig 6B and 6C , details ) . GluRIIA deprivation induces excessive recruitment of Brp at the active zones which sometimes results in active zone profiles with two or more T-bars [32] . Likewise , the excessive T-bar structures observed primarily in netoβnull mutant boutons may correlate to a homeostatic compensatory response triggered by the GluRIIA depletion ( Figs 3F and 4B and 4E ) . Since the SSR folds are reduced in neto-β mutants , it is possible that the reduced PAK and Dlg synaptic levels are indirectly caused by the lack of proper SSR structures . To test this possibility , we examined late embryos and first instar larvae , before the SSR develops [33] . If the SSR controls the synaptic accumulation of PAK , then PAK levels should be normal at neto-β mutant NMJs during the early stages of development . However , we found that PAK accumulated normally at the muscle attachment sites in control and neto-β mutant animals , but was severely diminished at neto-β mutant NMJs ( Fig 7A ) . Thus , loss of synaptic PAK is not secondary to the loss of SSR and appears to be directly caused by impairments in Neto-β . If Neto-β recruits PAK at the cell membrane , then loss of synaptic PAK in neto-β mutants should be rescued by muscle expression of PAKmyr , a membrane-tethered PAK variant [34] . We found that muscle overexpression of PAKmyr in neto-β mutants induced accumulation of PAK signals at perisynaptic but not synaptic sites , and could not rescue the GluRIIA synaptic accumulation ( netoβshort shown in Fig 7B ) . This indicates that Neto-β controls the recruitment of PAK at synaptic locations . Loss of PAK did not affect the synaptic recruitment of Neto-β: while PAK signals were lost at pak null NMJs ( pak11/Df ) or in mutants that fail to localize at cell peripheries ( pak6/Df or pak6/11 ) [35] , the synaptic Neto signals remained unaffected , irrespective of the pak genetic manipulations ( Fig 7C ) . Also , the levels of GluRIIA were 4-fold reduced in neto-β mutants ( Fig 4 ) , but only 2-fold in pak mutants [16] , suggesting that Neto-β has additional , PAK-independent functions in the synaptic recruitment/stabilization of GluRIIA . Together these data indicate that Neto-β is required for the synaptic accumulation of PAK . In flies and mammals , PAK membrane localization is controlled by Pix , a Rho-type guanine nucleotide exchange factor [16 , 36] . Mutations in dpix led to impairments in synaptic accumulation of PAK , GluRIIA , Dlg , and other postsynaptic components . Lack of Pix antibodies precluded us to test whether Neto-β recruits dPix at PSDs . Nonetheless , while the PAK synaptic signals were completely lost in dpix mutants , Neto signals accumulated at these mutant synapses albeit they appeared somewhat reduced , likely due to defects in the synapse organization ( S6 Fig ) . Thus , dPix is required for PAK but not for Neto recruitment of at synaptic sites . We have previously demonstrated that the net levels of Neto are critical for synapse assembly and NMJ development [12 , 14] . In particular , Neto depletion has profound consequences for synapse assembly and function . The levels of synaptic Neto are reduced in neto-β mutants compared with controls ( Fig 2 and S3 Fig ) raising the possibility that the phenotypes observed in neto-β allelic series could be partly due to reduced Neto levels . To distinguish between isoform specific and suboptimal Neto phenotypes we manipulated the muscle expression of Neto-α and Neto-β isoforms and examined their effects on the development and function of NMJ . Similar to neto-α , we found that muscle expression of neto-β transgenes rescued the viability and NMJ development of netonull mutants and induced gain-of-function phenotypes in a concentration-dependent manner ( Fig 8A and [27] ) . Low to moderate levels of Neto-β induced formation of NMJs with relatively normal morphology and clearly defined synaptic Neto clusters , resembling the neto mutants rescued with low and moderate levels of Neto-α . Excess Neto-β appeared detrimental to the NMJ development and to the overall growth and viability of rescued animals ( Fig 8A ) . Intriguingly , Neto-β but not Neto-α enabled the stabilization of PAK at PSDs , over a wide range of concentrations tested ( Fig 8A ) . Furthermore , increasing the levels of Neto- α in neto-β mutants could not restore the PAK synaptic accumulation , while introducing Neto-β effectively rescued this defect . This result cannot be explained by a difference between Neto- α and Neto-β cellular distribution , since both isoforms appear to concentrate at the NMJ . We confirmed that Neto-α and Neto-β synaptic levels correlate with the levels of Neto protein in the larval muscle , as detected by Western analysis ( Fig 8B ) . Thus , both Neto isoforms can traffic and accumulate at synaptic locations where they mediate synapse assembly . However , only Neto-β isoform can enable PAK accumulation at PSDs . Since PAK contributes to synaptic stabilization of GluRIIA , Neto-α-rescued NMJs are expected to exhibit a reduction in the synaptic GluRIIA signals and thus a reduction in quantal size . Indeed , we found that netonull mutants rescued with low/moderate levels of Neto-α had significantly reduced mini amplitudes and reduced GluRIIA/GluRIIB ratio ( Fig 8C and 8D , S7 Fig ) . In contrast , netonull NMJs rescued with neto-β transgenes had relatively normal mini amplitudes , indicating normal GluRIIA/GluRIIB ratio at these synapses . The mini frequency was largely normal , except for the neto-β transgenes which showed increased mini frequency when reared at 18°C ( Fig 8E ) . Interestingly , the evoked potentials were in the normal range in all Neto-α and Neto-β-rescued larvae tested , likely due to compensatory presynaptic response ( Fig 8F–8H ) . These results indicate that Neto-α-rescued NMJs have deficits in the synaptic accumulation of type-A receptors . Such defects are partly obscured by excess Neto-α , likely because the conserved domains of Neto confer high iGluRs “clustering capacity” in these rescue experiments . But under normal condition , Neto is a low abundance protein and a limiting factor for iGluRs clustering in the muscle . Furthermore , Neto-β appears to be the predominant isoform at the Drosophila NMJ . Together our findings demonstrate that Neto-β , the major Neto isoform at the Drosophila NMJ , controls the subtype composition of iGluRs partly by regulating the recruitment of the PSD-associated kinase PAK . Neto proteins have been initially characterized as auxiliary subunits that modulate the function of kainate ( KA ) and NMDA receptors [22 , 23] . In vertebrates , Neto1 and Neto2 directly interact with KAR subunits and increase channel function by modulating gating properties [23 , 37 , 38] . Since loss of KAR currents in mice lacking Neto1 and/or Neto2 exceed a reduction that could be attributed to alterations of channel gating , an additional role for Neto proteins in synaptic targeting of receptors has been proposed . The role for vertebrate Neto proteins in KAR membrane and/or synaptic targeting remains controversial and appears to be cell type- , receptor subunit- , and Neto isoform-dependent [23 , 39 , 40] . Furthermore , the C . elegans Neto has a very small intracellular domain ( 24 amino acids beyond the conserved domains ) [24] . This implies that 1 ) Neto without an intracellular domain constitutes the minimal conserved functional moiety , and 2 ) the divergent intracellular domains of Neto proteins may fulfill tissue and/or synapse specific modulatory functions . Indeed , Neto2 bears a class II PDZ binding motif that binds to the scaffold protein GRIP and appears to mediate KARs stabilization at selective synapses [41] . In flies , Neto is an essential protein that plays active roles in synapse assembly and in the formation and maintenance of postsynaptic structures at the NMJ . The Drosophila Neto isoforms do not have PDZ binding motifs , but they use at least two different mechanisms to regulate the synaptic accumulation and subunit composition of iGluRs . First , Neto participates in extracellular interactions that enable formation of iGluR/Neto synaptic complexes; formation of stable aggregates is presumably prevented by the inhibitory prodomain of Neto [27] . Second , the two Neto isoforms appear to facilitate the selective recruitment and/or stabilization of specific iGluR subtypes . We speculate that Neto-β may selectively associate with and recruit type-A receptors , perhaps by engaging the C-terminal domain of GluRIIA , which is critical for the synaptic stabilization of these receptors [42 , 43] . Aside from a possible role in the selective recruitment of iGluR subtypes , Neto-β participates in intracellular interactions that facilitate the recruitment of PAK at PSDs; in turn , PAK signals through two independent , genetically separable pathways ( a ) to modulate the GluRIIA synaptic abundance and ( b ) to facilitate formation of SSR [17] . Whether Neto-β recruits PAK directly or via a larger protein complex remains to be determined . Neto-β contains an SH3 domain that may bind to the proline-rich SH3 binding domain of PAK . However , in tissue culture experiments , we failed to detect a direct interaction between PAK and Neto-β ( full-length or intracellular domain ) . PAK synaptic accumulation is completely abolished at NMJ with mutations in dPix , although not all dpix defects are mediated through PAK [16] . Conversely , PAK together with Dreadlocks ( Dock ) controls the synaptic abundance of GluRIIA , while PAK and dPix regulate the Dlg distribution [17] . The reduction of GluRIIA and Dlg synaptic abundance observed at neto-β mutant NMJs suggests that Neto-β may interact with both dPix and Dock and enable both PAK activities . In addition , Neto-β may stabilize postsynaptic type-A receptors by enhancing their binding to Coracle , which anchors GluRIIA to the postsynaptic actin cytoskeleton [15] . Importantly , this study connects the complex regulatory networks that modulate the PSD composition to the Neto/iGluR clusters themselves . The Neto-β cytoplasmic domain is rich in putative protein interaction motifs ( S1 Fig ) , and may function as a scaffold platform to mediate multiple protein interactions that act synergistically during synapse development and homeostasis . Loss of Neto-β-mediated intracellular interactions at netoβshort NMJs reduced the GluRIIA synaptic abundance , but did not affect the GluRIIB synaptic signals ( Fig 4E ) . It is unlikely that the remaining cytoplasmic part of Neto-β facilitates the GluRIIB synaptic accumulation at these NMJs at the expense of GluRIIA and PAK . Instead , we favor a model whereby the synaptic stabilization of GluRIIA requires a Neto-β-dependent intracellular network . Disruption of this network diminishes GluRIIA and increases GluRIIB synaptic abundance , pending the availability of limiting subunits , GluRIIC-E and Neto . Conversely , the presence of this network ensures that at least some type-A receptors are stabilized at synaptic sites , even at Neto-deprived synapses , such as in netohypo larvae [12] . Assembly of this network does not require GluRIIA since both Neto-β and PAK accumulated normally at GluRIIA mutant NMJs ( Fig 8I ) . Furthermore , in the absence of Neto-β the synaptic abundance of GluRIIA can be partly restored by excess Neto-α or a delta-intracellular Neto variant , suggesting that excess iGluRs “clustering capacity” overrides the cellular signals that shape PSD composition [27] . What intracellular domain ( s ) of Neto bind to and how they are modified by post-translational modifications will be critical questions to understand how postsynaptic composition is regulated during development and homeostasis . The discovery of Drosophila Neto isoforms with alternative cytoplasmic domains and isoform specific activities expands the repertoire of Neto-mediated functions at glutamatergic synapses . On one hand , all Neto proteins share the highly conserved CUB1 , -2 , LDLa and transmembrane domains that have been implicated in engaging and modulating the receptors , the central function of Neto proteins [22 , 23 , 44] . In flies this conserved part is both required and sufficient for iGluRs clustering and NMJ development . In C . elegans the entire Neto appears to be reduced to this minimal functional unit [24] . The only exception is a retina-specific CUB1-only Neto1 isoform with unknown function [45] . In contrast , the cytoplasmic domains are highly divergent among Neto proteins . This diversity might have evolved to facilitate intracellular , context specific function for Neto proteins , such as the need to couple the iGluR complexes to neuron or muscle specific scaffolds in various phyla . Alternatively , by engaging in different intracellular interactions , via distinct cytoplasmic domains , different Neto isoforms may undergo differential targeting and/or retention at the synapses and thus acquire isoform-specific distributions and functions within the same cell . Phylogenetic analyses indicate that the intracellular domains of Neto are rapidly evolving in insects . Blocks of high conservations could be clearly found in the genome of short band insect Tribolium castaneum ( Coleoptera ) or in Apis mellifera ( Hymenoptera ) . However , most insects outside Diptera appear to have only one Neto isoform , more related to Neto-β . In fact , the only Neto-α isoform outside Drosophila was found in Musca domestica ( unplaced genomic scaffold NCBI Reference Sequence: XM_005187241 . 1 ) . Other neto loci , from Hydra to vertebrates , appear to encode Neto proteins with unique and highly divergent intracellular domains . An extreme example is the C . elegans Neto/Sol-2 , with a very short cytoplasmic tail , which requires additional auxiliary subunits , Sol-1 and Stargazin , to control the function of glutamate receptors [46 , 47] . Neto proteins appear to utilize their intracellular domains to connect to the signaling networks that regulate the distribution and subunit composition for iGluRs . Such cellular signals converge onto and are integrated by the intracellular domains of the receptors and/or by various auxiliary subunits associated with the iGluR complexes [1 , 6] . Neto proteins modulate the gating behavior of KAR but also play crucial roles in the synaptic recruitment of glutamate receptors in vivo [12 , 40 , 48] . At the fly NMJ , Neto enables iGluRs synaptic clustering and initiates synapse assembly . In addition , the intracellular domain of Neto-β recruits PSD components and triggers a cascade of events that organize postsynaptic structures and shape the composition of postsynaptic fields . The cytoplasmic domains of Neto proteins emerge as versatile signaling hubs and organizing platforms that directly control the iGluRs subunit composition and augment the previously known Neto functions in modulation of glutamatergic synapses . To generate neto-β alleles , the Minos transposomal element Mi ( ET1 ) Neto[MB07125] was mobilized with Minos transposase [25] . Several lines with precise excisions and imprecise excisions/small deletions were isolated and characterized by PCR amplification over the deficiencies and DNA sequencing . The genomic fragments removed in various neto alleles were as follows: neto203 X: 13 , 415 , 115–13 , 418 , 117 , including part of the exon 13 and 14 of the predicted neto gene , and neto204 X: 13 , 414 , 477–13 , 417 , 931 , containing exons 12 , 13 and 14 . The UAS-neto-β lines ( B lines ) were generated by insertion of the neto-β cDNA ( from RE42119 ) in pUAST vector and germline transformation ( BestGene , Inc . ) . Similarly , for the UAS-netoΔintra ( the H4 line ) the Neto coding sequence M1-R471 followed by a short linker ( DVPALE ) was placed in frame with the eGFP and cloned in pUAST . The neto-αRNAi lines were generated by insertion of a neto-α specific PCR fragment in pUAST-R57 followed by germline transformation . The PCR primers utilized were: RNAi-F ( 5′-AAGGCCTACATGGCCGGACCGGCGAACAAATGGAGGAAGACG-3′ ) and RNAi-Rev ( 5′-AATCTAGAGGTACCTGATTTTGTGCAGGAACTTGAGG-3′ ) . Other fly stocks used in this study were as follows: neto36 , neto109 , and UAS-neto-α -V5 [12]; UAS-neto-α ( A lines ) [27]; neto ( CUB1 ) RNAi [14]; Dp ( 1:3 ) DC270 [26]; pak6 , pak11 , and pakmyr [34]; dpix1 [16]; GluRIIASP16 , and Df ( 2L ) clh4 [7] ( from A . DiAntonio , Washington University ) . The G14-Gal4 was obtained from C . Goodman ( University of California at Berkeley ) . The rabbit polyclonal anti-Neto-β antibodies were generated against two synthetic peptides: β1 ( GRSHYGGLLVTSTAKQP ) and β2 ( LDDVSNRYYREAVPL ) ( 21st Century Biochemicals ) and separated by affinity purification . The rabbit polyclonal anti-GluRIIB and anti-GluRIIC were generated as previously described [8] against synthetic peptides ASSAKKKKKTRRIEK , and respectively QGSGSSSGSNNAGRGEKEARV ( Pacific Immunology Corp ) . To analyze muscle proteins , wandering third instar larvae were dissected , and all tissues except for the body wall ( muscle and cuticle ) were removed . The body walls were mechanically disrupted and lysed in lysis buffer ( 50 mM Tris-HCl , 150 mM NaCl , 1% Triton X-100 , 1% deoxycholate , protease inhibitor cocktail ( Roche ) for 30 min on ice . The lysates were separated by SDS-PAGE on 4%–12% NuPAGE gels ( Invitrogen ) and transferred onto PVDF membranes ( Millipore ) . Drosophila S2 cells were used for the production of recombinant proteins [12] . Full-length cDNA for neto-β from RE42119 was subcloned in pAcPA-based plasmids for expression in S2 cells under the actin promoter . The Neto-β truncation ( neto203-like ) was generated by looping out the deleted fragment using the QuickChange site-directed mutagenesis kit . All constructs were verified by DNA sequencing . Primary antibodies were used at the following dilutions: rat anti-Neto-ex [12] , 1:1000; anti-Tubulin ( Sigma-Aldrich ) , 1:1000; rabbit anti-Neto-β , 1:1000; rabbit anti-GluRIIC , 1:1000; rabbit anti-PAK , 1:5000 ( a gift from Nicholas Harden ) [35]; mouse anti-Dlg ( 4F3 ) , 1:1000; mouse anti-V5 ( Invitrogen ) , 1:1000 . Immune complexes were visualized using secondary antibodies coupled with IR-Dye 700 or IR-Dye 800 followed by scanning with the Odyssey infrared imaging system ( LI-COR Biosciences ) . Wandering third instar larvae were dissected as described previously in ice-cooled Ca2+-free HL-3 solution [49 , 50] . Embryos at 18h after egg laying ( AEL ) were dechorinated and genotyped and , after an additional incubation of 2 h at room temperature , were dissected as described previously [12] . First instar larvae were similarly dissected within 2 h from hatching . The samples were fixed in 4% paraformaldehyde ( Polysciences , Inc . ) for 20 min or in Bouin’s fixative ( Bio-Rad ) for 3 min and washed in PBS containing 0 . 5% Triton X-100 . Primary antibodies from Developmental Studies Hybridoma Bank were used at the following dilutions: mouse anti-GluRIIA ( 8B4D2 ) , 1:100; mouse anti-Dlg ( 4F3 ) , 1:1000; mouse anti-Brp ( Nc82 ) , 1:200; mouse anti-CSP ( 6D6 ) , 1:1000: mouse anti-α-Spectrin ( 3A9 ) , 1:50: mouse anti-FasII ( 1D4 ) , 1:10 . Other primary antibodies were utilized as follow: rabbit anti-PAK , 1:2000; rat anti-Neto-ex , 1:1000 [12]; and Cy5- conjugated goat anti-HRP , 1:1000 ( Jackson ImmunoResearch Laboratories , Inc . ) . Alexa Fluor 488- , Alexa Fluor 568- , and Alexa Fluor 647- conjugated secondary antibodies ( Molecular Probes ) were used at 1:200 . All samples were mounted in ProLong Gold ( Invitrogen ) . Samples of different genotypes were processed simultaneously and imaged under identical confocal settings in the same imaging session with a laser scanning confocal microscope ( CarlZeiss LSM780 ) . All images were collected as 0 . 2m optical sections and the z-stacks were analyzed with Imaris software ( Bitplane ) . To detect positive puncta we used the spot finding Imaris algorithm followed by manual inspection and correction . To quantify fluorescence intensities synaptic ROI areas surrounding anti-HRP immunoreactivities were selected and the signals measured individually at NMJs ( muscle 4 , segment A3 ) from ten or more different larvae for each genotype ( number of samples is indicated in the graph bar ) . The signal intensities were calculated relative to HRP volume and subsequently normalized to control . Boutons were counted in preparations double labeled with anti-HRP and anti-Dlg . All quantifications were performed while blinded to genotype . The numbers of samples analyzed are indicated inside the bars . Statistical analyses were performed using the Student t-test with a two-tailed distribution and a two-sample unequal variance . Error bars in all graphs indicate standard deviation ±SEM . ***; p<0 . 001 , **; p<0 . 005 , *; p<0 . 05 , ns; p>0 . 05 . The standard larval body wall muscle preparation first developed by Jan and Jan ( 1976 ) [51] was used for electrophysiological recordings [52] . Wandering third instar larvae were dissected in physiological saline HL-3 saline [49] , washed , and immersed in HL-3 containing 0 . 8 mM Ca2+ or 0 . 3 mM Ca2+ using a custom microscope stage system [53] . The nerve roots were cut near the exiting site of the ventral nerve cord so that the motor nerve could be picked up by a suction electrode . Intracellular recordings were made from muscle 6 . Data were used when the input resistance of the muscle was >5 MΩ and the resting membrane potential was between -60 mV and -80 mV . The input resistance of the recording microelectrode ( backfilled with 3 M KCl ) ranged from 20 to 25 MΩ . Muscle synaptic potentials were recorded using an Axon Clamp 2B amplifier ( Axon Instruments ) and pClamp 10 software . Following motor nerve stimulation with a suction electrode ( 100 μsec , 5 V ) , evoked EJPs were recorded . Four to six EJPs evoked by low frequency of stimulation ( 0 . 1 Hz ) were averaged . For mini recordings , TTX ( 1 μM ) was added to prevent evoked release [49] . To calculate mEJP mean amplitudes , 50–200 events from each muscle were measured and averaged using the Mini Analysis program ( Synaptosoft ) . Minis with a slow rise and falling time arising from neighboring electrically coupled muscle cells were excluded from analysis [54 , 55] . Quantal content was calculated by dividing the mean EJP by the mean mEJP after correction of EJP amplitude for nonlinear summation according to previously described methods [56 , 57] . Corrected EJP amplitude = E[Ln[E/ ( E—recorded EJP ) ]] , where E is the difference between reversal potential and resting potential . The reversal potential used in this correction was 0 mV [57 , 58] . Data are presented as mean ±SEM , unless otherwise specified; EJP amplitudes and quantal contents after the nonlinear correction are shown . Student T-test was used to assess statistically significant differences among the genotypes . Wandering third instar larvae were dissected in physiological saline HL-3 saline and fixed for 30 min on dissection plate in fixation buffer ( 0 . 1 M Sodium Cacodylate buffer , pH7 . 2; 2 mM MgCl2; 1% glutaraldehyde; 4% paraformaldehyde ) . The samples were trimmed to include only the abdominal segments A2 and A3 , transferred in a 1 . 5mL Eppendorf tube , fixed overnight at 4°C , then washed extensively with 0 . 1 M Sodium Cacodylate buffer with 132 mM Sucrose , pH 7 . 2 . The samples were further processed and analyzed according to published protocols [59] at the Microscopy and Imaging Facility , NICHD .
Ionotropic receptors assembled from different subunits have strikingly different properties and uses . In mammalian brain , the molecular mechanisms controlling the subunit composition of glutamate receptors are critical for the formation of neural circuits and for the long-term plasticity underlying learning and memory . Here we investigate how subunit composition is regulated at the Drosophila neuromuscular junction ( NMJ ) , a synapse similar in composition and physiology to mammalian AMPA/Kainate synapses . We find that an auxiliary protein , Neto , which is essential for functional receptors , has a key role in controlling which flavor of glutamate receptors will be at the synapses . In flies , synapse strength and plasticity is modulated by the interplay between two receptor subtypes , A and B . Mutations that eliminate or truncate the Neto-β isoform fail to accumulate the type-A receptors , as well as other postsynaptic proteins important for the synaptic stabilization of type-A receptors . This result indicates that Neto may use its cytoplasmic domains as signaling hubs and organizing platforms to sculpt postsynaptic composition . Neto proteins modulate the formation and function of glutamatergic synapses from worms to humans . Our findings expand the repertoire of Neto proteins and illustrate the richness in synapse modulation brought about by the growing family of auxiliary proteins .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[]
2015
Neto-Mediated Intracellular Interactions Shape Postsynaptic Composition at the Drosophila Neuromuscular Junction
We performed a genome-wide scan for muscle-specific cis-regulatory modules ( CRMs ) using three computational prediction programs . Based on the predictions , 339 candidate CRMs were tested in cell culture with NIH3T3 fibroblasts and C2C12 myoblasts for capacity to direct selective reporter gene expression to differentiated C2C12 myotubes . A subset of 19 CRMs validated as functional in the assay . The rate of predictive success reveals striking limitations of computational regulatory sequence analysis methods for CRM discovery . Motif-based methods performed no better than predictions based only on sequence conservation . Analysis of the properties of the functional sequences relative to inactive sequences identifies nucleotide sequence composition can be an important characteristic to incorporate in future methods for improved predictive specificity . Muscle-related TFBSs predicted within the functional sequences display greater sequence conservation than non-TFBS flanking regions . Comparison with recent MyoD and histone modification ChIP-Seq data supports the validity of the functional regions . A regulatory network represents the complex interplay between regulatory proteins and biochemical processes that govern when and where genes are expressed . Two important components of a regulatory network are cis-regulatory modules ( CRM ) , composed of functionally interacting clusters of transcription factor binding sites ( TFBS ) sufficient to confer a pattern of expression upon a promoter , and the corresponding trans-acting transcription factors ( TFs ) that bind to a CRM to regulate transcription initiation . The multiple TFBS that constitute a CRM allow for combinatorial control of expression; a limited number of TFs can participate in an exponential number of combinations with each potentially conferring specific patterns of gene activity [1] . CRMs can be situated almost anywhere relative to the structure of a gene: both near and far ( even exceptionally far ) from the promoter region ( s ) at which transcription initiates . While there are indications of quantitative orientation effects in some cases [2] , CRMs are generally thought to be active in either direction relative to a gene promoter . Linear distance in primary sequence is no indication of the three dimensional distance ( or orientation ) within the nucleus . Regulatory regions can be located in introns of an adjacent gene [3] , [4] , can skip over intervening genes [5] and there are suggestions that CRMs can act on genes located on different chromosomes [3] , [6] . Reflecting these properties , the discovery of CRMs stands out as a significant challenge for both computational and experimental research . In multicellular organisms , maintaining precise spatial and temporal control of transcription in various cell types is vital for correct tissue development and specialization [7]–[9] . One of the most widely studied “programs” of tissue development is the regulation of skeletal muscle differentiation . Myogenesis is a structured process , in which mononucleate myoblasts fuse together to form multinucleate myotubes , which then develop into classes of myofibres [10] . C2C12 cells provide a popular model for this process , with an easily triggered switch between the growth and differentiation phases [11] . Any tissue differentiation process requires complex transcriptional regulation controls . For skeletal muscle , differentiated cell gene expression involves at least two major TF families , the myogenin family and the MADS family [12]–[14] . In many differentiation processes , multiple proteins within a homology-based family can participate in the regulatory control of gene expression at overlapping temporal stages of the process . Skeletal muscle differentiation follows this model; thus the myogenin family may equally refer to Myogenin , MyoD , and Myf-2 while the MADS set encompasses both Srf and multiple members of the Mef2 gene family . Dozens of muscle-specific CRMs have been identified [15]–[17] , usually based on reporter gene assays in the C2C12 cell culture myogenesis model . Aided by the relatively plentiful set of skeletal muscle CRMs , much effort has been made by the bioinformatics research community to develop predictive algorithms for CRM discrimination . Multiple CRM detection programs have been developed , which look for clusters of TFBS specific to the TFs known to be involved in the cell type of interest . An original discriminative method to distinguish between CRM and non-CRM sequences based on a logistic regression analysis ( LRA ) procedure has been followed by a plethora of more advanced approaches ( Supplemental Table S1 in Text S1 ) [15] . For example , MSCAN makes use of motif-specific p-values to compute the statistical significance of sets of non-overlapping potential TFBSs [18] , while ClusterBuster is based on a hidden Markov model that incorporates heuristics to improve predictive performance [19] . None of the methods are sufficiently reliable for direct genome annotation; the specificity of predictions is sufficiently low that laboratory validation is essential to distinguish functional CRMs . The overall performance of the methods and the properties that differentiate the functional CRMs from the false candidates remain to be determined . In some cases , the prediction of CRMs has been coupled with phylogenetic footprinting under the premise that sequence conservation of known CRMs and TFBS is indicative of function and therefore a conservation filter will improve the positive predictive value of the CRM prediction methods [15] , [20]–[22] . It is often the case that the regulatory sequences display evidence of evolutionary selective pressure compared to the background rates of sequence change in non-functional sequence [23] , [24] . If the expression pattern of a gene is conserved between two species in the same taxonomy class , the CRM that confers the pattern is likely to be retained as well ( although the individual TFBS within the CRM may be altered ) . By applying phylogenetic footprinting to the analysis of closely related species ( i . e . 50–100 million years of separation for vertebrates ) , it becomes possible to concentrate predictions within a subset of regions in the conserved segments of genes . Improved specificity is balanced against the reduced sensitivity imposed by any filter . Once predictions of regulatory sequences have been made , laboratory validation is required to confirm regulatory function . One of the most widely used methods for validating computational predictions of regulatory regions are reporter gene assays in a cell culture model system [25] . A fusion construct of the predicted regulatory sequence and a reporter gene with a basal promoter in a plasmid is transiently transfected into cells , and the reporter gene activity is measured to determine the regulatory impact that the tested sequence exerts . It is feasible to conduct larger-scale experiments to investigate functional properties of panels of candidate CRMs and promoters within cells . Cooper et al performed a large screen of promoter activity in 16 cell lines on all predicted promoters in the 1% of the human genome targeted for in depth annotation by the ENCODE Project [26] . Similarly , relatively large-scale in vivo enhancer studies have been performed using highly conserved ( human to fish ) sequences driving reporter gene expression in transgenic mouse embryos , leading to the identification of 75 forebrain-specific enhancers [27] . Kappen et al . analyzed the regulatory controls for lsl , a LIM/homeodomain transcription factor , by inserting randomly sheared 8–10 kb fragments from the lsl genomic locus into reporter constructs and testing for expression both in vitro and in vivo [28] . Using a single copy insertion mouse transgenesis procedure , the Pleiades Promoter Project evaluated over 100 candidate regulatory sequences for the capacity to direct selective patterns of reporter gene expression in the developed brain [29] . The development of higher-throughput approaches to verify enhancer and promoter function has been a focus of recent efforts to annotate vertebrate genomes . The properties of skeletal muscle CRMs have been widely studied , but relatively few novel functional CRMs have been described since CRM prediction methods have emerged . To quantify the performance of CRM prediction methods requires a new body of reference data . We generated predictions of CRMs with three published methods and assessed the predictive benefit of sequence conservation and annotation of the expression patterns of proximal genes . We employed LRA , MSCAN , and ClusterBuster to scan the human genome for putative skeletal muscle regulatory regions , and tested a subset for the capacity to drive reporter gene expression in a selective manner in the C2C12 cell skeletal muscle differentiation assay . We compare the reporter gene expression in immature myoblasts against expression in mature myotubes , as well as in a fibroblast cell line . Based on the outcomes of the analysis , we define additional properties of sequence composition that are predictive of function and establish a new reference collection for the continuing development of predictive methods . Promoter regions are identified following the procedure described for the oPOSSUM database [30] . The oPOSSUM database contains the set of genes identified as being in one-to-one human and mouse ortholog pairs based on annotations in EnsEMBL v . 41 and UCSC hg18/mm8 whole genome alignments . For each ortholog pair , 10 kb upstream and downstream of a TSS is searched for CRMs . All noncoding regions are included in the search , including intergenic regions , introns , and untranslated regions ( UTR ) of exons; protein coding portions of exons are excluded . Any noncoding region that constitutes a portion of a coding exon in an alternative transcript is removed from the selection process . All alternative transcription start sites ( TSS ) supported by either human or mouse Fantoms3 CAGE evidence were identified and 50 bp on either side of each TSS was excluded [31] . CRM prediction tools were used to search for muscle-specific regulatory modules within the specified genome sequences . Logistic Regression Analysis ( LRA ) , MSCAN , and ClusterBuster were applied to the human genomic sequence regions specified above [15] , [18] , [19] . The input TFBS motif models were taken from JASPAR , a database of transcription factor binding site profiles [32] . The models used were MEF2A ( MA0052 ) , SRF ( MA0083 ) , MYF ( MA0055 ) , TEAD ( MA0090 ) , and SP1 ( MA0079 ) ; TFs with described key roles in muscle-specific gene expression . Predicted CRMs composed entirely of SP1 TFBS were excluded . The candidate regions were analyzed for conservation based on phastCons scores ( generated with 28 placental mammal genomes ) obtained from the UCSC Genome Annotation system [33] . For a region to be classified as conserved , the presence of at least one sub-region with phastCons scores of 0 . 7 or greater over 20 bp is required . For each region , both the mean and the maximum phastCons scores were calculated and sub-regions with phastCons scores over 0 . 7 were extracted and the ratio of the length of these sub-regions over the total length of the region calculated . For phylogenetic depth evaluation , three sets of human phyloP scores ( generated with 46 vertebrates , 46 placental mammals and 46 primates; database version hg19 ) were obtained from the UCSC Genome Annotation system . The ChIP-Seq peak locations for MyoD binding regions in the mouse genome were obtained from http://www . cs . washington . edu/homes/ruzzo/papers/DevCell/2010a/ , the companion web resource to the reference publication [34] . C2C12 cell ChIP-Seq peak locations for H3K4me1/2/3 , H3K9me3 , H3K9Ac , H3K18Ac , H3K27me3 , and H4K12Ac annotated by Asp et al . were downloaded from the NCBI GEO database ( GSE25308; [35] ) . MatrixAligner was used to calculate the profile similarity of two TFBSs [36] . This program generates scores from 0 to 2 , where a score of 2 indicates complete identity between two matrices being compared . Mouse C2C12 myoblasts ( ATCC CRL-1772; American Type Culture Collection; Manassas , VA , USA ) and mouse NIH-3T3 fibroblasts ( ATCC CRL-1658; American Type Culture Collection; Manassas , VA , USA ) were maintained in Dulbecco's modified Eagle's medium , supplemented with 10% ( v/v ) heat inactivated fetal bovine serum , 100 U/ml penicillin , and 100 µg/ml streptomycin . The cultures were grown at 37°C and 5% CO2 . Differentiation of myoblasts into myotubes was induced by transferring C2C12 cells to differentiating media consisting of 2% ( v/v ) horse serum , 100 U/ml penicillin , and 100 µg/ml streptomycin . The media and reagents for cell culture were obtained from Gibco-Invitrogen ( GIBCO-Invitrogen Canada , Canadian Life Technologies , Burlington , ON , Canada ) . Primer3 was used to design the flanking primers for each predicted CRM for PCR [37] . After performing PCR with the designed primers ( synthesized by Invitrogen Coporation ( Carlsbad , CA , USA ) ) , 20 ng of each PCR product was pooled , which were then purified using the PCR purification kit ( NEB , Mississauga , ON , Canada ) and subcloned into the pGL-3 promoter luciferase vector ( Promega; Fisher Scientific , Nepean , ON , Canada ) via Kpn I and Bgl II restriction enzymes sites . Restriction digest was performed overnight at 37°C . Post-digestion , the vector was dephosphorylated with calf intestinal alkaline phosphatase ( NEB , Mississauga , ON , Canada ) . The restriction enzyme-digested PCR products and the vector were gel-purified using QIAquick gel extraction kit ( Qiagen Inc . Mississauga , ON , Canada ) and ligated using T4 DNA ligase ( NEB , Mississauga , ON , Canada ) . A set of control clones and a sample of the library were prepared . Constructs were transformed into sub-cloning efficient DH5α chemically competent E . coli cells ( GIBCO Invitrogen Canada , Canadian Life Technologies , Burlington , ON , Canada ) via heatshock at 42°C and plated on LB agar plates containing 100 µg/ml of Ampicillin for preliminary bacterial colony screening . Colonies were picked and inoculated overnight in 3 ml LB broth with ampicillin . Plasmids were prepared using QIAprep Spin Miniprep Kit ( Qiagen Inc . Mississauga , ON , Canada ) . Sequence confirmation was performed by the CMMT/CFRI DNA Sequencing Core Facility . Large-scale transformation , colony picking , miniprep , and sequencing reactions with the constructs were performed ( Genome Science Centre , Vancouver , BC , Canada ) . 1 µl of ligation mix was transformed by electroporation into E . coli DH10B T1 resistant cells ( Invitrogen ) . Transformed cells were recovered using 1 ml of SOC medium and plated onto 22 cm×22 cm agar plates ( Genetix ) containing 100 ug/ul ampicillin . Bacterial colonies were picked from the agar plates and arrayed into 384-well microtiter plates ( Genetix ) using a QPIX automated colony 15 picker ( Genetix ) . Plasmid preparations were performed via an alkaline lysis protocol . DNA sequencing reactions were prepared using a Biomek FX workstation ( Beckman-Coulter ) and performed using BigDye 3 . 1 ( Applied Biosystems ) . Analysis of the resulting sequences to the target DNA regions was performed with AlignX from the Vector NTI software ( Invitrogen ) . Concentration of the plasmid products was quantified using Picogreen assays ( GIBCO-Invitrogen Canada , Canadian Life Technologies , Burlington , ON , Canada ) via fluorescence measurement with a POLARstar Omega microplate reader ( BMG Labtech; Fisher Scientific , Nepean , ON , Canada ) . All DNA samples were normalized to 100 ng/µl per well . Two sets of C2C12 myoblasts and one set of NIH-3T3 fibroblasts were seeded in 96-well plates at a density of 6000 cells per well . The myoblasts were divided into two sets so that one set could be harvested as myoblasts , while the other set could be differentiated into myotubes prior to harvest . After 24 h ( at 70% confluency ) in growth media , the cells were transfected with 200 ng of a pGL3-promoter firefly luciferase plasmid construct and 20 ng renilla phRL-TK internal control luciferase plasmid ( Promega , Madison , WI ) using Lipofectamine 2000 according to the manufacturer's protocol ( GIBCO-Invitrogen Canada , Canadian Life Technologies , Burlington , ON , Canada ) . At 24 h post-transfection , the myoblast C2C12 set and the NIH-3T3 fibroblasts were harvested and luciferase activity measured using the Dual-Luciferase Reporter Assay System ( Promega , Madison , WI ) and a POLARstar Omega microplate reader ( BMG Labtech; Fisher Scientific , Nepean , ON , Canada ) . The final set of C2C12 myoblasts was switched to differentiating media 24 h after transfection , and incubated for 96 h for differentiation into myotubes . For each clone , duplicate transfections ( technical replicates ) were performed . The reporter gene activity assays were carried out in two phases . In phase 1 , all plasmid constructs were tested in the three cell types . In the second phase , only myotube and myoblast activities were assessed . The following terminology will be used when discussing the experimental data: All statistical analyses were done using the R software [38] . The ratios of firefly luciferase expression values over the renilla luciferase expression values were calculated to measure the relative increase of the firefly luciferase activity over the renilla luciferase activity ( the internal control for transfection efficiency ) . Clones that did not produce both firefly and renilla luciferase expression values above the minimum threshold of 1000 luminescence relative light units ( LRUs ) were marked as failed transfections and removed from subsequent analyses . This heuristic filter was applied to exclude spurious expression ratio measurements , as the ratio of two small values can result in a disproportionately high value , and the VSN procedure intended to mitigate this effect was not sufficient [39] . For those clones where only the firefly luciferase values were above this threshold , the renilla luciferase value was set to the threshold level . This step was designed to minimize the occurrence of large ratios even when the firefly luciferase expression values are near the threshold . The threshold of 1000 LRUs is higher than the median machine background level , which was found to be below 250 LRUs . While this conservative heuristic filter may result in a decrease in sensitivity , the trade-off was deemed acceptable in order to avoid situations where spurious measurements are accepted as false positive results . The expression ratios from the two technical replicates for each clone were averaged , excepting the cases where a replicate transfection failed the minimum expression threshold filter ( in such cases the single replicate value was used ) . The expression ratios obtained for each cell type were normalized using the VSN package . Each clone was treated as an independent sample even though there were in some cases insert replicates . The stochastic variation in the number of insert replicates would otherwise have complicated the analysis . Differential expression between 1 ) fibroblasts and myotubes , 2 ) myoblasts and myotubes , and 3 ) fibroblasts and myotubes groups were determined using the SAM package [40] , applying a false discovery rate maximum of 0 . 05 . The two sets of clones selected from phase 1 and phase 2 at the FDR of 0 . 05 were combined and grouped according to the insert sequence . For each sequence , the number of clones that were identified as showing differential expression was counted , and those sequences with only one supporting clone and/or less than 50% of the available clones identified as positive were excluded from the final set . The overall region selection process is illustrated in Supplemental Figure S1 in Text S2 . Three sets of genomic sequences were identified for the study of skeletal muscle CRM predictions: ( i ) background regions randomly selected from conserved regions for control ( background set ) ; ( ii ) predicted skeletal muscle CRM regions proximal to skeletal muscle-expressed genes ( muscle set ) ; and ( iii ) predicted skeletal muscle CRM regions proximal to genes with no observed link to skeletal muscle ( non-muscle set ) . Prediction of CRMs was performed for the muscle and non-muscle sets , while the background sequences were randomly selected from conserved intergenic regions which may or may not contain predicted CRMs . The sets are further described below . To identify defining characteristics of the positive regions compared to the non-responding regions , the validated set was subjected to analyses based on sequence and conservation properties . We generated genome-wide predictions of muscle-specific CRMs using three CRM prediction programs , including Cluster-Buster , LRA and MSCAN . Based on the predictions , 339 candidate sequences were tested for CRM activity using promoter-reporter gene assays in a cell culture model of skeletal muscle development , of which 278 were successfully transfected into cells and had reporter expression measurements taken . The validation process revealed 19 myotube-restricted promoter-enhancing sequences . In addition to the known enrichment for sequence conservation of functional CRMs , phylogenetic depth analysis revealed that the individual TFBSs display even higher sequence conservation than the surrounding sequence . The active CRMs exhibited elevated G/C mononucleotide content indicating the value for including sequence composition measures in the implementation of future methods . Comparison of the ChIP-Seq results for MyoD and histone modification marks in C2C12 cells with the identified enhancing sequences further supports their recognized utility in the detection of active , functional CRMs . The performance of the CRM prediction programs used in this paper was not sufficient for genome annotation . The poor performance is likely reflective of the incomplete information presented for the prediction – the primary sequence and sequence conservation data does not convey information about the three dimensional properties of the nucleus nor the epigenetic state of the chromatin [60]–[63] . As evidenced by the significant increase in the proportion of responding regions that overlap with MyoD and histone modification peaks from ChIP-Seq studies , incorporating the results from ChIP-Seq assays for the relevant TFs , co-activating proteins or histone modification marks can improve the specificity of the predictions . In order for such data to be useful , data needs to be generated for each tissue type analyzed , as CRMs are anticipated to be differentially marked when activated . At this time , there is an insufficient amount of such large scale data available to make this a feasible strategy for many tissue types , but more complete data may become available as the costs of experiments come down and sensitivity increases . Ultimately an intersection of computational and experimental methods will be required for the highest quality annotation of CRMs . A fundamental question arising out of the work reported here is why methods that appeared to be doing well for skeletal muscle CRM discovery failed to demonstrate strong predictive capacity in application here . One key reason may be driven by selection bias for laboratory studies . The reports of CRMs from individual gene studies may in many cases have been influenced by the identification of muscle-related motifs in the available genomic sequences . Due to the selective publication of those sequences showing positive expression , the relative importance of motif enrichment may have been over emphasized . Another key limitation is that most of the methods generate sufficiently high false prediction rates that the reliability of any specific set of predictions is unlikely to be high . The results here demonstrate the driving need for experimental validation of computational predictions whenever feasible . One striking observation emerging from this study is the enrichment of G/C mononucleotides in the CRMs , observed both in the new muscle set as well as the brain-directing CRMs from the Pleiades Project [29] . The potential contribution of compositional properties to regulatory regions has been previously explored , including a statistical method for CRM prediction [64] and a recent approach from Evans to classify CRM-containing regions into compositional subsets of genome sequences [65] . These approaches and the data presented here are independent of the long-recognized role of CpG islands in demarcating promoter-containing regions and the influence of CpG enrichment on motif over-representation [66] , [67] . While there have been prediction methods released , such as Stubb , EMMA and PhylCRM , that directly incorporate phylogenetic footprinting in order to reduce the false positive rate of their predictions [21] , [22] , [68] , the joint incorporation of nucleotide composition properties and sequence conservation remains to be explored . The outcomes of this paper include both a novel set of 19 skeletal muscle-directing CRMs for use in future machine learning procedures and the specific call for the inclusion of nucleotide composition properties in the next generation of tools .
For efficient identification of genomic sequences responsible for regulating gene expression , a number of computer programs have been developed for automatic annotation of these regulatory regions . We searched for potential regulatory regions responsible for controlling the expression of skeletal muscle-specific genes using these programs , and validated the predictions in a popular cell culture model for muscle . We were able to identify 19 previously uncharacterized regulatory regions for muscle genes . The accuracy of the predictions made by these programs leaves much to be desired , leading us to conclude that other signals in addition to the sequence information will be required to achieve sufficient predictive power for genome annotation . Genomic regions with confirmed regulatory function were compared against non-functional sequences , revealing sequence conservation , composition and chromatin modification properties as important signals in determining regulatory region functionality .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "sequence", "analysis", "genome", "expression", "analysis", "genomics", "gene", "regulation", "genetics", "regulatory", "networks", "molecular", "genetics", "biology", "computational", "biology", "gene", "expression", "genetics", "and", "genomics", "dna", "transcription" ]
2011
Validation of Skeletal Muscle cis-Regulatory Module Predictions Reveals Nucleotide Composition Bias in Functional Enhancers
We demonstrated previously that 75% of infertile men with round , acrosomeless spermatozoa ( globozoospermia ) had a homozygous 200-Kb deletion removing the totality of DPY19L2 . We showed that this deletion occurred by Non-Allelic Homologous Recombination ( NAHR ) between two homologous 28-Kb Low Copy Repeats ( LCRs ) located on each side of the gene . The accepted NAHR model predicts that inter-chromatid and inter-chromosome NAHR create a deleted and a duplicated recombined allele , while intra-chromatid events only generate deletions . Therefore more deletions are expected to be produced de novo . Surprisingly , array CGH data show that , in the general population , DPY19L2 duplicated alleles are approximately three times as frequent as deleted alleles . In order to shed light on this paradox , we developed a sperm-based assay to measure the de novo rates of deletions and duplications at this locus . As predicted by the NAHR model , we identified an excess of de novo deletions over duplications . We calculated that the excess of de novo deletion was compensated by evolutionary loss , whereas duplications , not subjected to selection , increased gradually . Purifying selection against sterile , homozygous deleted men may be sufficient for this compensation , but heterozygously deleted men might also suffer a small fitness penalty . The recombined alleles were sequenced to pinpoint the localisation of the breakpoints . We analysed a total of 15 homozygous deleted patients and 17 heterozygous individuals carrying either a deletion ( n = 4 ) or a duplication ( n = 13 ) . All but two alleles fell within a 1 . 2-Kb region central to the 28-Kb LCR , indicating that >90% of the NAHR took place in that region . We showed that a PRDM9 13-mer recognition sequence is located right in the centre of that region . Our results therefore strengthen the link between this consensus sequence and the occurrence of NAHR . Several mechanisms have been proposed to cause genomic rearrangements , notably: Non Allelic Homologous Recombination ( NAHR ) , Non Homologous End Joining ( NHEJ ) , Fork Stalling and Template Switching ( FoSTeS ) and Break-Induced Replication ( BIR ) [1] , [2] . NAHR takes place between duplicated sequences with a high sequence identity ( usually >95% ) located in different genomic regions of the same chromosome [3] . These paralogous sequences or Low Copy Repeats ( LCR ) tend to generate polymorphic regions with deleted and duplicated alleles called Copy Number Variants ( CNVs ) . The consensual NAHR model predicts that recombinations between LCRs located on the same chromatid result in the production of a deleted allele and a small circular molecule that will be lost by the end of the cell cycle . Recombinations between LCRs located on two distinct chromatids ( whether sister-chromatids or chromatids from homologous chromosomes ) result in the production of a deleted allele and a complementary duplicated allele ( Figure 1A ) . In consequence NAHR is expected to produce an excess of deletions over duplications . This has been verified for several NAHR hotspots using sperm typing assays: on average twice as many deletions as duplications were generated de novo [4] . One study however describes similar deletion and duplication frequencies at the 7q11 . 23 , 15q11-q13 and 22q11 . 2 loci , suggesting a predominant inter-chromatid NAHR [5] . This study was carried out by fluorescent in situ hybridization ( FISH ) which allows the detection of all numerical anomalies occurring at these loci and not only the NAHR-mediated events . This could explain at least part of the discrepancy observed between the two studies , given that a recent study of the RAI1 locus suggested that complex genetic events generate an excess of duplications [6] . As NAHRs events occur at fixed LCRs they tend to be recurrent , and the recombined alleles normally share a common size defined by the distance separating the two LCRs . It is well-established that meiotic recombination events , whether resulting in crossing over or producing unbalanced alleles through NAHR , are not uniformly distributed along the human genome but occur preferentially at specific hot spots [7]–[9] . Myers et al . ( 2008 ) have characterized a degenerate 13 bp sequence motif ( CCNCCNTNNCCNC ) that is present in approximately 40% of the identified human crossover hotspots . A three nucleotide periodicity was observed within and beyond the 13-mer core , suggesting a direct interaction with a motif binding protein [10] . Subsequent work strengthened this hypothesis as it has been proposed that PRDM9 , a multi-unit zinc finger binding protein expressed mainly during early meiosis in germ cells [11] , specifies hotspot usage by binding specifically to this 13 bp consensus motif [12]–[14] . PRDM9 was then shown to be highly polymorphic , different alleles seemingly providing preferred targeted recombination hotspots [14] . Berg et al . ( 2010 ) measured the recombination rate at ten crossover hotspots , five with a PRDM9 recognition motif , five without a clear motif . Men with the rarer N allele showed a heavy reduction ( >30-fold ) at all hotspots , even at those which did not contain an obvious PRDM9 motif [12] . Further work revealed that specific PRDM9 alleles activated different hotspots [15] . The direct correlation between PRDM9 recognition sequence and PRDM9 genotype however remains elusive , indicating that the rules governing the interaction between PRDM9 and its targeted sequences must be subtle and complex [12] , [15] . CNVs and other unbalanced micro recombination events are involved in the aetiology of many human pathologies such as Alpha Thalassemia , Potocki-Lupski Syndrome , Charcot-Marie Tooth , Williams-Beuren syndrome , Prader Willi/Angelman syndrome , and infertility through the production of Y-chromosome microdeletions [16] . Here we focus on the DPY19L2 locus ( 12q14 . 2 ) which has recently been shown to be linked with Globozoospermia [17] , a rare syndrome of male infertility [18] characterized by the presence of 100% round , acrosomeless spermatozoa in the patient's ejaculate ( MIM #102530 ) . Reports of familial cases pointed to a genetic component to this pathology [19]–[21] , and this assumption was confirmed as a homozygous mutation of SPATA16 was identified in three siblings [22] and a homozygous missense mutation of PICK1 was identified in a Chinese patient [23] . We demonstrated recently that DPY19L2 was in fact the main locus associated with globozoospermia as 15 out of 20 analysed patients presented a 200 Kb homozygous deletion removing the totality of the gene [17] . DPY19L2 was described to have arisen , along with three other genes ( DPY19L1 , L3 and L4 ) , through the expansion and evolution of the DPY19L gene family from a single ortholog found in invertebrate animals [24] . We then identified DPY19L2 point mutations and heterozygous deletions and demonstrated that 84% of the 31 globozoospermia patients analysed had a molecular alteration of DPY19L2 [25] . Others find a slightly lower incidence of DPY19L2 deletions in globozoospermia patients [26] , [27] . Comparison of the spermiogenesis between wild type and Dpy19l2 knock out ( KO ) mice allowed us to demonstrate that Dpy19l2 is expressed in the inner nuclear membrane only in the section facing the acrosome , and that it is necessary to anchor the acrosome to the nucleus . This indicates that DPY19 proteins ( DPY19L1-4 in mammals ) might constitute a new family of structural transmembrane proteins of the nuclear envelope that likely participate in a function that was so far known to be only carried out by SUN proteins: constituting a bridge between the nucleoskeleton and cytoplasmic organelles and/or the cytoskeleton [28] . In our previous work we had demonstrated that DPY19L2 was homozygously deleted in a majority of patients with globozoospermia and that this deletion occurred by NAHR between two highly homologous 28 Kb LCRs located on each side of the gene [17] . Strengthening the case for the occurrence of NAHR at the DPY19L2 locus , heterozygous deletions and duplications have been identified in several large array CGH studies and this locus is classified as a CNV [29]–[33] . Surprisingly , considering that NAHR is known to generate an excess of deletions , these databases contain a large excess of duplications . We developed a PCR assay to specifically amplify the recombined LCRs corresponding to deleted and duplicated alleles allowing the precise localisation of the breakpoints ( BP ) . We observed that all identified BPs clustered in the center of the LCR . We analysed this region and identified a 13-mer PRDM9 pro-recombination sequences in the middle of the hotspot . We also developed a digital PCR assay that enabled us to estimate the rates of de novo deletion and duplication at this locus . Contrary to the allelic frequency observed in the general population we measured an approximate 2 fold excess of deletions over duplications . We show that the negative selection against the deleted alleles could explain this apparent paradox . The DPY19L2 CNV was analysed using array CGH data available from web servers [29]–[33] for a total of 6575 control individuals , mainly from the Database of Genomic Variants ( http://projects . tcag . ca/variation/ ) . A total of 83 gains and 26 heterozygous losses are reported for the DPY19L2 CNV in this pool , indicating a threefold excess of duplications over deletions . We wanted to confirm this result and exclude a potential technical bias towards duplications that could be caused by the presence on chromosome 7 of DPY19L2P1 , a pseudogene highly homologous to DPY19L2 [24] . To this end we re-analysed the array CGH data produced for the diagnosis of syndromic mental retardation in Grenoble and Lyon hospitals , and searched for DPY19L2 deleted and duplicated alleles in this dataset . A total of 1699 array CGH profiles were re-analysed ( see Figure S1 for illustration ) . We identified a total of 15 duplications and 3 heterozygous deletions . The recombined alleles were secondarily amplified with the long PCR primers to confirm the validity of the array CGH results . Presence of the deletion could be confirmed by our deletion-specific PCR in the three individuals putatively carrying a heterozygous deletion . DNA from 3 individuals expected to carry a duplicated allele could not be obtained . Ten out of the 12 remaining individuals putatively carrying a DPY19L2 duplication were amplified by our duplication-specific PCR . For the two individuals that could not be amplified , the duplication was nevertheless confirmed by Multiplex Ligation-dependent Probe Amplification ( MLPA ) . Theses results show that our reanalysis of the array CGH data did not yield any false positives . Overall reanalysis of these 15 individuals showed that 2 out of 15 recombinant alleles could not be detected by our PCR assay , indicating that the breakpoints of 2/15 recombined alleles fell outside of our amplified region . We also wanted to obtain an estimation of the frequency of the deleted and duplicated alleles in the general population using our recombination-specific PCR assay . For that we designed primers that amplified a smaller sequence which could be co-amplified with an additional pair of primers ( RYR2 primers ) used as a positive amplification control ( Figure 1B and Table S1 ) . This duplex PCR setup controls for poor DNA quality or technical variations . We analysed 150 control individuals originating from North Africa and 150 individuals of European origin with these two duplex PCRs ( for the detection of deleted and duplicated LCRs , respectively ) . We identified only one heterozygous deletion in an individual of North African origin and two duplications in one European and in one North African individuals . Overall a total of 8574 individuals have been analysed , including 6575 individuals from array CGH public databases , 1699 individuals from Grenoble-Lyon array CGH data and 300 individuals analysed by recombination-specific PCR . From these cohorts we identified 30 deletions ( frequency of approximately 1/290 ) and 100 duplications ( approximate frequency 1/85 ) ( Table S2 ) . These values indicate that the allele frequencies of the recombined deleted and duplicated alleles are 1 . 7×10−3 ( 95% CI: 1 . 2×10−3; 2 . 5×10−3 ) and 5 . 8×10−3 ( 95% CI: 4 . 7×10−3; 7 . 1×10−3 ) , respectively . Confidence intervals ( CI ) were calculated assuming a binomial model , with binom . test in R . We note that our PCR-based assay only allows the identification of breakpoints occurring between the selected primers ( 1392 bp ) . The location of the breakpoints of each CNV detected by array CGH ( an unbiased approach ) located in the DPY19L2 locus was scrutinised to establish if they were located within the LCR and hence were caused by NAHR ( Table S3 ) . This analysis shows that 87% of the deletions and 76% of the duplication fell within the LCR limits . Overall , we believe that our PCR assay permits to identify the majority of recombinations occurring at the DPY19L2 locus , since: 1 ) amplification was obtained for all 15/15 globozoospermia patients analysed , and 2 ) amplification was obtained for 13/15 ( 87% ) recombined array CGH patients . As the previous results consistently showed an excess of duplications over deletions in the general population , we wanted to measure the rates of de novo duplications and deletions to verify if the observed skew was due to the selection of duplications over deletions or if more duplications were produced de novo . The rate of genetic events occurring de novo can be measured on sperm DNA since each spermatozoon is the product of meiosis and corresponds to a new haploid genome . We first tried to develop a semi-quantitative PCR assay to directly measure the frequencies of deletions and duplications using sperm from control donors ( with two copies of DPY19L2 ) . The shortest fragment that could provide a reliable specific amplification and amplify the whole breakpoint area was 1392 nt long . Reliable quantitative PCR for fragments longer than 500 nt is difficult with current techniques . We therefore resorted to performing a digital PCR . First , the DNA was serially diluted and distributed in 96-well plates so that approximately 25% of the wells produced an amplicon . The appropriate quantity of sperm DNA was determined by trial experiments for each of the two PCR assays: 50 ng of sperm DNA per well ( corresponding to approximately 17 , 000 copies of chromosome 12 , assuming one haploid genome represents 3 pg of DNA ) were used for the PCR specific of the DPY19L2 deletion , and 100 ng per well ( ∼33 , 000 copies under the same assumption ) were used for the duplication-specific PCR . For example , for donor A the deletion-specific PCR produced 26 positive wells . The deletion recombination frequency λ and its 95% confidence interval were then calculated as described ( see Methods ) , resulting in a rate of de novo DPY19L2 deletion for donor A estimated at 1 . 9×10−5 ( 95% CI: 1 . 3×10−5; 2 . 7×10−5 ) . Similarly , the duplication-specific PCR for donor A produced 23 positive wells , but because there was twice as much starting DNA this results in a rate of de novo DPY19L2 duplication estimated at 8 . 1×10−6 ( 95% CI: 5 . 3×10−6;1 . 2×10−5 ) for this donor ( Table 1 and Figure 2 ) . When pooling the results from the three sperm donors , more robust estimates are obtained: the de novo DPY19L2 deletion rate is estimated at 1 . 8×10−5 ( 95% CI: 1 . 4×10−5; 2 . 2×10−5 ) , while the de novo duplication rate is estimated at 7 . 7×10−6 ( 95% CI: 6 . 1×10−6; 9 . 7×10−6 ) ( Table 1 ) . There is a significant approximately two-fold enrichment of deletions over duplications at the DPY19L2 NAHR hotspot . We investigated whether differential amplification efficiency between the deletion and duplication assays could explain the observed difference between deletion and duplication de novo rates . To this end , we performed a control experiment as described ( see Methods ) . No significant difference in amplification efficiency was observed: the deletion-specific control PCR amplified 37 wells , and the duplication-specific PCR amplified 40 wells . Amplification of the LCRs in the deleted alleles had not been achieved in our previous study and the breakpoint minimal region had only been narrowed down to a 15 Kb region within the LCRs ( 8 ) . Here we designed and validated PCR primers that amplify a 2 Kb product in deleted individuals only ( Figure 1B ) . We quickly realised that mapping the breakpoints was complicated by the fact that many of the nucleotides that differed between LCR1 and LCR2 in the reference sequence were in fact not specific to one or the other LCR . Since mapping the breakpoints requires markers specific to each LCR , we decided to amplify and sequence the 2 Kb breakpoint region for each LCR in 20 control individuals . To achieve the specific amplification of LCR 1 and 2 we had to rely on the reference human genome sequence to design the primers . We had no way of confirming that the targeted LCRs were specifically amplified in control individuals , but no amplification was obtained when assaying twenty homozygous deleted patients , vouching for the specificity of the primers . We then amplified and sequenced LCR1 and 2 from a total of 20 control individuals: 10 of North African origin and 10 of European origin . Thirty-four nucleotides were indicated as specific to either LCR 1 or 2 in hg19 reference sequence but 14 of these were in fact arbitrarily found in the two LCRs ( Table S4 ) : we consider that these are non-LCR-specific single nucleotide polymorphisms ( SNPs ) . The remaining 20 nucleotides were indeed LCR-specific: these 20 fixed markers were used to map the recombination breakpoints , and we used the 14 SNPs to establish a haplotype map of the patients' deleted alleles ( Table S4 ) . Allele-specific amplification of the deleted LCR was carried out on 15 homozygously deleted globozoospermia patients . Each amplification yielded a single 2088 bp product , while the PCR was negative for all the healthy controls tested ( n = 20 ) . We sequenced all the amplicons in order to better characterize the breakpoint region . Fourteen out of the 15 patients analysed were homozygous for all markers tested . Three different breakpoints ( BPs ) were identified based on the presence of the 20 invariant markers . The three recombination events ( BP1–3 ) were included in a 1153 bp maximal region ( Table S4 and Figure 3 ) . The breakpoints could not be mapped more accurately for lack of nucleotides specific to each LCR . One patient was heterozygous for markers 13 and 14 , indicating that this patient was heterozygous and carried two different deleted alleles ( BPs 2 and 3 ) . If we consider that the other 14 patients carried two recombined deleted alleles each , we have a total of 14 alleles with BP1 ( between markers 17 and 18 ) , 13 alleles with BP2 ( between markers 18 and 24 ) and 3 with BP3 ( between markers 25 and 28 ) ( Figure 3B ) . The 14 identified SNPs were then used to map the different haplotypes in patients presenting the same breakpoint ( Table S4 ) . This shows the presence of a total of 7 distinct haplotypes , indicating that at least 7 recombination events are at the origin of our patients' pathology ( 15 patients ) . We also observe that 5 patients with BP2 have the same haplotype and that two groups of 3 patients with BP1 have the same haplotype , suggesting the presence of several founding deletions in our patients' population . This is not surprising as all our patients came from the same region ( Tunis area ) and a majority had related parents ( often first cousins ) . One and three deletions were identified respectively in the 300 individuals analysed by PCR and in the 1699 Grenoble-Lyon array CGH patients group . There were 3 occurrences of BP2 and 1 of BP3 . Overall , including the globozoospermia patients , a total of 34 somatic deleted alleles were examined , resulting in the detection of three different recombination breakpoints . Fourteen alleles ( 41 . 2% ) had a deletion between markers 17 and 18 ( BP1 ) , 16 alleles ( 47 . 0% ) between markers 18 and 24 ( BP2 ) , and 4 alleles ( 11 . 8% ) were recombined between markers 25 and 28 ( BP3 ) ( Figure 4 top left ) . Two and fifteen genomic duplicated alleles were detected respectively in the 300 control individuals analysed by PCR and in the Grenoble-Lyon array CGH patients . Only 12 duplicated alleles could be sequenced ( for lack of DNA from 3 control subjects and because two of the subjects had breakpoints falling outside the range of the duplication-specific PCR ) . Seven alleles ( 58 . 3% ) corresponded to the reciprocal alleles of deletion 2 ( BP2 ) with a recombination between markers 18 and 24 , and 5 alleles ( 41 . 70% ) corresponded to the reciprocal alleles of deletion 3 ( BP3 ) with a recombination between markers 25 and 28 ( Figure 3B ) . The position of the meiotic recombination events ( deletion and duplication ) obtained from three sperm donors were also characterized by DNA sequencing . A total of 74 de novo deleted alleles and 65 de novo duplicated alleles were sequenced . All recombination events ( from both duplications and deletions ) clustered into five breakpoints ( Figure 4 ) . Two of them are new ( BP4 and BP5 ) i . e . not previously identified in globozoospermic patients or in the CGH control cohort . The number and percentages of deleted and duplicated breakpoints respectively are: BP1: 2 ( 2 . 7% ) and 4 ( 6 . 1% ) ; BP2: 56 ( 75 . 7% ) and 38 ( 58 . 5% ) ; BP3: 10 ( 13 . 5% ) and 13 ( 20% ) ; BP4: 2 ( 2 . 7% ) and 3 ( 4 . 6% ) and BP5: 4 ( 5 . 4% ) and 7 ( 10 . 8% ) ( Figure 4 ) . BP2 is by far the most frequent BP , followed by BP3 , explained by the fact that these two breakpoints correspond to the largest regions . Interestingly in sperm , the distributions of the deleted and duplicated breakpoints are quite similar . This is logical as the duplicated alleles are expected to be the reciprocal alleles of some of the deleted alleles . In genomic DNA the correlation is not as good , and we note that the frequency of the deleted BP1 is particularly high . Most of the deleted alleles come from globozoospermia patients ( and a few detected in CGHarray patients ) most of whom were recruited in Tunis . As suggested by the shared haplotypes observed between some deleted patients ( Table S4 ) a founder's effect is likely to account for some of the most frequent deletions , in particular BP1 . Sequencing of the PRDM9 ZF array was performed in the 3 sperm donors . All three donors were homozygous for the A allele which represents over 90% of the European alleles . It comprises 13 copies of the 84-bp ZF repeat that binds the 13-bp Myers recombination motif [12] , [14] . This result is concordant with the ethnicity of the donors . A comparison of the two LCRs is presented in Figure 3A . The illustration was produced from the results of a megablast search ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ? PROGRAM ) . All identified recombined alleles ( n = 185 ) cluster between markers 10 and 28 within a 1153 bp region . This recombination hotspot is roughly located in the middle of the 28 Kb LCR ( Figure 3A ) . Five 13 bp PRDM9 consensus recognition sites ( CCNCCNTNNCCNC ) are present along the LCR ( Figure 3A ) . One of these sites is located in the centre of the 1153 bp hotspot ( less than 35 nt away from the hotspot median position ) ( Figure 3B ) . We note that the most central BP ( BP2 ) which encompasses the 13 bp site , represents 117 out of 185 recombined alleles or 63% of the detected recombined alleles ( Figure 4 ) . Given that five PRDM9 consensus recognition sites are found within the 28 Kb LCR1 sequence , the probability that a site would occur by chance less than 35 bp away from the centre of the hotspot is 1− ( 1−5/28000 ) 70 = 0 . 012 . The Thymin at the centre of the consensus recognition site ( CCNCCNTNNCCNC ) was present only in the reference sequence of LCR1 . Sequence analysis of our control individuals showed that this nucleotide was in fact a SNP ( Marker 20 in Figure 3 and Table S4 ) with a T allele frequently found in both LCR1 and LCR2 ( Table S4 ) . In our globozoospermia patients we observed that all patients with the BP1 ( with marker 20 located after the breakpoint thus on LCR2 sequence ) have the T allele , indicating the presence of a T allele on LCR2 of the original unrecombined allele ( Table S4 ) . Conversely patients with BP3 ( with marker 20 located before the breakpoint thus on LCR1 sequence ) have the C allele indicating the presence of a C allele on LCR1 of the original unrecombined allele . All patients with BP2 have the C allele . As marker 20 is located within the breakpoint maximal sequence we can only conclude that at least a C allele was present on either LCR1 or LCR2 of the original unrecombined allele . We sequenced LCR1 and LCR2 of our three sperm donors and realised that all where homozygous for the C allele at both LCR1 and LCR2 , suggesting that the presence of the thymine in the CCNCCNTNNCCNC consensus sequence is not necessary to initiate recombination in the DPY19L2 LCR central region . Myers et al . [8] , [11] indicated that although the core 13-mer recognition sequence was associated with recombination hotspots , the recognition motif extended beyond the core sequence with preferentially associated nucleotides identified within a 39 bp sequence encompassing the PRDM9 core sequence . We therefore aligned this extended motif with the sequence of the 5 PRDM9 motifs identified within the LCR ( Figure 3C ) . We observe a good correlation within all 5 sequences , especially for the nucleotides that had been shown to be significantly associated with hotspots ( indicated in red in Figure 3C ) . We also observe that the sequence central to our recombination hotspot ( motif c ) presents the highest homology ( 53% ) with Myers' extended recognition sequence ( Figure 3C ) . It appears paradoxical that de novo deletions are produced twice more frequently than de novo duplications during meiosis , while duplicated alleles are three times more frequent than deleted alleles in the general population . We investigated whether this could be explained parsimoniously through the combined effects of selection and mutation . Men carrying a homozygous deletion of DPY19L2 are 100% infertile , but currently there is no evidence that a heterozygous deletion of DPY19L2 causes a phenotype or that homozygous women are affected . Additionally , the deleted allele is rare . Under these assumptions , according to the General Selection Model ( GSM ) , natural selection results in a decrease in the frequency of the deleted allele of approximately q2/2 per generation , where q is the frequency of the deleted allele ( see Methods ) . Given that the deleted allele has a frequency of 1 . 7×10−3 ( 95% CI: 1 . 2×10−3; 2 . 5×10−3 ) in the general population according to our combined control data , the GSM predicts that this frequency decreases by 1 . 5×10−6 ( 95% CI: 7×10−7;3 . 1×10−6 ) per generation . Conversely , deleted alleles are produced de novo by NAHR at an estimated rate of 1 . 8×10−5 ( 95% CI: 1 . 4×10−5; 2 . 2×10−5 ) according to our digital PCR data . Assuming the allele frequency is at an equilibrium , these two rates should balance out . In fact they are somewhat similar but the 95% confidence intervals do not overlap . However the CIs only represent the uncertainty induced by the sampling procedure , i . e . the fact that the allele frequency and recombination rate are estimated from a sample of the whole population: they do not take into account experimental biases or imperfections that may exist at various steps . In addition , the GSM is a theoretical model that assumes an infinite population size and panmixia , whereas in practice stochastic effects and population structure ( including for example any potential consanguinity or local founder effects ) come into play . These could result in a significantly increased impact of purifying selection on the deleted allele , so that the frequency decrease resulting from selection and the de novo production of deleted alleles through NAHR may in fact cancel out . Alternatively , it is possible that heterozygously deleted men suffer a fitness penalty . This can be taken into account within the GSM , and one can calculate the relative fitness of heterozygous individuals such that the GSM-predicted decrease of the deleted allele's frequency compensates the measured NAHR-induced production of new deleted alleles . In fact , assuming women are not affected , a 98% relative fitness of heterozygous men is sufficient ( see Methods ) . Such a small effect could have easily remained undetected , and this scenario cannot be ruled out . This potential selection could be caused by meiotic segregation distortion as was observed for the T/t mouse locus [34] . Finally we only studied the recombination rate in male germ cells and we cannot exclude the possibility that the frequency and ratio of deletion and duplication might be different in female gametes . All in all we believe the rates are reconcilable: whether the discrepancy observed when assuming heterozygous individuals have no phenotype is due to imperfections in the data and/or to population structure which disrupts the theoretical GSM model , or whether heterozygously deleted men suffer a small fitness penalty , we propose that the frequency decrease due to purifying selection and the de novo production of deleted alleles through NAHR cancel out , and that the frequency of the deleted DPY19L2 allele is today at a selection-recombination equilibrium in the population . On the other hand , to the best of our knowledge there is no evidence that the duplicated DPY19L2 allele is either deleterious or advantageous . We therefore assume that the duplicated DPY19L2 allele is not under selection , so its frequency can increase in the population by recurrent NAHR . This resolves the paradox . Liu and colleagues ( 2011 ) proposed that the frequency of NAHR occurring between two paralogous LCRs was proportional to the LCR length and sequence homology but inversely proportional to the distance between the LCRs [6] . The authors logically proposed that the probability of ectopic chromosome synapsis increases with LCR length , and that ectopic synapsis is a necessary precursor to ectopic crossing-over . Here we measured that the average rate of de novo recombination ( deletion plus duplication ) by NAHR at the DPY19L2 recombination hotspot was 2 . 6×10−5 . This rate is higher than what was measured at other loci such as the Williams-Beurren syndrome ( WBS ) locus or the LCR17p locus [4] . In our case the relatively small LCR size ( 28 Kb ) is compensated by the proximity of the repeats ( 200 Kb ) compared with much greater distances separating the paralogous LCRs for WBS and LCR17p . DPY19L2 LCR1 and 2 also present a very high sequence identity ( 98% ) which could also reinforce their synapsis and recombination . Our results are in agreement with previous work suggesting that the distance separating the two LCRs , as well as their sequence homology and length are parameters likely influencing recombination frequency . We observed that >90% of DPY19L2 NAHR events occurred within a 1 . 2 Kb region located in the centre of the 28 Kb LCR , suggesting the presence of a pro-recombination sequence within this hotspot . Myers et al . ( 2008 ) have characterized a degenerate 13 bp sequence motif that is present in approximately 40% of the identified human hotspots and which constitutes a PRDM9 recognition signal [12]–[14] . PRDM9 codes for a zinc finger array which catalyses the trimethylation of the lysine 4 of histone H3 ( H3K4me3 ) [11] . This PRDM9-mediated post-translational histone modification likely initiates the recruitment of the recombination initiation complex , creating a favourable chromatin environment and allowing access of SPO11 to the DNA . SPO11 then initiates the formation of double-strand breaks ( DSBs ) which will be repaired by homologous recombination [35] . Here we identified a hotspot of NAHR located in the centre of a 28 Kb LCR . We showed that a PRDM9 13-mer recognition sequence is present at the epicentre of all the identified breakpoints . We however realised that the thymine , central to the 13-mer motif ( CCNCCNTNNCCNC ) , was a T/C SNP , each nucleotide being found arbitrarily within LCR1 or LCR2 . Following this observation one can wonder if recombination events at the DPY19L2 hotspot occur preferentially in the presence of fully matching PRDM9 13-mer alleles . We measured the frequency of de novo recombination in sperm from three donors . As it happens , sequencing revealed that all three were homozygous for the C allele on both LCR1 and LCR2 . This indicates that , at this locus , the presence of the 13-mer exact match is not necessary to initiate recombination . This observation is concordant with what was described previously at different loci and confirms that PRDM9 tropism for the 13-mer recognition site might not be very strong and/or that other mechanisms also intervene in the choosing of double strand break localization [12] , [15] . One explanation can come from the extended sequence surrounding the 13-mer motif . Myers and colleagues ( 2008 ) [10] described a 39 bp pro-recombination sequence encompassing the 13-mer motif . We observe a greater than 50% sequence identity for the complete 39-mer sequence , indicating that a good match to the extended motif might be at least as important as a perfect match of the core 13-mer motif . We identified a total of 5 distinct breakpoints ( BP ) , all localized within a 1 . 2 Kb region located in the centre of the 28 Kb LCR . Others have described the localization of the deletions of globozoospermia patients [22] . They described a total of 9 separate BPs in the DPY19L2 LCR . Looking at the precise localizations of the described BPs , we noticed that the nucleotides used to delimit BPs 1–6 in that study are in fact nucleotides that we identified as SNPs ( markers 19–23 and 26 ) , which strongly questions the validity of the BP localization in that study . Reanalyzing the presented data and using LCR-specific markers only , we conclude that Elinati et al . ( 2012 ) BPs 1 , 2 , 4 , 5 , 6 fall within the boundaries of “our” BP2 and that “their” BP3 corresponds to “our” BP3 . This illustrates the difficulty in precisely identifying the localization of BPs and demonstrates that this can only be achieved with a high level of confidence after confirmation that the markers used to define the BP positions are indeed locus-specific . From our reanalysis , Elinati et al . ( 2012 ) identified deletions in 27 globozoospermia patients , 23 had our BP2 , one had BP3 and one had a BP that fell just outside of our studied region . These results thus confirm the importance of the recombination hotspot described here . Two additional BPs ( BP8 and 9 ) were also identified in Elinati's study which fell well outside of our recombination hotspot . This might constitute a second , less frequent recombination hotspot within the LCRs . We noticed that these two BPs are located 1200 bp telomeric from the 13-mer PRDM9 site d ( as indicated in Figure 3A ) . Thus this second putative hotspot is further away from a consensus 13-mer motif than our hotspot ( the greatest distance of the BPs we identified from the 13-mer is 600 bp ) , but we can question again the accuracy of the positioning of these two breakpoints . Here , while analyzing the array CGH recombined patients we identified two recombined alleles which did not fall within our studied BP area . It is possible that these recombination events are also located within this second putative hotspot . With the DPY19L2 locus we believe that we have a good model to study the effect of the PRDM9 recognition site on NAHR . We plan to accurately position the yet uncharacterized BPs in relation to other PRDM9 sites . We are also currently screening an anonymized sperm bank to identify donors that are homozygous for the central 13-mer PRDM9 recognition T allele and/or who present rarer PRDM9 alleles to investigate how the recombination rate is affected by both the PRDM9 genotype and the extended PRDM9 recognition motif . We believe that although much work remains to be done , our study illustrates and consolidates the hotspot models described previously . In a moving environment we can imagine that the central region of the LCR will have the most opportunities to synapse with its paralogous sequence . The presence of an extended PRDM9 recognition motif in the centre of the LCR then very likely contributes to DSB and NAHR . The combination of these parameters therefore probably explains why approximately 90% of the breakpoints occurred within a few hundred nucleotides from the most centrally located PRDM9 recognition site . All patients , family members and anonymous DNA and sperm donors gave their written informed consent , and all national laws and regulations were respected . Ethical approval was obtained from Grenoble CHU review board . We previously reported that 15 out of 20 patients with globozoospermia had a homozygous deletion of the DPY19L2 region [17] . These patients are included in this study . All patients are unrelated apart from two who are brothers . All patients originated from North Africa ( Tunisia , n = 12; Morocco , n = 2 and Algeria , n = 1 ) . Array CGH data from a total of 1699 control anonymous individuals were re-analysed . These analyses had been carried out as a diagnosis for syndromic mental retardation either at Grenoble or Lyon's hospital . As our aim was to identify DPY19L2 centred CNVs in this cohort of patients and since there is no known link between DPY19L2 and mental retardation , we believe that this cohort can serve as a control in this study . All individuals agreed to the anonymous use of their DNA in genetic studies and signed an informed consent . The fertility and ethnic origin of these individuals was not documented . All were French citizens . We estimate that in excess of 90% of these individuals are of European origin and that the vast majority of the others are of North African origin . There was no gender selection but this cohort contained approximately 2/3rd of males . Array CGH results from these patients were scrutinized for the DPY19L2 region . Three hundred control individuals were analysed independently with recombinant DPY19L2-specific PCR ( deleted and duplicated ) to identify deleted and duplicated alleles . One hundred and fifty individuals originated from North Africa ( Algeria , Morocco , and Tunisia ) and 150 originated from Europe . All individuals gave their informed consent to constitute an anonymous DNA bank . Non-recombined LCR1 and 2 of twenty of these individuals were amplified and sequenced to identify LCR-specific SNPs . There was no gender selection and this cohort contained a similar number of males and females . Lastly the DPY19L2 CNV was also analysed from array CGH data available from web servers [29]–[33] for a total of 6575 control individuals , mainly from the Database of Genomic Variants ( http://projects . tcag . ca/variation/ ) . Most of these individuals originated from Europe ( 75% ) , Africa ( 18% ) or Asia . Individual CNV could however not be linked to a particular individuals and its geographical origin . The location of the breakpoints of each CNV located in the DPY19L2 locus was scrutinised to establish if they were located within the LCR and hence were caused by NAHR ( Tables S2 and S3 ) . Genomic DNA was extracted either from peripheral blood leucocytes using a guanidium chloride extraction procedure [36] or from saliva using Oragene DNA Self-Collection Kit ( DNAgenotech , Ottawa , Canada ) . Sperm DNA was extracted from 2 ml of semen which were transferred to a 25 ml Falcon Tube ( BD Biosciences ) . Ten ml of PBS was added , mixed gently and centrifuged at 3 , 000 rpm for 5 minutes . Supernatant was discarded and the pellet was resuspended again in 10 ml of PBS , mixed and centrifuged as before . Pellets were then resuspended in 1 ml digestion buffer ( NTE buffer 0 . 5 mM NaCl , 10 mM Tris-HCl pH 7 . 5 , 5 mM EDTA , pH 8 ( 100∶10∶1 ) , 0 . 4% SDS ) , 25 µl of 10 mg/ml proteinase K solution ( Sigma ) were added and the mix was incubated overnight at 42°C with occasional mixing . Three hundred microliters of the contents of each Falcon tube were transferred into SafeLock tubes ( Eppendorf ) . An equal volume of phenol/chloroform/isoamyl alcohol ( 25∶24∶1 ) was added and mixed gently until emulsified . The tube was centrifuged at 3 , 000 rpm for 5 minutes . We repeated this process a second time , adding an equal volume of chloroform/isoamyl alcohol . The upper aqueous layer was transferred into a clean Eppendorf tube . The aqueous layers from the two phenol/chloroform extractions were combined and an ethanol precipitation was performed: 25 µl 3M sodium acetate pH 5 . 4 and 1 ml 100% ethanol were added to the aqueous phase , mixed gently and centrifuged as before . The pellets were washed twice with 70% ethanol and finally resuspended in 300 µl TE buffer ( 10 mM Tris-Cl pH 8 . 0 , 0 . 1 mM EDTA , pH 8 . 0 ( 10∶1 ) ) by incubating overnight at 50°C with gentle shaking . DNA was extracted from three fertile anonymous donors of European origin with normal sperm parameters and of similar age ( between 30 and 35 years old ) . In each case a spermogram was realised according to WHO's 2010 guidelines [37] . Sperm concentration ranged between 60–120×106 spz/ml , with , in each case less than 1×106 leucocytes/ml . We therefore considered that the presence of this small percentage of leucocytes had a negligible effect on the quantification of the sperm ( only ) DNA and on the ensuing calculations . A molecular analysis was carried out to determine PRDM9 ZF array genotype . PCR amplification and sequencing of the PRDM9 ZF array were performed using primers and protocols as described previously ( [12] . PCR and sequencing primer sequences are listed in the Table S1 . All primers were designed to have at least their 3′ nucleotide specific to the LCR of interest ( Table S1 ) . PCR primers were designed to amplify specifically LCR 1 or LCR 2 , in order to perform a sequence comparison of the two LCRs . For each recombined LCR locus ( resulting from deletion or duplication ) , two sets of specific primers were designed ( Figure 1B ) . The external primers ( long primers ) were used for sequencing analysis . They were also used as an outer primer for the digital PCR that was devised to measure the rate of de novo recombination in sperm . The short internal primers ( SI ) were used in duplex with RYR2 primers that were used as a positive amplification control . These two sets of primers were used to detect the presence of recombined alleles in the 300 control individuals . They were also used as inner primers for the digital PCR . PCR amplification was carried out on an Applied Biosystems genAmp 2700 thermocycler . Due to the high sequence homology between the two LCRs , the use of a precise annealing temperature was critical . The same thermocycler had to be used throughout the study as small variations in block temperature could introduce discrepancies in the amplification . Both the long and short PCR cycles were preceded by a 7 minutes denaturation at 95°C and followed by a 10 minutes elongation at 72°C . The specific annealing temperature of each primer set is indicated in Table S1 . Thirty-five cycles were carried out for the long PCRs , with 30 seconds of denaturation at 95°C , 30 seconds of annealing and 2 minutes of elongation at 72°C . Forty-five cycles were carried out for the short PCRs with 30 seconds of denaturation at 95°C , 20 seconds of annealing and 2 minutes of elongation at 72°C . We performed the long and short PCRs in 1× Takara Ex Taq buffer ( Takara ) , 250 µM dNTPs ( Takara dNTP mixture ) , 300 nM each primer , 1 unit Takara Ex Taq Takara ) with 200 ng of somatic DNA in a total volume of 25 µl . All sequences ( native LCR 1 and 2 and deleted and duplicated LCRs ) were carried out with BigDye Terminator v3 . 1 ( Applied Biosystems Courtaboeuf , France ) on an ABI 3130XL ( Applied Biosystems , Courtaboeuf , France ) . Oligonucleotide array CGH was performed with the Agilent 105K or 180K Human Genome CGH Microarray ( Agilent Technologies , Santa Clara , CA , USA ) ( Hospices Civils de Lyon array CGH Platform and CHU Grenoble array CGH Platform ) . Extracted DNAs were labelled according to the instructions of the supplier and incubated overnight . The samples were purified and hybridised as described previously [17] . Graphical display and analysis of the data were performed with the Agilent DNA Analytics software version 4 . 0 . 81 ( statistical algorithm: ADM-2 , sensitivity threshold: 2 . 5 , window: 0 . 5 ) . A value of zero represents equal fluorescence intensities between sample and reference DNA . Copy-number losses shift the value to the left ( ≤−1 ) , and copy-number gains shift it to the right ( ≥0 . 58 ) . The design of the MLPA probes , MLPA reaction and data analysis were performed according to the recommendation of the MRC-Holland synthetic protocol ( www . mlpa . com ) and as described in Coutton et al . ( 2012 ) [25] . We designed two nested LCR-specific PCRs as described in the PCR section . In addition , we designed a TaqMan dual labeled probe ( Table S1 ) to allow the second step of the nested PCRs to be run on Biorad iCycler IQ real time PCR detection . We tested each recombinant-specific combination of primers for specificity and sensitivity on negative and positive ( DPY19L2 deleted and duplicated ) control blood DNA ( Figure 1B ) . Each of the two rearrangements was assayed on DNA extracted from three unrelated sperm donors . Each donor was confirmed to carry two copies of DPY19L2 by MLPA analysis ( data not shown ) . We note that our assay will not distinguish triplications of the DPY19L2 locus , which are likely to occur at extremely low frequencies . We performed the first LCR-specific PCR ( long PCR ) in 1× Takara Ex Taq buffer ( Takara ) , 250 µM dNTPs ( Takara dNTP mixture ) , 300 nM of each primer , 1 unit Takara Ex Taq ( Takara ) , using sufficient copies of template DNA to give approximately 24 positive wells per 96-well plate ( exact quantities determined empirically by successive dilutions ) and 2 . 5 mM MgCl2 , in a total volume of 50 µl . Following thermal cycling we incubated 10 µl of the long PCR products with 5 µl of Exosap-IT PCR Clean-up Kit ( GE Healthcare ) for 15 min at 37°C to digest the long PCR primers followed by enzyme inactivation at 80°C for a further 15 min . Two µl of 10× diluted long PCR products was used as a template in the second PCR ( short PCR ) . In the short PCR we used the same concentrations of buffer , dNTPs , primers and enzyme as in the Long PCR , but the total volume was 25 µl and we added a dual-labeled probe ( final concentration 250 nM; Eurofins MWG Operon ) ( Table S1 ) . To map the locations of breakpoints we re-amplified wells that we had previously identified as positive in the long PCR plate using the short primers and sequenced these amplicons . The quantity of input sperm DNA was experimentally determined by serial dilutions to obtain approximately 24 positive breakpoint-specific amplifications per 96-well plate . The number of positive amplifications was then counted to estimate the number of recombinants in the input sperm . Each well contains a sample drawn from the input DNA without replacement , hence the number of recombinants in a given well is appropriately modeled using a hypergeometric distribution . We note that this hypergeometric distribution has often been approximated in the literature by Poisson ( 6 , 22 ) or binomial ( 23 ) distributions , but although such approximations are acceptable we find no need for them in this study , as the direct calculation is simple . Indeed , using the hypergeometric distribution the probability that a well contains no recombinants is:where N is the total number of copies of chromosome 12 in the input DNA ( i . e . 1 . 6×106 for the deletion assay and 3 . 2×106 for the duplication assay , see Results section on digital PCR ) , W = N/96 is the number of copies per well , and R is the total number of recombinants . The value of R such that this probability is closest to the observed ratio of negative wells ( i . e . one minus the fraction of wells that produced a positive amplification ) is easily found by tabulation . This leads to an estimation of the de novo recombination rate λ = R/N , and a 95% confidence interval is calculated by modeling the initial dilution to obtain the input DNA using the binomial distribution ( with binom . test in the R stats package , http://www . r-project . org ) . In order to evaluate the amplification efficiency of our duplication/deletion assays , we used as positive controls genomic DNA from one heterozygous duplicated individual and from one heterozygous deleted individuals . We believe that this type of control is more accurate than the use of cloned recombinant deleted and duplicated alleles as this reduces dilution factors . More importantly it reproduces faithfully the possible inhibitions due to the presence of the over majoritarian non-target genomic DNA or the potential amplification of homologous sequences that are present in the actual quantifying experiments . The DNA concentration was measured by Nanodrop ( ThermoScientific ) and DNA quality was evaluated using an agarose gel electrophoresis ( 0 . 8% ) . No smear or fragments were observed . Considering that a human diploid genome represents 6 pg of DNA , we performed serial dilutions of the duplication and deletion controls to obtain a concentration of 1 . 5 pg/µl . One microliter of each solution was aliquoted in a 96-well plate , so that approximately 25% of the wells are expected to contain a recombinant allele ( as we used heterozygous controls who carry only one copy of the deleted or duplicated alleles ) . The number of positive wells was then counted when amplifying deleted and duplicated DNA . Given a locus with two alleles ( e . g . wild-type DPY19L2 allele and deleted allele ) , and noting q the frequency of the minor ( deleted ) allele , the GSM predicts the change in allele frequency Δq at each generation given the relative fitness of each genotype . In our case the homozygous wild-type is used as a reference ( fitness 1 ) , and the homozygous deleted men are known to be 100% infertile while the deletion is considered to have no effect in women , hence the fitness of the homozygous deleted genotype is 0 . 5 . Let W be the relative fitness of the heterozygous genotype , and p = 1-q the frequency of the wild-type allele . Note that q≈1 . 7×10−3 , hence p≈998×10−3 . The GSM therefore simplifies to: Δq≈−q[W ( q−p ) +p−q/2] . In the first scenario , heterozygous individuals are assumed to have no phenotype , hence W = 1 and the equation simplifies to: . In the second scenario , we no longer assume W = 1 and instead wish to calculate the value of W such that the GSM-predicted Δq exactly compensates the de novo rate of production of deleted alleles through NAHR , i . e . Δq = −1 . 8×10−5 . Turning the previous equation around , we obtain: . Substituting the values of p , q and Δq , this yields W = 0 . 99 . Assuming that heterozygous women have no phenotype , we finally obtain a relative fitness of 98% for heterozygous males .
We demonstrated previously that most men with globozoospermia , who produce only round acrosomeless spermatozoa and are 100% infertile , had a homozygous deletion removing the totality of DPY19L2 . We also showed that this deletion occurred by Non-Allelic Homologous Recombination ( NAHR ) . NAHR results in the production of deletions and duplications of regions encompassed by two homologous sequences , normally with a higher occurrence of deletions over duplications . Analysis of public databases at the DPY19L2 locus paradoxically revealed that , in the general population , duplications were approximately three times as frequent as deletions . Analysis of sperm DNA permits us to quantify de novo events that take place during male meiosis . We therefore measured the rates of de novo deletion and duplication in the sperm of three healthy donors . As predicted by the NAHR theoretical model and contrary to the allelic frequency observed in the general population , we identified an approximate 2-fold excess of deletions over duplications . We calculated that the measured rate of de novo deletion was compensated by evolutionary loss , whereas duplications , not subjected to selection , increased gradually . Purifying selection against infertile homozygous deleted men may be sufficient for this compensation , or heterozygously deleted men may also suffer a small fitness penalty .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "mutation", "genetic", "mutation", "mutation", "types", "natural", "selection", "gene", "identification", "and", "analysis", "genetics", "molecular", "genetics", "biology", "gene", "duplication", "population", "genetics", "genetics", "of", "disease", "genetics", "and", "genomics", "mutational", "hypotheses" ]
2013
Fine Characterisation of a Recombination Hotspot at the DPY19L2 Locus and Resolution of the Paradoxical Excess of Duplications over Deletions in the General Population
With increasing appreciation for the extent and importance of intratumor heterogeneity , much attention in cancer research has focused on profiling heterogeneity on a single patient level . Although true single-cell genomic technologies are rapidly improving , they remain too noisy and costly at present for population-level studies . Bulk sequencing remains the standard for population-scale tumor genomics , creating a need for computational tools to separate contributions of multiple tumor clones and assorted stromal and infiltrating cell populations to pooled genomic data . All such methods are limited to coarse approximations of only a few cell subpopulations , however . In prior work , we demonstrated the feasibility of improving cell type deconvolution by taking advantage of substructure in genomic mixtures via a strategy called simplicial complex unmixing . We improve on past work by introducing enhancements to automate learning of substructured genomic mixtures , with specific emphasis on genome-wide copy number variation ( CNV ) data , as well as the ability to process quantitative RNA expression data , and heterogeneous combinations of RNA and CNV data . We introduce methods for dimensionality estimation to better decompose mixture model substructure; fuzzy clustering to better identify substructure in sparse , noisy data; and automated model inference methods for other key model parameters . We further demonstrate their effectiveness in identifying mixture substructure in true breast cancer CNV data from the Cancer Genome Atlas ( TCGA ) . Source code is available at https://github . com/tedroman/WSCUnmix Tumor heterogeneity is now recognized as a pervasive feature of cancer biology with implications for every step of cancer development , progression , metastasis , and mortality . Most solid tumors exhibit some form of hypermutability phenotype [1] , leading to extensive genomic variability as tumor cell populations expand [2] . Studies of single cells by fluorescence in situ hybridization ( FISH ) [3 , 4] have long revealed extensive cell-to-cell variability in single tumors , an observation that has since been shown , by single-cell sequencing technologies , to occur with a far greater scale and variety of mechanisms than previously suspected ( e . g . , [5 , 6] ) . Furthermore , studies of clonal populations across progression stages have revealed that it is often rare cell populations that underlie progression , rather than the dominant clones [4] . Indeed , heterogeneity itself has been shown to be predictive of progression and patient outcomes [7] . All of these observations have suggested the importance of having ways of accurately profiling tumor heterogeneity , for both basic cancer research and translational applications . Experimental technologies for profiling tumor heterogeneity are constantly improving , but are so far impractical for systematically profiling variability genome-wide in large patient populations . FISH and related imaging technologies can profile many thousands of cells , but only at limited sets of preselected markers [4] . Single-cell sequencing can derive genome-wide profiles of hundreds to thousands of cells in single tumors [5 , 8 , 9] , but is so far cost-prohibitive for doing so in more than very small patient populations . Furthermore , technical challenges make it difficult to develop accurate profiles of structural variations , such as copy number variations ( CNVs ) , which are the major drivers of progression in most solid tumors [10] . Bulk regional sequencing can profile small numbers of tumor sites per patient in large patient populations [11] but provides only a coarse picture of the heterogeneity within each site . RNA sequencing ( RNA-Seq ) provides a measure of the quantity of RNA expression and is practical on substantially larger numbers of single-cells than DNA-Seq [9]; however , it is subject to greater noise than DNA-Seq [12] and provides a more indirect measure of clonal heterogeneity . These technical challenges to assessing heterogeneity experimentally have led to enormous interest in computational deconvolution ( also known as mixed membership modeling or unmixing ) methods as a way of computationally separating cell populations from mixed samples . Originally proposed as a way of correcting for stromal contamination in genomic measurements [13] , such methods were later extended to reconstructing clonal substructure [14] and subclonal evolution [15] among tumor cell populations . The past few years have seen an explosion of such methods for deconvolution of numerous forms of genomic data sources ( e . g . , [16–31] ) . All such methods , however , are limited in accuracy and capable of resolving at best a few major clonal subpopulations , a small fraction of the heterogeneity revealed by single-cell experimental studies . These limits result from an inherent difficulty of separating high-dimensional mixtures , especially from sparse , noisy data . The gap between the heterogeneity we know to be present and what we can resolve by deconvolution is enormous , suggesting a need for further methodological advances . Genomic deconvolution is a burgeoning field in which many different approaches are now available , often differing in models , algorithms , and the kinds of data or study design for which they are well suited . Leading contemporary approaches include TITAN [19] , THetA [18] , THetA2 [32] , PhyloWGS [33] , SPRUCE [34] , Canopy [35] , BitPhylogeny [36] , and PyClone [37] , each of which we briefly discuss here . TITAN uses a graphical model to estimate subpopulations based on copy number alterations and loss of heterozygosity events for whole genome or whole exome sequencing data , assuming as input read depths and allelic ratios at single nucleotide variant ( SNV ) sites . THetA and its follow-up version THetA2 perform tumor composition estimation using both SNV and copy number data derived from sequence read depths . PhyloWGS uses a probabilistic model to perform deconvolution jointly with phylogeny inference specifically on low-coverage whole genome sequencing data , making use of copy number estimates and variant allele frequencies ( VAFs ) of simple somatic variants . SPRUCE uses SNV and CNV data similar to that of THetA/THetA2 to make inferences as to the composition of heterogeneous tumor samples , but via a combinatorial enumeration strategy to explore the space of possible phylogenies consistent with a data set . Canopy optimizes for a probabilistic model to perform joint phylogenetic inference and tumor deconvolution from a data set based on several data sources , including VAFs and allele-specific copy numbers . BitPhylogeny similarly performs joint phylogenetics and deconvolution using Markov chain Monte Carlo ( MCMC ) sampling , but is unusual among methods in this domain in making use of DNA methylation data . PyClone performs tumor deconvolution for multiple samples from a single patient using SNV data , CNV data , and combinations thereof as input and is designed to work specifically with targeted deep sequencing data ( >1000X coverage ) . In prior work , we proposed that one could better resolve genomic mixtures by taking account of extensive substructure we would expect such mixtures to exhibit [21] . That is , an individual tumor or tumor site is not likely to be a uniform mixture of all cell types observed across all tumor samples in a study . Rather , one can expect distinct samples to group into subsets that share more or fewer cells depending on how closely related they are to one another . For example , all tumor samples can be expected to share some contamination by normal cells while tumors with common subtypes can be expected to share both normal cells and cell states characteristic of those subtypes . Likewise , tumor regions might be expected to share more similarity with those nearby than those more distant in a single patient . This kind of substructure is in principle exploitable to improve our ability to reconstruct accurate mixed membership models . Specifically , by deconstructing tumor samples into subgroups with similar mixtures , one can decompose the problem of reconstructing a high-dimensional mixture into the easier problem of reconstructing several overlapping lower-dimensional mixtures . We previously showed how to implement such an approach to substructured mixture deconvolution , adapting an earlier deconvolution strategy for uniform mixtures that was based on identifying geometric structures ( simplices ) of tumor point clouds in genomic space [15 , 38] but subdividing these point clouds into low-dimensional subsimplices that collectively constitute a higher-level object known as a simplicial complex . This prior work used a pipeline of several sequential steps to transform a genomic point cloud into a structured mixed membership model [21]: The resulting pipeline established a proof-of-concept for the approach , but also introduced several difficult computational challenges . For example , it required accurately pre-specifying the number of partitions and the dimensionality of each of the partitions , both difficult inference problems in themselves that require significant knowledge of the system under study . In the present work , we improve on this proof-of-concept method by tackling several subproblems on the path to more completely automating inference of substructured genomic mixtures from populations of tumor samples . We have eliminated several nuisance parameters from the prior work , most notably by introducing methods for automated dimensionality estimation of subsimplicies and automated maximum likelihood inference of other previously user-defined parameters . We also improve upon our earlier work by proposing a model better suited to capture the uncertainty in cluster assignments through use of a fuzzy clustering representation of data points ( samples ) with respect to the inferred simplicial complex ( and therefore the tumor phylogeny ) , allowing tumor samples to exhibit partial or uncertain membership in multiple phylogenetic branches . This flexibility is of particular importance when a sample is near a branch point in the simplicial structure , which corresponds biologically to a sample having a genomic profile similar to a most recent common ancestor of multiple tumor lineages . In addition , we develop a more comprehensive likelihood function , allowing us to optimize over and thus eliminate nuisance parameters from prior work . Although the approach we introduce makes inferences as to intraturmor heterogeneity , we use information present across multiple patients ( that is , intertumor heterogeneity ) to make those inferences . This application assumes that commonalities in progression processes can be observed across subgroups of patients , even if the exact presentation is unique for each tumor . Because the model presumes common subgroups of tumors proceeding along similar evolutionary trajectories , an inferred mixture vertex will correspond to a coarse-grained model of a shared progression stage among a subset of tumors . That is , the vertex , would be interpreted as an approximate representation of a recurring cell type appearing in the course of progression of multiple samples . Since no two samples have exactly the same evolutionary history , however , it would be expected to reflect the common features of a cluster of similar cell types while averaging out their differences . The overall simplicial complex structure will correspond to a model of the space of evolutionary trajectories among all of these progressions stages across all observed tumor subgroups . Paths in the evolutionary tree will correspond to the recurring evolutionary pathways between the averaged progression stages represented by the vertices . Based on those reconstructions , we can then make inferences for each sample as to the relative amounts of each progression stage represented in that tumor , providing a coarse-grained inference of intratumor heterogeneity . We validate the approach through application to breast tumor data from The Cancer Genome Atlas ( TCGA ) [39] and comparison with the widely-cited PyClone software [37] . We also compare with a more recent deconvolution method using DNA methylation data , providing an independent basis for comparison to the DNA copy number and RNA expression-derived deconvolution of our method [28] . We conceptually model input data as a matrix M ∈ R s × g , where the s ∈ N rows correspond to distinct samples ( which might be biopsies of tumors in a patient population , tumor sites in a single patient , or regions of a single tumor ) and the g ∈ N columns correspond to probes along a genome ( typically one per gene , although potentially at lower or higher resolution ) . Note , however , that as the underlying data types input to the method are changed , the interpretation of output is changed correspondingly . For instance , if the features used as input are not gene copy numbers , but rather SNV sites , then the components of the matrix M will be SNV VAFs for the given samples . Similarly , if samples are different regions from a single patient , the inferred phylogeny is for a single patient , rather than across a patient panel . For ease of exposition , we refer to rows as samples and columns as genes below . We use this generic matrix format because data from many sources can be preprocessed into such a matrix ( e . g . , array-based CNV , SNV , or expression data or whole-genome or whole-exome sequence-derived CNVs , SNVs , or expression levels ) . Although the basic strategy is intended to be generic with respect to platform and genomic datatype , we specifically consider here three scenarios: 1 ) CNV data as might be derived from array comparative genomic hybridization ( aCGH ) or DNA-Seq read depths , 2 ) RNA expression data as might be derived from expression microarrays or RNA-Seq , and 3 ) a heterogeneous combination of DNA CNV and RNA expression data . Our goal is to decompose the rows of M into an approximately convex combination of a smaller set of unknown mixture components ( putative cell populations ) . More formally , we seek a decomposition M = F V + ϵ ( 1 ) where F ∈ R s × k are mixture proportions , V ∈ R k × g are unmixed subpopulations , k ∈ N is the number of inferred cell subpopulations , and ϵ ∈ R s × g is an error matrix . F is interpreted as the mixture fractions of the pure subpopulations , also called mixing proportions , and V as the inferred genomic profiles of the pure subpopulations , also called mixture components . This interpretation leads to natural constraints on the problem: 1 ) ∑i Fij = 1 for a fixed j and 2 ) ∀i , j: 0 ≤ Fi , j ≤ 1 . Given these constraints , the formal goal of the method is to compute F and V given M , with an intermediate step of determining the mixture dimension k . Our approach to performing this deconvolution involves constructing a more involved simplicial complex mixed membership model , which will imply F and V , through a series of discrete inference steps . While most aspects of model inference are automated , as detailed in the remainder of Materials and Methods , the following parameters and hyperparameters still require manual selection: To begin analysis , we first pre-process M into a matrix of Z-scores: M z = M − μ M σ M ( 2 ) where μM is a vector of the mean copy numbers of each gene across all samples , and σM is a vector of the standard deviations of the copy numbers . This process is altered slightly to accommodate heterogeneous DNA and RNA data that have been concatenated as features . We assume that the distributions of read counts will differ for DNA and RNA data , so instead of μ and σ for all samples column-wise , we use a μ and σ for pools of all data for each data type . That is , we evaluate the mean and standard deviation for Z-score computation for all samples and for all DNA features , and separately for all samples and all RNA features . In the RNA only case , we use the framework outlined in Eq 2 . Next , to facilitate analysis of genomic point clouds , we reduce the dimension of the data using principal components analysis ( PCA ) [40] . While there are more sophisticated dimensionality reconstruction strategies available , we favor PCA as a simple , standard method that has relatively modest data needs . We identify a total of kupper PCs , using the Matlab pca routine in economy mode , where k u p p e r ∈ N < g is an upper bound on the number of cell subpopulations we will infer . In the present work , we use kupper = 12 , intended to be approximately an upper limit on the number of distinct mixture components a method of this class might be able to infer . We denote the PCA scores , corresponding to amounts of each PC in each tumor , as S M ∈ R s × k u p p e r . Then , in order to fine-tune the automated dimensionality detection , we implement the sliver method of dimensionality estimation described in [41] . The core model proposed by that work relies on testing for the presence of “slivers” , geometric objects with poor aspect ratios , which occur when the following expression , which we call Assertion 3 , is satisfied: ν < δ j r wherer = L j j ! ( 3 ) where ν represents the volume of some enclosing structure , j represents the current estimate of dimension , increasing for each time Assertion 3 is false up until the limit of 12 , and δ represents a tolerance factor between 0 and 1 . For a quick estimate of an enclosing structure , we use the algorithm proposed in [15] . We then use the top j − 1 PCs after the algorithm terminates . To automate the selection of the δ parameter , we use all values spaced 0 . 05 apart between 0 and 1 . The range of possible δ values is 0 to 1 for this parameter based on the approach outlined by [41] . Because some values of the parameter lead to the same estimate of the dimensionality of the dataset , we choose one representative value from each partition of the range of dimension estimate values , then choose the model that has the highest likelihood . Lastly , we normalize the scores for each PC to a [0 , 1] range , which is then assumed by the pre-clustering technique applied in the next section [42] . We compute the 0–1 normalized version of SM as S [ 0 , 1 ] = S M − min S M max S M − min S M ( 4 ) where the minimums and maximums are computed for each PC , taken over all samples . We next pre-cluster data to identify initial candidate subsets of samples inferred to have drawn from the same set of mixture components . Each such subset will correspond to a distinct subsimplex of the full simplicial complex to be inferred . While this is a clustering problem , it is a non-standard one in that we seek to cluster data into distinct low-dimensional subspaces of a contiguous higher-dimensional point cloud , rather than into disjoint subclouds as is in conventional clustering . We developed a specialized clustering method for this purpose [42] , based on a two-stage variant of medoidshift clustering [43] . We initially cluster in Euclidean PC space to reduce the raw data to a smaller set of representative data points . We then cluster these representatives under a negative-weight exponential kernel function using the ISOMAP distance measure [44] , a form of geodesic metric measuring distance between data points through a k-nearest-neighbor graph of the input point cloud , which collectively draws on features of manifold learning and related technologies . The combination of ISOMAP distance and negative exponential kernel produces a clustering in which cluster representatives are approximately extremal points of the simplicial complex that serve to pull apart distinct subspaces of the point cloud . The initial Euclidean clustering suppresses noise , which otherwise makes the negative exponential kernel highly sensitive to outlier data . We refer the reader to [42] for full details . At the end of this process , we are left with a small set of cluster representatives M2stage , defined as the union over clusters i of a neighborhood N ( xi ) of points associated with each cluster representative xi: M 2 s t a g e = ∪ i M N ( x i ) , ( 5 ) where each representative is itself a point in S[0 , 1] , and a corresponding clustering of all samples C = {C1 , … , Cr} , S[0 , 1] = ⋃Ci∈CCi . We further assess uncertainty of the cluster assignments by determining a relative statistical weight of each data point in each cluster . We use a weight function based on a folded multivariate normal distribution , where the mean of the function is a 0 vector , the covariance matrix is the identity multiplied by the distance from each cluster center to the mean of all cluster centers , and the value at which the density function is evaluated is the distance from xi to Cj in ISOMAP space . After these relative weights have been derived , we convert them to probabilities of assignment of each point to each cluster . If we denote the raw weight of the ith data point as a vector Ri , then we can define the normalized weight vector: W i = R i − min C j ∈ C R i max C j ∈ C R i − min C j ∈ C R i ( 6 ) In the above formula , Cj refers to an arbitrary cluster in the clustering C , over which we maximize or minimize . The clustering in principle depends on a chosen neighborhood size for the k-nearest-neighbors graph , although a scan over all possible neighborhood sizes found no sensitivity of the final model likelihood to this parameter . We next seek to estimate the dimension of each cluster , which will correspond to the number of mixture components inferred for that cluster . The major challenge of this step is distinguishing a genuine axis of variation from random noise stemming from biological and technical limitations , particularly when working with sparse , noisy genomic measurements . Intuitively , we identify dimension by iteratively adding axes of variation via PCA until we can no longer reject the hypothesis that variance in the next dimension is distinguishable from noise . We first build a model of expected noise per dimension by randomly sampling data points of pure Gaussian noise with mean 0 and identity covariance . We then perform PCA on this random point cloud and estimate the mean μG ( i ) and standard deviation σG ( i ) of the point cloud for each PC i ∈ 1 , … , kupper . We then identify the smallest i ≤ kupper such that the standard deviation of the true data in PC i is smaller than μG ( i ) + κσG ( i ) , where κ defines a significance threshold in standard deviations . In the present work , we set κ = 3 to yield effectively a significance threshold of < 0 . 001 for rejecting the hypothesis that the next dimension can be explained by Gaussian noise . The result of this module , then , is a vector of inferred dimensions of each of the clusters: D ∈ {1 , … , kupper}r . We would expect this test to be conservative ( underestimate true dimension ) , although less so as the size of the data set and its precision increases . We found it necessary to use a custom-made conservative dimensionality estimator , as opposed to a more standard technique ( e . g . , [41] ) , because the number of data points available in this application is much smaller than is typically assumed by methods in this problem domain . We use the approach outlined in [41] in the initial phase , as it is prior to the pre-clustering , and therefore typically has a several-fold increase in the minimum number of data points considered , bringing it better in line with the data needs of that method . We next seek to establish an initial mixed membership model by separately unmixing each cluster , using the inferred dimension from the previous step as the number of mixture components . We establish the model by minimizing an objective function based on the noise-tolerant geometric unmixing method of [38]: P ( θ | X ) ∝ ∏ i = 1 r ( e x p ( − ∑ j = 1 s ( | x i − F j i V j i | W j i ) ) M S T ( V j , A j ) − γ β ) ( 7 ) Where γ is a regularization penalty set based on an estimated signal-to-noise ratio ( SNR ) of the data source [21] , V are the inferred vertices , A is the adjacency matrix , MST is a minimum spanning tree cost , W is the relative weight function computed above , F are the inferred mixture components , xi is the ith data point , β is a BIC penalty for model complexity [45] and , |⋅| is L1 distance . The first term penalizes data points outside the bounding simplex via an exponentially-weighted L1 penalty . The MST term captures a form of minimum evolution model on the simplex itself intended to penalize the amount of mutation from a common source needed to explain the simplex vertices ( mixture components ) [21] . We optimize for the objective function via the Matlab fmincon function , fitting V and F to assign mixture components and mixture fractions to each cluster independently . In practice , we use a transformed version of the equation into negative log space , as the optimization packages are built for minimization rather than maximization , and log domain better handles underflow for small likelihoods while preserving the ordering of solutions . We next seek to join the discrete simplices , each modeling a subset of samples as a uniform mixture , into a unified simplicial complex . We accomplish this by merging simplex vertices if we cannot reject the hypothesis that they represent distinct points in genomic space . We first establish a probability model using the k-nearest-neighbors graph on samples and vertices by modeling the set of overlapping neighbors between two vertices via a hypergeometric distribution . On the assumption two vertices draw their neighbor sets independently from the pool of all samples , the expected number of data points in common would be | N 1 | | N 2 | N ( 8 ) where there are N data points , |N1| nearest neighbors of the first vertex , and |N2| neighbors of the second vertex . We merge two vertices when the number of observed overlapping nearest neighbors is above expectation . We empirically determined on our synthetic data that the method is insensitive to the number of nearest neighbors for choices between 2 and N and chose k = 15 nearest neighbors arbitrarily within this range for the real data . This approach replaces computationally costly bootstrap estimates used in our prior work [21] . For those instances in which the process above does not result in a single connected simplicial complex , we add a step of post-processing to reconcile the geometric body into a single , connected simplicial complex . For those collections of bodies that do not consist of one connected component after the hypergeometric distribution correction , we iterate over all pairs of simplex vertices , merge the two vertices by creating a new vertex from the mean of the previous two vertices in all features , set the adjacency matrix to the union of the adjacency matrices of the two previous vertices , and compute the value of the objective function outlined in Cluster-wise Unmixing . We continue to merge points until there is at least one candidate consisting of a single connected component . If there are multiple such candidates , the candidate with the lowest objective function value , corresponding to the maximum of the likelihood function , is chosen . Pseudocode for this algorithm is provided in Fig 2 . To demonstrate the efficacy of the algorithm , we use breast cancer ( BRCA ) CNV and RNA-Seq data from The Cancer Genome Atlas ( TCGA ) [39] . We downloaded level 4 DNA CNV data on 2 Jun 2016 ( 1 , 080 samples ) and RNA-SeqV2 data on 1 Jun 2016 ( 1 , 041 samples ) , of which 1 , 022 samples were in common , along with clinical data for this cohort . For copy number data at level 4 , gene features are extracted and a list of genes is provided , in contrast to the blocking procedure required by earlier work [42]; however , the platform is flexible to represent more or less granular data . We ran the pipeline using the following parameters: maximum number of dimensions supplied to the pre-processing sliver method: 12; number of bootstrapped replicates for pre-clustering: 1000; neighborhood size for pre-clustering: 1; number of nearest neighbors for vertex merger: 15; cutoff for dimensionality estimation: 3 standard deviations; maximum number of iterations of fmincon per simplex: 1000 . The choices reflect computational resource limitations , as well as a stable number of bootstrapped replicates , and choices to ensure convergence of the methods . The neighborhood size was chosen based on assumptions implicit in our normalization technique—for full details , see [42] . The number of nearest neighbors was chosen based on the test of simulated data similar to [42] demonstrating insensitivity to this parameter up to approximately N neighbors . The 3 standard deviations chosen correspond to a p-value of approximately 0 . 001 . The runtime of the experiments depends largely on the dimension of the maximally likely clusters ( i . e . , the number of subpopulations in the tumor dataset that our model chooses as most likely ) and the number of iterations in the minimization phase ( iterations of fmincon ) . In order to assess the consistency of our method with respect to outlier data points , we conducted a sensitivity analysis using the TCGA CNV data . The sensitivity analysis was structured in an analogous fashion to 10-fold cross validation . For each of ten iterations , we excluded 10% of the data set , selected by a random uniform distribution . For the remaining data , the model was run to completion to produce a simplicial complex and assignment of mixture components and mixture fractions to the data points in that set of replicates . We then compared inferences by several measures to assess consistency across subsamples of the data . We assessed similarity of the inferred component sets between replicates . To assess similarity of two sets of inferred vertex components A and B , we first identified for each component in A the closest matching component B , based on normalized Euclidean distance in PC space . We likewise identified for each component in B , the closest matching component in A . We assigned a score for the similarity of two vertex sets based on the mean distance between each component and its closest match relative to the mean distance between pairs of distinct components within A and within B . RNA-Seq data was downloaded from TCGA . The data consists of lists of gene expression in normalized counts , as well as gene name lists identifying each feature . Data from each of the samples were concatenated into a matrix of samples by genes . Using the parameters described above , the weighted unmixing procedure produces a tetrahedral simplex . Although other simplicies and simplicial complexes were considered by our algorithm , the tetrahedron was determined to be the maximum likelihood model . The results are illustrated in Fig 3 , which shows the true point cloud as well as our inferred structure , where samples are colored by the clinical subtype . The DNA level 4 data consists of log2 ( ⋅ ) copy number ratios , which are exponentiated and Z-scored prior to unmixing following the methods outlined above . We also considered application to DNA CNV data from TCGA . The results are visualized in Fig 4 . The decreased noise of DNA CNV technology relative to RNA-Seq technology results in a more sharply defined simplicial complex structure than was apparent with RNA-Seq data , consisting of three lines connected at a shared fulcrum . We attribute the clearer structure to the lower inherent stochasticity of DNA versus RNA data , which would be expected to better approximate the assumption that mixtures of cells will behave as linear combinations of their underlying cell types . We note that the central vertex , labeled 4 , appears skewed away from the apparent junction of the three subsimplices . We attribute this skew in the position of the junction to the difficulty of accurately clustering samples near such subsimplicial boundaries , leading to imprecise positioning of the shared vertex in the distinct subsimplices that is only partly corrected when the vertices are merged . Lastly , we considered a combination of DNA and RNA features . Because of the varying noise profiles of the data types [12] , we adjusted the normalization procedure as outlined above . We have plotted the results of the unmixing below in Fig 5 , using the same color code for tumor subtypes as with the RNA-only and DNA-only data . The combined data leads to a somewhat more complex structure than either individual data type alone , consisting of a tetrahedron and triangle connected at a point . The higher dimension compared to the individual data types may reflect changes in the overall noise profile or to the complementary aspects of progression that are revealed by the two data types in isolation . We further used the TCGA CNV data to assess sensitivity of the method to subsamples of the data . We assessed reproducibility across ten replicates of 90% subsamples of the TCGA data and quantified reproducibility of inferred mixture component sets based on the ratio of Euclidean distances between best matching component pairs between replicates versus Euclidean distances within replicate sets . A score below one would then indicate general consistency between vertex sets relative to variability within each set , while a higher score would then be interpreted to mean that vertex components are highly distinct between runs relative to the variability among components within a set . Across all 45 comparisons among pairs of replicates , we found a mean distance of 0 . 6806 by this measure . This result suggests there is sensitivity to outliers in the simplicial inference leading to variability replicate-to-replicate , but that there is nonetheless similarity run-to-run relative to the variability in individual data sets . To assess the functional and biological significance of the inferences made by our model in each of the three test cases , we projected the data points from PC space back into genome Z-score space . We then identified genes lists with statistically-significant increase or decrease in Z-score as assessed by Bonferroni-corrected p-values . In the RNA and combined cases , we used p = 0 . 01 after correction . In the DNA case , at the p = 0 . 01 level , DAVID [47] reported that the number of genes provided was too large to process the results . As a result , we chose a stricter threshold of p = 2 . 1905 × 10−11 , the smallest value we could choose without producing underflows in the p-value calculation . Those genes that were statistically significantly upregulated were then evaluated on a per-vertex basis by DAVID [47] for enrichment by functional terms corresponding to specific networks , pathways , or other functional classes . In our case , we have specific interest in enriched tissues , diseases , and disease classes , as these areas provide the ability for the database to point specifically to our dataset . As expected , the DAVID analysis revealed enrichment for several terms related to breast cancer specifically , as well as breast tissue more broadly . In Tables 1–3 , we provide the most significantly enriched terms for each of RNA , DNA , and combined RNA/DNA deconvolution . Tables 1 and 2 present the ten most significantly enriched terms for RNA and DNA , respectively . Complete lists of significantly enriched genes ( p ≤ 0 . 05 ) appear in Supplementary Material as S1 and S2 Tables . Only seven terms were significantly enriched for combined RNA/DNA deconvolution and therefore only those are listed in Table 3 . Comparison of the method shows that DNA-only results in the largest number of distinct pathways enriched , followed by RNA-only , then combined . Combined , however , is most specifically enriched for expected term classes broadly related to cancers and breast tissue . These results may suggest that the combined data is more effective at achieving high specificity at a trade-off in sensitivity . While there are many deconvolution tools in this domain , the variations in data assumptions of the methods make direct head-to-head comparison difficult . While TITAN [19] can make inferences from similar copy number variation data to our method , it depends on knowledge of alleleic frequency data at SNV sites unavailable to us in the present analysis . THetA [18] and THetA2 [32] perform a comparable form of inference but are tuned specifically for inference from a single tumor , making them unsuitable for comparison on a patient cohort for which our methods are designed . PhyloWGS [33] is designed for whole-genome analysis like our method , but depends on availability of variant allele fractions of novel somatic variants , a model and data type again unsuited to the kind of cross-cohort analysis performed by our method . SPRUCE [34] likewise depends on VAF data under the assumption that all samples are drawn from a single patient , making it poorly suited to the kind of data for which our method is designed . Canopy [35] likewise makes use of VAFs and allele-specific copy number data unavailable to us and poorly suited to the kind of cross-cohort analysis for which our method is designed . BitPhylogeny [36] likewise assumes a data type unavailable in our application , methylation data in that case , making direct comparison on real data infeasible . In order to allow for some comparison to an alternative in the literature , we choose to compare to PyClone [37] , as it is is a highly cited method producing similar output to our method that can in principle make inferences from a common set of data to our method . PyClone can work with copy number data and can optionally omit allele-specific frequency information ( although it is designed to make use of such information if it is available ) . We emphasize that although PyClone can be run on a common data set to our method with some preprocessing , it is tuned for very different assumptions on those data than our method . PyClone assumes precise frequency estimates on small numbers of sites , as is appropriate to the targeted deep sequencing data for which it was designed , while our method is designed to use less precise data on large numbers of markers , as is appropriate for the whole exome or whole genome data for which it was designed . Furthermore , PyClone is also designed for multiple samples from a single tumor while ours is designed to work with cross-sectional data from distinct tumors . While we can run both methods on a common set of data , we thus cannot devise a single dataset that provides a fair test of both . Our intention in comparing the methods , then , is not to show that our method is superior to PyClone but rather that our method is filling a niche for which prior tools are not designed and to which they do not generalize well . In order to preprocess the data in a format amenable to the PyClone system , we assume a read length of 300 , and baseline copy number of 2 . For this analysis , we assume a copy number of 2 for any region for which which there is no copy number alteration call in the data . We also omit analysis of sex chromosomes . We omitted allele-specific copy numbers as input to PyClone because this information is not part of the publicly-available version of TCGA data . Although both our approach and PyClone can run on SNV data , it proved computationally infeasible to include the SNVs in this dataset for the PyClone analysis , as PyClone is not designed to handle such a large marker set nor to work with markers drawn from many genetically distinct tumors . We first attempted to run the full dataset of all level 4 gene copy number breast tumor samples from TCGA through the PyClone pipeline on a workstation equipped with an Intel i7-4770K processor at 3 . 5GHz per core , with 32GB of RAM . However , the approach was unable to complete in approximately 1 week of running time , which we inferred may be due to the large number of genes ( > 20 , 000 ) present in the full dataset , an amount well in excess of the small targeted sequencing data assumed by PyClone , as well as by the fact the PyClone algorithm runs on a single core . We thus pruned the list of genes ( features ) to a subset of corresponding to known breast cancer driver genes from [48] . PyClone then successfully ran on the set of tumor data points from the TCGA breast tumor dataset . PyClone output also differs somewhat from that of our method , requiring some post-processing to facilitate comparison . PyClone outputs mean and variance scores for each sample , for each cluster of mutations , which can approximately relate to our vertices . Further , we consider a version of the means of the scores normalized to sum to one analogous to the mixture fraction scores we generate . We then test for similarities in Spearman correlation of our model’s inferred mixture fraction rank to the rank of mixture fraction provided by PyClone . Results of comparison of our method with PyClone appear in Table 4 . The PyClone comparison points ( Py1 to Py4 ) correspond to the inferred mutational cluster prevalences . To make a fair comparison , we normalized the prevalences by their sums on a per-cluster basis to derive a fractional composition estimate based on PyClone . We then used Spearman correlation as a comparative tool to examine how similar in rank PyClone’s inferences of which clusters of genes were dysregulated are to our ranking of fractional composition with respect to inferred vertex amount . Because the vertices represent inferred pure subpopulations within tumor samples and are in PC space , the vertices are equivalent to genomic profiles of the subpopulations , where sets of genes are mutated . The correlation analysis provides a matrix of correlations where each element corresponds to the correlation between dysregulation of one Pyclone cluster and representation of one inferred subpopulation by our method . While in this case the two methods produced equal numbers of clusters , we would not expect that to be true for all data sets and the analysis does not assume the matrix dimensions are equal . There is significant ( p < 0 . 01 ) positive correlation between 3 of our 4 vertices and 3 of the 4 clusters inferred by PyClone , signaling general agreement between methods in their estimate of substructure . We further sought to validate our approach by comparing correlation of PyClone inferences to clinical labels supplied by TCGA ( Table 5 ) with correlation of the inferences of our simplicial complex approach to the TCGA clinical labels ( Table 6 ) . Considering both positive and negative correlations , at the p≤0 . 0001 level , our approach has four entries significantly correlating to the clinical labels across two vertices , as compared to three entries across two clusters in the case of PyClone . Additionally , including those at a weakly significant ( p ≤ 0 . 05 ) level , our approach has five entries correlating three of the four vertices’ mixture fractions to clinical subtypes , while PyClone has four entries across three of the four clusters’ mixture fractions to clinical subtypes . To provide an additional point of comparison , we also applied our method to TCGA RNA-Seq data . PyClone is not designed to accommodate RNA-Seq data , so we provide results only for our method . Table 7 shows the results . In this test case , the simplicial complex approach retrieved significant ( p ≤ 0 . 01 ) correlation at each of the inferred vertices , and for each of the subtypes , with a total of 9 significant entries . While a perfect comparison of our method with PyClone , or any prevailing method known to us , is impossible given different data assumptions and input and output types , these comparisons provide clear evidence that our method is at least comparable in ability to identify substructure among tumor data sets when given appropriate input data to its model assumptions . On the whole , we interpret the results as being in agreement on the tested BRCA TCGA dataset , with our method providing the additional benefits of Having the option to run on expression data , gene copy data , or heterogeneous combinations thereof , Not requiring matched tumor-normal data or assumptions about normal samples , and Being amenable to much larger numbers of features , such as might be derived from whole-genome data ( WGS/WES ) , in comparison to PyClone or similar tools In cases where the constraints of PyClone ( deep targeted sequencing , matched tumor-normal samples ) are well-satisfied , it may perform more accurately than the general approach we have developed , but in cases of lower read depth , datasets missing some or all normal matched samples , or with whole-genome coverage , the simplicial complex approach may be more appropriate . We note that our method was not able to produce useful results on the trimmed list of genes we produced to yield a manageable gene set for PyClone . We speculate that the high noise in the data makes it infeasible to estimate simplicial structure from a small targeted gene set , resulting in our method fragmenting the samples into many more clusters . Our approach thus appears to be poorly suited to targeted gene sets and better to large or whole-genome data sets , in contrast to PyClone , which is well tuned for small numbers of genes but not computationally feasible for whole-genome data . In order to further validate the approach , we examined Spearman correlation with an orthogonal data set . Onuchic et al . [28] developed a deconvolution approach based on DNA methylation data from TCGA [46] . The result of the Onuchic et al . [28] approach was a deconvolution of the data into constituent subtypes categorized into 5 cancer subgroups , a stromal group , an immune group , and a normal group . The results of correlating our results to theirs are shown in Table 8 . There is significant ( p < 0 . 01 ) positive correlation between what we estimate as the fulcrum of the simplicial complex—correspondening to the most recent ancestor in a phylogenetic interpretation—and the Onuchic et al . [28] estimate of stromal , immune , and normal composition . Further , our vertex 1 correlates in a statistically significant and positive way to their estimates of cancer subtype 1 and cancer subtype 5 . We have developed a novel method for taking better advantage of mixture substructure in deconvolution of mixed genomic data from heterogeneous tumor samples . This contribution is intended to advance a theoretical strategy for better resolving substructure in complex genomic mixtures , a general strategy that might be incorporated into many existing approaches for cell type deconvolution using assorted data types and inference models . The advances in the present paper bring us closer to the goal of deriving precise models of complex mixture substructure in the face of sparse , noisy genomic data without the need for extensive expert intervention . For this purpose , we have introduced new strategies for automated inference of subcluster dimensions , automated construction of a global simplicial complex structure , and better deconvolution of submixtures on small samples with uncertain subclustering . We have shown that we can automatically learn model structure from realistic sizes of data set without degrading performance of the model relative to methods requiring significantly more user intervention . We have further shown that this general approach is effective to varying degrees on CNV , RNA-Seq , and heterogeneous data sets . We have further shown that our method has comparable ability to resolve mixture structure to a leading deconvolution method , PyClone , on a common data set , while demonstrating several advantages in relaxing assumptions on data type , source , and quality . The ultimate goal of the present work is to make sophisticated mixture deconvolution approaches more widely accessible to a non-expert community , by allowing them to be incorporated more broadly into a variety of deconvolution approaches in the literature . Much work still remains , though , both in better automating these approaches and improving inference quality . There are still several ( hyper- ) parameters for which the task of automated learning remains challenging . While automated dimension estimation appears valuable in improving simplicial complex models , deriving accurate estimates is a significant challenge for sparse , noisy data [49] . Integration of additional forms of genomic data into a common mixture framework is likewise a promising but challenging direction for improving inference quality . The computational framework presented here could also in principle be applied to many genomic samples from a single patient ( e . g . , distinct tumor regions , sites , or timepoints ) , although we do not explore that application here as data of this form is still scarce . The exact data needs of the method would depend on the heterogeneity across samples . We would expect this inter-sample heterogeneity to be substantially smaller for multiple samples from a single tumor than for the application to distinct tumors examined here , but nonetheless higher than is required for other tumor deconvolution methods that infer simpler underlying mixture models . Further , while we have applied this approach here to two data types and their combination , the same general strategy might be applied to many forms of genomic measurement ( CNV , RNA expression , SNV , epigenetic , proteomic ) and technologies for assessing them ( array , sequence , or other high-throughput methods ) . Furthermore , as single-cell methods become more cost-effective , combinations of bulk and single-cell data may prove particularly informative . Finally , the simplicial complex models themselves require refinement to better capture the real sources of genomic mixture substructure they are meant to model , including substructure imposed by common pathways of subtype evolution , spatial constraints in the tumor microenvironment , and other sources of mixture substructure that do not conform well to our current simplicial complex model .
One of the major challenges in making sense of cancer genomics is high heterogeneity cell-to-cell , as a tumor is typically made up of multiple cell populations with distinct genomes and gene expression patterns . The difficulty of working with such data has led to interest in computationally inferring the components of genomic mixtures . We develop a new approach to this problem designed to take better advantage of the fact that mixtures of cells across tumors or tumor regions can be expected to be highly non-uniform; samples that share greater common ancestry or progression mechanisms are likely to have more similar mixtures of cell types . We present new work on reconstructing mixtures from multiple genomic samples where the samples can be presumed to share such a pattern of similarity . Our methods automate the process of reconstructing these mixtures and the relationships between samples . We demonstrate their effectiveness on tumor genomic data in comparison to alternative methods in the literature .
[ "Abstract", "Introduction", "Materials", "and", "methods", "Results", "Discussion" ]
[ "cancer", "genomics", "taxonomy", "medicine", "and", "health", "sciences", "breast", "tumors", "cancers", "and", "neoplasms", "basic", "cancer", "research", "oncology", "phylogenetics", "data", "management", "materials", "science", "copy", "number", "variation", "materials", "by", "structure", "genome", "complexity", "computer", "and", "information", "sciences", "breast", "cancer", "comparative", "genomics", "evolutionary", "systematics", "genetics", "biology", "and", "life", "sciences", "physical", "sciences", "genomics", "evolutionary", "biology", "mixtures", "computational", "biology", "genomic", "medicine" ]
2017
Automated deconvolution of structured mixtures from heterogeneous tumor genomic data
Respiratory infectious diseases are the third cause of worldwide death . The nasopharynx is the portal of entry and the ecological niche of many microorganisms , of which some are pathogenic to humans , such as Neisseria meningitidis and Moraxella catarrhalis . These microbes possess several surface structures that interact with the actors of the innate immune system . In our attempt to understand the past evolution of these bacteria and their adaption to the nasopharynx , we first studied differences in cell wall structure , one of the strongest immune-modulators . We were able to show that a modification of peptidoglycan ( PG ) composition ( increased proportion of pentapeptides ) and a cell shape change from rod to cocci had been selected for along the past evolution of N . meningitidis . Using genomic comparison across species , we correlated the emergence of the new cell shape ( cocci ) with the deletion , from the genome of N . meningitidis ancestor , of only one gene: yacF . Moreover , the reconstruction of this genetic deletion in a bacterium harboring the ancestral version of the locus together with the analysis of the PG structure , suggest that this gene is coordinating the transition from cell elongation to cell division . Accompanying the loss of yacF , the elongation machinery was also lost by several of the descendants leading to the change in the PG structure observed in N . meningitidis . Finally , the same evolution was observed for the ancestor of M . catarrhalis . This suggests a strong selection of these genetic events during the colonization of the nasopharynx . This selection may have been forced by the requirement of evolving permissive interaction with the immune system , the need to reduce the cellular surface exposed to immune attacks without reducing the intracellular storage capacity , or the necessity to better compete for adhesion to target cells . Some pathogenic bacteria like Neisseria meningitidis , Streptococcus pneumoniae , Haemophilus influenzae , and Moraxella catarrhalis are highly adapted to the ecological niche of the human nasopharynx ( NP ) . This one defines the upper part of the pharynx from the end of nasal cavities ( choanoe ) to the upper surface of the soft palate . On the lateral parts it communicates with the Eustachian tubes by the pharyngeal ostium whereas the posterior part is composed of the pharyngeal tonsil ( adenoids ) . The aforementioned species are part of the normal human NP microbiome where they generally live in asymptomatic symbiosis . However , some strains can occasionally cause local diseases of the upper-respiratory tract ( pharyngitis , laryngitis , bronchitis , sinusitis and otitis ) or an invasive infection leading to life threatening diseases , such as pneumonia , septicemia and meningitis . It is expected that the bacterial adaptation to human mucosa at the NP has occurred through some evolutionary events that allowed immune tolerance of the bacteria by the immune system and/or conferred new properties for bacteria to respond to the novel physical and chemical constraints . In this sense , we hypothesized that modifications of the peptidoglycan ( PG ) , a MAMP ( Microbe-Associated Molecular Pattern ) , may have been selected during NP adaptation . PG is strongly recognized by the host via the specialized receptors NOD1 and NOD2 [1–4] . But , PG is also an essential component of bacterial cell walls that shapes the cell and serves as an exoskeleton conferring resistance to internal turgor pressure [5] . It is composed of polymerized repeats of disaccharide units ( N-acetylglucosamine or G and N-acetylmuramic acid or M ) cross-linked by short stem peptides . Several reports have already described the selection of different bacterial shapes , varying from rod ( bacilli ) to spheres ( cocci ) to helical and spirals ( spirochetes ) , among others , during adaptation to new ecosystems [6–8] . These cell shape changes can be a transition that can happen on the developmental time scale , during a single cell cycle or it can become permanent through the course of bacterial evolution . Except curvature , cell shape is governed by two mechanisms: cell elongation and division . Numerous proteins allow and orchestrate the spatial and temporal coordination of these mechanisms . Most analyses of Gram-negative cell wall biogenesis have been performed in Escherichia coli and are reviewed here [9 , 10] . In this model organism , inner membrane-bound penicillin binding proteins ( PBPs ) are responsible of transglycosylation ( polymerization of disaccharide-pentapeptide precursors ) and transpeptidation ( crosslink of peptide residues ) . Two bifunctionals PBPs , PBP1A and PBP1B , are central factors of the cell elongation and division machinery , respectively . In addition to PBP1A , the elongation machinery is also composed , among others , of MreB , an actin structural homologue , necessary for a proper localization of the machinery or the monofunctional PBP2 ( called PBPX in Neisseriaceae ) another PBP essential for lateral PG synthesis . The division machinery is , for example , composed of PBP3 ( called PBP2 in Neisseriaceae ) essential for septal/polar PG synthesis . Due to the high numbers of players and the multiple interconnections , the coordination between cell elongation and division is not yet completely understood , although FtsZ plays a major role . FtsZ is a tubulin-like protein that forms , prior to cell division , a dynamic ring-like structure at the mid-cell that initiates the assembly of the divisome [11] . In this article , we described the evolution from rod-to-cocci of the ancestor of several NP pathogens and show that PG structure evolved during nasopharyngeal adaptation . Furthermore , we show that in the Neisseriaceae family , one of the starting points of this evolution event is the deletion of a coordinator of both cell elongation and division called YacF or ZapD . Our data also highlighted changes that accompanied this cell shape transition such as a decreased recognition by the innate immune system , the optimization of the ratio cell surface over the volume or the localization of some surface-exposed structure ( e . g . pili ) . Bacteria from the Neisseriaceae family have variable cell shape: some are elongated ( e . g . Kingella oralis or N . elongata ) whereas others present a coccoïd form ( e . g . N . meningitidis and N . gonorrhoeae ) . To establish that these differences were linked to the evolution within the family , we correlated the phylogeny ( determined using core genome analysis as described in the M&M ) with cell shape verified by scanning electronic microscopy for some representative strains that were received at the Centre National de Reference des Méningocoques or with information found in the literature for other well described strains [12] ( Fig 1 ) . Our results suggested that Neisseria cell shape evolved from rod to coccus at a node of evolution that we have called 1 in the Fig 1 . As a note , the coccus Neisseria wadsworthii 9715 is related to bacilli Neisseria clade with the closest bacterium being the bacillus Neisseria waeveri . This phylogeny could be biased by a low quality genomic sequence . Nevertheless it is consistent with previous phylogeny ( based on the same genomic sequence ) [13] . Therefore , this strain may have undergone an independent coccus cell-shape transition . As PG is the main determinant of cell shape in bacteria , we determined the muropeptides composition of PG from different Neisseriaceae representatives of each lineage . We found a stepwise increase of the proportion of pentapeptide-containing muropeptides ( GM5 ) correlated with a decrease of the proportion of tetrapeptide-containing muropeptides ( GM4 ) along the phylogeny toward N . meningitidis lineage at node 1 but also at node 2 ( Fig 1 ) . This second node corresponded to the divergence of the meningococcal highly related species [14] . Overall , we observed a two-fold reduction of the mean total ratio ( GM4+GM4_GM4 ) / ( GM5+GM5_GM4 ) at node 1 ( 5 . 1 vs 2 . 9 ) and a further two-fold reduction at node 2 ( 2 . 9 vs 1 . 3 ) . We hypothesized that this ratio may represent a difference in the structure of the PG septal/polar versus the PG lateral produced during elongation . In other words , the PG septal/polar could be enriched in GM5 containing muropeptides . To test this possibility , we used immune-gold detection of vancomycin binding to the PG saculli of N . bacilliformis ( Fig 2A ) . The vancomycin has the useful property to specifically bind to GM5 [15] . We measured the number of gold-beads by 10 μm2 in both the lateral and polar PG ( Fig 2B ) and observed an increased occurrence of GM5 in the polar PG . Overall , these results suggest that the change in cell shape led to a global enrichment in GM5 due to a decreased occurrence in the sacculi of lateral PG produced during elongation ( less rich in GM5 ) . To determine which genetic differences could be responsible for these changes , we screened for gene differences between genomes of species that diverged before and after node 1 and node 2 ( excluding N . wadsworthii 9715 ) . We used MycoHIT [16] as previously described . This software was designed to investigate the presence/absence of genes encoding orthologous proteins in conjunction with the phylogeny to finally detect horizontal gene transfers or deletion . To detect gene insertions , we used N . meningitidis proteins as the reference ( database ) that was compared by BLAST ( TblastN ) against all the other genomes . Similarly , for gene deletion , we used proteins from our complete genome of N . elongata as reference to search for orthologs in the other genomes . For potential evolutionary events that could have helped the coccoïd transition and the first increase of GM5 at node 1 , we were not able to find any horizontal gene transfers but we found a unique deletion of a gene annotated as yacF . This gene is absent in all bacteria that diverged after node 1 concomitantly with the appearance of the cell-shape change and it is present in all bacteria that diverged before node 1 except N . wadsworthii 9715 ( Fig 1 ) . Importantly , yacF ( but not mreBCD , pbpX , rodA and rodZ ) was also deleted from the coccus N . wadsworthii 9715 that could have undergone an independent cell-shape transformation . Notably , as we detected a second increase of GM5 in the structure of the PG at node 2 , we also screened for gene insertions and deletions that correlated with this event . We detected numerous genes deletions ( 7 ) and insertions ( 22 ) including several genes of unknown function ( S2 Table ) . Hence some of them may be directly involved in the PG structural change observed . Interestingly , among all the events observed at this node , we detected the deletion of the elongation machinery ( mreBCD , pbpX , rodA , rodZ ) ( Fig 1 ) . We found only one evolutionary event that correlated with cell shape change at node 1 , the deletion of yacF . Interestingly , this gene encodes a protein , YacF ( or ZapD ) that has been recently shown to be implicated in the E . coli cell cycle . It is localized at the midcell in a FtsZ-dependent manner [17] . In addition , in vitro data showed that the interaction of YacF with FtsZ promotes the bundling of FtsZ protofilaments [17] . Based on these results , it was suggested that YacF , in vivo , promotes FtsZ-ring assembly in E . coli [17] . Thus we hypothesized that the natural deletion of yacF in the ancestor of coccus Neisseria could have participated in the observed cell shape transition . Consequently , we investigated the correlation between the presence of yacF in bacterial genomes and the cell morphology ( Fig 3 ) . We observed that yacF is only present in β-proteobacteria or γ-proteobacteria and its presence is strongly correlated with bacilli cell shape . All the species harboring yacF are bacilli and inversely no coccoid bacteria harbor yacF . There is one exception to this rule in the Methylococcales order . This order has genera such as Methylomonas and Methylomicrobium that are bacilli and Methylococcus , which are cocci . The genome of the coccus Methylococcus capsulatus contains yacF but also all the elongation machinery genes ( mreBCD , pbpX , rodA , rodZ ) . The cell cycle of methanotrophs is clearly less studied , and it remains to be determined how elongation and division is regulated in this bacteria . Finally , if yacF is almost exclusively present in bacilli , it is important to notice that some bacilli ( such as Xhantomonadeles or Pasteurellales ) do not harbor yacF . This is also the case for other accessory partners of the elongation machinery ( such as MreD in Acidithiobacillales and RodZ in Bordetella sp . ) ( Fig 3 ) . Overall , this distribution suggests an important role of YacF in bacilli As we noticed that yacF was present only in bacilli , but not in cocci , we hypothesized that its role may be restricted to bacteria with rod cell shape . To gain insight into its function , we deleted the gene in two elongated Neisseria: N . elongata ( Fig 4A ) and N . bacilliformis ( Fig 4B ) . The ORF of yacF is potentially part of a conserved operon that includes yacG , a DNA gyrase inhibitor , coaE , a dephosphocoenzyme A kinase , and genes encoding proteins implicated in type IV pili production ( pilDFG ) ( Fig 1 ) . To exclude polar effects , due to yacG split-up from the rest of the operon , we developed an alternative genetic approach for N . elongata . We fused the start codon of yacG with the ATG of yacF . This permitted to produce a non-interrupted operon that resulted in the control of yacG by the putative native promoter of the operon . In this condition , a similar expression of yacG was measured using RT-PCR for the N . elongata wild type and the ΔyacF strains ( S1 Fig ) therefore , excluding a potential role of YacG in phenotypes observed . When yacF was deleted , we noticed strong morphological defects for all cells observed in both species ( N . elongata and N . bacilliformis ) ( Fig 4 ) with a multitude of morphological aberration from multipolar cells , to abnormally elongated cells . These defects were restored by complementation with yacF in N . elongata ( Fig 4A ) . This important morphological defect was accompanied by a moderate reduction of the growth rate ( S2 Fig ) . To verify if this aberrant morphology was linked to cell wall synthesis , we measured the muropeptides content of the PG extracted from these mutants using reverse-phase HPLC ( Fig 5 ) . The yacF mutant in both N . elongata and N . bacilliformis showed an increase of muropeptides composed of pentapetides ( GM5 ) and a decreased of tetra-peptides ( GM4 ) compared to wild-type bacteria ( Fig 5 ) as observed during natural deletion of yacF at node 1 ( Fig 1 ) . The quantification of these differences in N . elongata is also presented in S3 Fig . In E . coli YacF has a FtsZ-dependent mid-cell localization , that takes place before the septum formation [17] . As we observed abnormal elongation and abnormal division in N . elongata and N . bacilliformis lacking yacF , we hypothesized that this protein may play a role in regulating the transition between elongation and division at the mid-cell of elongated Neisseria . If this is true , in the absence of one of these events the role of YacF may be minimal . To test for the role of YacF in absence of division , we used penicillin G . This antibiotic targeting PBPs , used at an optimal concentration close to the inhibitory dose , blocks cell division but not elongation [18] . This consequently leads to the filamentation of N . elongata cells [18] by inhibiting specifically PBP2 but not PBPX . We used this property and assessed for potential differences in morphology ( Fig 6A ) or PG structure ( Fig 6B ) as the ratio between GM4/GM5 ( Fig 6C ) . We observed no differences between the wild type strain and its yacF mutant when the division was inhibited . To test the role of YacF in absence of elongation , we constructed a strain deleted from the entire mreBCD , pbpX , rodA locus and assessed for potential differences in morphology ( Fig 7A ) or PG structure ( Figs 7B and S2 ) as the ratio between GM4/GM5 ( Fig 7C ) correlated with the absence of yacF . We observed neither aberrant morphological problems nor PG composition differences between the mutant ΔmreBCD , pbpX , rodA and the double mutant ΔyacF; ΔmreBCD , pbpX , rodA ( Fig 7A , 7B and 7C ) . All together our data shown that YacF is not required in absence of the elongation or the division for normal morphology or PG structure suggesting that YacF is implicated in the coordination/transition between these two events . As seen in Fig 7C , wild type N . elongata ( a species that diverged before node 1 ) has a ratio GM4/GM5 ( or GM4_GM4/GM4_GM5 ) superior to ΔyacF ( representation of bacteria that diverged after node 1 and before node 2 ) that has itself a ratio superior to the double mutant ΔyacF; ΔmreBCD , pbpX , rodA mutant ( representation of bacteria that diverged after node 2 ) ( Fig 7C ) . Thus , by reconstructing two different genetic events associated with node 1 and 2 , we were able to recapitulate the step-wise change in the PG structure observed in Neisseriaceae . Based on the suggested role of YacF , in regulating the transition between elongation and division and the well-characterized role of MreBCD , PBPX , RodA , RodZ in elongation , it was surprising to notice that during evolution yacF deletion occurred prior to that of the elongation machinery . To better understand this evolutionary time-lapse , we compared the natural evolution to the alternative expected order of events , i . e . prior deletion of the elongation machinery . Thus , we compared the growth fitness of the wild type N . elongata , the simple mutant ΔyacF or ΔmreBCD , pbpX , rodA and the double mutant ΔyacF; ΔmreBCD , pbpX , rodA . The ΔmreBCD , pbpX , rodA showed a strong growth defect that can be partially complemented by yacF deletion ( S2 Fig ) . This suggests that deletion of yacF was prior to the elongation machinery to reduce the fitness burden of changing cell shape . The deletion of the elongation machinery was difficult to achieve in Neisseriaceae . We were unsuccessful in deleting the locus in N . bacilliformis and we obtained only one clone after several assays in N . elongata . This clearly suggested a need for bacterial adjustment to lose the elongation machinery . We reasoned that this in vitro evolution could reveal some additional genetic changes ( for example , suppressor mutations ) that might have also occurred naturally during the evolution of the Neisseriaceae familly . Therefore , we Illumina-sequenced the genome of the N . elongata ΔmreBCD , pbpX , rodA and compared it to our complete wild-type N . elongata PacBio-sequenced genome . We identified only two SNPs: 1 ) position 2052208 in ccoN . It is a frameshift mutation that restores ccoN . As ccoN is not a pseudogene in the other N . elongata sequence , we concluded to a sequencing error in our wild-type bacteria genome . 2 ) position 3614 in NELON_00025; a pseudogene encoding an insertase . These two SNPs are unlikely to be linked to a physiological adaptation to the cell shape change . Besides these two SNPs , we also observed a duplication of a 350kb region that is surrounded by two repeated tuf genes ( Fig 8A ) . Interestingly , the region encompasses the dcw ( division cell-wall ) cluster and ends with ftsZ the major actor of cell division . We hypothesized that this duplication , although without SNPs , could affect gene expression by changing the chromosome structure . We performed RNA sequencing analyses ( Fig 8A ) and qRT-PCR validation ( Fig 8B ) on RNA from N . elongata strains harboring deletions of mreBCD , pbpX , rodA compared to the wild-type strain . The effect of the mreBCD , pbpX , rodA deletion on gene expression was reproducible between the single and double mutants and mainly restricted to genes in the region surrounding the second tuf gene around 0 . 5 Mb ( Fig 8A ) . The duplication of this 350 kb region lead to the specific up-regulation of genes linked with cell wall remodeling such as murF , murC , murI , ftsA , ftsQ . This strongly suggests that during in vitro evolution , the division machinery needed to be over-expressed in order to compensate for the loss of the elongation . We showed that the deletion of yacF that encodes a putative coordinator of the elongation and division , was one of the starting points of the coccoïd transition in Neisseriaceae . This is also true for other bacteria , such as the coccoïd endosymbiote Blochmania sp . that lost yacF but preserved other components of the elongation machinery ( as mreBCD see Fig 3 ) . Additionally in the pseudomonadales order composed of the well-known Acinetobacter and Moraxella genera , all sequenced bacteria lack yacF suggesting a common ancestral deletion ( Fig 3 ) . Similar to the Neisseriaceae family , Moraxellaceae also comprises species with different cell shape varying from rod to coccobacilli to cocci ( M . catarrhalis species ) . By using the same approach described above , we observed that this cell shape change was also correlated with the family phylogeny ( Fig 9 ) . In addition , as observed for Neisseriaceae , the coccobacillus-to-coccus transition was concomitant with a decrease in the GM4 proportion over the GM5 in the PG composition measured by ( GM4+GM4_GM4 ) / ( GM5+GM5_GM4 ) ratio . This coccus transition was concomitant with the deletion of mreBCD , pbpX , rodA , rodZ , ( Fig 9 ) . Twice during the evolution of nasopharyngeal symbiote , we observed an evolution that ends with a cell shape change and a modification of the structure of the PG . It was recently shown that changes in the N . meningitidis PG composition affects the bacterial detection by dedicated host receptors [19] . It is therefore possible that the changes observed during the evolution toward N . meningitidis will also affect the host-pathogen relationship by affecting the Nod1 and Nod2 recognition . To test this , we measured NF-κB luciferase expression in response to PG recognition by Nod proteins using previously described assay [20 , 21] . The Nod1 and Nod2 responses to N . elongata ( bacillus ) purified sacculi were superior to the response of the double mutant sacculi ( coccus ) ( Fig 10A ) suggesting that the coccal PG , exclusively composed of polar/septal PG , is a less potent Nod activator than the bacilli in vitro . Except from the host-pathogen interaction , we also hypothesized that these changes may have other unsuspected impacts important for colonization of the NP . First of all , using SEM images , we estimated the volume and the surface of bacteria cells of wild-type N . elongata ( bacillus ) and the double mutant ( coccus ) and calculated the ratio surface/volume ( Fig 10B ) . Our results showed that the coccoïd cell shape has the property to present a reduced cell surface over the intracellular volume . Finally , the absence of elongation led to cells composed of PG produced by PBP2 ( septal/polar PG , enriched in GM5 ) and a complete absence of lateral PG . We hypothesized that the presence of GM5 in the septal/polar PG may be a marker of localization of some cell apparatus . As such , a structure that localized preferentially to the poles in diplo-bacilli ( wild type N . elongata ) would be localized all over the cell in the diplo-cocci ( N . elongata ΔmreBCD , pbpX , rodA; ΔyacF ) . Using negative contrast TEM , we studied pili localization in the two types of isogenic bacteria . We observed a clear preference for polar localization of this apparatus in N . elongata ( Fig 10C ) while it was evenly distributed all over the cell in the double mutant ∆yacF;∆mreBCD , pbpX , rodA . This result indicates that the observed differential localization of pili between N . elongata ( polar/septal ) and coccoïd Neisseria as N . meningitidis or N . gonorrhoeae ( all-over the cell ) is linked to the coccoïd transition that took place during the emergence of these species . N . meningitidis and M . catarrhalis are two bacteria that share the same ecosystem as proved by recent horizontal genes transfer between these species [22] . The present work establishes that the ancestor of those bacteria has undergone the same cell shape transformation from a rod to a coccus accompanied by a PG enrichment in GM5 . Our evolutionary approach highlighted the central role of yacF as a starting point of the coccus transition for several bacilli . Importantly , this approach allows description of the role of YacF ( ZapD ) , in the coordination of the elongation and division . This work is reminiscent of the recent description of YacF in E . coli [17] . Durand-Heredia and colleagues described a link between YacF and FtsZ . However , they were not able to establish a role for YacF as yacF deletion has no effect on cell morphology in E . coli , suggesting a redundancy with unknown YacF functional homologues . This is also strengthened by the fact that some bacilli lack homologues of the yacF gene . When the deletion was reconstructed in N . elongata , we were not able to completely mimic the evolution suggesting that some additional events and compensatory mutation are also necessary . We tried to reverse the evolution in N . meningitidis by re-introducing yacF , rodZ , mreBCD , pbpX , rodA but this had no effect on cell morphology or PG composition . This supports the notion that additional changes , independent of those genes , are necessary . It seems unlikely to involve defects of the dcw cluster ( the cluster of division and cell wall synthesis ) of N . meningitidis as it is similar to bacilli dcw cluster and not to cocci cluster [6 , 23] . In contrast , it could involve an increased expression/activity of the divisome as suggested by the in vitro coccus evolution . Therefore , further studies will be necessary to completely understand the coccoïd transition by , for example , applying several rounds of in vitro passage of N . elongata ΔyacF to select for cell shape stable variants . It was surprising to us not to observe the deletion of the elongation machinery associated with the cell shape transition in Neisseriaceae . Indeed several Neisseria cocci retain the machinery ( Fig 1 ) . This suggests a role of this machinery in cocci bacteria that may be independent of elongation . Indeed our PG structural data showed an intermediate GM4 over GM5 ratio compared to cocci that deleted the machinery . These results suggest that some components on the machinery are still involved in PG synthesis . It may be possible that their presence is still required to localize properly or organized temporally the divisome in bacteria that recently underwent cell shape transition . It has been shown in E . coli that some components of the elongation machinery interact with components of the divisome [24] . This hypothesis could explain the strong growth defect of the mreBCD , pbpX , rodA mutant in N . elongata , which correlated with an increased expression of some proteins of the divisome ( AmiC , MurI , FtsA , FtsQ ) and the failure to delete the same locus in N . bacilliformis . Evolution may have to first select events that will phenocopy the deletion of the elongation machinery ( ΔyacF ) to stepwise adapt the divisome ( or other physiological processes ) that still required the presence of elongasome proteins . Two phylogenetically distinct bacteria specialized in the colonization of the NP underwent convergent evolution . Several hypothesis have been emitted to explain different cell shapes as these would be beneficial for motility , to compete for nutrient or to resist to predation and physical constraint ( turgor pressure ) among other [25–27] . Kingella sp . Simonsiella sp . Eikinella sp . and some Neisseria are often found in the oral cavity in the saliva , an aqueous environment , whereas N . meningitidis is attached to the NP mucosa a drier environment . One hypothesis may be that bacilli , with polar pili , are more adapted to move ( twitching motility ) in the saliva in contact with the buccal mucosa with directional movement [28] whereas cocci with pili all over the cell may be more adapted to attach the dryer nasopharyngeal mucosa . An alternative hypothesis is that the coccus is less susceptible to attacks from the immune system or bacterial killing systems ( as Contact-Dependent Inhibition CDI system ) as the ratio surface/volume is smaller than that of bacilli . In support for this hypothesis several studies has shown that cell surface size is a key factor when facing immune attacks [29 , 30] . We therefore think this cell shape change may represent an advantage in the case of N . meningitidis in the NP to increase resistance to aggressions . The increased proportion of GM5 in the PG of NP pathogens may also represent some advantages . Our group recently showed that , when N . meningitidis experiences changes in the PG composition , including a decreased ratio GM4/GM5 , the recognition by the innate immune system is altered with less induction of the immune response [19] . In this work , we observe similar results using PG from engineered N . elongata coccus strain that harbors a decreased ratio GM4/GM5 . It is therefore possible that the PG with increased proportion of GM5 is processed or recognized differently . In support of this , a study reported that ligand-induced structural rearrangements occurred in the PG-binding site of the human PGRP ( PG recognition protein ) co-crystalized with pentapeptide muropeptides but not with tripeptide muropeptides [31] . It can also be possible that an optimal ratio of GM5 over the GM4 is required for permissive interaction . Apart from a role in host innate immune system interaction , the presence of GM5 in the PG may also be a way to avoid PG bacteriolytic effectors , injected in the periplasm by neighboring bacteria , by altering their natural substrate [32] . In this work , we characterized the process of cell shape transition during bacterial evolution within a defined ecological niche by highlighting some of the genetic events that have been selected as well as the factors and advantages that may have driven this selection . Using an evolutionary approach , we added a new layer of understanding of the function of a protein involved in coordinating cell elongation and division . Understanding the fitness of these pathogens to the NP and their survival and their invasive capacity may help tailoring intervention strategies to favor asymptomatic carriage and to prevent invasive life-threatening infections All Neisseria were grown in GCB agar medium with Kellogg supplements . For cloning experiments , E . coli DH5α was grown at 37°C in Luria-Bertani Media ( Difco ) . When required , antibiotics were added as follows: chloramphenicol ( 30 μg/ml for E . coli; 5 μg/ml for Neisseria sp . ) , kanamycin ( 50 μg/ml for E . coli; 100 μg/ml for Neisseria sp . ) and erythromycin ( 300 μg/ml for E . coli; 3 μg/ml for Neisseria sp . ) and sub-inhibiting concentration of penicillin G ( 0 . 12 μg/ml ) . N . elongata subsp . glycolytica ( 29315 ) , N . lactamica ( 23970 ) , N . bacilliformis ( BAA-1220 ) , N . sicca ( 29256 ) , K . oralis ( 51147 ) were purchased at the American Type Culture Collection ( ATCC ) . E . corrodens and M . bovis were obtained from Dr . Gaillot and Dr . S . Highlander , respectively . Other strains were from the collection of Centre National de Reference des Meningocoques ( CNRM , Institut Pasteur , Paris ) . To generate the deletion of mreBCD , pbpX , rodA in N . elongata , we first amplified the surrounding region in 5’ or 3’ with the respective couple of primers 5’RD1NeF-5’RD1NeR and 3’RD1NeF-3’RD1NeR . We ligated the 5’ fragment digested with XmaI and XbaI into the plasmid pCom-Pind [33] . The resulting plasmid was digested with BamHI and SpeI and ligated with the 3’ fragment digested with the same enzymes to generate plasmid p3’KORD1Ne3’::Cm . To generate the yacF deletion in N . elongata , we first amplified the upstream 5’ region of yacF with the primer set 5’RD2NeF-5’RD2NeR . This PCR fragment was digested with NsiI and PstI and ligated into the plasmid pGEM::Km [34] digested with NsiI to generate p5’RD2Ne::Km . yacG was amplified using primer set YacGF-YacGR and fused to the rest of the operon by inserting the PCR fragment digested with MluI and SpeI to p5’RD2Ne::Km digested with the same enzymes . The resulting plasmid was called p5’RD2GNe::Km . The construct along with the kanamycin resistance cassette was amplified using 5’RD2NeF-KmXbaIR primer set and fused to the downstream 3’ region of yacG amplified with 3’RD2NeMoinsF-3’RD2NeR . To achieve this fusion , we used a multi-step PCR reaction with the two PCR product as template and 5’RD2NeF-3’RD2NeR as primers . The resulting 3kb PCR product was named 5’RD2GNe3’::Km . To complement the deletion of yacF in N . elongata in trans , we constructed a plasmid that allows insertion in an intergenic region at the end of nrqF . We amplified 5’ and 3’ regions surrounding the target locus of insertion with 5nrqF-5nrqR and 3nrqF-3nrqR primer set , respectively . We ligated the 5’ fragment digested with SpeI and BamHI with the plasmid pCom-Pind [33] . The resulting plasmid was digested with XbaI and NcoI and ligated with the 3’ PCR fragment digested with the same enzymes to generate p5nrq3::Cm . In parallel , the firefly luciferase orf was inserted into pBAD28 ( Life Technologies ) using NcoI and PstI to generate pBAD::luc . A Neisseria pilEp promoter was amplified from N . meningitidis MC58 DNA using pilEpF-pilEpR primers and cloned in front of the luciferase genes using NcoI and NheI in pBAD::luc to generate ppilEpLuc . In this plasmid , luciferase was subsequently replaced by yacF that has been amplified using yacFNcoIF-YacFPstIR primer set and digested with NcoI-PstI to generate ppilEpyacF . Finally the cassette containing pilEp and yacF was excised using NheI and PstI , treated with T4-polymerase ( Fermentas ) and inserted into p5nrq3::Cm that was digested with BamHI and blunt-ended with T4-polymerase to generate the final plasmid pcompYacF::Cm . To generate the yacF deletion in N . bacilliformis , we first amplified the 5’ upstream or 3’ downstream region with the respective couple of primers 5’KORD2NbF-5’KORD2NbR and 3’KORD2NbF-3’KORD2NbR , respectively . We first ligated the 3’ fragment digested with NcoI and SphI into the plasmid pGEM::Km [34] . The resulting plasmid was digested with NsiI and ligated with the 5’ digested with NsiI and PstI to generate the plasmid p5’KORD2Nb3’::Km . Bacteria were transformed with linearized plasmids or PCR products for 5’RD2GNe3’::Km as followed: bacteria were inoculating on a GCB agar plate and 10 μl of around 500 ng of DNA was deposed on top of the culture . After an overnight incubation , bacteria were collected and inoculated in selective GCB agar plates containing the corresponding antibiotics . Bacteria were inoculated on GCB agar plates without antibiotics ( except in the case of the penicillin G treatment experiments ) and incubated at 37°C in a 5% CO2 atmosphere during 16h . PG was isolated by an adapted version of the method developed for E . coli [35] with all steps carried out at a pH below 7 . 0 . Bacteria were collected and dispersed in 10 ml of cold distilled water ( pH 6 . 0 ) . The cells were added drop-wise to 10 ml of boiling 8% SDS buffered and boiled for a further 30 min . After cooling to room temperature overnight , the SDS-insoluble material was collected by centrifugation at 39000 × g for 30 min . The pellet was washed seven times with warm distilled water ( pH 6 . 0 ) , lyophilized , resuspended to a final concentration of 6 mg/ml and stored at –20°C . The procedure used has been described elsewhere [30 , 36] . The PG ( 200 μg ) was digested with 20 μg of mutanolysin from Streptomyces globisporus ( Sigma ) for 18 h at 37°C in 12 . 5 mM sodium phosphate buffer ( pH 5 . 8 ) . The enzyme reaction was stopped by boiling the sample for 3 min , the digested PG was mixed with sodium borohydride ( 2 mg ) for 15 min . The pH of the samples was then adjusted to 2 . 0 , and the samples were centrifuged to remove insoluble material . We used a linear gradient from 50 mM sodium phosphate buffer ( pH 4 . 3 ) to 50 mM sodium phosphate buffer ( pH 5 . 1 ) containing 15% methanol for 120 min on a Hypersil ODS column ( 4 . 6 × 250 nm; 5 μm particles; ThermoHypersil-Keystone ) at 52°C using a flow rate of 0 . 5 ml/min . UV detection was carried out at 205 nm . The quantity of each muropeptides was assessed by measuring the area of the corresponding peak . We only compared PGs that were extracted simultaneously . For assessing evolution of the PG structure in the different phyla , we determined for each species the total ratio of GM4/GM5 that is calculated as followed ( GM4+GM4_GM4 ) / ( GM5+GM5_GM4 ) where GM4_GM4 and GM5_GM4 represent dimers . Muropeptides recognition by Nod receptors has been performed as described [20 , 21] . Briefly , in one well of 24 well plates , HEK293T cells grown to mid confluency were transfected using 1μl of Fugene with a mixture containing a NF-κB-luciferase reporter construct ( 50ng ) , a ß-galactosidase expressing vector ( 25ng ) and the pCDNA3 plasmid ( 225ng ) , alone ( None ) or together with human Nod1 ( hNod1 ) or human Nod2 ( hNod2 ) or murine Nod1 ( mNod1 ) ( 1ng each ) . Control muropeptides ( MurTriDap as hNod1 agonist or MDP as hNod2 agonist or FK156 as mNod1 agonist , 100nM each ) or PG to be tested ( 2 , 1 or 0 . 5μg of non digested PG from the different bacteria ) were added dropwise to the cells 30 min prior to the transfection in order to favor the intracellular delivery of the muropeptides and their subsequent recognition by the cytosolic Nod proteins , which results in activation of the NF-κB-driven luciferase expression . The luciferase and ß-galactosidase activities were measured 24h post stimulation/transfection from cellular lysates in the presence of their substrates . Data ( mean values of triplicates ) are expressed as relative light units , normalized to the β-galactosidase activity and are representative of two independent experiments . For scanning electronic microscopy , the strains were grown on GCB agar plates during 6h and prefixed in 2 . 5% glutaraldehyde in 0 . 1 M cacodylate buffer for at least 1h and then rinsed in 0 . 2 M cacodylate buffer . After post-fixation in 1% osmium tetroxide ( in 0 . 2 M cacodylate ) , bacteria were dehydrated with increasing ethanol concentrations . Specimens were critical point dried using carbon dioxide , coated with gold and examined with a JEOL JSM-6700F scanning electron microscope . The ratio surface/volume was calculated as followed: for coccus , the radius perpendicular to the septum ( r ) and the diameter ( d ) parallel to the septum were measured and a mean radius ( mr ) was calculated as followed: ( 2r+d ) /4 . Finally , the ratio surface/volume was calculated using the following formula: [4π ( mr ) 2]/[4/3π ( mr ) 3] . For bacillus , as they are composed of a cylinder with a height ( h ) and a length ( l ) fused to two half sphere ( see above for calculation ) , the ratio surface/volume was calculated using this formula: [4π ( mr ) 2 + πlh]/[4/3π ( mr ) 3 + π ( l/2 ) ²h] . For immune-gold labeling of GM5 , freshly extracted PG ( 12 ug ) from N . bacilliformis was incubated with 0 . 25 μg/ml of vancomycin ( Sigma ) in a small volume ( 4 μL ) over-night . The samples were subsequently diluted 1/50 and laid on the grids by ultracentrifugation in a Beckman Airfuge ( 120 000g for 5 min ) . The grids were washed three times with PBS and blocked by incubation in 1% ovalbumine PBS for 5 min . The grids were subsequently incubated for 60 min with the Anti-vancomycin ( ab19968; Abcam ) serum diluted 1/150 in PBS-1% ovalbumine . After three washes and one additional blocking step with 1% ovalbumine , the grids were incubated with an anti-IgG mouse antibody ( 1/20 in PBS-1% ovalbumine ) coupled with 6 nm gold beads . Finally , after three washes , the grids were stained with 3% PTA ( phosphotungstic acid ) . Genomic DNA was extracted using Genomic Tip 20/G kit ( Qiagen ) from an overnight culture grown on G2 plates . Whole-genome sequencing was performed either using PacBio RSII ( N . elongata wild-type ) or Illumina HiSeq 2000 sequencer ( which generated 150-bp paired reads; N . elongata ΔmreBCD , pbpX , rodA ) . The sequencing was done by GATC Biotech using standard protocols as recommend by the manufacturer’s instructions . For N . elongata wild-type , the sequences ( 2 cells ) were de novo assembled using SMRT analysis v2 . 1 . 1 using default settings , obtaining wherein 1 contig with ≈200× average genome coverage . The genome was annotated using the NCBI Prokaryotic Genomes Automatic Annotation Pipeline ( PGAAP ) ( http://www . ncbi . nlm . nih . gov/genomes/static/Pipeline . html ) . This Whole Genome project has been deposited at DDBJ/EMBL/GenBank under the accession CP007726 . For N . elongata ΔmreBCD , pbpX , rodA the reads were aligned to CP007726 and analysed with defaults settings with seqman NGEN v12 ( DNASTAR ) . Neisseriaceae: the core-genome consists in the set of genes present in all genomes and was defined as the intersection of the pairwise lists of orthologs . Orthologs were identified as bidirectional best hits , using end-gap free global alignments , between the proteome of Eikenella corrodens ATCC 23834 as a pivot and each of the other 27 proteomes ( see Touchon , GBE , 14 for details ) . Hits with less than 40% similarity in amino acid sequence or more than 20% difference in protein length were discarded . Each of the 343 families of proteins of the core-genome was used to produce a multiple alignment with muscle v3 . 52 ( default parameters ) [37 , 38] . Poorly aligned regions were removed with BMGE using the BLOSUM62 matrix [39] . Alignments were then concatenated , producing an alignment with 107286 columns and 47462 patterns . The phylogenetic tree was inferred using the maximum-likelihood method implemented in IQ-Tree 1 . 2 . 2 [40] . Initially , we set the program to test all the 144 combinations of substitution models available in the program . Both the AIC and the BIC criteria pointed the LG+I+G4+F model as the one most appropriate for the dataset . Hence , we built the maximum likelihood tree using IQtree with this model and default parameters . We produced 1000 rapid bootstraps to assess the robustness of the topology . The tree was rooted with the outgroup Snodgrassella alvi wkB2 . Moraxellaceae: As only few genomes are available , the schematic phylogeny is based on 16s sequencing and alignment with Mauve of sequences from isolates from our collection and M . bovis . In addition to 16s sequencing , all the strains used in this study were verified for the presence of rodA in the case of Neisseriaceae and pbpX in the case of Moraxellaceae using the family specific primers ( RodAF-RodAR and PbpXMcF-PbpXMcR respectively ) . To detect genes deletion or insertion in Neisseriaceae concomitant with cell shape change , it was necessary to determine genes that do or do not have orthologues among all the available genomes . We used complete genomes of N . meningitidis MC58 , N . gonorrhoeae 8013 , N . lactamica 020–06 , N . elongata ATCC 29315 and incomplete genomes of N . lactamica ATCC 23970 , N . polysaccharea ATCC 43768 , N . cinerea ATCC 14685 , N . subflava NJ9703 , N . mucosa C102 , N . flavescens SK114 , N . flavescens NRL300031/H210 , N . sp GT4A CT1 , N . sicca VK64 , N . macacae ATCC 33926 , N . sicca ATCC 29256 , N . mucosa ATCC 25996 , N . sp . oral taxon 014 , N . bacilliformis ATCC BA1200 , N . weaveri ATCC 51223 , N . weaveri LMG5135 , Simonsiella muelleri ATCC 29453 , Kingella oralis ATCC 51147 , K . kingae ATCC 23330 , K . denitrificans ATCC 33394 , Snodgrassella alvi wkB2 , N . shayeganii 871 , and Eikenella corrodens ATCC 23834 . We excluded Neisseria wadsworthii 9715 . We performed an alignment search with the StandAlone TBLASTN program [41] , using the 2063 predicted proteins from N . meningitidis MC58 or 2105 predicted proteins from N . elongata ATCC29315 as the query sequences to search for matches in the genomic DNA of other organisms . We obtained two matrix of around 40000 scores ( 2063 or 2105 protein sequences blasted against 18 genomes ) providing two types of output: categorical ( hit versus no hit ) and quantitative ( degree of similarity ) . To categorically assign that there was no hit , we employed the default E-value ( or Expectation value ) of e-10 which is provided at NCBI and has been used in a similar study[42] . Thus , if the statistical significance ascribed to a comparison is greater than this E value , we assigned a percentage of similarity of 0% to that comparison . To analyze quantitative results , we used MycoHIT [16] to assign absence or presence of an orthologue as previously described . “Absence” was defined as lower values and “presence” as higher values , than the 95 percentile of tested species . The presence or absence in other bacteria of orthologs of YacF , MreB , MreD and RodZ , were identified using String database [43] based on the Clusters of Orthologous Groups ( COG ) method [44] as previously described [45] . RNA was extracted using QIAGEN RNeasy mini Kits . Enriched mRNA was obtained from 7 μg of total RNA using the rRNA capture hybridization approach from the MicrobExpress kit ( Ambion ) , according to the manufacturer's instructions . For strand-specific high-throughput sequencing , directional cDNA libraries were prepared from enriched fragmented mRNA using the TruSeq Stranded mRNA LT sample preparation kit , set A ( Illumina ) . Fragments of cDNA of ± 150 bp , ligated with Illumina adapters and amplified per PCR , were purified from each library . Quality was confirmed on a Bioanalyzer ( Agilent ) and quantification done using a Qubit dsDNA HS Assay Kit ( Invitrogen ) . Sequencing of 51 bases was performed in single-end mode , using an Illumina HiSeq2000 instrument ( Illumina ) . Reads were cleaned from the adapter sequences and from sequences of low quality using an in-house program . Only sequences with a minimum length of 25 nucleotides were considered for further analysis . The Seqman NGEN ( DNASTAR ) was used to align the reads to the N . elongata CP007726 genome . qRT-PCR was performed using Power SYBR Green PCR master mix and StrepOne plus ( Applied Biosystems ) using the primers listed in S1 Table . A ΔΔCt was calculated by subtracting with ΔCt of gyrA . In addition , a standard t-test was applied to assess statistically significant difference in comparison to the wild-type ΔΔCt . A t-test was used to determine statistical significance of observed differences ( GraphPad Prism v5 . 0; GraphPad Software , CA ) . For RNAseq , count data were analyzed using DNASTAR Qseq program . Data were normalized using RPKM method ( Summarize and normalize all RNA-Seq experiments by assigned Reads Per Kilobase of template per Million mapped reads ) . The generalized linear model was set with condition "WT" as the reference . Raw p-values were adjusted according to the Benjamini and Hochsberg ( BH ) procedure and genes with an adjusted p-value below 0 . 001 and a 3 fold differences were considered differentially expressed .
The nasopharynx hosts an important microbial community that comprises some well-known pathogens such as Neisseria meningitidis and Moraxella catarrhalis . In some circumstances , it also represents the portal of entry of systemic infections such as septicemia and meningitis , or infections of the respiratory system , middle ear , eye , central nervous system and joints of humans , caused by N . meningitidis and M . catarrhalis , respectively . In this article , we demonstrated that both bacteria underwent a similar cell shape evolution that resulted in a transition from a bacillus to a coccus . This was consequently accompanied by a change , similar for both bacteria , in the structure of the PG , the major bacterial cell shape determinant and also a strongly recognized molecule by the immune system . In our efforts in understanding the evolutionary events that led to the cell shape transition in N . meningitidis , we identified two genetic deletion events required for the shape transition , i . e . of yacF ( zapD ) and the cell elongation machinery . Furthermore , we delineated the importance of YacF ( ZapD ) in the coordination of the cell elongation and division . Finally , we suggest that this transition was selected to reduce the cell surface sensible to immune attacks and to redistribute surface appendages , such as pili , to acquire new properties of cell adhesion or movement necessary for the proper colonization of the nasopharynx .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[]
2015
Common Cell Shape Evolution of Two Nasopharyngeal Pathogens
Extreme weather events affect the development and survival of disease pathogens and vectors . Our aim was to investigate the potential effects of heat waves on the population dynamics of Asian tiger mosquito ( Aedes albopictus ) , which is a major vector of dengue and Zika viruses . We modeled the population abundance of blood-fed mosquito adults based on a mechanistic population model of Ae . albopictus with the consideration of diapause . Using simulated heat wave events derived from a 35-year historical dataset , we assessed how the mosquito population responded to different heat wave characteristics , including the onset day , duration , and the average temperature . Two important observations are made: ( 1 ) a heat wave event facilitates the population growth in the early development phase but tends to have an overall inhibitive effect; and ( 2 ) two primary factors affecting the development are the unusual onset time of a heat wave and a relatively high temperature over an extended period . We also performed a sensitivity analysis using different heat wave definitions , justifying the robustness of the findings . The study suggests that particular attention should be paid to future heat wave events with an abnormal onset time or a lasting high temperature in order to develop effective strategies to prevent and control Ae . albopictus-borne diseases . Originated from Southeast Asia , Asian tiger mosquito ( Aedes albopictus ) is the most prevalent vector in all continents except the Antarctica [1 , 2 , 3 , 4 , 5] . The pathogens it transmits pose a severe threat to human health by global epidemics , including dengue and Zika arboviruses . For instance , the dengue incidence has increased six-fold from 1990 to 2013 , with cases more than doubled every decade [6] . This historical evidence suggests a crucial need to develop effective disease control and intervention strategies in order to minimize the risk of epidemic spread and infection [7 , 8] . Meanwhile , the Zika virus , being detrimental to children born with microcephaly and neurological disorders , has spread from Brazil to twenty-six other countries or territories in the Americas within one year [1] . Despite the increasing infections and rapid spread of these arboviruses , no effective antiviral treatment exists . Thus , controlling the development of mosquito vectors becomes a viable option for curbing the disease transmission , especially in regions with limited public health resources [9] . The life cycle and transmission of most infectious agents are inextricably linked to climate [10] . Ae . albopictus is a small-bodied ectotherm; its population abundance and dynamics are firmly regulated by meteorological factors [11] and are sensitive to climate change [5] , [12] . Temperature influences many aspects of Ae . albopictus’ life cycle in a non-linear fashion [13 , 14 , 15] . Lukewarm temperature fosters the development of mosquito at the stages of egg incubation [13] , larval pupation [14] , and pupal eclosion [15]; and it shortens the extrinsic incubation period , eventually expediting the transmission cycle and adult production [15] . However , when temperature exceeds a certain threshold , the effects on the mosquito development become contrastingly different and even detrimental [15] . It was tested that the duration of the gonotrophic cycle or the oviposition extended and the number of laid eggs decreased when the temperature rose above 35 . 0°C [16] . Findings from forecasting models also proved that the mosquito population tended to decrease in certain tropical regions under extremely hot weather [17] . The mechanism leading to the population dynamics of Ae . albopictus has yet to be elucidated [14 , 15 , 16 , 18] . An obstacle to the identification is the uncertainty of climatic conditions , such as the onset , peak , and duration of extreme weather events , which are globally heterogeneous and regionally specific . Furthermore , seeking the theoretic pathway to the mosquito development is becoming more challenging , since the global climate manifests a higher degree of oscillation [19 , 20 , 21] . A coupled global climate model predicts that heat waves , as common extreme weather events , will become more frequent and longer-lasting in the second half of the 21st century [22] . Despite few instances exploring the statistical links between heat waves and the mosquito ecology , the climate-driven mechanism has been poorly understood [23] . Specifically , little is known about how heat wave characterisitics ( e . g . , the onset day of a heat wave , the duration of a heat wave ) affect the development . This existing knowledge gap obfuscates developing effective strategies to prevent and control mosquito-borne epidemics . Many studies have employed controlled experiments to identify the response of Ae . albopictus to extremely high temperature [14 , 15 , 16 , 18] . These studies , however , cannot capture the full range of parameters in the mosquito’s life stages , since the development process is relatively slow , complicated , and unrepeatable . Statistical methods ( e . g . , multivariate regression models ) are able to establish the long-term association between environmental factors and population growth , but they are invariably focused on the aquatic stages ( e . g . , larvae ) and are thus unable to characterize the growth parameters in the aerial stages ( i . e . , adults ) and explain the intricacy of the transition between stages [24] . Most importantly , the few recorded heat wave events at data collection sites pose a considerable challenge to the model validation . To overcome the data issue , computer-based simulations of the weather processes offer an alternative solution [25]; however , very few existing studies are focused on the impact of extreme weather [23] . In addition , most simulation studies rely on statistical models while overlooking the intrinsic process of the development within the mosquito’s life-history stages . The mechanistic population model , which establishes the multi-stage development of the mosquito by a series of differential equations , has become popular in the entomological research of mosquitoes [26 , 27 , 28] . Recently , Jia et al . [29] proposed a mechanistic population model that accounts for the diapause behavior , referring to the inactive state in which the mosquito is unable to hatch and ceases from the development in order to survive extreme environmental conditions ( e . g . , high temperature , extreme desiccation ) . This model , termed the mechanistic population model of Ae . albopictus with diapause ( MPAD ) , has been further explored in this paper to identify the mechanistic associations between heat waves and the population abundance of Ae . albopictus . A 35-year historical heat wave dataset was employed to extract key climatic elements . Finally , a rich set of mathematical simulations were conducted to thoroughly investigate the important mechanisms responsible for the population dynamics of Ae . albopictus caused by heat waves . Our study area is in Guangzhou ( 113 . 23°E , 23 . 17°N ) ( Fig 1 ) —the largest city of Southwest China with over 12 . 7 million population and a population density of 1 , 708 residents per km2 [30] . This mega-city has a distinct subtropical climate with an average annual temperature of 21 . 9°C and an annual rainfall ranging from 1 , 370 to 2 , 353 mm . The humid and warm climate is favorable for Ae . albopictus to survive and grow . In 2014 , an unprecedented outbreak of dengue fever occurred in Guangzhou , causing 37 , 305 cases of infections [30] . This outbreak was attributed to the combined effects of the urban heat island and climate change , including more frequent and intense heat wave events [31] . The theoretical foundation of the study is the climate-driven and process-based MPAD model [29 , 32] . The MPAD model formulates the continuous development of Ae . albopictus in a seven-stage process using a bottom-up approach , as shown in Eqs ( 1 ) through ( 7 ) . These seven stages include eggs ( E , including non-diapause E0 and diapause Edia , Eq ( 1 ) ) , larvae ( L , Eq ( 2 ) ) , pupae ( P , Eq ( 3 ) ) , emerging adults ( Aem , Eq ( 4 ) ) , blood-fed adults ( Ab , Eq ( 5 ) ) , gestating adults ( Ag , Eq ( 6 ) ) , and ovipositing adults ( Ao , Eq ( 7 ) ) . In each equation ( representing one development stage ) , the variation of daily population abundance ( marked in the prime notation ) is determined by ( 1 ) the accumulated population from the last stage , ( 2 ) the mortality at the current stage , and ( 3 ) the population developing into the next stage . The life-history traits are driven by both climate-dependent parameters and climate-independent parameters . The climate-dependent parameters include daily mean temperature , daily accumulated precipitation , and daily photoperiod . These variables , derived from the experimental results [12 , 16 , 33 , 34] , are given in S1 Table and S2 Table . One highlight of the MPAD model is the consideration of diapause . Diapause-related parameters , as indicated by the subscript dia in Eqs ( 1 ) through ( 7 ) , are defined to indicate whether the mosquito eggs are dormant or whether adults suspend the hatching activity under extreme conditions [35 , 36] . The performance of the MPAD model was evaluated in our previous work by comparing against field Ae . abopictus container index ( CI ) in two Chinese cities: Guangzhou and Shanghai [29] . The coefficient of determination ( r2 ) was 0 . 84 in Guangzhou and 0 . 90 in Shanghai , which showed a significant improvement over previous mechanistic population models . The better performance was attributed to the inclusion of diapause-related parameters and the modification of temperature-driven parameters . These adjustments are of critical importance in regions characterized by considerable seasonality ( e . g . , temperate zones ) , where the intra-annual dynamics of mosquito population only emerges with one peak . The heat wave ( HW ) is an extended period of continuously hot weather , typically followed by a high level of humidity [22] . However , since local acclimatization and adaptation influence the impact of extreme heat , there is no globally accepted measure of heat waves [37] . A widely used strategy is to define heat wave locally using both intensity and duration indicators [38 , 39] . Here , we first adopted one heat wave definition given by the China National Standard: a heat wave refers to an extreme weather event where the daily maximum temperature is greater than or equal to 35 . 0°C for at least three consecutive days ( HW Definition I ) [40] . To extract the historical heat wave events , we acquired all available daily temperature and precipitation measurement data in Guangzhou from the China Meteorological Data Sharing Service System , and generated a 35-year climate dataset spanning from 1980 to 2014 [41] . We also derived the photoperiod data from the National Oceanic and Atmospheric Administration [42] . Using the temperature dataset , we identified all heat waves in the study area . Heat wave events operate at both fast and slow rates with various degrees of severity . These processes can be characterized by the onset day ( OHW , the first day in day of year [DOY] when a heat wave occurs ) , the duration ( DHW , the period of consecutive heat wave days ) , and the average daily mean temperature ( TaveHW ) . Their descriptive statistics are shown in Table 1 . The frequency distributions ( fit by trend curves ) of their characteristics are summarized in Fig 2 . The only year without an occurrence is 1996 , after which an increased frequency can be identified ( Fig 2A ) . The occurrences have a strong seasonality , where the most frequent DOYs range from mid-July to mid-August ( Fig 2B ) . More than two-thirds of events ( n = 86 ) have lasted three to four days ( Fig 2C ) . In addition , the peak of TaveHW ranges from 29 . 7–30 . 5°C ( Fig 2D ) and the peak of the maximum of the daily maximum temperature ( TmaxHW ) is around 35 . 5–36 . 8°C ( Fig 2E ) . As the heat wave is a complex extreme weather event , the estimates of the recurrence probabilities of heat waves are used as the proxy for the temporality of their occurrences [43] , which are calculated from the probability distributions ( Pdf ) of OHW , DHW , and TaveHW , as given by Eqs ( 8 ) through ( 10 ) . In order to evaluate the effects of heat waves on population abundance , one assumption is to make the non-heat wave conditions constant across the period of observation without inter-annual variability . Thus , we calculated the averaged annual daily mean temperature ( T ) over 35 years using Eq ( 11 ) , as shown in Fig 3A . We also derived the time series of two other key climatic variables required by the MPAD model: the daily accumulated precipitation ( P ) and the photoperiod ( PP ) ( Eq 11 ) . The temperature series T was then replaced by the temperature of a heat wave event ( THW , the red line in Fig 3B ) during the heat wave DOYs ( Eq 12 ) . This new synthetic temperature series was labeled as T’ ( Fig 3B ) . A total of 127 such temperature series T’ were generated . In addition , we also found that using a single year is insufficient to identify the climate-driven mechanism , as the result is largely dependent on the conditions in Year 1 [27 , 28 , 29] . Thus , we extrapolated the 3-year temperature curve by placing T’ in Year 2 ( Fig 3C ) . We then designed experiments to test the effect of the 3-year temperature curve on the population abundance in Year 2 and Year 3 . i = 1…35 ( year ) j = 1…365 ( day ) X = T , P , PP T′={THWOHW≤DOYs≤OHW+DHWTotherDOYs ( 12 ) The stage of blood-fed adults is of critical importance in the disease ecology . During this period , the mosquito becomes an active transmission vector of disease pathogens [44] . For this reason , we used the daily population abundance of the blood-fed adults to examine the heat wave effects . Here we compared the population dynamics between two groups of blood-fed adults: the control group ( A ) under the non-heat wave scenario ( T ) and the test group ( AHW ) under the heat wave scenario ( T’ ) . After deriving the daily population abundance of A and AHW by the MPAD model , we calculated the relative difference in population ( R ( j ) ) , as shown in Eq ( 13 ) . We then derived the duration of consecutive days ( RD ) when this relative difference exceeds 10% as a proxy for the heat wave effect , as shown in Eq ( 14 ) . R ( j ) =|AHW ( j ) −A ( j ) |A ( j ) , j=1…365 ( 13 ) RD=|tE−tB| ( 14 ) where tB denotes the first day when R ( j ) exceeds 10% and tE denotes the last day when R ( j ) exceeds 10% . Then we tested the effect of each heat wave characteristic . Specifically , the proposed indicator RD is treated as a function of three heat wave variables ( OHW , DHW , and TaveHW ) , as shown in Eq ( 15 ) . To test the contribution of each climatic factor , we designed three groups of sensitivity analysis . In each group , only one factor was treated as a test variable while the two other factors were held constant as controlled variables , as shown in Table 2 . For example , in the first group ( {RD}~OHW ) , the value of OHW was randomly drawn from its probability distribution ( Eq ( 8 ) ) for 1 , 000 times , while the two other factors DHW and TaveHW were selected as the combinations of their first , second , and third quartiles ( the values were drawn from Table 1 ) . This group of simulation generated a total of 9 , 000 runs . The given heat wave definition generated a total of 127 synthetic heat wave temperature series T’ . These series of T’ served as the input into the MPAD model , further generating 127 daily blood-fed adult population abundance curve AHW as the outcome . Comparatively , the population abundance A under the non-heat wave scenario T was also derived . The overlay of simulated heat wave population curves AHW is shown in Fig 4 , which reveals that the historical heat waves only occurred briefly from early summer into early autumn ( DOYs∈[144 , 271] , DHW∈[3 , 18] ) . Their effects on the population abundance were also limited to the time period when heat waves stroke and would not carry over to winter or the next year . In addition , the heat wave occurrences mostly suppressed the mosquito development rather than promoted it , as demonstrated by comparing A and one selected AHW ( Fig 4 inset ) . We further examined how the population dynamics responded to the variation of individual heat wave characteristics , including OHW ( Fig 5A and 5B ) , DHW ( Fig 5C ) , and TaveHW ( Fig 5D ) . For each test variable , we held the other two variables constant and included three specific cases for discussion . In addition , we generated the population abundance under the non-heat wave scenario T ( black curve in Fig 5 ) and derived its peak at DOY 192 . Fig 5A shows the examples of three heat waves with different onset days ( i . e . , OHW = 169 , 179 , and 189 ) . These scenarios , with an onset day earlier than DOY 192 , generated population curves similar to that under the non-heat wave scenario . The early onset of heat wave slightly advances the emergence of the population peak but has no cascading effect on the late stage development ( DOY > 225 ) . However , when heat waves occur after DOY 192 ( i . e . , OHW = 205 , 214 , and 244 ) , the population curves largely shift , where a greater level of variation is observed ( Fig 5B ) . Fig 5C shows three heat waves with different durations ( i . e . , DHW = 4 , 7 , and 18 ) , which demonstrates that the longer the event lasts , the greater extent it suppresses the population growth . Lastly , Fig 5D shows three scenarios under different temperature conditions ( i . e . , TaveHW = 29 . 1 , 29 . 6 , and 30 . 7 ) , where the resulting effects on the population abundance are not significant . One noticeable pattern in all of these scenarios is that the population grows when the heat wave strikes but plummets after a short period . Several factors may contribute to this phenomenon . Environmentally , long-lasting heat waves can dry up shallow bodies of water and subsequently deprive mosquitos of breeding grounds . Physiologically , heat waves can also cause most mosquito species to spawn at once and then dry in unison when weather becomes extreme . Several genes of heat shock protein—known to overcome high temperature stress—tend to show downregulation in larvae when subject to thermal stress at 39°C [45] . Besides the visual assessment , we further quantified the effects via statistical regressions based on the experimental design in Table 2 . Specifically , we used RD—consecutive days when the relative difference in the population abundance exceeds 10%—as a population index representing the heat wave effects . Fig 6 shows the associations between RD and OHW , DHW , TaveHW . In Fig 6A–6C , the relationship between RD and OHW generally follows a quadratic form ( average r2 is around 0 . 90 ) with the trough appearing in late July ( DOY 203–204 ) . We noticed that in Fig 6C , in addition to the quadratic curve , two peaks emerge in early June ( DOY 160 ) and late September ( DOY 265 ) when both DHW and TaveHW are at their third quantiles ( i . e , blue curve in Fig 6C ) . In Fig 6D–6F , a significant linear correlation is observed between RD and DHW only with a large OHW ( i . e . , late heat wave onset , blue lines in Fig 6D–6F ) . In Fig 6G–6I , RD and TaveHW have a piecewise association , which is relatively flat before TaveHW = 30 . 5 and follows a linear pattern afterwards . The full list of the mathematical relationships are included in S3 Table . The definition of a heat wave event is regionally specific [39] . Since there is a lack of consensus about the heat wave definition , we would like to examine if our results are robust when a different definition applies . To test the sensitivity of the MPAD model , we adopted two other heat wave definitions that have been previously employed in Guangzhou [46 , 47] . The second definition is less restrict: a heat wave is defined as ≥ 2 consecutive days with the daily mean temperature at or above the 95th percentile of the year ( HW Definition II ) [46] . The last definition is a stricter criterion: a heat wave is defined as ≥ 7 consecutive heat days with the daily mean temperature at or above the 95th percentile ( HW Definition III ) [47] . Based on the same experimental design , we extracted the historical heat waves according to each new definition . Their descriptive statistics are shown in S4 Table . Then , we simulated the mosquito population AHW under each new heat wave definition and tested the relationship between RD and the three heat wave variables OHW , DHW , and TaveHW following the simulation design in Table 2 . The results are shown in S1 Fig ( for HW Definition II ) and S2 Fig ( for HW Definition III ) . A total of 489 heat waves were extracted by using HW Definition II . It can be observed from the results that the correlation patterns are in consistent with HW Definition I . However , when HW Definition III was employed , only 12 heat waves were extracted . With the few identified events , we were unable to establish a significant correlation pattern . It is thus demonstrated that our simulation results are robust , when a sufficient number of observations can be generated using a new definition . There is much epidemiological evidence demonstrating how climate variations and trends affect human health outcomes [55 , 56 , 57] . Despite the many explorations on the disease pathogens , the complicated interplay between heat waves and Ae . albopictus remains unclear . This paper explores the variability of Ae . albopictus responding to heat waves events using a 35-year historical climate dataset via mathematical modeling and a simulation design . Our simulation results reveal that the unusual onset of a heat wave and a relatively high temperature over an extended period are the two primary factors inhibiting the population development . As the frequency and severity of heat waves are likely to increase in the future [22] , this study provides insights into assessing the potential effects on the mosquito introduced by the global climate . Understanding this climate-driven mechanism is crucial to developing effective strategies to prevent and control dengue fever , Zika , as well as other far-reaching mosquito-borne epidemics .
Understanding the population dynamics of Asian Tiger mosquito ( Ae . albopictus ) –the most prevalent vector of global epidemics including West Nile virus , dengue fever , Zika–could shed lights on improving the understanding of vector transmission as well as developing effective disease control strategies . It is widely acknowledged that the life cycle of Ae . albopictus is firmly regulated by meteorological factors in a non-linear way and is sensitive to climate change . Our study extends the understanding about how extreme heat events manipulate the mosquito population abundance . We adopted an existing mechanistic population model of Ae . albopictus , combined with a rich set of simulated heat wave events derived from a 35-year historical dataset , to quantify the mosquito’s responses to different heat wave characteristics . We found that an abnormal onset time and a lasting high temperature play the most important role in affecting the mosquito population dynamics . We also performed a sensitive analysis by changing the definition of the heat wave , justifying the rigor of the conclusion . This research provides implications for developing public health intervention strategies: to control dengue fever , Zika , as well as other far-reaching mosquito-borne epidemics , priority should be given to heat wave events with an abnormal onset time or a lasting high temperature .
[ "Abstract", "Introduction", "Materials", "and", "methods", "Results", "Discussion" ]
[ "ocean", "waves", "invertebrates", "medicine", "and", "health", "sciences", "atmospheric", "science", "population", "dynamics", "experimental", "design", "animals", "research", "design", "developmental", "biology", "physiological", "processes", "diapause", "population", "biology", "infectious", "disease", "control", "insect", "vectors", "research", "and", "analysis", "methods", "climate", "change", "infectious", "diseases", "oceanography", "marine", "and", "aquatic", "sciences", "disease", "vectors", "insects", "arthropoda", "mosquitoes", "eukaryota", "climatology", "earth", "sciences", "physiology", "biology", "and", "life", "sciences", "species", "interactions", "organisms" ]
2019
Potential effects of heat waves on the population dynamics of the dengue mosquito Aedes albopictus
In developing tissues , cell polarization and proliferation are regulated by morphogens and signaling pathways . Cells throughout the Drosophila wing primordium typically show subcellular localization of the unconventional myosin Dachs on the distal side of cells ( nearest the center of the disc ) . Dachs localization depends on the spatial distribution of bonds between the protocadherins Fat ( Ft ) and Dachsous ( Ds ) , which form heterodimers between adjacent cells; and the Golgi kinase Four-jointed ( Fj ) , which affects the binding affinities of Ft and Ds . The Fj concentration forms a linear gradient while the Ds concentration is roughly uniform throughout most of the wing pouch with a steep transition region that propagates from the center to the edge of the pouch during the third larval instar . Although the Fj gradient is an important cue for polarization , it is unclear how the polarization is affected by cell division and the expanding Ds transition region , both of which can alter the distribution of Ft-Ds heterodimers around the cell periphery . We have developed a computational model to address these questions . In our model , the binding affinity of Ft and Ds depends on phosphorylation by Fj . We assume that the asymmetry of the Ft-Ds bond distribution around the cell periphery defines the polarization , with greater asymmetry promoting cell proliferation . Our model predicts that this asymmetry is greatest in the radially-expanding transition region that leaves polarized cells in its wake . These cells naturally retain their bond distribution asymmetry after division by rapidly replenishing Ft-Ds bonds at new cell-cell interfaces . Thus we predict that the distal localization of Dachs in cells throughout the pouch requires the movement of the Ds transition region and the simple presence , rather than any specific spatial pattern , of Fj . The growth and patterning of developing tissues are inextricably intertwined; epithelial cells typically become polarized along a body axis as they proliferate . This polarization is regarded as being dependent upon spatial variations in the concentration of morphogens . However , there are cases where cells become polarized even where there is little spatial variation in local signaling profiles . Furthermore , this polarization is maintained over time despite ongoing cell proliferation . A good example of this can be found in the Drosophila wing imaginal disc , a larval tissue that develops into the adult wing where cell polarization is manifested in the orientation of trichomes [1 , 2] . In the wing disc , pattern formation is orchestrated by the morphogens Decapentaplegic ( Dpp ) and Wingless ( Wg ) whose concentration gradients are sketched in Fig 1A [3 , 4] . Dpp and Wg signaling regulate both growth and polarization via the protocadherins Ft and Dachsous ( Ds ) that form heterodimers between adjacent cells ( Fig 1B ) [1] . In particular , Dpp and Wg signaling activate the transcription factor Vestigial ( Vg ) , which in turn transcriptionally activates expression of the Golgi kinase Four-jointed ( Fj ) and represses Ds expression [5] . The asymmetric arrangement of Ft-Ds heterodimers around the periphery of each cell defines the subcellular localization of the unconventional myosin Dachs that determines the extent of the cell’s polarization . ( In this paper , we use the term “Dachs localization” to refer to the direction within a cell , typically toward the distal part of the wing pouch where Dachs tends to reside . We use the term “Dachs polarization” primarily to describe the magnitude of this Dachs localization . ) In particular , Dachs normally localizes to the side of the cell with the least amount of bound Ft [6–9] . On the other hand , the Golgi kinase Four-jointed ( Fj ) influences the magnitude of the polarization by affecting the binding affinities of Ft and Ds [10–12] , i . e . , phosphorylation by Fj makes Ft more likely and Ds less likely to bind . Recent modeling studies have asked whether cell polarization patterns in the wing disc are established by the Fj gradient . A recent model by Jolly et al . [14] showed that when Fj has a spatially uniform slope or gradient as seen experimentally [12] , i . e . , a linearly sloping profile , the result is a pattern of Ft-Ds bond asymmetry similar to that seen experimentally . Moreover , if the model’s Fj profile is flattened in this model , Ft-Ds bonds become symmetrically distributed . Hale et al . [12] used a one-dimensional model to show that a linear gradient of Fj expression produces asymmetry in the cellular distribution of Ft-Ds bonds that is qualitatively consistent with the observed pattern , even when unphosphorylated Ft and phosphorylated Ds are capable of binding . However , these models did not incorporate dynamics , i . e . , how cell polarization is affected by a moving Ds expression profile ( see below ) and how polarization is retained after cell division . Experimentally , Ds is expressed at low levels with a fairly flat spatial profile near the center of the wing disc and higher levels at the edge of the wing pouch , with a steep slope in a narrow transition region in between the edge and the center [5 , 15–21] , which expands radially outward as we sketch in Figs 1A and 2C . ( By comparison , Ft expression is fairly uniform throughout the disc [22] . ) Since cells in the transition region have amounts of Ds that differ substantially from those of their neighbors , these differences can produce an asymmetric distribution of Ft-Ds bonds [7 , 9 , 11 , 23–25] . In contrast , cells located far from the transition region have roughly the same concentrations of Ds as their neighbors . However , cells throughout the wing primordium , not only those near the graded region , still exhibit an asymmetric distribution of Ft pathway components [8 , 26] . One could attribute this asymmetry to the linearly graded profile of Fj [12] . However , even in the absence of Fj , there is still a weak asymmetric Dachs polarization within cells [26] . Thus , Dachs polarization cannot simply be a readout of the current local concentrations of Ft , Ds , and Fj . Mani et al . [27] proposed that even very slight cell-cell differences in Ds could lead to an asymmetric distribution of Ft-Ds bonds . By assuming that new bonds form preferentially with the same orientation as existing bonds , they showed that even a very shallow Ds profile can be amplified to produce the bond asymmetry and polarization seen experimentally . However , there is no specific experimental evidence that Ft-Ds binding exhibits cooperativity in this way . In addition this model focuses on the steady-state distribution of bonds , making the assumption that Ft-Ds kinetics occur on a much shorter time scale than other processes . The model thus does not directly address how polarization is retained after cell division . In the wing disc , cells divide asynchronously approximately every 8 hours [28–30] so new cell-cell interfaces are constantly being created . Because the Ft pathway depends on the formation of Ft-Ds bonds at these interfaces , one might expect Ft pathway activity and the subsequent localization of Dachs to be disrupted by the creation of new interfaces with no Ft-Ds bonds . However , in a growing wing pouch , Dachs is typically localized at the distal side of each cell [26] . To the best of our knowledge , no evidence suggests that Dachs localization is disrupted by cell division . This result suggests that cells have some mechanism to recover or retain their polarization after dividing . For comparison , dividing epithelial cells in Drosophila have a reduced level of E-cadherin at the new cell-cell interface , requiring the formation of new adherens junctions ( reviewed in [31] ) . A model of cell polarization should take into account the growth of the wing pouch and the time evolution of the Fj and Ds expression profiles . In particular , the Fj expression domain grows as the pattern of Vg expands , pushing the Ds domain to the edge of the wing primordium [5] . Although the mechanism governing the movement of the wing pouch boundary is little understood , it involves increasing Dpp signaling [32] combined with the tendency of Dpp signaling to affect Fj and Ds expression [7] . Aegerter-Wilmsen et al . [33] have proposed an intriguing model in which differences in mechanical compression induce Vg and inhibit Ds activity , thus regulating cell growth . This model accounts for the experimentally observed patterns of cell proliferation and changes in Fj and Ds expression over time . However , this model does not explicitly include Ft-Ds binding at cell interfaces , instead giving each cell overall Fj and Ds concentrations . It then uses the extant Fj and Ds gradients in the tissue , rather than the arrangement of Ft-Ds bonds around a cell , to determine Dachs localization . This approach has the advantage of showing that Dachs localization can be polarized as long as either Fj or Ds is graded across the tissue . However , since it does not have explicit Ft-Ds bonds at interfaces , it cannot address the question of how cells respond to their history of Ds and Fj expression , or how cells retain their Ft-Ds bond arrangements after they divide and create a new interface . The Ft pathway is associated not only with polarization but also with growth . In particular , greater asymmetry in the distribution of Ft-Ds bonds around a cell is associated with both stronger Dachs polarization and faster cell proliferation [7 , 9 , 34] . Finding the origin of distal Dachs localization throughout the wing pouch , then , would help to explain the rough spatial uniformity of cell proliferation that is observed experimentally [28 , 35 , 36] . However , cell proliferation can in turn affect the distribution of Ft-Ds bonds and Dachs localization , because it involves the creation of a new cell-cell interface between the two daughter cells . In summary , there are two questions that have not been addressed by previous models . First , how is polarization ( in the form of the Ft-Ds bond distribution and Dachs localization ) affected by changes in the expression of Ds over time ? Although the graded expression of Fj is one potential polarization cue , boundaries where Ds expression changes markedly also affect the polarization of nearby cells [8 , 34] and the region of steeply graded Ds expression has been shown to move with respect to individual cells [5] . Second , how do cells retain or recover their polarization after cell division ? To answer these two questions , a more realistic model of asymmetry in the Ft-Ds bond distribution is needed that incorporates Ft-Ds binding dynamics and cell proliferation . Our model implements the changing expression profile of Ds and simulates the dynamics of Ft-Ds bond formation . The model gives rise to a consistently distal polarization direction throughout the pouch that arises from each cell’s history of Fj and Ds expression and naturally preserves this polarization after cell division . This paper is organized as follows . We present a computational model in which cell proliferation depends on the rate of change of the morphogen concentration as well as on the asymmetry of the spatial distribution of Ft-Ds bonds around the periphery of a cell . The greater the asymmetry of the distribution is , the greater the polarization and growth rate of the cell . We show that cells near the edge of the wing pouch have an asymmetric distribution of Ft-Ds bonds around their periphery as a result of the steep transition in the concentration of Ds . This model reproduces the distalward polarization of cells in the wing pouch by taking into account the dynamic expansion of the wing pouch boundary . Ds-expressing cells in the periphery of the disc gradually become part of the Fj-expressing wing pouch , so that the wing pouch becomes an increasing fraction of the overall disc [5] . As the transition region sweeps outwards , it leaves in its wake polarized cells . Our model explains how dividing cells and their progeny retain this polarization after the transition region has swept over them . Cells throughout the wing primordium have asymmetrically distributed Ft-Ds bonds leading to cell polarization [8 , 26] . How does this pattern of asymmetry arise ? Certainly the linearly graded profile of Fj plays a role [7 , 12 , 14 , 27] . However , the cell polarization pattern does not necessarily mirror the local Fj and Ds concentration profiles . For example , inducing a steep boundary of Ds expression can affect the polarization of cells some distance away [8] . The edge of the wing pouch , which is itself a steep boundary of Ds expression , sweeps over a large fraction of the disc during development [5] . This raises the question as to whether cells retain a memory of the history of the Ds expression levels over time . Specifically , we ask: what role do temporal changes in the expression of Ft pathway components play in Dachs polarization ? Once polarization is established , how do cells maintain the polarization of their Ft-Ds bonds over multiple cell cycles ? Cells in the disc divide several times during the larval phase , forming new cell-cell interfaces . Since polarization depends on the arrangement of Ft-Ds bonds around the periphery of a cell , how is the polarization of a cell retained after subsequent cell divisions ? In the next section , we describe the model that we have developed to address these questions . Here we give an overview of our model; a more detailed description , including equations , is in Sections 2–3 of the Supplemental Text . We model the wing disc as a two-dimensional , roughly circular , cluster of cells ( S1 Fig ) , because the wing pouch , our region of interest , consists of a single layer of columnar epithelial cells , bounded by the peripodial membrane . The cluster of cells is surrounded by unbounded free space . Each cell can grow and divide . Bulk forces prevent adjacent cells from overlapping or separating ( S2 Fig ) but have no other effect . The program keeps track of the location , size , and neighbors of each cell . Every cell also has certain amounts of morphogen , Ft , Ds , and Fj . Ft and Ds are distributed homogenously on the cell surface and form as many bonds as possible with Ds and Ft molecules in neighboring cells . Cell polarization and growth rate are functions of the asymmetry of Ft-Ds bonds ( S3 Fig ) . The simulation progresses in discrete time steps with each time step corresponding to one minute of real time . ( We obtain similar results when the length of the time step varies , which we discuss in Section 8 . 7 of the Supplemental Text . ) Experiments show that the amplitude of the Dpp concentration profile increases over time , due to some combination of increasing proliferation of Dpp-expressing cells and decreases in the Dpp degradation rate [32] . In addition , each cell in the disc experiences an increase in Dpp signaling over time even though , as the disc grows , individual cells move away from the Dpp source [32] . Experiments suggest , and previous models assume , that the Dpp profile scales with the size of the disc [4 , 32 , 33 , 37] . This scaling may be the result of another spatially graded signaling factor , e . g . , Magu ( Pentagone ) , that affects the rate of Dpp binding or degradation [38 , 39] . In our model we include a generic morphogen that can be thought of as a rough superposition of Dpp and Wg . ( Although the Dpp and Wg profiles are not necessarily formed via the same mechanism , we simplify this aspect of the model for the sake of symmetry and to focus on the interaction between Ft , Ds , and Fj . Past models of wing disc growth and patterning [37 , 40] have also made a similar simplification . ) When the simulation starts , each cell has an initial amount of morphogen that depends on its distance from the center . The morphogen concentration is radially symmetric and decays exponentially with radial distance r from the center with a decay length that scales with the size of the disc ( see Fig 2 ) . As Fig 2B shows , the amplitude of the morphogen concentration increases exponentially with time as does the morphogen concentration in every cell . Thus the morphogen concentration [M ( r , t ) ] at a given radial position r in μm and time step t in minutes is given by [M ( r , t ) ]= C0 exp ( tt0−ArR ) ( 1 ) where C0 is the initial morphogen concentration in the center of the disc , A is a unitless constant relating the decay length of the morphogen profile to the size of the disc , R is the radius of the disc in microns at that time step , and t0 is the characteristic time over which the morphogen concentration increases by a factor of e , set to 1200 minutes . This value of t0 is based on the experimentally measured rate of increase of the Dpp concentration [32] . Fig 2 shows an example of the initial Fj , Ds , and morphogen expression patterns used in our model . Notice that the shape of the Fj and Ds profiles differs from the morphogen profile that drove them . The initial Ds concentration as well as the Ds expression rate ( i . e . , the number of proteins produced per time step ) in a cell are Hill functions of the local morphogen concentration . This produces a sigmoidal profile with two plateaus connected by a region with a steep slope . The ring , i . e . , the radial position , where the morphogen concentration is equal to the threshold value corresponds to the inflection point of the Ds profile . Over time , the morphogen concentration at all positions increases exponentially as described in ( Eq 1 ) , moving the location of the threshold concentration radially outward with respect to individual cells . The Fj profile , by contrast , is a linear function of radial position . Over time , its intercept increases , but its slope remains constant . Here is an outline of the steps followed in our simulation leading to the establishment and maintenance of Ft-Ds bonds around the periphery of a cell . ( A full description can be found in Section 3 of the Supplemental Text . ) The model begins at time step 0 with a population of 1000 cells , which is roughly the number of cells in the disc at the beginning of the third instar [28 , 35] when the Vg-expressing wing pouch begins to differentiate from the rest of the disc [41] . ( The periphery of the simulated tissue is not recruited into the pouch because the model is not intended to make specific predictions about the prospective hinge region . ) Roughly 150 cells of the starting population of cells lie within the initial Ds front and represent the growing wing pouch . We assume that the Ft concentration at time step 0 is the same for all cells , and that the Ft expression rate is constant in time . The Ft and Ds in a cell are separated into phosphorylated and unphosphorylated pools based on the amount of Fj in the cell . ( The more Fj that there is in a cell , the more Ft and Ds that will be phosphorylated in the cell . ) Since phosphorylation by Fj makes Ft more likely and Ds less likely to form heterodimers [10 , 11] , the binding probability of any given Ft or Ds depends on its phosphorylation state . At each time step , a cell partitions its available Ft and Ds evenly among all its adjacent neighbors and forms Ft-Ds bonds at each interface , with probabilities weighted by the phosphorylation states of the individual Ft and Ds proteins involved . ( Experimental data show that E-cadherin at mature adherens junctions is replaced on time scales on the order of minutes [42] , so we assume that Ft-Ds binding takes place on similarly fast time scales . ) Free , i . e . , unbound , phosphorylated Ft and Ds can become unphosphorylated . However , unphosphorylated Ft and Ds at the membrane do not become phosphorylated , because we would not expect Fj in the Golgi to act upon Ft and Ds at the cell membrane . Ft-Ds bonds can dissolve , and only free Ft and Ds can degrade . In our model the cellular growth rate depends on three factors . First , experiments find that Dpp is associated with cell proliferation [43–45] so , in our model , cells proliferated faster when the local morphogen concentration was higher . Second , ft- ds- double mutant discs are known to overgrow to a greater degree than either ft- or ds- single mutants [46] . Since a disc lacking either Ft or Ds would have no Ft-Ds bonds , the single and double mutants should differ from one another only in their number of free protocadherins , suggesting a link between the level of free Ft or Ds and slower proliferation . ( However , ft- and ds- single mutants still overgrow compared to wild-type discs [46] despite having more free Ds and Ft , respectively . This is because mutants lack Ft-Ds bonds , causing the model to regard them as having a very high degree of bond asymmetry which produces overgrowth compared to wild-type . In other words , there are other factors that can outweigh the penalty of free Ft and Ds to the growth rate . ) In our model , each cell’s growth rate was penalized based on the cell’s number of free Ft and Ds , whether phosphorylated or not . Third , experiments find that clones of cells that overexpress Fj or Ds exhibit increased cell proliferation near their boundaries , particularly when the surrounding wild-type cells express comparatively little Fj or Ds [7 , 34] . Experiments also show that increased spatial uniformity in Fj or Ds expression leads to slower cell proliferation [7] . These results suggest that greater differences in Fj and Ds expression between adjacent cells are associated with faster cell proliferation , likely via the asymmetry of the Ft-Ds bond distribution around each cell . So in our model , cell growth is enhanced when there is more asymmetry in the spatial distribution of Ft-Ds bonds around the boundary of a cell . We measure this asymmetry on a scale from 0 to 1 , using the “minimum fraction” method described in Section 5 . 2 in the Supplemental Text . We describe the instantaneous growth rate of a given cell with index i in terms of the ratio of its radius rn , i at one time step to its radius rn-1 , i at the previous time step using the following formula: rn , irn−1 , i=1 + G0 ( 1+CFtxFt , i ) ( 1+CDs , ixDs , i ) ( 1+CMxM , i ) 1+Ui ( 2 ) In ( Eq 2 ) , G0 is a constant scaling factor and xM is a measure of the morphogen concentration . xFt , i and xDs , i reflect the asymmetry of the distribution of bound Ft and Ds protocadherins around a given cell with index i , respectively , such that the asymmetry of bound Ft and Ds varies between 0 ( equal number of bonds on all neighbors ) and 1 ( at least one neighbor with no bonds ) . We then apply an adaptation mechanism in the form of integral feedback [47] , as outlined in Section 5 . 1 of the Supplemental Text , to obtain the values of xM , i , xFt , i , and xDs , i . ( Note that this feedback affects only the growth rate , and not the bond asymmetries or morphogen concentrations themselves . Thus it should affect the distribution and retention of Ft-Ds bonds only inasmuch as it affects the rate of cell division . ) The constant coefficients CM , CFt , and CDs represent the relative strengths of the effects on growth of the Ft-Ds bond distribution asymmetry and morphogen signaling . ( Since CM xM , i , CFt xFt , i , and CDs xDs , i are all less than unity , the numerator could be represented by ( 1+CFt xFt , i + CDs xDs , i + CM xM , i ) with only a modest effect on the overall growth rate . ) These effects are then divided by ( 1+Ui ) where Ui is proportional to the number of free ( i . e . , unbound ) Ft and Ds in cell i . This equation has the desired traits as can be seen by taking various limits . For example , if there were no morphogen , no asymmetry in the distribution of bound Ft and Ds and no unbound Ft or Ds in a cell , i . e . , if Ui = xM , i = xDs , i = xFt , i = 0 , then the radius of the cell would grow at a ( small ) constant rate G0 , i . e . , ( rn+1 , i/rn , i ) = 1 + G0 . Notice that the “1” means that the cell cannot shrink . If there is morphogen present ( xM , i > 0 ) and/or asymmetry in the distribution of Ft-Ds bonds ( xFt , i > 0; and/or xDs , i > 0 ) , then the cell grows faster . The more free Ft and Ds there is , i . e , the larger U is , the slower the rate of growth . Dachs is localized on the cell edge which has the least amount of Ft bound to the Ds of an adjacent cell . We represent Dachs polarization by a vector that points toward this edge . This vector has a magnitude equal to the cell’s bond asymmetry , a quantity between zero and unity . ( This metric for bond asymmetry , its use in calculating xFt , and the “grace period” for cell adjacency mentioned above , are described in detail in Section 5 . 2 and Eqns . S26 and S27 of the Supplemental Text . ) A cell’s probability of dividing in a given time step rises sharply as its radius approaches a given threshold . ( Note that we do not account for the rapid increase in apical surface area often observed in mitotic cells in the wing disc [31]; the “cell size” we use corresponds more closely to a cell’s overall volume which tends to increase steadily over time . ) More details , including mathematical expressions , values of these quantities , and division probabilities , are given in Section 2 . 2 of the Supplemental Text . Zecca and Struhl [5] have noted that the expression patterns of Fj and Ds are not static . Rather , over time , the boundary of the wing pouch expands radially outward as Ds-expressing cells are recruited into the Vg-expressing population of cells . In our model , as the amplitude and the length scale of the morphogen profile grew with the disc ( Eq 1 ) , the steeply graded Ds expression front ( transition region ) moved outward , causing cells behind the front to express Ds at a lower rate ( see Fig 2 ) . Over the course of a run , the transition region swept over a large fraction of the disc . In our model , Fj is graded and decreases linearly from the center to the edge of the disc , producing spatial variation in the Ft-Ds binding affinity from cell to cell . In addition , the Ds expression front expands outward through the disc . As a result , cells in the disc had an asymmetric distribution of Ft-Ds bonds and Dachs localized to the distal side of each cell , as observed experimentally [6 , 7 , 9] . ( In our model , cells that had only recently become adjacent , e . g . , via cell division , did not count as neighbors as far as Dachs localization and the minimum fraction were concerned . Otherwise , newly adjacent cells would have had very little bound Ft , and Dachs would simply have tended to localize toward a cell’s newest neighbor . ) The general algorithm to determine cell adjacency is described in Section 7 of the Supplemental Text , while the special case of cells that recently became adjacent is discussed in Section 5 . 2 of the Supplemental Text . ) Fig 3 shows the direction ( panels A-D ) and magnitude ( panels E-H ) of Dachs polarization at four different times during a simulation run . Near the start of a run ( Fig 3 , panels A-B and E-F ) , only cells close to the front had strongly polarized Dachs , while cells outside the front had Dachs vectors that are randomly oriented . As the simulation continued and the front passed over more cells , cells that had been recruited into the wing pouch due to movement of the Ds front also began to display Dachs polarization ( Fig 3 , panels C and G ) . However , changing Ds expression rates took some time to affect Ds concentrations and , in turn , Ft-Ds bond populations , so the region of strongest Dachs polarization lagged somewhat behind the location of the Ds expression front . After 35 hours , nearly all of the cells had Dachs localized to their distal , or center-facing , side ( Fig 3 , panels D and H ) in agreement with experiments [7 , 8 , 26 , 48] . Note that the region nearest the center of the disc , whose cells have never had the front pass over them , remained weakly polarized for the duration of the simulation . To differentiate the effects of the Fj gradient and the moving Ds front on Dachs localization , we also ran simulations in which either the Fj or the Ds expression pattern was spatially uniform . In the absence of Fj but with a moving Ds front , Dachs was still distally localized but with a drastically reduced average magnitude ( Fig 4 , panels B and E and S17 Fig in the Supporting Information ) , in agreement with experimental results [26] . Similarly , when Fj was uniformly expressed at a high level ( Fig 4 , panels C and F and S14 Fig in the Supporting Information ) , Dachs was distally localized in the wing pouch but at a lower magnitude than in wild-type . Thus , we predict that Dachs polarization , like Ft-Ds bond asymmetry , originates primarily from the Ds front and is amplified by the Fj gradient . Next , we considered a disc whose Ds expression front was held stationary in terms of its relative position between the disc’s center and edge , rather than being determined by a critical value of the morphogen concentration . In this case , individual cells primarily expressed either high or low levels of Ds for the entirety of the run without the front passing over them . Even though the front was stationary , the morphogen and Fj profiles were the same as with the moving front; i . e . , the amplitude of the morphogen profile still increased with time and Fj had a linearly sloping profile that decreased with increasing radial distance from the center . ( The results were essentially unchanged if the morphogen amplitude was constant in time . ) When the front did not sweep over a large fraction of the disc , only cells close to the front where the local gradient of Ds remained relatively steep had asymmetrically distributed Ft-Ds bonds and asymmetrically localized Dachs . Furthermore , cells near the disc’s center and edge consistently experienced spatially uniform Ds concentrations , leading to more symmetric bond distribution and less polarized Dachs . Fig 5 shows the direction ( panels A-D ) and magnitude ( panels E-H ) of Dachs localization in a disc with a stationary front . ( Note that individual cells could still move away from the front as they and their neighbors proliferated , but the spread of polarization was less pronounced when the front’s location was fixed . ) In contrast to the polarization pattern observed with the moving front in Fig 3 , the case of the stationary front produces a polarization pattern that is strongly correlated to the spatial expression pattern of Ds . To differentiate the effects of Ds and Fj on Dachs localization , we also examined polarization patterns with a stationary front with uniformly expressed Fj and in the absence of Fj ( Fig 4 , panels H , I , K , and L and S20 and S21 Figs in the Supporting Information ) . With uniform Fj and a stationary Ds front , cells exhibited a broadly similar but weaker polarization pattern than that shown in Fig 5 . However , when Fj was absent , Dachs polarization was very weak throughout the disc . This suggests that the Ds front is sufficient to induce distal Dachs localization in nearby cells , but the degree of Dachs polarization is greater when Fj is present and greater still when Fj is spatially graded . Both a stationary and a moving front produced a pattern of Dachs localization near the front that is broadly similar to that seen experimentally in wild-type discs [7 , 26] ( Figs 3 and 5 ) . The average magnitude of Dachs polarization showed little dependence on the movement of the front ( S8 Fig in the Supporting Information ) . In both cases , Dachs in cells near the front tended to localize toward the distal side of each cell . However , in the case of a moving front , the average direction of Dachs localization in cells near the center of the disc was consistently distal , while Dachs was randomly localized in the center of a disc with a stationary front ( Fig 5A–5D ) . Our model then predicts that the Ds front is a stronger polarizing cue for Dachs than the Fj gradient , and that cells can retain their Dachs polarization well after this front has moved away . To differentiate further the effects of the graded Fj profile and the moving Ds front , we compared Dachs polarization at minute 2100 under a variety of conditions of Fj and Ds expression ( Fig 4 ) . In particular , we examined moving and stationary Ds fronts , and Fj that was either graded , absent , or uniformly overexpressed . With a normal ( wild-type ) , graded Fj profile ( Fig 4 , left column ) , Dachs localization was the same as in Figs 3 and 4 . When Fj was absent ( Fig 4 , center column ) , the overall magnitude of Dachs localization was much lower , but still distally oriented , in agreement with experimental results which found very weak Dachs asymmetry in a Fj mutant disc [26] . With uniformly overexpressed Fj ( Fig 4 , right column ) , the overall pattern of Dachs localization was similar to that with graded Fj , but with a slightly smaller average magnitude . In all cases , Dachs was distally localized in cells near the front . However , when the front moved , Dachs was also distally localized in the cells near the center of the disc that the front had previously passed over . These results suggest that a moving Ds front is sufficient to induce distal Dachs localization in the entire wing pouch , but the presence of Fj amplifies this effect , and a gradient of Fj amplifies it more . S14 , S17 , S18 , S19 , and S20 Figs in the Supporting Information examine the cases of uniform and absent Fj with moving and stationary Ds fronts in greater detail . The Fj gradient alone is also sufficient to give rise to weak polarization . S15 Fig shows that when Ds expression is uniform and Fj is graded , some weak distal Dachs localization is still observed . However , when both Fj and Ds are expressed uniformly as in S16 Fig , Dachs localization is randomly oriented . Thus , the gradient ( slope ) of the Fj concentration is less important for polarization . We investigate the Fj profile’s effect on Dachs localization in finer detail in S18 Fig in the Supporting Information . Cell division and proliferation are important components of our model so it is worthwhile to check the model by comparing the growth of the wing disc to experimental outcomes under a variety of conditions . Since a number of experimental results on growth are given in terms of the overall wing disc size , Fig 6A summarizes our results in terms of the number of cells in the disc under various conditions . ( Additional results on disc growth and polarization patterns are given in S7 Fig and section 8 . 1 of the Supplemental Text . ) The growth of a wild-type disc was the control . All other parameters were fixed . Recall that in our model we assumed that the more asymmetric the bond distribution was , the faster the growth rate was . Cells in discs with uniformly expressed Fj or Ds tended to have more symmetrically distributed bonds and the disc undergrew , consistent with experiment [7 , 18] . A disc that expressed both Fj and Ds uniformly had even more symmetrically distributed bonds and hence , undergrew more than a disc that only uniformly expressed one of the two , consistent with experiment [7] . Additional results that were qualitatively consistent with experiment , including results on the roughly uniform spatial pattern of cell proliferation , are presented in detail in Section 8 of the Supplemental Text . Several features of this model are dependent upon parameter values . For example , the degree to which cells retain their polarization after the Ds expression front has passed over them depends on the rate at which Ft-Ds bonds turn over ( i . e . , dissolve and are possibly replaced with different bonds ) which is on the order of 1% per time step . While this rate of bond turnover is dependent on several parameters , it depends most strongly on the degradation rate of free Ft and Ds and the rate of dissociation of Ft-Ds bonds ( note that only free , unbound , Ft or Ds undergoes degradation in our model , since endocytosis is usually necessary for the degradation of receptors ) . These rates affect the turnover rate of Ft-Ds bonds at a given interface as well as the ability of cells to respond to changing Ds expression . As a result , they must have a short time scale compared to that of the movement of the Ds front in order for Dachs localization to respond to the movement of the Ds front . On the other hand , they must have a long time scale compared to that of Ft and Ds expression in order to have a stable arrangement of Ft-Ds bonds and retain a memory of the passage of the Ds front . The overall pattern of Dachs localization is fairly insensitive to changes in these degradation and dissociation rates ( see S9–S12 Figs in the Supporting Information ) . However , the overall growth rate is more sensitive to these changes , as shown in Fig 6B . A detailed discussion of the dependence of bond asymmetry and cell proliferation on these degradation and dissociation rates is available in section 8 . 3 of the Supporting Information . In Fig 6A the difference in disc undergrowth ( compared to WT ) between the “uniform Ds” and the “uniform Fj and Ds” cases is not as dramatic as that seen experimentally . However , relative wing disc sizes will depend in part on the length of the simulation , since growth is roughly exponential . Thus , longer growth times will lead to greater differences between wing disc sizes . In particular , we note that our simulation ends at the end of the third larval instar , while the experiment measures the areas of adult wings [7] . We could have adjusted our parameters to give better agreement with experiment for the growth under conditions of “uniform Ds” and “uniform Fj and Ds” , but this would have resulted in poorer agreement with other experimental results . In particular , many different parameters affect the relationship between Fj and Ds expression and the growth rate that we examine in Fig 6A . These include the rates of Fj and Ds overexpression when Fj or Ds concentrations are uniform , the concentration and spatial slope of Fj , the penalty to the growth rate for excess free Ft or Ds , the relationship between the Fj concentration and the Ft and Ds phosphorylation rates , the relative rates of bond formation for Ft and Ds with different phosphorylation states , and the contribution of the Ft-Ds bond distribution to the growth rate . Parameters are chosen to fit a number of experimental results regarding Ft-Ds binding and the overall growth rate of the disc , and changes would substantially affect our other results . Some aspects of the model , including the dependence of the proliferation rate on the arrangement of Ft-Ds bonds and the retention of the overall bond arrangement after cell division , are dependent on the kinetics of Ft-Ds bond formation . In particular , these effects require the number of Ft-Ds bonds at an interface to reach a steady state on a time scale much shorter than a cell cycle . While the overall pattern of Dachs localization is once again fairly robust to changes in the time scale of Ft-Ds bond formation , the proliferation rate will be affected when cells frequently have at least one interface that is depleted of Ft-Ds bonds . We discuss this effect , along with the remaining parameters , in detail in Section 8 . 4 of the Supplemental Text . Our simulations show that Dachs is still asymmetrically localized when Fj is uniform and its starting concentration and expression rate are both increased by approximately a factor of 10 compared to wild-type , though the magnitude of this polarization is very weak in the absence of Fj ( S14 and S17 Figs in the Supporting Information show the distalward direction of polarization in the upper panels ) . Thus , our model predicts that the presence of Fj , more than its gradient , together with a moving Ds transition region , results in widespread distal polarization in the wing pouch . This is in contrast to previous models [12 , 14] that relied on a linear gradient of Fj to achieve polarization . Furthermore , our model shows that a stationary front of Ds expression with a wild-type Fj gradient also produces asymmetric Dachs localization in much of the wing pouch , suggesting that either the moving Ds front or the Fj profile alone can give rise to the polarization seen experimentally . This redundancy appears to confer some degree of robustness to variations in Ds and Fj expression . To compare our model and previous models , it would be useful to do an experiment that assesses the polarization in a disc with uniform Fj levels . In our model the Ft-Ds bonds that each cell forms with its neighbors constitute the cell’s “record” of previous patterning events in the disc . The distribution of these bonds reflects not only current Ft and Ds availability , but also the entire history of Ft and Ds availability in the cell . The Ft-Ds bond asymmetry persists after the transition region or front has moved beyond the cell . For this reason , the growth rate and polarization direction can be maintained throughout the wing pouch to a much greater extent than one would expect from the currently existing expression patterns . Furthermore , because a cell’s Ft-Ds bonds are the result of its entire history , each cell engages in a form of temporal averaging when determining its polarization , reducing its sensitivity to noise in the Ft pathway signaling . To understand the mechanism underlying this memory , note that all the processes described earlier ( Ft/Ds/Fj expression and degradation , bond formation and dissolution , phosphorylation and dephosphorylation ) happen at finite rates , with turnover times on the order of hours , comparable to the time scales of the movement of the front and of the cell cycle . This means that the asymmetric arrangement of bonds around a cell can persist well after the cell is no longer near the front , suggesting that the net dissociation rates of the Ft-Ds bonds should be significantly smaller than the rate at which the Ds front propagates . We examine this time scale dependence further in section 8 . 3 of the Supplemental Text . How do cells retain their polarization over multiple cell cycles ? While Ft-Ds bonds are being formed and dissolved , cells are constantly growing and dividing , forming new interfaces between adjacent cells . In our model , after a cell divides , adjacent cells have new interfaces that initially have no Ft-Ds bonds , so the polarization of recently divided cells is potentially disrupted . Fig 7 illustrates what happens to the arrangement of Ft-Ds bonds around a newly divided cell . Before division ( Fig 7A ) , cells all have similarly polarized Ft-Ds bonds . These cells are assumed to be far from the transition region , and so express Ft , Ds , and Fj at similar rates . When a cell divides , it creates a new interface between the two daughter cells with no bonds on it , temporarily disrupting the polarization of the daughter cells ( Fig 7B ) . Over time , however , new bonds of both polarities form at the interface , and the overall pattern of Ft-Ds bond polarization is restored ( Fig 7C ) . Bonds of both polarities can form at the new interface because the formation of new Ft-Ds bonds in our model is independent of any existing bonds . However , if Ft-Ds binding exhibits cooperativity [27] , bond formation in a newly divided cell will tend to follow the pattern of existing bonds on that cell , potentially allowing cells to recover their Dachs localization even faster . We also examined the results of a long run of the simulation covering 120 hours , more than double the duration of the third instar ( roughly 48 hours and 6 cell cycles ) under otherwise wild-type conditions ( Fig 7D and 7E ) and using a uniform Fj profile ( Fig 7F and 7G ) . In both cases , the disc largely retains its pattern of Dachs localization , suggesting that the graded Fj profile is not necessary for long term Dachs polarization . However , this result depends on the rate of Ft-Ds bond formation being fast compared to the rate of cell proliferation . When Ft-Ds bond formation is substantially slowed , cells at the center of the disc rapidly lose their Dachs polarization ( see S13 Fig in the Supporting Information ) . The most direct test of our model’s predictions would be to measure the magnitude and direction of Dachs localization over time . In particular , we would expect the pattern of Dachs polarization to correlate closely with the pattern of Ds expression , with strong Dachs polarization near the boundary of Ds expression ( i . e . , the edge of the wing pouch ) and relatively weak Dachs polarization outside it . We would then expect the degree of Dachs polarization in any individual cell to change over time; for example , a cell might begin the third instar with very little Dachs polarization , have its polarization increase dramatically as the Ds front approaches it , then have its polarization decrease slightly as the Ds front moves on . Moreover , we would expect the overall degree of Dachs polarization to be lessened in the absence of graded Fj or Ds , and undetectable when neither Fj nor Ds is graded [26] . A different result would suggest that factors other than the Fj and Ds profiles play a role in Dachs polarization . Some of the parameters of the Ft-Ds-Fj interaction described above may be measurable experimentally . For example , to measure the rate of Ft-Ds bond dissociation and investigate the relationship between bond asymmetry and growth rate , one could halt the expression of either Ft or Ds in a clone of an otherwise wild-type disc . The expression of a gene can be induced in a single clone via , for example , treatment with a drug [43] . If this gene coded for an RNA that inhibited Ft or Ds expression ( similar to those used in [8] ) , the resulting clone would have its Ft or Ds expression rates dramatically lowered after the drug was administered . For example , consider such a clone of cells in which Ft expression is suddenly stopped . In our model , this would drastically slow the formation of new Ft-Ds bonds , since no new Ft would be expressed and existing Ft would continue to degrade . We would therefore expect the overall number of bonds to decline over time , but by assumption , not the bond distribution asymmetry or Dachs polarization . According to our model , such an experiment would cause the clone’s growth rate to stay fairly constant ( or even decrease due to the new excess of free Ds ) , until suddenly the growth rate would increase when cells lose all of their bonds . A similar reversal in growth rate is seen experimentally; discs with reduced Ft expression undergrow [27] but discs mutant for Ft overgrow [46 , 51–53] . Experimentalists could also potentially alter the degradation rates of free Ft and Ds to examine their effects on the distribution of Ft-Ds bonds and cell growth . In our simulations we determined the spatial symmetry of the distribution of Ft-Ds bonds by counting the number of bonds a cell forms with each of its neighbors . An actual cell could detect the spatial distribution of bonds via mechanotransduction . Although bulk mechanical forces between cells do not interact with the Ft pathway in our model , Ft-Ds bonds could be involved in mechanical signaling by mechanically linking the cytoskeletons of adjacent cells . There is evidence that mechanical forces play a role in the Ft pathway . For instance , Dachs is a myosin and so may be directly involved in mechanotransduction . In addition , Yorkie , which is downstream of Dachs in the Ft pathway , is homologous to YAP ( Yes-associated protein ) . Yorkie/YAP is a transcriptional coactivator associated with cell-ECM interactions and mechanotransduction [54 , 55] . The Ft pathway may thus be involved in transducing the mechanical stresses that a cell experiences from its neighbors via Ft-Ds bonds . Other models have also suggested that bulk mechanical compression decreases Ds activity and could be responsible for the movement of the Ds expression front [33] . In addition , the counteracting effects of morphogen signaling and mechanical compression on growth could give rise to a negative feedback loop that controls the final size of the disc [33 , 37 , 40 , 56] . The possible interactions between mechanical stresses and the Ft pathway merit further study . In our model , the movement of the front of Ds expression is driven solely by the increasing amplitude of the morphogen profile . We chose this mechanism largely for its simplicity; it makes few assumptions about other signaling pathways . However , recent studies have shown that discs with spatially uniform Dpp signaling still exhibit a pattern of Ds expression roughly resembling that in wild-type discs , with Ds expressed at high levels near the edge of the disc [57] . In our model , a disc with spatially uniform morphogen expression would uniformly express Ds at a low rate , and not have a transition region . This implies that other mechanisms besides morphogen signaling may underlie Ds expression , such as the recruitment of cells from the Ds-expressing periphery into the wing primordium [5] , or differences in mechanical compression such as described above [33] . Fj expression is also affected by the levels of Ft , Dachs and Ds , suggesting the existence of feedback mechanisms within the Ft pathway . In particular , Fj expression is upregulated in Ft mutant clones [6] and downregulated in Dachs mutant clones [6] . Similarly , clones that overexpress Ds reveal nonautonomously elevated levels of Fj near their boundaries , both inside and outside their boundaries [34] , providing further evidence for possible feedback mechanisms . In our model , the magnitude and direction of Dachs localization are outputs that do not directly affect other parts of the model . However , evidence shows that Dachs localization can affect the orientation of cell divisions [48] . ( In our model , the orientation of cell divisions has very little effect on the overall results; see section 2 . 2 of the Supplemental Text . ) Although cells in the wing disc prior to pupariation do not substantially rearrange or sort [49] , Dachs has been shown to affect the rearrangement of cells in the thorax during the pupal stage [9] . The relationship between Dachs localization , cell division patterns , and cell rearrangement may present an interesting challenge for future modeling studies . Our model also shows that complementary patterns of morphogen and Ft pathway signaling can lead to roughly uniform growth in the wing pouch ( i . e . , within the wake of the expression front ) ; see S20 and S21 Figs and sections 8 . 8–8 . 9 of the Supplemental Text . We have developed a model of the Drosophila wing disc showing how cell polarization in the form of an asymmetric distribution of Ft-Ds bonds survives cell division . Experimentally , the cells of the Drosophila wing have asymmetrically distributed Ft-Ds bonds with their neighbors [8 , 26] . Our model indicates that this distribution is due to more than just the graded slope of Fj in agreement with experiment [26] which found that Dachs polarity is lost when Ds is uniform and Fj is absent , but not when just Fj is absent . In our model , it is the movement of the Ds expression boundary with respect to individual cells that is a major contributor to Dachs polarity . Even though Ft and Ds have a fairly flat spatial concentration profile in most of the wing pouch [5 , 15–21] , the Ds concentration profile is not static . The boundary of the wing pouch ( or “transition region” ) , where Ds has a steep gradient , expands outward over time [5] . In our model , cells in the wake of this expanding front are more strongly polarized than cells that have never had the front pass over them . Furthermore , we propose that cells retain their Ft-Ds bond asymmetry after the front has passed and after cell division by quickly replenishing Ft-Ds bonds at the newly formed cell-cell interface . The movement of the front in our model , together with the presence of Fj , allows cells throughout the wing primordium to experience roughly uniform growth and patterning in the presence of dynamic , nonuniform signals . By integrating this mechanism into a simple model for proliferation , we obtain results consistent with a number of experimental observations of disc size and polarization [7 , 8 , 18 , 26] . We propose that the wing disc is an example of a biological system that depends not only on the current state of gene expression , but also on its history and on multiple interacting factors that change over time . There are other examples of this , e . g . , somites , or body segments , of vertebrates are formed by a propagating wave front that passes over cells undergoing oscillatory changes in their gene expression [58] . The fate of each cell depends on its state when the front reaches it with the result being a periodic spatial pattern of somites that persists after the front has passed . Thus , dynamics in tissue size , cell number , signaling levels , and expression patterns are important during development .
In the tissues of a developing organism , specialized proteins can control cell growth and give cells a sense of direction , e . g . , which way is the head or the tail , by having their concentration vary throughout the tissue . In cells of the developing fruit fly wing , a protein called Dachs localizes on the side of the cell closest to the center of the tissue , indicating a directionality . The localization of Dachs is determined by the spatial distribution , around the periphery of a cell , of intercellular bonds of the proteins Fat and Dachsous between adjacent cells . Here we asked how this cell directionality is affected when cells divide and when the concentration of Dachsous changes over time . We use a computational model to show that as the circular step-up region of the Dachsous concentration profile sweeps radially outward , like rings radiating outward from where a pebble was dropped in a pond , it leaves polarized cells in its wake . Our model also shows how cells can naturally recover their directionality after cell division .
[ "Abstract", "Introduction", "Models", "Results", "Discussion", "Conclusions" ]
[ "cell", "binding", "phosphorylation", "cell", "physiology", "cell", "cycle", "and", "cell", "division", "cell", "processes", "cloning", "developmental", "biology", "cell", "growth", "molecular", "development", "molecular", "biology", "techniques", "chemical", "dissociation", "research", "and", "analysis", "methods", "cell", "proliferation", "proteins", "chemistry", "molecular", "biology", "morphogens", "biochemistry", "cell", "biology", "post-translational", "modification", "biology", "and", "life", "sciences", "chemical", "reactions", "physical", "sciences" ]
2017
Expanding signaling-molecule wavefront model of cell polarization in the Drosophila wing primordium
Aedes aegypti , the “yellow fever mosquito” , is the primary vector to humans of the four serotypes of dengue viruses ( DENV1-4 ) and yellow fever virus ( YFV ) and is a known vector of Chikungunya virus . There are two recognized subspecies of Ae . aegypti sensu latu ( s . l . ) : the presumed ancestral form , Ae . aegypti formosus ( Aaf ) , a primarily sylvan mosquito in sub-Saharan Africa , and Ae . aegypti aegypti ( Aaa ) , found globally in tropical and subtropical regions typically in association with humans . The designation of Ae . aegypti s . l . subspecies arose from observations made in East Africa in the late 1950s that the frequency of pale “forms” of Ae . aegypti was higher in populations in and around human dwellings than in those of the nearby bush . But few studies have been made of Ae . aegypti s . l . in West Africa . To address this deficiency we have been studying the population genetics , subspecies composition and vector competence for DENV-2 of Ae . aegypti s . l . in Senegal . A population genetic analysis of gene flow was conducted among 1 , 040 Aedes aegypti s . l . from 19 collections distributed across the five phytogeographic regions of Senegal . Adults lacking pale scales on their first abdominal tergite were classified as Aedes aegypti formosus ( Aaf ) following the original description of the subspecies and the remainder were classified as Aedes aegypti aegypti ( Aaa ) . There was a clear northwest–southeast cline in the abundance of Aaa and Aaf . Collections from the northern Sahelian region contained only Aaa while southern Forest gallery collections contained only Aaf . The two subspecies occurred in sympatry in four collections north of the Gambia in the central Savannah region and Aaa was a minor component of two collections from the Forest gallery area . Mosquitoes from 11 collections were orally challenged with DENV-2 virus . In agreement with the early literature , Aaf had significantly lower vector competence than Aaa . Among pure Aaa collections , the disseminated infection rate ( DIR ) was 73 . 9% with a midgut infection barrier ( MIB ) rate of 6 . 8% , and a midgut escape barrier ( MEB ) rate of 19 . 3% , while among pure Aaf collections , DIR = 34 . 2% , MIB rate = 7 . 4% , and MEB rate = 58 . 4% . Allele and genotype frequencies were analyzed at 11 nuclear single nucleotide polymorphism ( SNP ) loci using allele specific PCR and melting curve analysis . In agreement with a published isozyme gene flow study in Senegal , only a small and statistically insignificant percentage of the variance in allele frequencies was associated with subspecies . These results add to our understanding of the global phylogeny of Aedes aegypti s . l . , suggesting that West African Aaa and Aaf are monophyletic and that Aaa evolved in West Africa from an Aaf ancestor . Aedes aegypti , the “yellow fever mosquito” , is the primary vector to humans of the four serotypes of dengue viruses ( DENV1-4 ) , yellow fever virus ( YFV ) and is a known vector of Chikungunya virus . Dengue is a major public health problem in tropical regions of the world , causing millions of dengue fever and hundreds of thousands of dengue hemorrhagic fever cases annually [1] . In endemic areas the annual number of cases has risen steeply since the 1950s [2] . With multiple serotypes circulating in endemic areas , 100 million infections of dengue fever ( DF ) occur annually , including up to 500 , 000 cases of the more severe form of disease called dengue hemorrhagic fever ( DHF ) with a case fatality rate of up to 5% [3] . Despite the development of a safe , effective YFV vaccine , yellow fever remains an important health risk in sub-Saharan Africa and tropical South America [4] , [5] . The WHO estimates there are 200 , 000 cases and 30 , 000 deaths attributable to YFV infection each year , most of which occur in Africa [6] . There are two recognized subspecies of Ae . aegypti s . l . : the presumed ancestral form , Ae . aegypti formosus ( Aaf ) , a primarily sylvan mosquito in sub-Saharan Africa , and Ae . aegypti aegypti ( Aaa ) , found globally in tropical and subtropical regions typically in association with humans . The designation of Ae . aegypti s . l . subspecies arose from observations made in East Africa in the late 1950s that the frequency of pale “forms” of Ae . aegypti was higher in populations in and around human dwellings than in those of the nearby bush [7] . The implied correlation between color and behavior prompted Mattingly [8] to revisit the biology and taxonomy of Ae . aegypti . He described formosus ( Walker ) as a subspecies of Ae . aegypti that was restricted to sub-Saharan Africa and in West Africa “is the only form known to occur except in coastal districts and in one or two areas of limited island penetration . ” He also suggested that it most frequently breeds in natural containers such as tree holes and feeds on wild animals . Mattingly also stated that , in addition to the dark-scaled parts of the body being generally blacker , “ssp . formosus never has any pale scales on the first abdominal tergite . ” The type form of Ae . aegypti aegypti was alternatively defined as “either distinctly paler and browner ( at least in the female ) than ssp . formosus or with pale scaling on the first abdominal tergite or both . ” He also suggested that Aaa breeds in artificial containers provided by humans , will breed indoors , and has a preference for feeding on human blood [9] . McClelland [10] made a comprehensive study of differences in scale patterns in the abdominal dorsum in 74 Ae . aegypti s . l . collected from 69 different worldwide locations . He concluded that many of Mattingly's subspecies distinctions were not always clear cut in Africa , the only region in the world where both forms are found . In East Africa , pure Aaa or Aaf collections as defined by both color and behavior were found but there were also collections where the subspecies were mixed . In areas of sympatry , he found intermediate forms , with peridomestic habits and a wide range of pale scaling . Populations widely overlapped in the extent of pale scaling . McClelland [10] concluded that , with a large enough sample size , populations could be distinguished on the basis of body color , although peridomestic populations overlapped with the distributions of both Aaa and Aaf populations . Body color alone , however , was unreliable as a means to assign individuals to a particular subspecies and instead , he recommended using the number of pale scales on the first abdominal tergite . Later , mark-release-recapture studies in Kenya [11] demonstrated that immature mosquitoes collected from sylvan , peridomestic , or domestic breeding containers showed an overwhelming preference for their respective habitat as adults . In contrast , in West Africa , mosquitoes morphologically consistent with Aaf were found breeding domestically indoors in Nigeria [12] and Gabon [13] . Therefore , the classic behavioral/habitat descriptions given by Mattingly [8] for these two subspecies were not valid throughout Africa . In eastern Kenya , genetic crosses between Aaf and Aaa showed that preferences for endophily had a strong genetic component [14] . These authors speculated that these sympatric populations remained behaviorally and morphologically distinct because of adaptations that limited genetic exchange . Aaf rarely entered houses , and the authors proposed that those that did would not be likely to oviposit in water jars but would instead seek natural breeding sites in the forest . They speculated that the offspring of those that oviposit in water jars would not be adapted to surviving in the low nutritional content of drinking water . Conversely , they argued that gravid Aaa rarely enter the forest , and were not therefore attracted to tree holes . If they oviposited there , the larvae would not be adapted to avoiding predators found in natural containers . Those larvae that survived to adults would be anthropophilic and unlikely to find a suitable host . It was further hypothesized that the subspecies evolved allopatrically , and that Aaa was reintroduced into East Africa after adaptation to human habitats . Therefore these layers of behavioral differences were fully developed when the subspecies came into contact again , greatly restricting gene flow between them . Laboratory experiments crossing Aaa and Aaf from Kenya showed no evidence of assortative mating [15] . Furthermore , there was no decrease in fecundity in hybrids , nor any morphological defects . The monumental works of Tabachnick , Powell , Munstermann and Wallis [16]–[27] on the global population genetics and vector competence of Ae . aegypti s . l . showed that collections made throughout the species distribution fell into one of two clades ( Figure 1 ) . One clade contained Aaa from East Africa , South America , the Caribbean and Texas/Northeastern Mexico suggesting that these New World populations were derived from East Africa . The other clade contained Asian and Southeastern U . S . Aaa and a basal clade consisting of Aaf from East and West Africa . This tree topology suggested therefore independent New World and Asian introductions . Their parallel work with Beaty [17]–[19] on vector competence suggested that West African Aaf had lower competence for YFV than other global collections of Aaf and Aaa . Despite the importance of these early groundbreaking studies they had , in retrospect , a number of deficiencies . They did not use the number of pale scales on the first thoracic tergite [9] to identify individual mosquitoes . Instead , whole Ae . aegypti s . l . collections were classified as either Aaa or Aaf based upon geographic origin , collection location ( indoor Aaa vs . outdoor Aaf ) and/or their general body coloration of “light” ( Aaa ) or “dark” ( Aaf ) . Furthermore , they assumed that all West African Ae . aegypti were Aaf . Thus notice in Figure 1 that no Aaa were sampled from West Africa . This assumption was based upon Mattingly's [8] claim that in West Africa “formosus is the only form known to occur except in coastal districts and in one or two areas of limited island penetration . ” But this statement was based largely upon collections from Ghana and Burkina Faso . Finally , all early vector competence work was based upon the Asibi strain of YFV . No work was done with DENV because dengue was not a prevalent disease at that time . In order to address these deficiencies , we have been studying the population genetics , subspecies composition and vector competence for DENV-2 of Ae . aegypti s . l . in Senegal . Here we report an analysis of 1 , 040 Aedes aegypti sensu latu ( s . l . ) from 19 collections distributed across the 5 phytogeographic regions of Senegal . From January 8 , 2005–July 20 , 2007 we collected Ae . aegypti s . l . immature stages ( larvae and pupae ) and eggs from the 19 locations in Senegal listed in Table 1 and mapped in Figure 2 . At each urban and rural site , we collected immature stages from at least 30 different breeding sites in each of three different , distant locations at least 100 m apart . Breeding sites consisted of water storage containers and discarded trash such as plastic pails , tires , and cans . In the forest gallery sites of PK10 and Deux Rivieres , immature stages were collected from treeholes and from the discarded husks of Saba senegalensis ( Apocynacea ) which collect water during the rainy season . Eggs collection were also made using ten ovitraps in both of these forest gallery sites . Eggs and immature stages were returned to the laboratory where they were reared to adults and then identified to species [28] . Aedes aegypti s . l were further identified as Aaa or Aaf based upon the number of pale scales on the first abdominal tergite [10] . If the first abdominal tergite lacked pale scales ( McClelland's F range [10] ) it was scored as Aaf and was otherwise scored as Aaa . These adults were provided access to sugar , allowed to mate for three days; males were then aspirated , and stored at −80°C . Every third day , over a two-week period , sugar was removed from the cages 24 h prior to bloodfeeding on mice . Bloodfed females were then given constant access to wet paper towels as an oviposition substrate . After two weeks females were aspirated and stored at −80°C . DNA was obtained from individual adults by salt extraction [29] , suspended in 300 µl of TE buffer ( 10 mM Tris-HCl , 1 mM EDTA pH 8 . 0 ) , and stored at −80°C . Mosquito collections were characterized for vector competence using an immunofluorescence assay ( IFA ) at 14 days post-oral challenge . The DENV-2 strain used was dengue 2 JAM1409 which was isolated in 1983 in Jamaica [30] and belongs to the American Asian genotype [31] . This DENV-2 strain was used rather than one from West Africa because we wished to compare vector competence data in Ae . aegypti from Senegal with all of our other collections including our standard susceptible Dengue 2 Susceptible on 3 chromosomes ( D2S3 ) strain and our resistant Dengue 2 Midgut Escape Barrier ( D2MEB ) strains [32]; all of which have been characterized with JAM1409 . All procedures for growing virus in 14 day cell culture , quantifying the virus , and infecting mosquitoes with membrane feeders covered with sterile hog-gut are published [33] . D2S3 [32] served as a positive control to test for consistency in the quality and quantity of DENV-2 preparation and infection . Undiluted virus titers ranged from 7 . 5–8 . 5 log10 infectious virus/mL . After exposure to the infectious bloodmeal , fully engorged mosquitoes were removed from the feeding carton and held for 14 days at a constant 27°C and 80% relative humidity in an insectary with a 12-hour photoperiod . Heads and abdomens were assayed for infection by IFA using a mouse derived primary monoclonal antibody directed against a flavivirus E gene epitope [34] , [35] . Heads were checked first for DENV-2 infections . If the head was uninfected , the abdomen was checked for infection . Table 2 lists the primers and annealing temperatures for the eight gene regions from which we identified SNPs . Figure 3 shows the locations of SNPs in the amplified regions . These gene regions were amplified in the 57 Ae . aegypti listed in Table 3 . Amplified products were screened for polymorphisms with Single Stranded Conformation Polymorphism ( SSCP ) analysis [29] . All novel SSCP genotypes were then sequenced to screen for SNPs . These sequences were then assembled into a single dataset and translated to assess whether each SNP encoded a synonymous or replacement substitution . Once a SNP locus was selected it was assigned the name of the gene followed by a numeric label indicating its distance in nucleotides from the adenine in the ATG start site . Genotypes at SNP loci were detected using allele specific PCR . Genotypes were determined in a single-tube PCR using two different “allele-specific” primers , each of which contained a 3′ nucleotide corresponding to one of the two alleles and an opposite primer that amplified both alleles . Allele specific primers were manufactured ( Operon Inc . , Huntsville , AL ) with 5′ tails [36] , [37] designed to allow discrimination between SNP alleles based on size or melting temperature . Primer sequences are provided in Table 4 . An intentional transversion mismatch was introduced three bases in from the 3′ end of allele specific primers to improve specificity and each allele specific primer differed by a transition at this site [38] . Melting curve PCR was performed as previously described [39] . Variation in allele frequencies among and within years , subspecies , phytogeographic regions , vegetation zones and habitats was determined by analysis of molecular variance ( AMOVA ) using the computer program Arlequin 3 . 01 [40] . This program also estimated pairwise FST values and Slatkin's linearized FST [FST/ ( 1−FST ) ] [41] among collections and computed the significance of the variance components associated with each level of genetic structure by a nonparametric permutation test with 100 , 000 pseudoreplicates [40] . Pairwise linearized FST values were used to construct a dendrogram among all collections by means of unweighted pair-group method with arithmetic averaging analysis [42] in the NEIGHBOR procedure in PHYLIP3 . 5C [43] . Wright's F-Statistics were calculated using Weir and Cockerham's method [44] . Figure 4 shows the proportion and distribution of mosquitoes classified as Aaa or Aaf in the 19 collection sites . This figure suggests a northwest-southeast cline in the abundance of the two subspecies . Six collections from the Sahelian region in northwest Senegal where the primary vegetation type is Acacia-Savannah contained only Aaa . Six collections from the southern Forest gallery area in southern Senegal where the primary vegetation type is deciduous forest and scrub consisted of only Aaf ( Ngari , PK-10 and Deux Rivieres are placed under a single pie chart in Figure 4 ) . Only Aaf was found in Goudiry in the central Acacia-Savannah region . The two subspecies were sympatric in four sites north of The Gambia in the central Savannah region containing predominantly tall grass savanna and scrub and in Dienoudialla and Saraya in the southern Forest gallery area . Letters in the pie charts in Figure 4 indicate the results of pairwise 2×2 heterogeneity χ2 tests . Four statistically homogeneous groups were detected . Group ‘a’ are the pure Aaa collections while group ‘d’ are the pure Aaf , and the Dienoudialla and Saraya collections , groups ‘b’ and ‘c’ overlap and contain all of the collections in which the two subspecies are sympatric . We incorporated our standard D2S3 strain [32] as a positive control and standard refractory D2MEB [32] strain as a negative control . The Disseminated Infection Rate ( DIR ) was 92 . 3% in D2S3 and 51 . 2% in D2MEB ( sample sizes = 65 and 80 females , respectively ) . Figure 5 shows the proportion and distribution of mosquitoes with a disseminated infection ( DIR ) , a midgut infection barrier ( MIB ) and a midgut escape barrier ( MEB ) . There is a northwest-southeast cline in the susceptibility of Ae . aegypti s . l . populations . Northwestern Aaa collections have a high disseminated infection rate ( DIR ) while southeast Aaf collections have a low DIR associated with a MEB . Letters in the pie charts in Figure 5 indicate the results of pairwise 2×2 heterogeneity χ2 tests . Five statistically homogeneous groups were detected . N'goye ( group ‘a’ ) had a higher DIR than the other 10 collections . Group ‘b’ contains the pure Aaa collections from the Sahel . Group ‘e’ contains the pure Aaf collections from the Forest Gallery . Groups ‘c’ and ‘d’ overlap and contain all of the other collections . There was a positive correlation between the proportion of Aaf among Ae . aegypti s . l . and the proportion of mosquitoes with a midgut escape barrier for the 11 sites ( Spearman's rank correlation; ρs = 0 . 797 , P = 0 . 003 ) . Using the primers in Table 2 , the regions of the Aminopeptidase N ( Apn ) ( 3 . 4 . 11 . 2 ) AAEL012783 , α-amylase 2 ( Amy2 ) ( 3 . 2 . 1 . 1 ) AAEL013421 , α-Glycerophosphate dehydrogenase ( aGPDH ) ( 1 . 1 . 1 . 8 ) AAEL003873 , Glucose-6-phosphate isomerase ( GPI ) ( 5 . 3 . 1 . 9 ) AAEL012994 , Glutamate dehydrogenase ( GluDH ) ( 1 . 4 . 1 . 2 ) AAEL010464 , Fumarase ( Fum ) ( 4 . 2 . 1 . 2 ) AAEL008167 , and Phosphoglucomutase ( Pgm ) ( 5 . 4 . 2 . 2 ) AAEL010037 genes shown in Figure 3 were amplified in the 57 mosquitoes listed in Table 3 . These were then screened for sequence variation using SSCP . All of the primers and the associated analyses for the Early Trypsin gene are published [45] . Figure 3 shows the region that was amplified with the PCR primers underlined . All SNP sites are also underlined and the chosen SNP site is in a box . Our selection of SNPs was biased in many ways . We only used SNP loci that demonstrated two alternate nucleotides because more nucleotides would require additional , more expensive SNP detection . In addition only those SNPs were used in which the most common allele had a frequency ≤0 . 95 among the 57 initial mosquitoes . The remaining SNPs were then screened as candidates for allele specific PCR . Each SNP was analyzed using Primer Premier 5 . 0® ( Premier Biosoft International , Palo Alto , CA ) to identify primers that would amplify a product ≤70 bp because this was the maximum size for discrimination by melting curve PCR . Furthermore , primers were eliminated that had potential to form hairpins or might anneal to one another . αGPDH . 55 is a synonymous G↔A transition in the third position of a Arg codon . Apn . 1938 is a synonymous G↔A transition in the third position of a Gln codon . Amy2 . 447 is a synonymous G↔T transversion in the third position of a Pro codon , while Amy2 . 450 is a synonymous G↔T transversion in the third position of the adjacent Pro codon ( Figure 3 ) . Fum . -294 resides 294 bp upstream from the ATG start in the Fumarate hydratase gene . GPI . 1 , 500 is a synonymous G↔A transition in the third position of a Lys codon . GlutDH . 507 , 567 , and 627 are all synonymous transitions in the third position of Val , Glu , and Iso codons , respectively . Pgm . 954 is a synonymous A↔C transversion the third position of a Leu codon . TrypEarl detects a 13 bp deletion immediately 5′ to the ATG start in the Early Trypsin gene [45] . SNP allele frequencies were compared among and within years , subspecies , phytogeographic regions , vegetation zones and habitats by AMOVA [40] . We first tested whether alleles shifted in frequency among collection years ( Table 5A ) because this would have required partitioning by year any further analyses . Results indicate that 1% of the variation in allele frequencies arose among the three years and this was not significant in the permutation tests . All subsequent analyses , therefore , combined samples from different years . Next , we tested for variation in allele frequencies between the subspecies . In the first AMOVA we analyzed only the six collections in which the two subspecies were sympatric to avoid confounding differences among sites with differences among subspecies . Table 5B indicates that no variation was found between the subspecies . We then compared all Aaa collections with all Aaf collections , and again no variation was found between the subspecies . All subsequent analyses combined the subspecies in the six sympatric collection sites . We next analyzed for variation among northern , central and eastern collections and Table 5C indicates that 1 . 3% of the variation in allele frequencies arose among the three regions but this was not significant in the permutation tests . All collections were next grouped into one of the three vegetation zones in Figure 2 . Table 5D indicates that 0 . 6% of the variation in allele frequencies arose among these zones and that this was not significant . All collections were next grouped into the five phytogeographic regions ( Table 1 ) . Table 5E shows that 3 . 2% of the variation in allele frequencies arose among these regions and this was significant . Finally , all collections were grouped into the three habitat types ( Table 1 ) , and Table 5F indicates that 0 . 9% of the variation in allele frequencies arose among habitats and that this was not significant . Table 6 lists Wright's F-statistics estimated using Weir and Cockerham's methods [44] for the entire study . FST estimates at each locus were significantly ( P≤0 . 0001 ) greater than 0 . The largest amount of variance was detected at the GlutDH . 507 locus , the least occurred at the TrypEarl locus . Many FIS estimates at each locus were significantly ( P≤0 . 0001 ) greater or less than 0 . Of 185 independent tests 56 were significant; far in excess of the nine expected with 5% Type 1 error rate . However , there was no general trend towards excess homozygotes ( FIS>0 ) or excess heterozygotes ( FIS<0 ) . In half of the tests FIS>0 and in the other half FIS<0 . The largest deviance in FIS was seen at GlutDH . 627 ( FIS = −0 . 276 ) with excess heterozygotes in six collections . The smallest deviance in FIS was seen at GlutDH . 507 ( FIS = −0 . 012 ) with a slight excess of heterozygotes in one collection . Unweighted pair-group method with arithmetic mean ( UPGMA ) cluster analysis [46] of pairwise FST/ ( 1−FST ) among the Senegalese collections ( Figure 6 ) indicates four clusters labeled A–D . The collection year was distributed independently among clades ( Fisher's Exact Test ( FET ) , p = 0 . 1397 ) . Subspecies were distributed independently among clades ( FET , p = 1 . 0000 ) . The vegetative zone in which the collection was made was also independent among clades ( FET , p = 0 . 0643 ) . However , collections were clustered by phytogeographic region ( FET , p = 0 . 0010 ) and habitats ( FET , p = 0 . 0068 ) with a disproportionately large number of Urban and Acacia Savanna collections occurring in Clade A . Thus , aside from habitats , the cluster analysis largely confirms the AMOVA results . A Mantel analysis of pairwise FST/ ( 1−FST ) against geographic distances indicated a highly significant correlation between genetic and geographic distances among collections ( Figure 7 ) . While a significant correlation is usually interpreted as evidence of isolation by distance , the regression coefficients were small ( R2 = 0 . 03–0 . 05 ) and general inspection of the data points in the untransformed geographic distance graph suggests only a weak trend . We have demonstrated a northwest–southeast cline in the abundance of Aaa and Aaf in Senegal as determined by the number of pale scales on the first abdominal tergite of individual mosquitoes . The vector competence of mosquitoes in some of these collections was analyzed for susceptibility to DENV-2 susceptibility and was correlated with the distribution of the two subspecies . Population genetic analyses with SNPs revealed large and significant differences in allele frequencies among collections . However , none of this variation was attributable to the year of collection , subspecies , the vegetation zone , or the habitat in which the collections were made . Minor amounts of the variation in allele frequencies were attributable to the geographic distance among collection sites and to the phytogeographic region in which the collections were made . Huber et al . [47] recently published an in-depth examination of gene flow among five cities in Senegal using variation at 10 isozyme markers . They collected five samples from Barkedji in the Sahel; Diourbel , Kaffrine and Koungheul from the Savannah region; and Kedougou from the Forest gallery for a total of 25 samples containing 1 , 086 mosquitoes . Their overall FST value was 0 . 078 . Most ( 74% ) of FST was accounted for by variation among the five collections within each city , while the remainder was accounted for by differences among the five cities . Our overall FST value was slightly larger ( 0 . 109 ) but we did not compare multiple collections within cities; some of our sites had small sample sizes ( which inflate FST estimates [48] ) and our study included 19 sites over a much larger geographic range . Huber et al . [47] also performed an AMOVA among collections in the same vegetation zones as in Figure 2 and , as with our study , more variation arose within ( 5 . 5% ) rather than among ( 2 . 6% ) zones . Huber et al . also performed an AMOVA on subspecies . As with our study , more of the variation arose among collections within a subspecies ( 5 . 7% ) rather than among subspecies ( 3 . 6% ) . However , even though this was a small percentage , it was significant in their permutation tests . We only examined gene flow in the six collections where the subspecies are sympatric and found a non-significant 1 . 4% of frequency variation arose between subspecies . In contrast Huber et al . compared Kedougou ( Aaf ) with all other cities ( Aaa ) . Thus their subspecies variance included , and was therefore inflated by , variation among cities . Huber et al . performed a cluster analysis of linear FST values and , as in Figure 6 , found no clusters corresponding to cities , subspecies or vegetation zones . They also tested for isolation by distance using the same analyses as presented here and found none . While our regression was significant , the linear regression model explained little of the overall variance . There is a major discrepancy between our FIS results and those of Huber et al . The number of significant tests in their study was the number expected with a 5% Type 1 error rate but the number of significant tests in our study was far in excess of this expected rate . This initially suggested to us that our melting curve PCR assay was inaccurate . The assay might not be equally sensitive to both nucleotides at a locus and thus indicate an apparent homozygote for one allele in mosquitoes that are in reality heterozygotes , thus yielding FIS>0 . The assay might also not be specific and thus indicate an apparent heterozygote in mosquitoes that are in reality homozygotes , thus yielding FIS<0 . The problem with this interpretation is that FIS = 0 for the majority of tests at each locus and FIS was not consistently greater or less than zero in any one collection or at any one locus . Nevertheless , we amplified and sequenced PCR products from 2–3 individuals in a collection and at a locus where FIS≠0 and in every case confirmed the genotype reported by melting curve PCR assay . In addition , we reviewed our initial sequence results from some of the 57 mosquitoes listed in Table 3 . These also did not conform to Hardy-Weinberg expectations . Sometimes there was an excess of homozygotes at a locus but for other loci there was an excess of heterozygotes . At this time , we have no explanation for this discrepancy . Both studies agree that very little or no variation exists between the subspecies . This is in stark contrast to similar studies [25] done in East Africa where allozyme frequencies differed markedly between the subspecies . Our results were presaged by McClelland [10] who found intermediate forms in areas of sympatry . These forms exhibited a wide range of pale scaling and occurred in peridomestic habitats . More recently , mosquitoes morphologically consistent with Aaf were found breeding domestically indoors in Nigeria [12] and Gabon [13] . Huber et al . [47] readily identified both forms in Senegal . Therefore , the classic behavioral/habitat descriptions given by Mattingly [8] for these two subspecies are not valid throughout Africa . This tautology between Aaa and Aaf in West Africa therefore suggests a revision to Figure 1 in which West African Aaa and Aaf are monophyletic within the upper clade ( Figure 8 ) . This revision suggests three fundamental conclusions . First , because Aaf is only found in Sub-Saharan Africa , and West African Aaa and Aaf are monophyletic , our results strongly support Mattingly's original suggestion [9] that Aaa arose from a sylvan Aaf population probably in West African forests . Second , Asian and Southeastern US Aaa populations originated from West Africa Aaa rather than Aaf as was previously suggested [27] . Third , West African Aaa subsequently spread into East Africa where they adapted to human habitats , and subsequently gave rise to the Texas/Northeastern Mexico , Caribbean , and South American Aaa . In agreement with the early literature [17]–[19] , we also found that Aaf had significantly lower vector competence than Aaa . Among pure Aaa collections , the disseminated infection rate ( DIR ) was 73 . 9% with a midgut infection barrier ( MIB ) rate of 6 . 8% , and a midgut escape barrier ( MEB ) rate of 19 . 3% while among pure Aaf collections , DIR = 34 . 2% , MIB rate = 7 . 4% , and MEB rate = 58 . 4% . These patterns are consistent with those reported earlier for the two subspecies with YFV and DENV1-4 [17]–[19] , [49] , but are inconsistent for specific locations . DENV-2 virus has been isolated from both western Senegal ( *Bandia Village in Figure 2 ) [50] and extensively from the Kédougou area in eastern Senegal ( near Ngari in Figure 2 ) [51] , [52] . However , a comprehensive serosurvey for DENV exposure has not been made and so we cannot test for a correlation between Aaa abundance and risk for DENV exposure . When Tabachnick et al . [17] examined the susceptibility of “West African Sylvan” populations from Dakar and N'goye to YFV infection they found the DIR to be 11 and 7% respectively . This is odd in two respects . First we found no Aaf in our Dakar and N'goye collections , and secondly , the DIRs with DENV-2 were 50 and 90% respectively . It is possible that vector competence for the long passaged Asibi strain of YFV used by Tabachnick et al . [17] is low ( their most competent population only had a 53% DIR ) . But it is also possible that the subspecies composition of these sites has changed . A group at Institut Pasteur de Dakar published a paper in 2008 [53] also measuring vector competence of Ae . aegypti s . l . populations from six locations in different bioclimatic zones and habitats of Senegal . They examined competence using a sylvatic ( ArD 140875 ) and an epidemic ( ArA 6894 ) DENV-2 isolate . They found that Senegalese Ae . aegypti s . l . populations had a high MIB rate ( 74–100% ) and a highly variable DIR ( 10–100% ) . Both their study and ours examined vector competence in Dakar and N'goye and their findings are completely incongruent with ours . We believe three factors explain the discrepancies . First , they did not use standard susceptible and refractory strains as controls . Thus they have no baseline for comparison . Secondly , their MIB rates were very high resulting in DIR estimates based on ≤2–10 midgut-infected females . Third , their TCID50/ml titers were 106 . 5–7 . 0 plaque forming units ( pfu ) while we used titers of 107 . 5–8 . 5 pfu and Tabachnick et al . [17] used YFV TCID50/ml titers of 107 . 8–8 . 8 pfu . Their low DIR was therefore probably due to low blood meal titers of both DENV-2 isolates . Taken as a whole , our descriptions of subspecies distributions , vector competence and allele frequencies provide a very incomplete picture . In fact , they present a paradox . Why are the distributions of subspecies and vector competence rates distributed along a northwestern-southeastern cline while no such pattern is seen with either isozymes or SNPs ? Why are SNP or isozyme phylogenies not distributed along the same cline ? Our current knowledge of the distribution and vector competence of the two subspecies in West Africa in general and in Senegal in particular is still very incomplete . An additional deficiency in the current study is that no data were collected as to feeding , resting , or oviposition behaviors exhibited by mosquitoes at each sites . In addition , Figures 4 and 5 suggest a northwest-southeast cline in subspecies composition and vector competence but , in fact , the sampling locations were mostly distributed from northwest to southeast . Note that there are no collections from the northern or western marshes , the southern broadleaf evergreen forest , the western tall grass savanna and scrub , nor from the western deciduous forest and scrub south of The Gambia . A broader study of subspecies , vector competence and allele frequencies throughout West Africa may provide clues towards resolving this paradox .
We conducted a population genetic study with 1 , 040 Aedes aegypti sensu latu ( s . l . ) collected from 19 sites distributed across the five phytogeographic regions of Senegal . Adult mosquitoes without pale scales on their first abdominal tergite were classified as Aedes aegypti formosus ( Aaf ) and those having pale scales as Aedes aegypti aegypti ( Aaa ) . We found the two forms distributed along a northwest–southeast cline . Northern Sahelian collections contained only Aaa while the southern Forest gallery collections consisted of only Aaf . The two subspecies were sympatric in four collections north of The Gambia . Aaa was a minor component of two collections from the Forest gallery area . Eleven of these collections were fed a dengue-2 virus–infected bloodmeal . Consistent with the early literature , Aaf had lower vector competence than Aaa . In agreement with a recently published isozyme gene flow study in Senegal , analyzes of allele frequencies indicated only a small , nonsignificant percentage of the variance associated with subspecies . These results improve our understanding of the global phylogeny of Aedes aegypti s . l . , suggesting that West African Aaa and Aaf are monophyletic and that Aaf , the black “sylvan” species , is the ancestor of Aaa , the lighter “domestic” species in West Africa .
[ "Abstract", "Introduction", "Materials", "and", "Methods", "Results", "Discussion" ]
[ "public", "health", "and", "epidemiology/infectious", "diseases", "virology/animal", "models", "of", "infection", "genetics", "and", "genomics/population", "genetics" ]
2009
Gene Flow, Subspecies Composition, and Dengue Virus-2 Susceptibility among Aedes aegypti Collections in Senegal
Explaining the maintenance of communicative behavior in the face of incentives to deceive , conceal information , or exaggerate is an important problem in behavioral biology . When the interests of agents diverge , some form of signal cost is often seen as essential to maintaining honesty . Here , novel computational methods are used to investigate the role of common interest between the sender and receiver of messages in maintaining cost-free informative signaling in a signaling game . Two measures of common interest are defined . These quantify the divergence between sender and receiver in their preference orderings over acts the receiver might perform in each state of the world . Sampling from a large space of signaling games finds that informative signaling is possible at equilibrium with zero common interest in both senses . Games of this kind are rare , however , and the proportion of games that include at least one equilibrium in which informative signals are used increases monotonically with common interest . Common interest as a predictor of informative signaling also interacts with the extent to which agents' preferences vary with the state of the world . Our findings provide a quantitative description of the relation between common interest and informative signaling , employing exact measures of common interest , information use , and contingency of payoff under environmental variation that may be applied to a wide range of models and empirical systems . Many theorists have seen communication as a fundamentally cooperative phenomenon [1]–[4] . In an evolutionary context , however , cooperation cannot be taken for granted , because of problems of subversion and free-riding [5] . In the case of communication , these problems include both refusal to share information , and deception , or lying for one's own advantage . If lying is common , there is no point in listening to what anyone says . If no one is listening , there is no point in talking . In recent work the situation is often sketched as follows: it is easy to see how communication can be viable if there is complete concordance of interests between senders and receivers of signs . Then communication can result in useful coordination and division of labor . There is no mystery about signaling within multicellular organisms , for example , including hormonal and cell-to-cell signaling ( although conflicts of interest may arise even here: [6] ) . In between-organism contexts , the problem of conflict of interest rapidly becomes acute . Special mechanisms are needed to explain how honesty is maintained . The main approach taken in recent years has been costly signaling theory [7]–[9] . Intrinsic costs of signaling prevent dishonesty , by differential expense to liars or differential benefits to the honest . “Cheap talk” models , where signaling has no costs , have seen some development [10]–[15] but have been minor players in recent years . Here we use a novel method to examine ways that informative signaling can be sustained without cost in a range of situations of partial and low common interest . We use a version of the Lewis sender-receiver model [1] , [16] , and employ a method of sampling and analyzing cases drawn from a large space of games with different relationships between sender and receiver payoffs . We then offer generalizations based on analysis of the sample of cases . The analysis uses coarse-grained measures of common interest between sender and receiver , and attends also to a feature that interacts with common interest: the degree to which payoffs for an agent depend on different acts being produced in different states , the contingency of payoff for that agent . We find that using a simple and intuitive measure of common interest based on comparisons of preference orderings over actions , it is possible , though rare , for informative signaling to be maintained at equilibrium with complete divergence of interests . We then construct a more fine-grained measure of common interest , one that is more demanding in its classification of a case as one of zero common interest , and find that informative signaling with zero common interest is possible in this stronger sense as well . Defining an information-using equilibrium as one where the receiver makes use of informative signals to guide behavior , the proportion of games that include at least one information-using equilibrium increases monotonically and rather smoothly with both measures of common interest . ( See below , in the Methods section , for the equilibrium concept we use throughout the paper . ) We then look at the equilibria that support the highest amount of information use for a given level of common interest , and again find a monotonic , though less smooth , relationship between degree of common interest and maximum information use . A third analysis , looking at the relationship between common interest and contingency of payoff for sender and receiver ( defined below ) , yields more complicated results . We conclude that informative signaling can be stable in situations of minimal , even zero , common interest . A combination of mixed strategies of signal use by both senders and receivers , and the selective pooling of states by the sender , makes possible the extreme cases of this phenomenon . Pooling alone can suffice in cases where divergence of interests is not so extreme . As interests converge , stability of informative signaling becomes easier to achieve . Our model complements other recent work on the adaptive importance of mixed strategies and partially informative signaling in evolution . Our modeling framework draws on Lewis [1] and Skyrms [16] . We assume that the world varies exogenously and has three equally probable states ( , , ) . The sender perceives ( without error ) the state of the world and responds by mapping states to signals ( , , ) . The mapping need not be one-to-one as the sender may “pool” some states , treating them equivalently , and the sender may also probabilistically “mix” signals in response to a given state . The receiver perceives ( without error ) the signal sent and maps signals to acts ( , , ) , with pooling and mixes possible again . So a combination of sender and receiver rules can be represented as follows: Sender: Receiver: For example , the sender here sends message 1 whenever they see state 1 , message 2 whenever they see state 2 , and in state 3 they flip a biased coin to send message 1 two thirds of the time and message 3 one third of the time . Both sides receive payoffs as a consequence of the combination of the receiver's action and the state of the world . Sender and receiver payoffs may differ , and can be represented in the form seen in Table 1 . The payoff matrix defines a preference ordering over acts in each state for both sender and receiver . For example , in Table 1 , the preference ordering for the sender in state 1 is [>>] , and for the receiver [>>] . A simple measure of the degree of common interest in a game tracks how similar the orderings for sender and receiver are , for each state: there is complete common interest when sender and receiver have the same preference ordering over acts in every state , and complete conflict of interest when these orderings are reversed in every state . Between these extremes are various kinds of partial common interest: sender and receiver might agree on the best act in each state , but disagree otherwise; they might always agree on what is worst , but not otherwise; they might agree entirely in some states but disagree in others . In cases of complete common interest , some consequences for informative signaling are easily seen . With complete common interest , sender and receiver can both receive their maximum payoffs when the sender maps states to signals one-to-one and the receiver uses these signals to guide appropriate actions . This is a signaling system in the sense of Lewis [1] , and neither party has any incentive to change what they are doing . This state might not be attained by the selection process shaping sender and receiver behaviors , but if it is reached it is stable [17] . With complete conflict of interest , it would appear that signaling cannot be maintained , as any information about the state of the world carried by signals can be used by the receiver to produce acts contrary to the sender's interests , and any sensitivity to signals in the receiver can be exploited by the sender . Exploring the generality of this phenomenon is one aim of this paper . Another is quantifying the relationship between common interest and informative signaling . The varieties of partial common interest described above do not form a complete ordering . However , a coarse-grained measure of the overall degree of common interest can be constructed by modifying the Kendall tau distance . This measure describes the similarity in the ordering of the items in two lists , by counting discordant pairs of items across the lists . The first two items in the two lists form a discordant pair with respect to a preference ordering , for example , if in list 1 the first item is preferred to the second item , whereas in list 2 the second item is preferred to the first . We define a measure C of the common interest in a payoff matrix of the form in Table 1 by counting the discordant pairs in the sender's and receiver's preference orderings over acts in each state of the world , and then averaging across states and rescaling the results to yield a number between 0 and 1 , where corresponds to complete common interest and corresponds to complete conflict of interest . In response to results outlined below we also make use of a refinement of ; which compares not only the agents' preference orderings of the actions in each state , but also tracks how the agents' payoffs for each action relate to the mean value of the payoffs the agent might receive in that state . ( For details see Text S1 . ) As discussed below , is one among several ways of refining the simpler measure , , and we do not claim it is best for all purposes . We also make use of a further description of payoff matrices . For each agent , how much does payoff depend on matching different actions to each state of the world ? A simple illustration of the importance of this feature is seen in a case where the receiver has the same best act for every state ( has a dominant strategy available ) . Then the receiver can achieve maximum payoff no matter what the sender does , by mapping all signals to that cover-all act . Even if no one act is best in all states , there may be a cover-all act that works well for an agent nearly all the time . This is a within-agent matter . So we define and , also making use of the Kendall tau distance . For each agent , we compare the preference orderings over acts that apply in different states of the world , comparing each pair of states in turn . K is high for an agent with respect to a pair of states if good acts in one state are bad acts in the other state . K for an agent averages all comparisons of states , rescaled to lie between zero and one , where corresponds to the highest degree of contingency of payoff . ( For details see Text S1 . ) Our aim is to generalize about games with different levels of common interest and contingency of payoff for the agents . The method used is to generate samples from the space of games with three states where sender and receiver payoffs are integers between 0 and 99 . Payoffs for each player for each act in a state are chosen randomly , so 18 random choices specify payoffs for a game . We then use the implementation of Lemke's [18] algorithm provided by the software package Gambit [19] to search for equilibria in that game where informative signals are being sent and used . The equilibrium concept used is the Nash equilibrium: a pair of strategies form a Nash equilibrium if neither player can improve their payoff by unilaterally modifying their strategy . We measure the degree to which agents engage in informative signaling with mutual information , a symmetrical measure of the degree of association between two variables , measured in bits [20 , p . 7] . An equilibrium is an information-using equilibrium if there is non-zero mutual information between states of the world and the receiver's acts . We focus on mutual information between states and acts for the following reasons . If there is mutual information between states and acts , the only way for this to arise is for senders to send informative signals and receivers to use these signals to guide variation in their actions to some extent . It is possible for senders to send signals with information about the state of the world that is not used – informative signals that are ignored by the receiver . It is possible also for receivers to guide actions with different signals sent randomly by the sender . The first of these – informative signals that are ignored – is a situation which may be an equilibrium and in which there is informative signaling , but it is not a situation in which the receiver is making use of that information . Our primary focus is situations in which informative signals are both sent and used . This requires that the signals carry information about states and acts carry information about signals . Given that receivers only have access to the state of the world by attending to signals , by the data processing inequality [20 , p . 34] it is not possible for acts to carry more information about states than signals do . ( States , signals , and acts form a Markov chain . ) Any mutual information between states and acts arises from the use by the receiver of information about states in the signals . Computational methods are described in Text S1 but one feature should be noted here: Lemke's algorithm is not guaranteed to find every equilibrium in a game [21] . So the reports of information-using equilibria below may be under-counts . To investigate the role of C we generated a random sample from the space of games with three equiprobable states , three receiver actions , and independently chosen payoffs for sender and receiver associated with each receiver action in each state of the world . ( Each value of C is represented by 1500 games . ) These sender and receiver payoffs are integers between 0 and 99 . For each game we asked whether there is at least one information-using equilibrium in that game – an equilibrium with nonzero mutual information between states and acts – and then asked what proportion of games at each level of C have at least one information-using equilibrium . ( All these games also have equilibria that are not information-using equilibria ) . The results are shown in Figure 1 . Very low degrees of C suffice to enable information-using equilibria , but at low C levels , only a small minority of games do so ( unless the algorithm used has significant bias ) . As C increases , the fraction of games with information-using equilibria increases monotonically . The curve in Figure 1 does not reach 100% for the case of complete common interest . Some games with are games with zero and . ( When , K is the same for sender and receiver . ) The same act is best in every state . Around 1/9 games with will also be . In such a game , the receiver can always take the system to an equilibrium by mapping all signals to the same , optimal , act . Then there is no mutual information between states and acts , regardless of what the sender is doing , as there is no variation in acts . Surprisingly , a small number of games with , where sender and receiver have reversed preference orderings over acts in every state , have information-using equilibria . Table 2 shows a case of this kind – not a case from one of our samples , but a simplified case constructed using the computer-generated cases as a guide . Despite zero , the game in Table 2 has an information-using equilibrium , whose sender and receiver rules are as follows: Sender: Receiver: The mutual information between states and acts at this equilibrium is 0 . 67 bits , where the highest possible value for a game with three equiprobable states ( a Lewisian signaling system ) is 1 . 58 bits . A feature of the case in Table 2 is that although sender and receiver have reversed preferences in every state , in they share a second-best outcome ( ) that is almost as good as their best . This is ignored by our measure , and it is one kind of common interest between the two agents . A way to modify C that takes this factor into account is to compare , across sender and receiver , their preference orderings over both the payoffs that arise from different actions and also the average of the payoffs for that agent in that state . This is done by defining a “dummy act” for the receiver in each state , an act that secures for each agent the mean of the other payoffs possible in that state . This dummy act and its payoff are then included in the determination of each agent's preference ordering over acts in that state; the two agents might agree , or disagree , for example , about whether the payoff of Act 1 is higher than the mean of their payoffs possible in that state . , like , counts discordant pairs of preferences and is scaled to lie between 0 and 1 . ( For further details see Text S1 ) . yields a similar relationship between common interest and the proportion of games with an information-using equilibrium to that seen in Figure 1 . The game in Table 2 has a nonzero , as sender and receiver agree about how one of their second-best outcomes compares to their means for that state , so is a more demanding criterion for complete conflict of interest . Even in this stronger sense , though , it is possible for a game to have an information-using equilibrium with complete conflict of interest . A case of this kind , also one modeled on a less transparent computer-generated case , is shown in Table 3 . This game has the following information-using equilibrium: Sender: Receiver: In all the cases with and/or with information-using equilibria we have found , the underlying pattern is as follows . Two signals are used by the sender and three acts are used by the receiver . In one state the receiver produces an act that is intermediate in value for both sides . In the cases in Tables 2 and 3 , this is . The receiver is prevented from shifting to their optimal act for this state by the fact that the signal sent in that state is ambiguous , and is sometimes also sent in a state for which the act that might “tempt” the receiver in would be very bad . In another state , the receiver mixes their actions between optimal acts for each side . ( This is in both Tables 2 and 3 . ) Again , the receiver is prevented from settling on their optimal act in by the fact that the message the sender sends in that state is ambiguous; state 2 is used by the sender to deter exploitation in the other two states , and in this state all three acts are produced . In both cases in Tables 2 and 3 the information-using equilbria are very fragile , as either the sender ( in 3 ) or the receiver ( in 2 ) can shift without penalty to a strategy in which the mutual information between states and acts goes to zero . Not all cases of information-using equilbria and zero common interest have this feature , however; sometimes information-use is less easily lost . The lowest level of common interest at which an information-using equilibrium is found in which neither sender nor receiver plays a mixed strategy , probabilistically varying their response to a state or a signal , is ( see Text S1 for examples of both phenomena described in this paragraph ) . A valuable feature of C is the weakness of the assumptions required for its measurement; C assumes only ordinal , not cardinal , utilities . assumes cardinal utilities . does not , however , assume that sender and receiver utilities are commensurable . If that further assumption is made , the notion of zero common interest can be analyzed instead by requiring that in every state , sender and receiver payoffs sum to a constant and the choice of action determines only how the division is made ( a “constant-sum game” ) . We do not claim in this paper that information-using equilibria exist in constant-sum games . All constant-sum games have , though the converse does not hold . Some constant-sum games have nonzero , on the other hand , and not all games are constant-sum . Due to its simplicity and weak assumptions , in the remainder of the body of this paper we will use C to measure common interest . and constant-sum games are discussed in Text S1 . Once we know how likely a given level of C is to maintain at least one information-using equilibrium , we can also ask what is the highest level of mutual information between states and acts that can be maintained in a game with a given degree of . Figure 2 shows the maximum amount of mutual information between states and acts generated by an equilibrium pair of strategies from any game examined with a given level of . In constructing the pool of cases for this analysis , we have included not just the sample of games used in Figure 1 but also games found in earlier samples . Figure 2 shows that the highest value for information use grows monotonically with common interest , as expected , but in a step-like way and with quite high values of mutual information between states and acts seen even at the lowest values of . Conversely , our sample includes cases with high values of C and very minimal information use at equilibrium ( , mutual information = 0 . 03 bits; see Text S1 ) . A further analysis of these cases takes into account the contingency of payoff for sender and receiver , as well as common interest . The importance of this factor has been evident already in some extreme cases . When there is complete common interest but K is zero for both sides , there is no problem for signaling to solve – a single act always delivers an optimal payoff . When there is less common interest , the contingency of payoff for sender and receiver can diverge , and in most cases will be different . Figure 3 charts the proportion of games with at least one information-using equilibrium as a function of both common interest and contingency of payoff for an agent; separate graphs are given for and ( left ) , and for and ( right ) . The sample used for this chart is not the same one used for Figure 1 , as a random sample of all games with a certain under-represents some combinations of and . Figure 3 uses a sample in which every combination of and is represented by 1500 games . As expected , higher values of generate more information-using equilibria than lower values of . A difference is seen , however , between the consequences of low values of and . When the sender's contingency of payoff is very low , the intermediate values of present a local maximum in the proportion of games with information-using equilibria . When is low and is intermediate , will be appreciable . The receiver seeks to vary their actions with the state of the world , and though the sender would ideally like the same act to always be performed , equilibria exist in which a compromise is reached . When the receiver's is low , on the other hand , they can achieve optimal payoffs by mapping every signal to the same act . The receiver can “go it alone” ( though information-using equilibria arise in a few cases with high because of ties for the optimal act in a state ) . We have given a treatment of the relation between informative signaling and common interest between sender and receiver , in a framework where signal use is associated with no differential costs and no role is given to iteration of interactions between agents . We find that informative signaling is possible in situations where sender and receiver have reversed preference orderings over receiver actions in every state of the world . This situation , where , is one sense of “complete conflict of interest , ” and a sense that has been employed more informally in a range of earlier discussions ( eg . , [22] , [23] . In the light of our results , is shown to be a somewhat undemanding sense of complete conflict . We discussed one refinement of , which requires stronger assumptions about payoffs , and found that information use at equilibrium is possible with complete conflict even in this stronger sense , where . Another way to refine the idea of complete conflict , a way that uses still stronger assumptions , is by appeal to the notion of a constant-sum game . We do not claim that informative signaling is possible at equilibrium in constant-sum games . Another way to interpret our results is to suggest that the degree of conflict of interest in a game cannot be analyzed by noting the relationships holding between preferences in particular states , and then generalizing across states . Moving beyond consideration of these extreme values , we find that is a good predictor of the existence of information-using equilibria in the space of games studied in this paper . We note several limitations of our model . First , the model assumes a particular relationship between sender and receiver , one where the sender has private knowledge of a state of the world , and payoffs result from the coordination of receiver actions with this state . This “state” of the world might be the condition or quality of the sender . Another kind of model assumes that neither side has privileged information about the state of the world , and the role of signaling is to coordinate acts with acts rather than acts with states ( the “battle of the sexes , ” for example ) . In further work we hope to extend our analysis to cover these cases . Another limitation involves our use of the Nash equilibrium concept . A Nash equilibrium need not be an evolutionarily stable strategy ( because rivals may increase in frequency due to “drift” ) . In addition , equilibria of this kind may not be easily found by an evolutionary process [17] . Further work is needed to explore the dynamic properties of the games discussed in this paper . Thirdly , our analysis gives no role to the biological plausibility of games . We close by comparing our treatment with two other papers , one classic and one recent . First , Crawford and Sobel [10] treated agreement in interests as a matter of degree , and found that when interests diverge , honest signaling is possible , but with lower informational content than there would be with complete agreement: “equilibrium signaling is more informative when agents' preferences are more similar . ” In their model , the state of the world ( sender quality ) and the available actions both vary continuously in one dimension , and the difference between sender and receiver interests corresponds to a constant that is the difference between the actions seen as optimal by sender and by receiver in a given state of the world . In their model the degree of common interest across games can be measured exactly , but the model makes strong assumptions about the pattern of variation in the world . Our model makes weaker assumptions in this area , with the consequence that common interest is only partially ordered , motivating the introduction of coarse-grained measures such as C and . Crawford and Sobel found that as agents' interest converge , a larger number of distinct signals can be sent at equilibrium . We found that informative signaling can exist with zero common interest , through a combination of pooling and mixing , though games of this kind are rare and the proportion of games with an information-using equilibrium increases as interests converge . Crawford and Sobel's model also did not allow for variation in , which we find has significant effects on the viability of information use . Second , Zollman et al . [24] investigated biologically plausible games with two possible states of the world ( again , sender quality ) that are usually analyzed with substantial differential costs enforcing honesty . These authors found that very small differences in cost or benefit across different types of senders can maintain honest signaling when both sender and receiver mix strategies in a particular way . Senders in one state mix two signals , and senders in another state send just one of those signals . Receivers mix their responses to the ambiguous signal and do not mix their responses to the other . A conclusion from their model is that variation in signal-using behavior within a given situation , on both sender and receiver sides , need not be a matter of mere “noise” but can be an essential feature of an equilibrium state . Our results , within a framework of zero signal cost , lead to a conclusion of the same kind: probabilistic mixing of strategies , along with partial “pooling” of inputs , by both sign producers and sign interpreters can be important in maintaining signaling in situations of low common interest .
How can honest communication evolve , given the many incentives to deceive , conceal information , or exaggerate ? In recent work , it has often been supposed that either common interest between the sender and receiver of messages must be present , or special factors ( such as a special cost for dishonest production of signals ) must be in place . When talk is cheap , what is the minimum degree of common interest that will suffice to maintain communication ? We give new quantitative measures of common interest between communicating agents , and then use a computer search of signaling games to work out the relationship between the degree of common interest and the maintenance of signaling that conveys real information . Surprisingly , we find that informative signaling can in some cases be maintained with zero common interest . These cases are rare , and we also find that the degree of common interest is a good predictor of whether informative signaling is a likely outcome of an interaction . The upshot is that two agents with highly incompatible preferences may still find ways to communicate , but the more they see eye-to-eye , the more likely it is that communication will be viable .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[]
2013
Communication and Common Interest
Membrane attack complex/perforin-like ( MACPF ) proteins comprise the largest superfamily of pore-forming proteins , playing crucial roles in immunity and pathogenesis . Soluble monomers assemble into large transmembrane pores via conformational transitions that remain to be structurally and mechanistically characterised . Here we present an 11 Å resolution cryo-electron microscopy ( cryo-EM ) structure of the two-part , fungal toxin Pleurotolysin ( Ply ) , together with crystal structures of both components ( the lipid binding PlyA protein and the pore-forming MACPF component PlyB ) . These data reveal a 13-fold pore 80 Å in diameter and 100 Å in height , with each subunit comprised of a PlyB molecule atop a membrane bound dimer of PlyA . The resolution of the EM map , together with biophysical and computational experiments , allowed confident assignment of subdomains in a MACPF pore assembly . The major conformational changes in PlyB are a ∼70° opening of the bent and distorted central β-sheet of the MACPF domain , accompanied by extrusion and refolding of two α-helical regions into transmembrane β-hairpins ( TMH1 and TMH2 ) . We determined the structures of three different disulphide bond-trapped prepore intermediates . Analysis of these data by molecular modelling and flexible fitting allows us to generate a potential trajectory of β-sheet unbending . The results suggest that MACPF conformational change is triggered through disruption of the interface between a conserved helix-turn-helix motif and the top of TMH2 . Following their release we propose that the transmembrane regions assemble into β-hairpins via top down zippering of backbone hydrogen bonds to form the membrane-inserted β-barrel . The intermediate structures of the MACPF domain during refolding into the β-barrel pore establish a structural paradigm for the transition from soluble monomer to pore , which may be conserved across the whole superfamily . The TMH2 region is critical for the release of both TMH clusters , suggesting why this region is targeted by endogenous inhibitors of MACPF function . Membrane pore-forming proteins have the unique property of being expressed as metastable , water-soluble monomers that convert into a membrane inserted form . These proteins typically assemble into prepore oligomers on the target membrane surface . A dramatic conformational change then permits membrane insertion and formation of transmembrane pores [1–4] . The membrane attack complex/perforin-like family ( MACPF ) proteins form the largest superfamily of pore-forming proteins identified to date . They include perforin and complement component-9 ( C9 ) , mammalian pore-forming proteins that function as weapons of the humoral and cellular immune system , respectively [5] . The superfamily also includes a wide range of molecules implicated in defense or attack [6–8] . For example , invasion by the protozoan parasites Plasmodium spp . and egress by Toxoplasma gondii requires MACPF proteins , plants utilize the MACPF fold to combat bacterial infection [9] , and MACPF-related proteins can be identified in numerous Gram negative and Gram positive bacteria . Finally , a significant group of MACPF proteins play important , but poorly understood , roles in embryonic development and neurobiology [10–12] . Despite the absence of detectable sequence identity , the first crystal structures of MACPF proteins revealed that the pore-forming domain unexpectedly shared homology with the pore-forming bacterial cholesterol dependent cytolysins ( CDCs ) family [13–15] . This structural similarity extended across the key elements involved in pore formation ( originally annotated as three non-contiguous domains 1–3 in CDCs ) . The central , common feature of the MACPF/CDC fold is a four stranded , highly twisted β-sheet decorated with three small clusters of α-helices . Two of these helical bundles contain the regions destined to insert into the membrane ( transmembrane hairpins TMH1 and TMH2 ) . The third α-helical region comprises a short helix-turn-helix ( HTH ) motif formed via a sequence insertion at the bend of the central β-sheet . The HTH motif packs on top of TMH2 . These structural similarities , together with commonality of a pore-forming function , suggested that MACPF proteins share a common ancestor with CDCs and assemble into giant pores via a CDC-like mechanism [13 , 14 , 16–19] . Previous studies have provided important insight into pore formation by CDCs . Electron microscopy ( EM ) , biochemical , and biophysical studies of CDCs showed that monomers assemble into prepore oligomers on the membrane surface without major conformational changes in the subunits [17 , 19–22] . However , conversion to the pore form involves dramatic secondary and tertiary conformational changes in which the highly twisted β-sheet opens up and the assembly collapses ∼40 Å towards the membrane surface , allowing unfurling of TMH1 and TMH2 and their insertion into the membrane as amphipathic β-hairpins [19–22] . The CDCs form initial interactions with the membrane through a C-terminal lipid binding immunoglobulin-like ( Ig ) domain . In the MACPF branch of the superfamily a wide variety of domains are found both N- and C-terminal to the pore-forming MACPF domain . For example , perforin includes a C-terminal lipid and calcium binding C2 domain ( a variation of the Ig fold ) . Similar to the CDC Ig domain , this region mediates initial interaction of perforin with the target membrane . The MACPF domains in the complement membrane attack complex proteins are flanked by arrays of small disulphide constrained domains ( e . g . , thrombospondin , epidermal growth factor , and complement control protein domains ) . Rather than interacting directly with membranes , the role of these regions includes mediation of key protein-protein interactions that recruit the MACPF domain to the target cell surface [23–25] . The molecular structures of key intermediates in the assembly of MACPF and CDC pore complexes remain obscure , but are necessary to understand the transition from a monomeric form into oligomeric membrane prepores and then into pores . Here we have analysed this transition , using a variety of structural and biophysical approaches . Structures of MACPF and CDC oligomeric assemblies by EM have been very limited in resolution , owing to their heterogeneity and flexibility . To gain further insight into the structural conversions in pore formation , we chose pleurotolysin ( Ply ) , a MACPF protein consisting of two components , PlyA and PlyB , from Pleurotus ostreatus [26 , 27] . Previous studies have shown that PlyA binds membranes and is required to recruit the pore-forming MACPF protein PlyB to the membrane surface . PlyA and PlyB together form relatively small and regular pores in liposomes [27 , 28] . As well as determining the structure of the pleurotolysin pore , we used protein-engineering approaches to trap and structurally characterise three distinct prepore intermediates . Together these approaches allowed us to visualise a potential molecular trajectory of a MACPF protein during pore formation . The 1 . 85 Å X-ray crystal structure of PlyA ( Fig . 1A; S1 Table ) revealed a β-sandwich fold , unexpectedly related to the actinoporin-like family of pore-forming toxins [29] . Previous studies suggest that actinoporin-like proteins interact with membranes via one end of the β-sandwich , with the N-terminal sequence responsible for forming the pore [29] . However , PlyA lacks the proposed actinoporin N-terminal transmembrane region consistent with the observation that PlyA binds membranes , but is unable to form pores on its own [27] . The 2 . 2 Å structure of PlyB ( Fig . 1B and 1C; S2 Table ) reveals an N-terminal MACPF domain ( blue/red/yellow ) followed by three small β-rich domains clustered in a globular trefoil-like arrangement ( green ) . The MACPF domain of PlyB contains a central , four-stranded bent and twisted β-sheet characteristic of the MACPF/CDC superfamily ( red ) . The TMH1 cluster of helices ( yellow ) is located on the inside of PlyB , next to the concave face of the central β-sheet . TMH2 ( yellow ) comprises a single large α-helix and an additional β-strand ( termed “strand β5” ) , located on the edge of the central β-sheet . Together , the central β-sheet and the TMH regions constitute the topologically conserved MACPF/CDC pore-forming fold . EM images of liposomes with added PlyAB showed distinctive , ring shaped pore structures ( Fig . 2A and 2B ) . Analysis of negative stain EM images of oligomeric rings of Ply on membranes showed that the majority of the oligomers had 13-fold symmetry ( 75% ) , but 12- ( 15% ) , 11- ( 5% ) , and 14-fold ( 5% ) rings were also present ( Fig . 2C ) . For 3-D reconstruction , we extracted 14 , 700 individual cryo-EM images of pore side views in liposomes ( Fig . 2D ) . The images were analysed by the single particle approach , following the method developed for the CDC pneumolysin [17] . This allowed us to sort the pore views by symmetry , enabling determination of an 11 Å resolution cryo-EM map of a liposome-embedded 13-fold pleurotolysin pore from 8 , 770 views ( Fig . 3A and 3B ) . We used the crystal structures of PlyA and PlyB together with biophysical data ( S1 Fig . ) to interpret the map . A single PlyB moiety was fitted into the upper part of the pore structure ( Fig . 3C ) . The C-terminal trefoil ( green ) and the α-helices at the top of the MACPF domain ( blue ) unambiguously fit the EM density with only minor structural rearrangement . The core of the MACPF domain undergoes a massive opening but does not collapse as in CDCs ( Fig . 3C ) . The structure was modeled by flexible fitting in a multistep procedure [30] . In the pore map , the position of PlyB is clearly recognizable in the upper part of each subunit , while the V-shaped density at the base of each asymmetric unit accommodates two PlyA molecules . The positions of PlyB subdomains were refined without TMH1 and TMH2 , because these transmembrane regions are expected to refold to form the β-barrel of the pore . The best fits were further refined with Flex-EM [30] via simulated annealing rigid-body dynamics . To identify the sequence forming the transmembrane β-hairpins we carried out fluorescence spectroscopy studies using single cysteine mutants in TMH1 , as previously performed on CDCs [20] . This approach revealed an alternating pattern of emission between residues 128–147 consistent with a ∼30 Å membrane-spanning amphipathic β-hairpin structure ( S1 Fig . ) . This information provided a useful restraint for the fitting . In the resulting pore model , each MACPF domain forms a four-stranded β-sheet ( Fig . 3A–3C ) . β-barrels are limited to discrete architectures , each with a characteristic strand tilt relative to the barrel axis [31] . For a barrel composed of n strands , the shear number S describes the register of hydrogen bonding between residues in adjacent β-strands and defines the strand tilt and the dimensions of the formed barrel: the greater the strand tilt , the wider and shorter the barrel [32] . Only three Ply barrel models , with S = 0 ( 0° tilt ) , S = n/2 ( 20° tilt ) , and S = n ( 36° tilt ) have dimensions comparable with the Ply pore cryo-EM map ( S2 Fig . ) . The S = n/2 model gave the best fit in diameter and height ( CC = 0 . 90 versus 0 . 73 for S = 0 barrel and 0 . 74 for S = n ) . This 52-stranded β-barrel was combined with a 13-mer ring of fitted PlyB molecules . Because of steric clashes with the barrel , further refinement using Flex-EM was performed on the HTH motif ( residues 298–313 ) ( Figs . 1B and 3C , 3D ) . After refinement of the central asymmetric unit , the pore was rebuilt with C13 symmetry in Chimera [33] to give the final pore model . In this pore , the central β-sheet has straightened and opened by ∼70° , as measured from the fitting , and TMH1 and TMH2 are fully unwound into β-hairpins to form a β-barrel spanning the membrane bilayer ( Fig . 3A–3C ) . The pore channel is thus formed by a 52-stranded β-barrel that is 80 Å in inner diameter and over 100 Å in height . The PlyB C-terminal trefoil sits in the cavity formed by a V-shaped wedge of density contacting the membrane ( Figs . 3C and 4A ) . This density can be accounted for by two PlyA molecules , revealing a tridecameric PlyB/2xPlyA pore assembly . The symmetrical shape of PlyA precludes discrimination of up/down orientation in the density . However , in the crystal structure of PlyA , we noted two different V-shaped dimers ( termed N-dimer and C-dimer ) in the asymmetric unit ( S3A and S3D Fig . ) . Both forms fitted adequately into EM density , placing either the PlyA N-terminus ( N-dimer ) or C-terminus ( C-dimer ) in proximity to the membrane surface . We tested the orientation of PlyA by adding a hexahistidine tag to the N-terminus ( Fig . 4A and 4B ) , which abrogated membrane binding of PlyA to red blood cells whereas a C-terminal tag had no effect on binding ( Fig . 4B ) . Also , mutation of Trp 6 ( W6E ) , located in the PlyA N-dimer interface , reduced membrane binding and led to 100-fold lower pore-forming activity ( Fig . 4A , denoted as purple spheres; S4A and S4B Fig . ) . These data support an N-dimer-like arrangement of PlyA molecules ( Fig . 4A ) , consistent with the known orientation of actinoporins on the membrane surface [29] . The resulting fit of 26 PlyA and 13 PlyB subunits had a cross-correlation coefficient of 0 . 8 with the map which includes part of the membrane as measured in Chimera [33] . To evaluate the local quality of fit , the segment-based cross-correlation coefficient ( SCCC ) [34] was determined and plotted on the pore subunit structure ( S5 Fig . ) . This analysis shows that the fit is more reliable for PlyB than for PlyA , because the map resolution is better in the region occupied by PlyB . To probe the mechanism of pore assembly , we engineered a series of disulphide bonds to limit movement in either TMH1 or TMH2 . As performed for perfringolysin O and other CDCs [35] , the TMH regions were trapped by introducing cross-links to the central sheet or other adjacent regions in the monomer structure . This trapping allows oligomer assembly but prevents the TMH region from unfolding enough to insert into the membrane . The disulphide trap mutants were engineered on a background PlyB variant that lacks the wild type cysteine ( C487A ) in order to avoid incorrect disulphide bond formation . PlyBC487A retains wild type activity according to haemolysis assay ( S6A Fig . ) . We then determined the cryo-EM structures of three different prepore-locked variants . Oxidised TMH1 variant PlyBF138C , H221C ( TMH1 lock ) ( Fig . 5A ) possessed no detectable lytic activity ( S6B Fig . ) , but reduction of the disulphide restored wild type lytic activity . Furthermore , the oxidised form could assemble into oligomeric prepores on erythrocytes or liposomes , and these prepores could be converted into lytic pores by disulphide reduction ( S6C Fig . ) . These data suggest that the TMH1 lock prepore structure is an intermediate in the formation of the pore . The crystal structure of the TMH1 trapped variant was determined and is otherwise indistinguishable from the wild type ( S7 Fig . ; S3 Table ) . In order to analyse the degree of β-sheet opening we created a library of thousands of molecular models and then performed constrained fitting into the prepore map . This procedure is described in S8 Fig . Briefly , using the monomer and pore structures as end points , we generated two series of angular sweeps of the beta sheet opening or closing , using the TEMPy software [36] . Each of these 2 , 300 generated β-sheet conformations was combined twice with the rest of the PlyB structure , either with the monomer or with the pore conformation , using MODELLER/Flex-EM [30] . The resulting 4 , 600 models were assessed by their fit to the prepore map and by statistical potentials , and the best ranking fits were used to estimate the angle of β-sheet opening in the prepore conformation ( S4 Table ) . Remarkably , the cryo-EM 3-D structure of the TMH1 lock prepore showed that the central sheet of the MACPF domain in the prepore assembly had opened substantially ( 53° ± 9° ) ( Fig . 5A ) . In these prepores , the top half of the barrel has formed , but not the lower , transmembrane part . Indeed , no density could be observed for most of TMH1 and TMH2 and it appears that these regions are largely unstructured . Thus , these data reveal that locking TMH1 has little effect in limiting the MACPF β-sheet opening . To lock TMH2 to the central core β-sheet of the MACPF domain we identified a second variant PlyBY166C , G266C ( TMH2 helix lock ) that could be trapped in the prepore state ( Fig . 5B ) . As with TMH1 lock , addition of reducing agent allows it to continue its trajectory to the pore form ( S6D and S6E Fig . ) . The cryo-EM structure of the TMH2 helix lock prepore revealed a very different picture from the TMH1 lock . The core sheet opening is only 37° ± 11° with some density remaining for TMH1 , suggesting that this region remains partly ordered ( Fig . 5B ) . These data show that the MACPF fold oligomerises without substantial relief of the twist in the core sheet or TMH1 release . As in the TMH1 lock prepore , no density can be seen extending to the membrane surface , consistent with the absence of lytic function . The β-sheet opening was analysed as above ( S8 Fig . ) . Remarkably , the results reveal that restricting the movement of the top of the TMH2 α-helix prevents unbending of the MACPF core β-sheet . This finding is opposite to expectation , since TMH1 forms an extensive buried interface , whereas TMH2 makes fewer contacts with the domain core sheet . However , our results are consistent with observations that TMH2 is important for controlling pore formation in other superfamily members , including interactions with the MAC inhibitor CD59 , and the pH trigger of the CDC listeriolysin O [25 , 37 , 38] . To further probe TMH2 function , a third PlyB disulphide lock was created to join strands β4 of the central β-sheet to β5 of TMH2 , PlyBV277C , K291C ( TMH2 strand lock; Figs . 5C , S6F , and S6G ) . Cryo-EM and modelling analysis showed that the central β-sheet was open to the same extent as in the TMH1 lock ( Fig . 4C , 49° ± 8° ) . This result provides an interesting contrast to the consequences of the TMH2 helix lock and highlights that simply restricting TMH2 movement through locking strands β4 and β5 does not prevent opening of the core sheet . The restrictions enforced by the TMH2 strand lock are , however , sufficient to prevent extension of the TMH2 hairpin since no density is seen extending into the membrane . These data collectively imply that neither TMH region can enter the membrane without the other , suggesting that the TMH sequences have evolved for cooperative folding and assembly . Here , we present a series of structures that identify the major conformational changes during MACPF pore formation . The final pore structure reveals that individual PlyB monomers in the pore have the orientation seen for those in the distantly related CDCs [17] . Although sequence-related to perforin , their pores differ in several respects . Like CDCs , perforin is a thin , key-shaped molecule , but it does not open up in the pore state [18] . This difference likely arises from the divergent structures surrounding the conserved MACPF core , as well as from its longer TMH regions . In addition , C-terminal labelling indicated the opposite β-sheet orientation in the perforin pore [18] . A model based on a more recently determined C8 structure [39] suggests that the closely related terminal complement proteins would have the CDC orientation , but there are currently no other data available for a more definitive conclusion on perforin . Our findings highlight a critical role of the interface between the top of TMH2 and the surrounding region in controlling sheet opening . The results of the constrained fitting suggest that a key trigger for the conformational change includes displacement of the HTH motif away from the bend in the sheet . Highly conserved glycine residues [14] adjacent to the HTH motif may provide the hinge point for this motion ( Fig . 3D ) . Consistent with this model , mutation of the equivalent glycine residues in a CDC prevents oligomerisation [40] . It is notable that the HTH packs against the top of TMH2 , suggesting that interactions between these two regions may govern unlocking of the bent conformation ( Fig . 3D ) . After sheet unbending , we propose that membrane insertion and pore formation follow a top down , zippering mechanism with the barrel assembling towards the membrane surface , energetically driven by refolding of the TMH regions . This mechanism would also minimize the free energy cost of inserting naked hairpins with unsatisfied hydrogen bond potential into the membrane . Analysis of intermediate prepore structures provides the basis for a molecular movie ( S1 Movie ) that illustrates a possible trajectory of the core β-sheet in a MACPF protein unbending from the soluble monomer conformation to the transmembrane pore ( Fig . 6 ) . The pore structure shows that Ply shares some features with CDCs , in particular the orientation of monomers and opening of the molecule to release the TMH regions . On the other hand it resembles perforin regarding its longer TMH regions that refold into a ∼100-Å-long beta-barrel that reaches down through the membrane without any collapse of the molecule . This work provides new insights into the assembly of a two-component MACPF/CDC family member , suggesting a basis for the study of more complex assembly systems such as the complement MAC . Furthermore , the intermediate structures of the MACPF/CDC domain during its refolding into the β-barrel pore establish a structural paradigm for the transition of the prepore to pore , which is likely to be conserved across the MACPF/CDC protein family . PlyA and PlyB were expressed in Escherichia coli . PlyA was expressed as a soluble protein; PlyB required refolding . Crystals of selenomethionine-labelled PlyA were grown in 50 mM Na citrate ( pH 5 . 6 ) , 12% ( w/v ) PEG3350 , 0 . 2 M MgSO4 , and the structure determined using single-wavelength anomalous dispersion ( S1 Table ) . Crystals of PlyB were grown in 0 . 2 M NH4Ac , 0 . 1 M Na citrate ( pH 5 . 0 ) and 30% ( w/v ) PEG8000 . In addition to two native datasets , diffraction data were also collected from four different heavy atom derivatives ( S2 Table ) . The PlyB structure was determined using multiple isomorphous replacement with anomalous scattering . The PlyB TMH1 lock structure was determined by molecular replacement using the structure of wild type PlyB . Pleurotolysin WT pores and engineered disulphide bond oligomers assembled on sphingomyelin/cholesterol liposomes were imaged by negative stain and cryo-EM , and sorted according to diameter and symmetry by multivariate statistical analysis and multireference alignment . Three-dimensional reconstructions were obtained by a combination of angular reconstitution and projection matching for tridecameric pores and prepores . The PlyA and PlyB crystal structures and a model for a 52-stranded β-barrel [31] were fitted into the electron microscopy maps using a combination of manual fitting , rigid body refinement , and flexible fitting . The degree of unbending of the MACPF β-sheet in the prepore intermediates was estimated by using a multistep procedure ( S8 Fig . ) to generate a library of sheet conformations in angular sweeps , and selecting the top 20 best fitting ones for each prepore map . The goodness-of-fit of the central asymmetric unit was assessed locally using SCCC ( S5 Fig . ) [33 , 34] . Detailed protocols are available in S1 Methods .
Animals , plants , fungi , and bacteria all use pore-forming proteins of the membrane attack complex-perforin ( MACPF ) family as lethal , cell-killing weapons . These proteins are able to insert into the plasma membranes of target cells , creating large pores that short circuit the natural separation between the intracellular and extracellular milieu , with catastrophic results . However , the pore-forming proteins must undergo a substantial transformation from soluble precursors to a large barrel-shaped transmembrane complex as they punch their way into cells . Using a combination of X-ray crystallography and cryo electron microscopy , we have visualized , for the first time , the mechanism of action of one of these pore-forming proteins—pleurotolysin , a MACPF protein from the edible oyster mushroom . This enabled us to propose a model of the pleurotolysin pore by fitting the crystallographic structures of the pore proteins into a three-dimensional map of the pore obtained by cryo electron microscopy . We then designed a set of double mutants that allowed us to chemically trap intermediate states along the trajectory of the pore formation process , and to determine their structures too . By combining these data we proposed a detailed molecular mechanism for pore formation . The pleurotolysin first assembles into rings of 13 subunits , each of which then opens up by about 70° during pore formation . This process is accompanied by refolding and extrusion of two compact regions from each subunit into long hairpins that then zipper together to form an 80-Å wide barrel-shaped channel through the membrane .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[]
2015
Conformational Changes during Pore Formation by the Perforin-Related Protein Pleurotolysin
Water plays an important role in the transmission of many infectious diseases , which pose a great burden on global public health . However , the global distribution of these water-associated infectious diseases and underlying factors remain largely unexplored . Based on the Global Infectious Disease and Epidemiology Network ( GIDEON ) , a global database including water-associated pathogens and diseases was developed . In this study , reported outbreak events associated with corresponding water-associated infectious diseases from 1991 to 2008 were extracted from the database . The location of each reported outbreak event was identified and geocoded into a GIS database . Also collected in the GIS database included geo-referenced socio-environmental information including population density ( 2000 ) , annual accumulated temperature , surface water area , and average annual precipitation . Poisson models with Bayesian inference were developed to explore the association between these socio-environmental factors and distribution of the reported outbreak events . Based on model predictions a global relative risk map was generated . A total of 1 , 428 reported outbreak events were retrieved from the database . The analysis suggested that outbreaks of water-associated diseases are significantly correlated with socio-environmental factors . Population density is a significant risk factor for all categories of reported outbreaks of water-associated diseases; water-related diseases ( e . g . , vector-borne diseases ) are associated with accumulated temperature; water-washed diseases ( e . g . , conjunctivitis ) are inversely related to surface water area; both water-borne and water-related diseases are inversely related to average annual rainfall . Based on the model predictions , “hotspots” of risks for all categories of water-associated diseases were explored . At the global scale , water-associated infectious diseases are significantly correlated with socio-environmental factors , impacting all regions which are affected disproportionately by different categories of water-associated infectious diseases . Although substantial advances in biomedical sciences and public health measures have facilitated control of many infectious diseases in the past century , the world has witnessed an increasing incidence and geographical expansion of emerging and re-emerging infectious diseases [1] , which , together with some other old ones , remain among the leading causes of deaths and disability worldwide [2] , [3] . The global environmental , ecological , and socio-economic changes have a significant impact on the distribution , emergence and re-emergence of infectious diseases and are expected to continue to influence such trend [1] , [4] , [5] , [6] , [7] , [8] , [9] . Some recent studies at both global and regional scales have suggested that climatic factors , human movement , and agricultural practices are important factors underlying the distribution , emergence , and re-emergence of infectious diseases [1] , [6] , [10] . Water is essential for maintaining life on Earth . Meanwhile , water can also serve as a media for hazardous substances and pathogenic organisms , posing substantial health threats to humans through a variety of pathways . During the past few decades , human development , population growth , extreme weather events , natural calamities , and climate change have exerted many diverse pressures on both the quality and quantity of water resources which may in turn impact conditions fostering water-associated diseases . Worldwide , water-associated infectious diseases are a major cause of morbidity and mortality [11] , [12] , [13] . A conservative estimate indicated that 4 . 0% of global deaths and 5 . 7% of the global disease burden ( in DALYs ) were attributable to a small subset of water , sanitation , and hygiene ( WSH ) related infectious diseases including diarrheal diseases , schistosomiasis , trachoma , ascariasis , trichuriasis , and hookworm infections [11] , [14] , [15] . Although unknown , the actual disease burden attributable to water-associated pathogens is expected to be much higher . A total of 1415 species of microorganisms have been reported to be pathogenic , among which approximately 348 are water-associated , causing 115 infectious diseases [5] . Yet , their distribution and associated factors at the global scale remain largely unexplored . Although the linkage between the hydrological cycle and infectious diseases has long been recognized , the underlying mechanisms shaping this relationship at global and regional scales are rarely characterized . Recent developments in hydrology and geo-spatial technology , and increasing availability of spatial socio-environmental information provide an opportunity to explore this issue . Geospatial techniques ( e . g . Geographic Information System , or GIS , and spatial analytical techniques ) offer a means for developing and organizing spatially explicit information . For example , the availability of information on terrestrial surface water area from the Global Lakes and Wetland Database [16] , could allow the exploration of the possible relationship between the availability of terrestrial surface water and distribution of water-associated diseases at the global scale . In this study , a comprehensive database has been developed for global water-associated infectious pathogens and diseases and socio-environmental information which have been integrated into a GIS database . The overall goal of our study is to explore the possible relationship between global distribution of water-associated infectious diseases and socio-environmental factors . In this study reported outbreaks of water-associated diseases were chosen as the study subject as they were available in the developed database and provided semiquantitative information ( e . g . yes or no , and frequency of outbreaks ) . Our specific aims in this study were to describe the global distribution of reported outbreaks caused by water-associated infectious diseases from 1991 to 2008 , to explore potential risk factors associated with spatio-temporal distributions of these outbreaks , and to develop a global risk map for these diseases . Primary source of information on water-associated pathogens and infectious diseases for the database developed in present study was based on the Global Infectious Disease and Epidemiology Network ( GIDEON ) , a subscription- and web-based comprehensive global infectious diseases database which provides extensive geographical and epidemiological information including outbreaks for 337 recognized infectious diseases in 231 countries and regions . Data in GIDEON are collated through a system of computer macros and dedicated source lists developed over the past 15 years . A monthly search of Medline is conducted against a list of GIDEON key words ( similar to Mesh terms in PubMed ) , and titles/abstracts of interest are reviewed . In addition , all standard publications of WHO and CDC are scanned for relevance before they are collated and entered into GIDEON . The GIDEON infectious diseases database provides a chronological listing of all reported outbreaks of infectious diseases , which are listed by year and country , with specific location information available for the majority of reported outbreaks . For those without specific location information , original publications or reports were searched to extract the information . To assess GIDEON's completeness on the reported outbreaks , a systematic search based on PubMed , ISI Web of Knowledge , WHO and CDC reports was conducted on reported outbreaks ( 1991–2008 ) for 10 randomly chosen water-associated diseases . Search terms included names of specific pathogen ( s ) /disease ( s ) and country/region , “outbreak” , “epidemic” , and “epidemics” , respectively . Chi-square test was performed to compare results from the independent search vs . that from GIDEON – our results were largely in agreement with that from GIDEON ( X2 = 591 . 2 , P<0 . 001 ) . Based on the database developed , water-associated diseases and their corresponding causal agents were systematically reviewed , together with extensive literature review for relevant environmental , biological , and epidemiologic characteristics . For each disease , the following information was included in the database we developed . The database included the following information - grid-based global human population density ( per km2 ) based on the 2000 global population dataset , which was developed by Socioeconomic Data and Applications Center ( SEDAC ) of Columbia University between 2003 and 2005 , providing globally consistent and spatially explicit human population information ( http://sedac . ciesin . columbia . edu/gpw/ ) ; global average accumulated temperature ( degree days , with a spatial resolution of 0 . 5 degree ) for the period between 1961 and1990 from United Nation Environmental Protection ( http://www . unep . org/ ) , which was based on the degree that the temperature rose above zero degree and the number of days in the period during which this excess was maintained [18]; surface area ( km2 ) of water bodies including large lakes , rivers , and wetland , collected from the global lakes and wetlands database ( http://www . worldwildlife . org/ ) ; the average rainfall ( mm ) per year for the period between 1961 and1990 from FAO ( http://www . fao . org ) ; and per capita Gross Domestic Product ( GDP ) which was based on each a country's GDP divided by the total number of people in the country ( http://sedac . ciesin . columbia . edu/ddc/baseline/ ) . The scale of all information collected was converted to one-degree grid in the GIS database . A total of 1 , 428 outbreak events had been reported from 1991 to 2008 . Outbreaks occurred all over the world and the clusters of reported outbreaks tended to be in west Europe , central Africa , north India and Southeast Asia ( Figure 2 ) . Among the reported outbreak events , 70 . 9% ( 1 , 012 ) were associated with water-borne diseases including 32 . 9% ( 471 ) water-carried , 12 . 2% ( 174 ) water-related , 6 . 8% ( 97 ) water-washed , 2 . 9% ( 41 ) water-based , and 7 . 3% ( 104 ) water-dispersed . 46 . 7% ( 667 ) of the outbreak events were associated with emerging or reemerging pathogens , which appeared in humans for the first time or had occurred previously but were increasing in incidence or expanding into areas where they had not previously been reported [5] . It is found that 49 . 6% ( 709 ) of the outbreak events was caused by bacteria , 39 . 3% ( 561 ) by viruses , and 11 . 1% ( 158 ) by parasites . 6 . 5% ( 93 ) of the outbreak events was caused by agents that could be transmitted by direct contact , 1 . 1% ( 16 ) transmitted through vectors , 63 . 5% ( 907 ) through environmental transmission , and 28 . 9% ( 412 ) by zoonotic routes . The reported outbreak events had shown a significant increase since 1991 , which had been accompanied by a significant increase in the number of published articles ( Figure 1 , Pearson correlation - 0 . 935 , P<0 . 001 ) . We used a generalized linear model to test the temporal trend in the outbreak events and found it insignificant ( t = 0 . 046 , P = 0 . 940 ) after controlling for the publication efforts . The number of published articles was therefore used as a covariate in the subsequent statistical analyses . Table 1 summarizes analyses of the Poisson models without and with spatially structured random effects using Bayesian inference for the five categories of water-associated diseases . The DIC values of the Poisson model with spatial random effects are smaller than that without spatial structure , suggesting that the spatial models provided a better fit to the data . The Poisson models with spatial structure were therefore used for risk factor analysis and mapping . The population density was shown to be a significant risk factor for reported outbreaks of all categories of water-associated infectious diseases and the probability of outbreak occurrence increased with the population density . The accumulated temperature was a significant risk factor for water-related diseases only . The analysis suggested that occurrence of water-washed diseases had significantly inverse relationship with surface water areas . Such inverse relationship was also observed between the average annual rainfall and water-borne diseases ( including water-carried ) and water-related diseases . Figure 3 ( A–F ) shows the risk distribution based on the model predictions with the blue indicating lower risk while the red representing higher risk . The model predictions suggested that west Europe , central Africa , north India were at the higher risk for water-borne diseases ( e . g . Escherichia coli diarrhea ) , and notably , that the higher risk for water-borne diseases in west Europe was primarily driven by water-carried diseases ( e . g . cryptosporidiosis ) . West Europe , North Africa , and Latin America tended to be at higher risk to water-washed diseases ( e . g . viral conjunctivitis ) . Risks associated with water-based diseases ( e . g . schistosomiasis ) were higher in east Brazil , northwest Africa , central Africa , and southeast of China . High risk areas for water-related diseases ( e . g . malaria and dengue fever ) were clustered in central Africa in particular Ethiopia and Kenya , and north India . For water-dispersed diseases ( e . g . Legionellosis ) , west Europe seemed to be at higher risk . In the past decade there has been an increasing interest in understanding factors underlying the distribution of infectious pathogens , emerging and re-emerging infectious diseases . Some recent research efforts have been in attempt to determine large-scale ecological factors associated with diversity richness and distribution of infectious and parasitic pathogens [6] , socio-environmental determinants of emerging infectious disease [1] , and to explore the impact of global environmental change on distribution and spread of infectious diseases [23] , [24] . These studies have offered valuable insights into understanding socio-environmental processes and factors underlying the distribution of infectious diseases . In this study , we focused our attention on water-associated infectious diseases and attempted to explore whether these diseases follow similar patterns observed in other studies [1] , [6] , and whether the distribution and occurrence of these diseases were related to terrestrial water dynamics ( e . g . precipitation and land-surface water ) together with other socio-environmental factors . The transmission of many infectious diseases is closely linked to water and the water-infectious pathogen interactions exhibit a complicated relationship depending on the transmission characteristics of the pathogens and water's roles in the transmission . The study showed that water-associated infectious diseases and outbreaks were broadly distributed throughout the world but the distribution of specific agents/diseases varied greatly from region to region . The majority of reported outbreaks events were associated with water-borne pathogen including those water-carried . Water-borne diseases have a much broader distribution than other water-associated diseases , suggesting a broader impact of waterborne pathogens in particular those related to fecal-oral route and water , sanitation , and hygiene . In addition to water , other environmental factors have also been recognized to play a significant role in the distribution , transmission , and outbreaks of these water-associated diseases [25] , [26] , [27] . It should be noted that , though , the outbreaks reported here only reflected “the tip of the iceberg” of the much larger problem . A complete count of outbreaks attributable to water-associated pathogens is impossible as underreporting is a universal problem , and reporting efforts and effectiveness may vary from country to country , and pathogens to pathogens , depending on many factors particularly availability of research and surveillance resources , and epidemiological characteristics of causal agents . In developing countries , outbreaks of many vector-borne infectious diseases such as dengue and malaria [28] , [29] and gastrointestinal infections [30] were grossly underreported , partly due to their endemic characteristics . Even in the US , reporting completeness of notifiable infectious diseases varied from 9% to 99% , and was strongly associated with diseases being reported [31] . In general , water-borne pathogens usually exhibit acute manifestations and are more likely to be reported [32] . In contrast , other diseases such as water-based schistosomiasis , a disease of chronic infections and atypical symptoms , are more likely to be underreported . In this study , the primary source of outbreak information was from GIDEON , which is the most comprehensive database on infectious diseases and offers detailed information on epidemiology including distributions and outbreaks of infectious diseases for more than 205 countries and regions , as well as clinical manifestations and treatment associated with each disease [23] , [33] . As expected , GIDEON does not include all outbreak information due to underreporting of outbreak events , but we believe that information from GIDEON is representative and provides an overview of available and recognized outbreak data , as argued by some other studies [23] , [33] . The distribution of water-associated diseases , like many other infectious diseases , is highly heterogeneous . The spatial structure associated with the distribution of the outbreaks may be important in understanding underlying risk factors . To explore possible associations between socio-environmental factors and the outbreaks at the global scale , two Poisson models ( without and with spatial structures ) were developed . Among the two models explored , the one incorporating spatial effects provided a better fit to the data . Our findings suggested that the importance of these socio-environmental variables was dependent on the category of water-associated diseases . Human population density was a common significant risk factor for the outbreaks caused by all categories of water-associated diseases , in concurrence with the previous study suggesting that human population was an important predictor of emerging infectious diseases event at the global scale [1] . The accumulated temperature was a significant factor associated with water-related diseases , which was in agreement with many other studies [34] , [35] , [36] , [37] . The transmission of diseases in this category typically involves vectors ( e . g . mosquitoes ) which require certain energy level ( e . g . accumulated temperature ) allowing completion of development of vectors and pathogens [10] , [38] , [39] . In this study , terrestrial surface water area ( at each grid-region ) was found to be inversely proportional to the outbreak events associated with water-washed diseases such as trachoma . The primary determinant of water-washed diseases is poor personal and/or domestic hygiene typically due to insufficient sanitary water for hygienic purpose , and this has been reported in many site-specific studies [40] , [41] , [42] . Our result from a large-scale correlation study supported these points of the previous studies , suggesting that regional water availability may be indicative of local water availability which is closely linked to personal and domestic hygiene . Our analysis indicated a negative relationship between average annual rainfall and water-related diseases , in contrast with some previous studies showing that some outbreaks of water-related diseases are positively associated with heavy rainfall events [8] , [43] , [44] , [45] . This can be partly explained by issues related to scale and timing effects – the majority of studies reporting positive relationship between precipitation and waterborne illness was conducted at local scale and typically time-lag effects were considered . Indeed , the rainfall and water-related diseases exhibit complex relationships as shown in previous studies , and many rainfall-driven transmission and outbreaks were dependent on local circumstances . In addition to rainfall , multiple and covarying drivers have also been proposed for seasonal pattern of transmission and outbreaks of many water-associated diseases , including temperature , host demographic and biological characteristics [46] , [47] , [48] . However , due to lack of global information on seasonal patterns of outbreaks and the driving factors , temporal heterogeneity of outbreaks events , such as seasonality discussed here , was not included in the present study . Using the best-fitted models we predicted global distributions of relative risks associated with each category of water-related infectious diseases , as shown in Figure 3 . Surprisingly , the risk maps show that west Europe and central Africa were all at relatively higher risk for water-borne diseases . A closer look at pathogens associated with the reported outbreaks indicated different dominant species in the two regions – in Africa reports of water-borne outbreaks were primarily associated with Vibrio cholerae , whereas in west Europe giardia , cryptosporidium were common in the water-borne outbreaks , with the latter being particularly related to accidental ingestions of contaminated water ( e . g . in recreational settings ) and , to some extent , mixed with infections of food-borne sources [49] , [50] , [51] . Some limitations of the current study are recognized . Although possible reporting bias was adjusted for using publications for each country , the analysis may have missed countries/regions with outbreaks but no publications and/or reports . Second , only a few socio-environmental factors were considered in the present study and it is likely that some other factors might be associated with the outbreaks . In addition , significant prediction uncertainties were noted throughout the outbreak countries and regions , this was partly due to the temporal correlation of the outbreak events which was not considered in the analysis . The addition of such information ( e . g . temporal trend of outbreaks in places where repeated outbreaks occurred ) to the model may improve model prediction . In spite of these , we think that overall patterns of distribution and associated risk factors presented here are informative and offer insights into global distribution and risk factors associated with water-associated diseases , although further studies on other possible risk factors and modeling approaches to improving prediction are still needed . In conclusion , our study , to our knowledge , is the first to describe global distribution of outbreaks caused by water-associated infectious diseases and explore possible risk factors underlying the distribution of these outbreaks at the global scale . The risk maps may offer insights for future studies and for prioritizing health resources .
Water is essential for maintaining life on Earth but can also serve as a media for many pathogenic organisms , causing a high disease burden globally . However , how the global distribution of water-associated infectious pathogens/diseases looks like and how such distribution is related to possible social and environmental factors remain largely unknown . In this study , we compiled a database on distribution , biology , and epidemiology of water-associated infectious diseases and collected data on population density , annual accumulated temperature , surface water areas , average annual precipitation , and per capita GDP at the global scale . From the database we extracted reported outbreak events from 1991 to 2008 and developed models to explore the association between the distribution of these outbreaks and social and environmental factors . A total of1 , 428 outbreaks had been reported and this number only reflected ‘the tip of the iceberg’ of the much bigger problem . We found that the outbreaks of water-associated infectious diseases are significantly correlated with social and environmental factors and that all regions are affected disproportionately by different categories of diseases . Relative risk maps are generated to show ‘hotspots’ of risks for different diseases . Despite certain limitations , the findings may be instrumental for future studies and prioritizing health resources .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "medicine", "infectious", "diseases", "public", "health", "and", "epidemiology", "epidemiology", "global", "health" ]
2012
Global Distribution of Outbreaks of Water-Associated Infectious Diseases
Our work addresses two key challenges , one biological and one methodological . First , we aim to understand how proliferation and cell migration rates in the intestinal epithelium are related under healthy , damaged ( Ara-C treated ) and recovering conditions , and how these relations can be used to identify mechanisms of repair and regeneration . We analyse new data , presented in more detail in a companion paper , in which BrdU/IdU cell-labelling experiments were performed under these respective conditions . Second , in considering how to more rigorously process these data and interpret them using mathematical models , we use a probabilistic , hierarchical approach . This provides a best-practice approach for systematically modelling and understanding the uncertainties that can otherwise undermine the generation of reliable conclusions—uncertainties in experimental measurement and treatment , difficult-to-compare mathematical models of underlying mechanisms , and unknown or unobserved parameters . Both spatially discrete and continuous mechanistic models are considered and related via hierarchical conditional probability assumptions . We perform model checks on both in-sample and out-of-sample datasets and use them to show how to test possible model improvements and assess the robustness of our conclusions . We conclude , for the present set of experiments , that a primarily proliferation-driven model suffices to predict labelled cell dynamics over most time-scales . The intestinal epithelium provides crucial barrier , transport and homeostatic functions . These requirements lead it to undergo constant repair and regeneration , and dysfunctions can result in pathologies such as tumorigenesis [1–7] . Much work has been carried out on estimating the rate parameters in the intestine and other epithelia [1 , 8–10] . However , attempts to interpret these experimental data using mechanistic modelling remain inconclusive [11–14] , due to the lack of consistent and reproducible approaches for comparing models representing conjectured biological mechanisms , both to each other and to experimental data . This challenge goes in both directions: using experimental data ( taken to be ‘true’ ) to parameterise and test mathematical or computational formalisations of mechanistic theories , and using these models ( taken to be ‘true’ ) to predict , interpret and question experimental results . Both experimental measurements and mathematical models are subject to uncertainty , and we hence need systematic ways of quantifying these uncertainties and attributing them to the appropriate sources . Furthermore , establishing a common approach for analysing experimental results , formulating mechanistic models and generating new predictions has many potential advantages for enabling interdisciplinary teams to communicate in a common language so that they may efficiently discover and follow promising directions as and when they arise . We address the above issues by developing a hierarchical Bayesian model for combining measurements , models and inference procedures , and applying it to a set of experiments targeting mechanisms of repair and regeneration in the intestinal epithelium . While progress is now being made in tackling this challenge in other areas of biology ( see , for example , [15–17] ) , to our knowledge the problem of intestinal epithelial dynamics has not yet been investigated using such an approach . The experiments under investigation were performed by ourselves and are presented in more detail in [18] . The aim of these experiments was to determine how proliferation rates , tissue growth and cellular migration rates are related under healthy , damaged ( Ara-C treated ) and recovering conditions , and how these relations can be used to identify mechanisms of repair and regeneration . A notable feature of the Bayesian approach to probabilistic modelling is that all sources of uncertainty are represented via probability distributions [19–21] . Adopting this perspective , we consider both observations and parameters to be random variables . Within a modelling context , uncertainty may be associated with ( at least ) : parameters within a mechanistic model of a biological or physical process , the mechanistic model of the process itself and the measurements of the underlying process . This leads , initially , to the postulation of a full joint probability distribution for observable , unobservable/unobserved variables , parameters and data . Another key feature of the Bayesian perspective is that it provides a natural way of decomposing such full joint models in a hierarchical manner , e . g . by separating out processes occurring on different scales and at different analysis stages . A given set of hierarchical assumptions corresponds to assuming a factorisation of the full joint distribution mentioned above , and gives a more interpretable and tractable starting point . Our factorisation follows that described in [21–24] . This comprises: a ‘measurement model’ , which defines the observable ( sample ) features to be considered reproducible and the precision with which they are reproducible ( the measurement scale ) ; an underlying ‘process’ model , which captures the key mechanistic hypotheses of spatiotemporal evolution , and a prior parameter model which defines a broad class of a priori acceptable parameter values . In order to illustrate some of the modelling benefits of the hierarchical approach , we show how both discrete and continuous process models can be derived and related using the hierarchical perspective . We discuss the relationship between the conditional/hierarchical modelling and causal modelling literatures ( see [25–27] for reviews ) and illustrate the distinct roles of ( Bayesian ) predictive distributions vs . parameter distributions for model checking and the assessment of evidence , respectively [20 , 28–31] . Our hierarchical Bayesian model incorporates measurement , process and parameter models and facilitates separate checking of these components . This checking indicates , in the present application to the spatiotemporal dynamics of the intestinal epithelium , that much of the observed measurement variability can be adequately captured by a simple measurement model . Similarly we find that a relatively simple process model can account for the main spatiotemporal dynamics of interest; however , model checking also identifies a minor misfit in the process model appearing over long time-scales . This motivates possible model improvements: we consider the inclusion of additional finite-cell-size effects in the process model , derived from a discrete process model and a subsequent continuum approximation formulated in terms of conditional probability . This only gives a slightly better qualitative fit to experimental data , however . We instead find that the dominant sources of the long-time misfits are probably due to some other factors—most likely relatively slow , time-varying proliferation rates ( e . g . due to circadian rhythms ) . In summary , a primarily proliferation-driven model appears adequate for predictions over moderate time-scales . Our hierarchical probability model was constructed on the basis of conditional probability assumptions . These allowed us to factor out a measurement model , a mechanistic model and a parameter model . Fig 2 illustrates the process of updating from realisations of the prior distributions of the proliferation and velocity fields to realisations of their posterior ( post-data ) distributions . As discussed in the Materials and methods section above , these are generated by an underlying piecewise-constant Gaussian random field of proliferation rates , k . This has length m = 5 , and defines an assignment of the cell indices into biologically-motivated regions of proliferation activity . The left-hand side of the figure shows simulations from the prior distribution for proliferation field ( top ) and realisations from the induced distribution for the velocity field ( bottom ) , respectively . The right-hand side shows the corresponding simulations after the prior parameter distribution has been updated to a posterior parameter distribution . The prior-to-posterior parameter estimation was carried out using the MCMC sampling approach described above with t = 120 min ( 2 h ) as an initial condition and t = 360 min ( 6 h ) and 600 min ( 10 h ) as given data . The initial condition for the underlying labelled fraction ( occupancy probability ) was determined by fitting a smoothing spline to the data . The prior distribution for the proliferation field shown in Fig 2 incorporated a weak mean trend in net proliferation rates , rising from the crypt base to the mid-crypt before falling exponentially to zero over the last few parameter regions post-crypt end , and a parameter correlation length of 1 . These assumptions can be relaxed/varied with little effect , though typically a non-zero parameter correlation length and a shut-off in proliferation after the crypt end produce more stable ( well-identified ) estimates . Additional visualisations of the parameter inferences are provided in Fig A-C in S1 Supplementary information . Fig 3 is the same as Fig 2 described in the previous section , but this time under treatment by Ara-C . Results from the baseline case are shown in grey , while those from Ara-C treatment are shown in blue . Here 1140 min ( 19 h post IdU labelling , 2 h post Ara-C treatment ) was used as the initial condition and 1500 min ( 25 h post IdU labelling , 8 h post Ara-C treatment ) used for fitting . The intermediate time 1260 min ( 21 h post IdU labelling , 4 h post Ara-C treatment ) and later time 1620 min ( 27 h post IdU labelling , 10 h post Ara-C treatment ) were used as out-of-sample comparisons ( see later ) . As can be seen , there is clear inhibition of proliferation and an even clearer effect on the cell migration ( growth ) velocity . The greater variability in the underlying parameter results compared to the baseline case may indicate , for example , greater parameter underdetermination and/or inconsistency of the model . This is not surprising as we expect all proliferation parameters to be reduced to similar ( low ) values and hence to become less distinguishable . To add additional stability to the results we can attempt to reduce underdetermination in the parameters by increasing the parameter correlation length and inducing an effectively more ‘lumped’ representation of the parameter field ( since values tend to stick together more ) . Doing this removed the more extreme negative net proliferation in the posterior profile , however it still allowed for small amounts of negative net proliferation/velocity ( the available Jupyter notebook can be used to explore various prior assumptions ) . Again , the need to introduce more stability is likely due to some combination of the limitations of resolution , a consequence of trying to fit the data too closely , or an indication of model inadequacies . In particular , under inhibited-proliferation conditions the effective number of parameters would be expected to be reduced . When fitting the full model , with largely independent parameters for each region , it is to be expected that some additional regularisation would be required for greater stability . Ara-C is metabolised between 10–12 h post-treatment . The two times considered here , 1620 min and 2520 min , correspond to 10 h and 25 h post Ara-C treatment , respectively , i . e to the end of the effect and after the resumption of proliferation . Hence , to check for the recovery of proliferation , we fitted the model using 1620 min as the initial condition and 2520 min as the final time . Fig 4 is the same as Figs 2 and 3 described in the previous sections , but this time after/during recovering from treatment by Ara-C . The previous results from the baseline case are shown in grey , while the new results following recovery from Ara-C treatment are shown in blue . Here 1620 min ( 27 h post IdU labelling , 10 h post Ara-C treatment ) was used as the initial condition and 2520 min ( 42 h post IdU labelling , 25 h post Ara-C treatment ) used for fitting . We did not make additional out-of-sample comparisons in this case , though in-sample posterior predictive checks were still carried out ( see later ) . Here , as expected , the proliferation and velocity profiles indicate that proliferation has resumed . The rates of proliferation appear to be lower than under fully healthy conditions , however , perhaps due to incomplete recovery ( the initial condition being right at the beginning of the recovery period ) . The timing of the recovery of proliferation and the well-identified proliferation and velocity profiles inferred give no indication that any other mechanism is required to account for these data , however . Fig 5 illustrates simulations from the predictive distributions corresponding to the prior and posterior parameter distributions of Fig 2 . This enables a first self-consistency check—i . e . can the model re-simulate data similar to that to which it was fitted [20 , 58] ? If this is the case then we can ( provisionally ) trust the parameter estimates in the previous figure; if not , then the parameter estimates would be unreliable , no matter how well-determined they seem . In our case the model appears to adequately replicate the data used for fitting . Figs 6 and 7 illustrate two additional ways of visualising replicated datasets . The former visualises the label profile along the crypt-villus axis at the future unfitted/out-of-sample time 1080 min ( 18 h ) , while the latter visualises both fitted ( 120 min/2 h , 360 min/6 h and 600 min/10 h ) and unfitted/out-of-sample ( 1080 min/18 h ) predictions plotted in the characteristic plane ( t , x ) in which the slopes along lines of constant colour should be inversely proportional to the migration velocities at that point , due to the ( hyperbolic ) nature of our ‘colour’ equation ( see e . g . [59] ) . We have interpolated between the dotted grid lines . These figures , in combination with Fig 5 , indicate that the model is capable of reliably reproducing the data to which it was fitted , as well as predicting key features of unfitted datasets such as the rate of movement of the front . On the other hand , there is clearly a greater misfit with the predicted rather than fitted data . In order to locate the possible source of misfit we considered various model residuals and error terms—see ‘Locating model misfit’ below . Here data from 1140 min ( 19 h; post IdU labelling ) were used as the initial conditions and 1500 min ( 25 h ) used for fitting . 1260 min ( 21 h ) and 1620 min ( 27 h ) were used as out-of-sample ( non-fitted ) comparisons . Fig 8 is analogous to Fig 5 in the healthy case . In general all of the features up to 1620 min ( 27 h ) in Fig 8 , and for both fitted and predicted times , are reasonably well captured . The fit at 1620 min is generally good , but perhaps worse than the other cases . This could be due to errors in longer-time predictions and to the beginning of proliferation recovery: we explore these alternatives in what follows . As discussed above , Ara-C is metabolised between 10–12 h post-treatment . The two times considered here , 1620 min and 2520 min , correspond to 10 h and 25 h post Ara-C treatment , respectively , i . e to the end of the effect and after the resumption of proliferation . As can be seen in Fig 9 , and as expected , the label has resumed movement in concert with the resumption in proliferation . The model appears to fit reasonably well . While the zeroth-order model behaves essentially as desired under experimental perturbation , and is likely capturing the essential features of interest , we observed some minor model misfit . We used posterior predictive checks to unpick the contributions of the various model parts and determine the source ( s ) of misfit . This in turn motivated potential model improvements . These checks were carried out under baseline ( healthy ) conditions as we were more confident of the experimental results under this scenario , but they can equally be carried out for the other datasets . Note , however , that time-varying effects are not expected to be as relevant under conditions of inhibited proliferation . Fig 10 shows the following checks: measurement error as determined by subtracting a smoothed spline from the observed data ( dark line ) and comparing these to the results obtained by subtracting the process model from the simulated data ( panels 1–4 , moving left-to-right and top-to-bottom , showing fitted—120 min/2 h , 360 min/6 h and 600 min/10 h—and unfitted/out-of-sample—1080 min/18 h—times ) . This presentation follows the noise-checking approach in [60] , as well as the general recommendations given in [20 , 58] . Reliable interpretation of these as ‘true’ measurement residuals depends on the validity of the normal approximation Eq 7 since these expressions are not directly interpretable in terms of the discrete binomial model ( see e . g . [20 , 58] ) . These are also visualised in terms of the corresponding cumulative distributions in the middle panel ( panel 5 , following as above ) . Panels 6–9 show the differences between the underlying process model and the smoothed spline fitted to the data . As can be seen , the measurement model appears approximately valid at all times , while the process model appears to have non-zero error for the 1080 min sample . We consider this in more detail next . As discussed in the process model section above , the presence of cellular structure in the epithelial tissue means that higher-order spatial effects could be present . One way of deciding whether these are important is to consider the extent to which these may account for the minor misfit identified above , as opposed to other factors such as time-varying proliferation rates . To do this we considered both uniform percentage reductions of the original parameter estimates ( approximating time-varying rates ) and the inclusion of higher-order spatial terms . Fig 11 gives an idea of the qualitative differences induced by including the higher-order spatial terms and those that could be induced by time-varying proliferation rates . This figure is based on the ( healthy ) 1080 min ( 18 h ) data in which we found some indication of a process model error . We see that while the higher-order model appears to give a slightly better qualitative fit to the data , both the higher-order and lower-order models require similar reductions of the parameter values to quantitatively improve the fit to our out-of-sample data . The reduced parameter values shown in Fig 11 correspond to a reduction of 20% , which was chosen visually as a reduction accounting for the bulk of the misfit . Thus the key ( yet relatively small ) difference between the model and out-of-sample data is likely due to an effect other than finite-cell sizes; in this case it is likely due to time-variation in parameter values due to circadian rhythms ( we have assumed steady-state parameter values ) . Other potential factors include label dilution or an unmodelled mixing phenomenon in the full two-dimensional case . We note however that these effects are small and appear to be important primarily for predicting much further ahead in time than the fitted data and the steady-state parameter assumption is likely valid for reasonable time intervals . This means that the more easily interpretable original model may be sufficient for many purposes . Understanding the intricate dynamics of the intestinal epithelium requires an interdisciplinary approach that integrates experimental measurements , mathematical and computational modelling , and statistical quantification of uncertainties . While a diverse range of mathematical models have been proposed for epithelial cell and tissue dynamics ( reviewed in [51 , 52 , 61–63] ) , from compartment models to individual-based models to continuum models , we lack consistent and reproducible approaches for comparing models representing conjectured biological mechanisms both to each other and to experimental data ( for an overview , see our review [44] ) . These shortcomings may explain why questions such as the connection between proliferation and migration and its variation under experimental perturbations remain open [8–14] . The aim of the present work was to acknowledge and confront these difficulties explicitly , and to present some initial constructive steps towards establishing such a framework . To do this we carried out new experiments ( described more fully in a companion paper [18] ) aimed at determining how cell proliferation rates , tissue growth and cell migration rates are related in the intestinal epithelium under healthy , damaged ( Ara-C treated ) and recovering conditions . We performed BrdU/IdU cell-labelling experiments under these respective conditions . We then developed a probabilistic , hierarchical ( conditional ) model to process and interpret these data . Our hierarchical model provides a best-practice approach for describing and understanding the uncertainties that could lead to unreliable mechanistic conclusions—uncertainties in experimental measurement and treatment , difficult-to-compare mathematical models of underlying mechanisms , and unknown or unobserved parameters . Our approach was influenced by recognising the benefits that the hierarchical Bayesian approach has demonstrated in applications across a number of different disciplines ( e . g . in environmental and geophysical science as in [64 , 65]; ecological modelling as in [66 , 67]; and in Bayesian statistical modelling and inverse problems more generally as in [20–24 , 68] ) . We also note that a hierarchical approach can have significant benefits outside the Bayesian framework ( see for example the ‘extended likelihood’ approach described in [69–71] ) . The hierarchical approach provides a framework , not only for combining disparate sources of uncertainty , but also for facilitating modelling derivations and relating discrete and continuous models . Though the resulting measurement , process and parameter models can be ( or have been ) derived by other means , as far as we are aware this particular perspective has not been systematically utilised in the manner considered here . We also note the connection between the choice of a measurement model as required here ( and/or process model error , and following e . g . [21–24 , 64 , 72] ) , and the development of approximate sampling and parameter fitting procedures , which are particularly useful for analytically difficult models . A key concern of the latter is the appropriate choice of summary statistics for constructing a ‘synthetic likelihood’ [73] or similarly-modified posterior target for Approximate Bayesian Computation ( ABC ) [74–76] . This choice determines ( implicitly or explicitly ) in which ways a given model or set of models can be considered an ‘adequate’ representation of the data , which features are considered to be reproducible and what the associated ‘noise’ structure should be ( [77] presents an alternative approach to characterising data features and model adequacy ) . These issues are crucial in deciding how to model the complexity of epithelial cell and tissue dynamics . An important next step , as described above , would be to consider other process model types and to evaluate and compare them under carefully modelled experimental conditions . Extensions incorporating other mechanical and/or cellular-level information ( e . g . [11 , 12] ) into process models would provide a natural next step . Importantly , due to the separation between measurement and process model components , these more complex process models could be incorporated into our present framework simply by replacing our process model component with a new model , while retaining the same measurement model . Of course additional parameters would require additional prior assumptions , and if additional data features were of interest then these would need to be incorporated into a modified measurement model . The benefit of a hierarchical model is that it offers an explicit guide as to where such modifications should be incorporated . In summary , the main results established using the above approach were
The intestinal epithelium is an important model system for studying the dynamics and regulation of multicellular populations . It is characterised by rapid rates of self-renewal and repair; dysregulation of these processes is thought to explain , in part , why many tumours form in the intestinal and similar epithelial tissues . These features have led to a large amount of work on estimating cell kinetic parameters in the intestine . There remain , however , large gaps between the raw data collected , the interpretation of these experimental data , and mechanistic models that describe the underlying processes . Hierarchical statistical modelling provides a natural method with which to bridge these gaps , but has , to date , been underutilised in the study of intestinal tissue self-renewal . As we illustrate , this approach makes essential use of the distinction between ‘measurement’ , ‘process’ and ‘parameter’ models , giving an explicit framework for combining experimental data and mechanistic modelling in the presence of multiple sources of uncertainty . We apply this approach to analyse experiments on healthy , damaged and recovering intestinal tissue , finding that observed data can be explained by a model in which cell movement is driven primarily by proliferation .
[ "Abstract", "Introduction", "Materials", "and", "methods", "Results", "Discussion" ]
[ "cell", "motility", "medicine", "and", "health", "sciences", "mathematical", "models", "animal", "models", "developmental", "biology", "model", "organisms", "probability", "distribution", "mathematics", "experimental", "organism", "systems", "information", "technology", "data", "processing", "digestive", "system", "research", "and", "analysis", "methods", "computer", "and", "information", "sciences", "mathematical", "and", "statistical", "techniques", "biological", "tissue", "mouse", "models", "probability", "theory", "approximation", "methods", "gastrointestinal", "tract", "anatomy", "cell", "biology", "cell", "migration", "epithelium", "biology", "and", "life", "sciences", "physical", "sciences" ]
2017
A hierarchical Bayesian model for understanding the spatiotemporal dynamics of the intestinal epithelium
Ehrlichia chaffeensis , an obligatory intracellular rickettsial pathogen , enters and replicates in monocytes/macrophages and several non-phagocytic cells . E . chaffeensis entry into mammalian cells is essential not only for causing the emerging zoonosis , human monocytic ehrlichiosis , but also for its survival . It remains unclear if E . chaffeensis has evolved a specific surface protein that functions as an ‘invasin’ to mediate its entry . We report a novel entry triggering protein of Ehrlichia , EtpE that functions as an invasin . EtpE is an outer membrane protein and an antibody against EtpE ( the C-terminal fragment , EtpE-C ) greatly inhibited E . chaffeensis binding , entry and infection of both phagocytes and non-phagocytes . EtpE-C-immunization of mice significantly inhibited E . chaffeensis infection . EtpE-C-coated latex beads , used to investigate whether EtpE-C can mediate cell invasion , entered both phagocytes and non-phagocytes and the entry was blocked by compounds that block E . chaffeensis entry . None of these compounds blocked uptake of non-coated beads by phagocytes . Yeast two-hybrid screening revealed that DNase X , a glycosylphosphatidyl inositol-anchored mammalian cell-surface protein binds EtpE-C . This was confirmed by far-Western blotting , affinity pull-down , co-immunoprecipitation , immunofluorescence labeling , and live-cell image analysis . EtpE-C-coated beads entered bone marrow-derived macrophages ( BMDMs ) from wild-type mice , whereas they neither bound nor entered BMDMs from DNase X-/- mice . Antibody against DNase X or DNase X knock-down by small interfering RNA impaired E . chaffeensis binding , entry , and infection . E . chaffeensis entry and infection rates of BMDMs from DNase X-/- mice and bacterial load in the peripheral blood in experimentally infected DNase X-/- mice , were significantly lower than those from wild-type mice . Thus this obligatory intracellular pathogen evolved a unique protein EtpE that binds DNase X to enter and infect eukaryotic cells . This study is the first to demonstrate the invasin and its mammalian receptor , and their in vivo relevance in any ehrlichial species . Ehrlichia chaffeensis causes human monocytic ehrlichiosis ( HME ) , an emerging tick-borne zoonosis . From the site of infected tick bite on human skin , E . chaffeensis infects monocytes and spreads via the bloodstream to various tissues , causing a systemic febrile disease . HME is characterized by fever , headache , myalgia , thrombocytopenia , leucopenia , and elevated liver-enzyme levels , but complications such as pulmonary insufficiency , renal failure , encephalopathy , and disseminated intravascular coagulation can cause death [1] . Early diagnosis and the proper treatment with doxycycline are critical to prevent complications . The disease is of particular threat to the immunocompromised and the elderly people [1] . E . chaffeensis is a small obligatory intracellular bacterium . It belongs to the family Anaplasmataceae in the order Rickettsiales that includes many understudied pathogens of veterinary and public health importance [2] . By electron microscopy , E . chaffeensis is a polymorphic bacterium ( 0 . 2–2 . 0 µm in diameter ) , and can be morphologically categorized as small dense-cored cells ( DCs ) or large reticulate cells ( RCs ) [3] . DCs are ∼0 . 2–0 . 5 µm in diameter , which is close to the size of the elementary body of Chlamydia and larger viruses such as Vaccinia virus . By light microscopy , it is not possible to distinguish individual RCs and DCs , since E . chaffeensis aggregates inside eukaryotic host cells . The characteristic clump of intracellular E . chaffeensis organisms is termed as “morula” ( mulberry in Latin ) [2] . However , when they are freshly isolated from host cells and dispersed , smaller bacteria ( <0 . 5 µm ) are more densely stained with basic dye than larger bacteria ( >0 . 5 µm ) ; therefore , they were defined as DCs and RCs , respectively [4] . DCs are more resistant to strong sonication and more infectious than RCs [5] . In cell culture , a biphasic developmental cycle has been reported: initially small infectious DCs bind to and internalize into host cells , which then develop into larger replicating RCs inside a membrane-lined compartment that resembles early endosomes . After replication in expanding inclusions , the mature RCs transform back into DCs prior to release from the host cells [4] , [5] . In patients' blood specimens , monocytes were primarily infected with E . chaffeensis , and hence , the disease was named as “monocytic ehrlichiosis” to distinguish it from “granulocytic ehrlichiosis” caused by infection with granulocyte-tropic Ehrlichia sp . [1] . E . chaffeensis can replicate well in several mammalian cell lines including canine histiocytic leukemia ( DH82 ) , human acute leukemia ( THP-1 ) , human promyelocytic leukemia ( HL-60 ) , human embryonic kidney ( HEK293 ) , and monkey endothelial ( RF/6A ) cells [6]–[8] . Entry into the permissive eukaryotic host cells is essential for E . chaffeensis to sustain its life , since its small genome of 1 . 18 Mb lacks a large portion of metabolic genes that are required for free living [9] . E . chaffeensis also lacks LPS , peptidoglycan , lipoteichoic acid , and flagella that engage Toll-like or NOD-like receptors , or scavenger receptors [2] , [10] . E . chaffeensis entry and subsequent infection of THP-1 cells , but not binding are almost completely inhibited by monodansylcadaverine ( MDC ) , a transglutaminase inhibitor [11] . MDC is known to block Neorickettsia risticii ( formerly Ehrlichia risticii ) entry and infection of P388D1 cells , vesicular stomatitis virus uptake and receptor-mediated endocytosis of α2-macroglobulin by Swiss 3T3 mouse cells , but not the uptake of latex beads by P388D1 mouse macrophages [12]–[14] . E . chaffeensis entry into THP-1 cells , leading to productive infection , is dependent on the host-cell surface lipid rafts and glycosylphosphatidyl inositol ( GPI ) -anchored proteins [15] . Furthermore , lipid raft-associated protein caveolin-1 , but not clathrin localizes to the E . chaffeensis entry site [15] . After entry , E . chaffeensis replicates in the membrane-bound compartment resembling an early endosome as it contains early endosome antigen 1 ( EEA1 ) , Rab5 , and transferrin receptor [6] . Several intracellular bacteria are known to enter host cells by using their specific surface protein collectively called as ‘invasin’ or ‘internalin’ [16] . However , detailed mechanisms of E . chaffeensis entry are unknown; particularly regarding the involvement of any specific bacterial surface protein that can function as an invasin and its cognitive host-cell receptor [2] . The comparative genome hybridization study of E . chaffeensis strains revealed that a hypothetical protein , ECH1038 , consists of highly conserved N- and C-terminal segments flanking its strain-variable central region [17] . ECH1038 expression is up-regulated in the DC stage of E . chaffeensis [18] . In this study , we uncovered that ECH1038 ( here named as entry triggering protein of Ehrlichia , EtpE ) , particularly its C-terminal conserved region ( EtpE-C ) , is critical for E . chaffeensis binding , entry , and infection of several different host cell types . In order to study whether EtpE-C can mediate the invasion into permissive host cells in the absence of other E . chaffeensis factors , we used latex beads coated with EtpE-C . The coated beads entered both phagocytes and non-phagocytes in a manner similar to E . chaffeensis entry . Subsequent investigation led to the discovery that DNase X , a host cell surface GPI-anchored protein , is the receptor of EtpE-C mediating the entry of E . chaffeensis into several mammalian cell types permissive to its replication . ECH1038 ( GenBank accession no . YP_507823 , EtpE ) consists of 1963 amino acid residues ( Mr 222 , 638 , pI: 7 . 0 ) and is predicted to be an outer membrane protein with an N-terminal secretion signal by PSORT analysis [17] . Although EtpE is variable in the central ∼950 residues , the N-terminal ∼700 residues and C-terminal ∼300 residues are conserved among multiple E . chaffeensis strains of distinct virulence , suggesting that these two regions are indispensable for E . chaffeensis . The amino acid sequence alignment of EtpE orthologs in the three strains of E . chaffeensis , Arkansas , Wakulla , and Liberty are shown ( suppl . Fig . S1 ) . Amino acid sequence alignment of EtpE orthologs among three genome-sequenced Ehrlichia species , E . chaffeensis Arkansas , E . canis Jake and E . ruminantium Welgevonden , revealed that while most of the N-terminal proximal region was conserved also among Ehrlichia species , the C-terminal region was not ( suppl . Fig . S2 ) . To begin probing EtpE function , we cloned N-terminal ( residues 29–708 ) and C-terminal ( residues 1658–1965 ) of EtpE as C-terminal histidine-tagged fusion proteins ( rEtpE-N and rEtpE-C ) and antisera were prepared against rEtpE-N and rEtpE-C . Western blot analysis using these antibodies showed that full-length EtpE was expressed by E . chaffeensis in DH82 cells ( Fig . 1A ) . DH82 cells were initially used , since this cell line has been successfully used to culture isolate E . chaffeensis from the blood of HME patients [19] , [20] . To determine whether EtpE is expressed by E . chaffeensis in human monocytes , the pathogen's primary in vivo target cells , EtpE expression was determined in E . chaffeensis cultured in human primary macrophages derived from peripheral blood monocytes by double immunofluorescence labeling after paraformaldehyde ( PFA ) fixation and saponin permeabilization . E . chaffeensis major outer membrane protein P28 [21] was used as positive control to label the bacterial membrane . The results showed that EtpE was abundantly expressed by E . chaffeensis in human macrophages , and localized at bacterial membrane like P28 [21] ( Fig . 1B ) . P28 is bacterial surface exposed [21] and is a β-barrel protein that functions as porin [22] . To determine whether EtpE is exposed on the bacterial surface , double immunofluorescence labeling with anti-EtpE-C and anti-P28 was performed after PFA fixation without saponin permeabilization using E . chaffeensis bound to the surface of DH82 cells . Unlike methanol or acetone fixation , PFA fixation does not allow antibody penetration across biological membranes unless with subsequent permeabilization , thereby limiting antibody staining to molecules exposed to the cell surface [23] . The result showed labeling of both EtpE and P28 ( Fig . 1C ) . Of note , labeled EtpE on E . chaffeensis had a beaded ( rosary-like ) pattern encircling individual bacterium , in contrast to P28 that had a uniform ring pattern ( Fig . 1C ) . When host cell-free bacteria were treated with pronase E , the surface immunofluorescence staining of EtpE was abolished , but not of CtrA which is an E . chaffeensis cytosolic response regulator of a two-component system [18] ( suppl . Fig . S3 ) . These data indicate the surface exposure of EtpE . In contrast to the punctate labeling pattern of EtpE in host cell-bound bacteria , homogeneous labeling of EtpE was observed on host cell-free bacteria ( suppl . Fig . S3 ) . Given the surface exposure of EtpE on E . chaffeensis , we examined whether the antibody against EtpE inhibits binding , entry , and infection of E . chaffeensis . Among the several host cell types used in this study , primary monocytes , macrophages , and myelocytic leukemia cell lines ( DH82 and THP-1 cells ) are referred to as phagocytes . Phagocytes are very efficient in bacteria and particle uptake as they have an array of dedicated phagocytic receptors , including pathogen pattern recognition receptors , mannose receptors , scavenger receptors , receptors for immunoglobulin ( FcR ) and complement ( CR3 ) that utilize opsonins for ingestion , to name a few [10] , [24] . The other two cell lines used in this study , RF/6A endothelial and HEK293 epithelial cells , are referred to as non-phagocytes , since they lack these features . We first used non-phagocytes to study the effect of in vitro antibody neutralization of EtpE as they lack the response to opsonization and will not readily take-up opsonized particles . E . chaffeensis was pre-incubated with mouse anti-EtpE-C or preimmune mouse sera , and then incubated with RF/6A cells . Binding and entry were determined by immunofluorescence labeling of E . chaffeensis with anti-P28 at 30 min and 2 h post-incubation/infection ( pi ) , respectively . Infection was determined at 48 h pi by quantitative real-time PCR ( qPCR ) . Anti-EtpE-C blocked E . chaffeensis binding , entry , and subsequently infection by approximately 80% compared to preimmune serum ( Fig . 1 , D–F ) . Similar level of inhibition of binding and entry was observed using mouse anti-EtpE-C in phagocytic cells such as human THP-1 cells ( suppl . Figs . S4A and S4B ) and canine DH82 cells ( data not shown ) . This suggests that human or canine FcR-mediated entry of E . chaffeensis opsonized with mouse anti-EtpE-C was negligible in this experiment . Immunization of mice with recombinant P28 , which functions as a porin [22] , protects mice from E . chaffeensis challenge [21] . Additionally , in a mouse model of HME , immunization of mice with Ehrlichia muris P28 conferred protection from E . muris challenge [25] . As another control , to rule out the possibility that inhibition of binding is a general property of antibody neutralization of any E . chaffeensis cell surface proteins , we examined whether antibody against P28 blocks E . chaffeensis binding and entry . Our result showed antibody ( Fab fragment ) against P28 did not block binding or entry of E . chaffeensis ( suppl . Fig . S5A and S5B ) . Taken together , these results suggest that EtpE-C potentially serves as an invasin to trigger E . chaffeensis entry in both phagocytes and non-phagocytes . EtpE is predicted to be anchored on the bacterial outer membrane at its N-terminus , based on analysis using the PRED-TMBB webserver [17] , [26] . In contrast to anti-EtpE-C , anti-EtpE-N-pretreatment reduced E . chaffeensis infection by only 20% ( suppl . Fig . S6A ) . Since both anti-EtpE-N and anti-EtpE-C reacted with native EtpE protein from E . chaffeensis equally well by Western blot analysis , we tested accessibility of anti-EtpE-N to EtpE molecules on live E . chaffeensis surface . For this purpose , we freshly prepared the host cell-free E . chaffeensis and incubated with the antibodies without pre-fixation . The result showed that E . chaffeensis was not as readily labeled with anti-EtpE-N as with anti-EtpE-C ( suppl . Fig . S6B ) , suggesting that the antibody access to the N-terminal conserved region might be limited in the native conformation of EtpE in live E . chaffeensis . Because EtpE is highly expressed by E . chaffeensis in mammalian cells in vitro , we next examined whether EtpE is expressed in vivo by Western blot analysis of defined HME patient sera [27] . Equal quantities of rEtpE-N and rEtpE-C ( GelCode Blue staining shown in Fig . 2A ) were used as antigens in the assay . Patient sera recognized both rEtpE-N and rEtpE-C , whereas the control serum from a healthy individual in an HME non-endemic region did not react with the recombinant proteins ( Fig . 2B ) . Similarly , sera from dogs experimentally infected with E . chaffeensis [28] , that were previously shown to recognize E . chaffeensis OmpA [18] and other E . chaffeensis lipoproteins [28] , recognized both rEtpE-N and rEtpE-C , but the control dog serum did not ( Fig . 2B ) . These data indicated that EtpE is expressed by E . chaffeensis in vivo during infection of its natural hosts , humans and dogs , and that an antibody ( humoral ) response is mounted against this protein during infection and disease . Previous studies showed that antibodies contribute to immunity against E . chaffeensis in immunocompetent mice [29] . Given the facts that anti-EtpE-C neutralized E . chaffeensis binding , consequently entry and infection in vitro , EtpE was expressed by E . chaffeensis in vivo and that a humoral immune response was mounted in infected mammals , we decided to examine whether rEtpE-C immunization could confer protection in mice from E . chaffeensis challenge . C3H/HeJ strain of mice was used , since this strain was reported to serve as a useful model for studying E . chaffeensis infection [30] . At 10 days after the last immunization , all mice were challenged intraperitoneally with E . chaffeensis . The E . chaffeensis load in the blood from rEtpE-C-immunized mice at 5 days post challenge was significantly lower than that of non-immunized mice ( Fig . 2C ) . These results indicate that rEtpE-C is a protective immunogen relevant in E . chaffeensis infection in vivo . Bacterial surface exposure of EtpE-C and effectiveness of EtpE-C as the target for both in vitro and in vivo neutralization suggest that EtpE-C may mediate E . chaffeensis invasion . To investigate this possibility , we utilized fluorescent latex beads of average size of 0 . 5 µm ( similar to the size of infectious DCs of E . chaffeensis ) coated with rEtpE-C protein . The presence of rEtpE-C on beads was confirmed by dot-blot analysis ( data not shown ) and immunofluorescence labeling with antiserum against EtpE-C ( Fig . 3A ) . Beads were incubated with mouse bone marrow-derived macrophages ( BMDMs ) for 45 min followed by trypsin treatment to remove beads that were not internalized . Mouse BMDMs were used here , also to serve as the wild-type control for the later studies using BMDMs from mutant mice . rEtpE-C-coated beads entered BMDMs ( Figs . 3B and 3C ) . Treatment with MDC , genistein ( broad-spectrum protein tyrosine kinase inhibitor ) , or phosphatidylinositol-specific phospholipase C ( PI-PLC that removes GPI-anchored proteins from the cell surface ) blocks E . chaffeensis entry and infection of THP-1 cells [11] , [15] . The entry of rEtpE-C-coated beads into BMDMs was almost completely blocked by these treatments ( Figs . 3B and 3C ) , suggesting rEtpE-C-coated beads enter BMDMs by the same signaling pathway as E . chaffeensis does . The latex bead is well-known to be taken up by macrophages and has been used as a model to study phagocytosis [31] . In striking contrast , entry of non-coated beads into BMDMs was not blocked by any of these treatments ( Fig . 3D ) . Non-coated beads did not bind or enter RF/6A and HEK293 non-phagocytic cells ( HEK293 cell data are shown in Fig 4B ) . Remarkably , rEtpE-C-coated beads did readily bind and enter non-phagocytes ( HEK293 data are shown in Figs . 4A and 4B ) . Beads coated with other recombinant E . chaffeensis proteins including rEtpE-N , rECH0825 ( a type IV secretion effector protein ) [7] or rECH0365 ( GroEL ) did not bind HEK293 cells ( Figs . 4A and 4B ) , indicating binding and entry of beads into non-phagocytes was due to specific coating with EtpE-C . Scanning and transmission electron microscopy revealed that rEtpE-C-coated beads bound to RF/6A cells were associated with filopodia-like membrane projections ( Figs . 4C and 4D left panel ) similar to those surrounding E . chaffeensis bound to DH82 cells [4] . Transmission electron microscopy of RF/6A cells incubated with rEtpE-C-coated beads verified that the beads were indeed internalized into RF/6A cells ( Fig . 4D right panel ) . MDC , genistein , and verapamil ( a Ca2+ channel blocker ) that block E . chaffeensis entry into THP-1 cells [11] , also blocked E . chaffeensis entry into RF/6A cells ( suppl . Fig . S7 , MDC data is shown ) . Treatment with any of these compounds almost completely blocked the entry of rEtpE-C-coated beads into RF/6A cells ( Figs . 4E and 4F ) . Once internalized , E . chaffeensis-containing vacuoles acquire characteristics of early endosomes [6] . To determine whether rEtpE-C-coated beads were delivered to early endosomes , immunofluorescence labeling was used to visualize the spatial relationship of the early endosomal marker , EEA1 with the rEtpE-C-coated beads , and observed by deconvolution microscopy . Individual as well as multiple beads were seen encased by EEA1-labeled membranous compartment , suggesting that some beads were in endosomes ( Fig . 4G ) . These results indicate that EtpE-C is an invasin , and even in the absence of any other E . chaffeensis factors , EtpE-C alone is sufficient to mediate the binding and entry of EtpE-C-coated beads into non-phagocytic cells . Since EtpE-C could mediate binding and entry of EtpE-C-coated beads , we next searched for the potential host-cell receptor for EtpE-C . We cloned EtpE-C into the yeast two-hybrid bait vector and screened a human bone marrow cDNA prey library to identify interacting proteins . Of the 5 clones detected and sequenced , all of them encoded a protein , deoxyribonuclease 1-like 1 ( DNase 1L1 , DN1L1 , or DNase X on chromosome Xq28 , GenBank accession no: X90392 , 302 residues ) . One of the clones contained an additional plasmid encoding S-adenosyl methionine-dependent methyltransferase but the coding sequence was out-of-frame; this prey construct alone did not support yeast growth when co-transformed with bait vector to test their interaction . All sequence hits corresponded to the C-terminal fragment of DNase X ( residues 105–302 ) . DNase X , one of the human DNase I–family endonucleases , is expressed on the cell surface as a GPI-anchored protein and also localized at early endocytic vesicles , endoplasmic reticulum , and Golgi [32] , [33] . To confirm EtpE-C binding to the native human DNase X , we performed far-Western blot analysis . DNase X from the THP-1 cell lysate bound to re-natured rEtpE-C on a nitrocellulose membrane , whereas DNase X did not bind the control rECH0825 ( Fig . 5A ) . Next , we utilized a protein pull-down assay wherein THP-1 cell lysate was applied to rEtpE-C bound to and renatured on a Ni-affinity matrix . Western blotting showed that native DNase X from the lysate bound to rEtpE-C , but not to the control rECH0825 ( Fig . 5B ) . In addition , co-immunoprecipitation showed that anti-EtpE-C , but not the control mouse IgG pulled down native DNase X from the lysate of THP-1 cells incubated with E . chaffeensis for 30 min ( Fig . 5C ) . Taken together , these results indicate that EtpE-C can bind to DNase X . We fixed RF/6A or HEK293 cells incubated with rEtpE-C-coated beads , and without membrane permeabilization , immunofluorescence labeled cell surface-exposed DNase X . DNase X localized to the areas on the surface of cells where rEtpE-C-coated beads were present ( RF/6A cell image shown in Fig . 5D ) . Time-lapse live-cell image analysis of rEtpE-C-coated beads bound to RF/6A cells ectopically expressing DNase X-GFP at 4°C showed that , upon warming up to 37°C , the initially separated DNase X-GFP signal and beads became closer and overlapped within 5 min ( Fig . 5E; see also suppl . Movie S1 ) . The fluorescence intensity profile analysis of red ( rEtpE-C-coated beads ) and green ( DNase X-GFP ) signal along the length of the line also revealed that the signals separated at initial time points converged in a few min after warming-up ( Fig . 5F ) . Since DNase X localized to EtpE-C-coated beads in non-phagocytes , we next examined this phenomenon in phagocytes . Human primary macrophages derived from peripheral blood monocytes were incubated with rEtpE-C-coated or non-coated beads , cell surface exposed DNase X was immunofluorescence-labeled without permeabilization and the distribution of beads and DNase X was examined by deconvolution microscopy . Surface DNase X was seen clustered with rEtpE-C-coated beads; whereas both surface DNase X and beads were randomly dispersed with non-coated beads ( image in a single z-plane shown in Fig . 6A ) . Orthogonal views of the cell from the reconstructed 3D view of serial z-stack images unequivocally demonstrated colocalization of DNase X with rEtpE-C-coated beads ( Fig . 6B left panel , see also 3D view in suppl . Movie S2 ) , whereas DNase X did not colocalize with non-coated beads ( Fig . 6B right panel , see also 3D view in suppl . Movie S3 ) . The intensity profile analysis of green ( DNase X ) and red ( beads ) signals of a single optical section showed that DNase X coincided with rEtpE-C-coated beads , but not with non-coated beads ( Fig . 6B right panels ) . Similar results were observed with canine primary macrophages derived from peripheral blood monocytes and DH82 cells ( suppl . Fig . S8 ) . These results indicate DNase X localizes to rEtpE-C-coated beads in primary human and canine macrophages , the pathogen's in vivo target cells . rEtpE-C-coated beads entered wild-type mouse BMDMs as shown in Figs . 3B and 3C . Therefore , we next examined whether rEtpE-C-coated beads can enter BMDMs from congenic DNase X−/− mice . Beads were incubated with BMDMs from DNase X−/− mice for 45 min followed by trypsin treatment to remove beads that were not internalized . Results showed rEtpE-C-coated beads did not enter DNase X−/− BMDMs ( Figs . 6C and 6D ) . In striking contrast , non-coated beads freely entered DNase X−/− BMDMs ( Figs . 6C and 6D ) . This lack of entry of rEtpE-C-coated beads into DNase X−/− BMDM , but not into the wild-type BMDM , was a direct consequence of its failure to bind DNase X−/− BMDM ( suppl . Fig . S9 ) . This phenomenon was specific to rEtpE-C coated beads , because neither the non-coated beads nor the rECH0825-coated beads showed any defect in binding DNase X−/− BMDMs ( suppl . Fig . S9 ) . Taken together , these results indicate that rEtpE-C coating dictates the latex bead binding and entry via DNase X-dependent pathway . Since DNase X was localized to EtpE-C-coated bead entry foci , we next examined whether DNase X localizes to the E . chaffeensis entry foci as well . Double immunofluorescence labeling of non-permeabilized DH82 cells and primary human macrophages derived from human peripheral blood monocytes showed surface DNase X colocalization with the bound E . chaffeensis ( Figs . 7A and 7B ) . DNase X also localized to the areas of E . chaffeensis binding on THP-1 cells ( data not shown ) . When DH82 cells were pre-incubated with monoclonal anti-DNase X IgG to block the surface-exposed DNase X , E . chaffeensis binding , entry , and overall infection were significantly reduced compared with the control mouse IgG-treated DH82 cells ( Figs . 7C–7E ) . Next , we utilized a small interfering RNA ( siRNA ) against DNase X to reduce the expression of endogenous DNase X in HEK293 cells ( Fig . 7F ) . Suppression of DNase X expression significantly reduced E . chaffeensis infection in HEK293 cells ( Fig . 7G ) . Moreover , E . chaffeensis binding and entry were reduced by 60% in DNase X−/− BMDMs compared to the wild-type BMDMs ( Fig . 7H ) . E . chaffeensis load at 56 h pi was significantly lower in DNase X−/− BMDMs compared to the wild-type BMDMs ( Fig . 7I ) . These results demonstrated that effective E . chaffeensis binding , entry , and infection depended on DNase X . Importantly , E . chaffeensis load in peripheral blood at 5 days pi in DNase X−/− mice was significantly lower than in wild-type mice ( Fig . 7J ) , indicating that effective in vivo infection of E . chaffeensis also requires involvement of DNase X . E . chaffeensis entry of permissive host cells is an absolute requisite , not only in the pathogenesis of HME , but also for the very existence of the bacterium in nature owing to its inability to survive outside of eukaryotic cells . The present study showed that E . chaffeensis surface protein EtpE is not only an adhesin that binds a specific receptor DNase X , a GPI-anchored mammalian cell surface protein , but also an invasin that subsequently mediates bacterial internalization . E . chaffeensis entry into phagocytes and non-phagocytes was similar , but completely different from the phagocytosis of non-coated latex beads . Differences include 1 ) ability of E . chaffeensis to bind and enter non-phagocytes vs . inability of non-coated beads to enter non-phagocytes , 2 ) inhibition of E . chaffeensis entry into phagocytes and non-phagocytes by MDC , genistein , verapamil , and PI-PLC vs . lack of inhibition of entry of non-coated beads into phagocytes by these compounds , 3 ) DNase X-dependent entry of E . chaffeensis into phagocytes and non-phagocytes , vs . DNase X-independent entry of non-coated beads into phagocytes , and 4 ) colocalization of DNase X and E . chaffeensis during entry vs . lack of colocalization of non-coated beads with DNase X . Remarkably , coating with a fragment of a single protein , EtpE-C made the beads to bind and enter non-phagocytes and phagocytes like E . chaffeensis does . Similarities between EtpE-C-coated beads and E . chaffeensis include 1 ) signaling pathways for invasion sensitive to MDC , genistein , verapamil , and PI-PLC , 2 ) DNase X-dependent adhesion and invasion , 3 ) colocalization with DNase X at entry foci during invasion , and 4 ) ultrastructure of entry process ( filopodia , [4] ) . Some of these features are summarized in Table 1 . Our results demonstrate that in the absence of any other bacterial proteins EtpE-C is sufficient to orchestrate the binding and entry of coated beads , and in extension E . chaffeensis binding and entry , into its host cells . Protein coating of latex beads has been widely used for phagocytosis study [34] and also been previously utilized for studies with Listeria internalin [35] . For studying rickettsial adhesins and invasins , Escherichia coli-based heterologous protein expression system has been utilized [36] , [37] . Using latex beads coated with rickettsial proteins provides an alternative way to study rickettsial invasin . The method is especially useful for those proteins poorly expressed on E . coli surface and/or when using host cells , such as macrophages , that are easily perturbed by E . coli . Function of EtpE-C as an E . chaffeensis invasin mediating its DNase X-dependent entry of host cells is strongly supported by in vitro and in vivo anti-EtpE-C neutralization results , and in vitro and in vivo requirement of DNase X for efficient E . chaffeensis infection . The results suggest E . chaffeensis invasion process , like inert rEtpE-C-coated latex beads , does not require bacterial energy . Adhesion alone seems to be sufficient in triggering cellular receptor-mediated signaling pathways resulting in filopodial extension and internalization . This is distinct from the triggering of bacterial uptake by the action of actively injected bacterial type III secretion system effector molecules [38] , [39] , but in agreement with the condensed and resistant features of E . chaffeensis DCs capable of invasion [18] . This also is in agreement with the regulation of EtpE expression by a DNA binding protein BolA , which is in turn regulated by CtrA , a response regulator of the two-component system [18] . E . chaffeensis CtrA positively regulates genes involved in the development of the resistant phenotype , at the late stage of E . chaffeensis growth cycle , when RCs convert to DCs [18] . The rosary-like pattern of EtpE in host cell surface-bound E . chaffeensis did not preexist in host cell-free E . chaffeensis; thus might be induced by its engagement with cell surface DNase X upon binding . Significance of this change in EtpE surface distribution awaits further investigation . Although both EtpE-N and EtpE-C were conserved among E . chaffeensis strains , only EtpE-C mediated binding and entry into non-phagocytes , indicating functional specificity of this segment . In addition to the poor accessibility of anti-EtpE-N to its target on live E . chaffeensis surface , the inability of EtpE-N to function as an invasin seems to be the primary reason for the weak in vitro neutralization of E . chaffeensis infection with anti-EtpE-N . While both EtpE-N and EtpE-C fragments were recognized and elicited humoral immune response in naturally infected humans and experimentally infected dogs , it seems that the antibody titer to EtpE-C is lower than to EtpE-N . This lower titer may help E . chaffeensis to establish infection . Although the present study did not probe the function of the central region of EtpE , being variable among E . chaffeensis strains , this segment of the protein may be under selective pressure in the host for E . chaffeensis to persist in nature . Although EtpE is found in all Ehrlichia spp . , amino acid sequences corresponding to EtpE-C are not conserved among EtpE orthologs of Ehrlichia spp . ; whether these orthologs also serve as adhesins/invasins in other Ehrlichia sp . remain to be studied . The present study showed that not only antibody against rEtpE-C neutralizes E . chaffeensis infection in vitro , but also immunization of mice with rEtpE-C confers protection against E . chaffeensis challenge . It has been reported that adoptive transfer of immune serum from immunocompetent C57BL/6 mice to immuno-compromised SCID mice confers significant protection from fatal E . chaffeensis infection [36] . Mice with depleted complements , or lacking B cells , FcγR1 , CR1 , CR2 , or Nox2 ( a subunit of NADPH oxidase ) are more susceptible to non-lethal dose infection with the HF strain of Ehrlichia ( Ixodes ovatus Ehrlichia ) that is closely related to E . chaffeensis [28] . N . risticii infects P388D1 macrophages , but FcR-mediated entry kills this bacterium and Fab fragment of the immune IgG blocks binding and entry of N . risticii [37] . Whether any of these mechanisms are involved in protection of mice from E . chaffeensis infection by rEtpE-C immunization awaits further study . Nonetheless , our results from in vitro neutralization studies suggest a direct inhibition of E . chaffeensis binding by anti-EtpE-C antibody and support the importance of antibodies in ehrlichial immunity . A recent study showed that EtpE mRNA is highly expressed by E . chaffeensis in cell lines derived from the ticks , Amblyomma americanum and Ixodes scapularis using E . chaffeensis genome microarrays [38] . Since E . chaffeensis is transmitted between mammals by tick bite , expression of EtpE in tick cells suggests an indispensable role for this protein throughout the E . chaffeensis life cycle . DNase X-mediated uptake is the first entry pathway to be uncovered for Ehrlichia spp . Compared to DNase I , DNase X has an extra hydrophobic stretch at its C-terminus [32] . This C-terminal stretch is conserved in all mammalian DNase X proteins examined , and required for GPI-anchoring to the plasma membrane [33] , [40] . Cellular functions of DNase X are not understood well . There is no obvious defect in DNase X−/− mice and their reproduction is normal [41] . DNase X was never reported to serve as a receptor for any infectious agents . –Overexpression of DNase X enhances degradation of the exogenous plasmid DNA , consequently suppressing transformation in RD myotubes; whereas siRNA-mediated DNase X knockdown reverses this inhibition [33] . DNase X is , therefore , considered as a barrier in naked DNA transfer [33] . rEtpE-C-coated beads , unlike non-coated beads or rECH0825-coated beads , were unable to bind or enter DNase X−/− BMDMs . Un-opsonized non-coated latex beads are taken up by macrophages predominantly via scavenger receptors [42]–[44] . This suggests that rEtpE-C-coating not only made beads to bind DNase X , but also to repel or avoid the scavenger receptor recognition . The detailed mechanism underlying this phenomenon remains to be clarified . The EtpE-DNase X-mediated entry of host cells by E . chaffeensis seems to be by a ‘zipper mechanism’ [39] initiated by specific contacts between bacterial ligand and host cell surface receptor and sequential engagement of the host cell surface membrane against bacterial surface . A schematic representation of a working model for E . chaffeensis binding and invasion of its target cells is depicted in Fig . 8 . The initial binding and engagement of DNase X in the lipid-raft enriched areas on the host cell surface by EtpE C-terminal region causes clustering and lateral re-distribution of DNase X molecules to the site of E . chaffeensis attachment . This binding elicits signals that culminate in host cytoskeletal remodeling , filopodial formation embracing the bacteria and engulfment of the bound bacteria into an early endosome in the host cell . This receptor-mediated endocytosis can be specifically disrupted by genistein , verapamil or MDC . Among members of the order Rickettsiales , the bacterial ligand and the cognitive host cell receptor pair for invasion have been reported only for Rickettsia conorii using in vitro cultured cells . For invasion , R . conorii uses an autotransporter protein OmpB as the ligand and Ku70 , one of the DNA-binding components of the DNA-dependent protein kinase of mammalian cells as the receptor [45] . Whether Ku70 is required for in vivo infection by Rickettsia spp . is unknown . OmpB was originally identified as an invasive rickettsial protein capable of mediating binding and entry of host cells in R . japonica , another spotted fever group Rickettsia closely related to R . conorii [36] . Anaplasma phagocytophilum , the causative agent of human granulocytic anaplasmosis , binds to fucosylated P-selectin glycoprotein ligand-1 ( PSGL-1 ) as a key initial receptor component [46] . α1 , 3-fucose is critical for A . phagocytophilum to infect neutrophils in mice and for the bacterium to colonize ticks [47] , [48] . α2 , 3-sialic acid at the PSGL-1 N-terminus was identified as the receptor for an A . phagocytophilum adhesin , the peptidoglycan-associated lipoprotein , OmpA [49] . The present study identifies the first Ehrlichia invasin-receptor pair and is a critical advancement as this provides the first evidence for the importance of a given invasin-receptor pair in vivo for any pathogens belonging to the order Rickettsiales . In contrast to the almost complete abrogation of entry of rEtpE-C-coated beads into DNase X−/− BMDMs , E . chaffeensis entry into DNase X−/− BMDMs was reduced by 60% compared to wild-type BMDMs . This suggests existence of additional mammalian receptors for E . chaffeensis infection . Similarly R . conorii invasion of embryonic fibroblasts derived from Ku70−/− mice is reduced by 50% compared to the cells derived from the wild-type litter mates [45] . A recent report has shown that E . coli expressing another autotransporter protein of R . conorii , OmpA , can invade endothelial cells by interacting with α2β1 integrin [50] . Rickettsial OmpA was originally suggested as an adhesin in R . rickettsii [51] . Although Rickettsia and Ehrlichia spp . are phylogenetically related ( both belong to the order Rickettsiales ) , and share similar wild animal reservoirs and vector ticks in nature , E . chaffeensis EtpE and R . conorii invasin OmpB/OmpA are uniquely evolved in them , respectively [52] . This perhaps enabled these bacteria to utilize independent host surface receptors: DNase X and Ku70 , respectively , for invasion to infect broader host range and cytoplasmic niche . In light of severe and potentially fatal outcomes of HME , the limited choice of antibiotics and lack of prophylactic measures , further understanding of invasion mechanisms is of great importance . Such information will assist in the development of new preventive and therapeutic measures against HME and similar diseases by specific pharmacological or immunologic disruption of invasin and the host cell receptor interaction . All animal experiments were performed in accordance with The Ohio State University Institutional Animal Care and Use Committee guidelines and approved e-protocol numbers 2009A0186 and 2008A0066 . The University program has Full Continued Accreditation by the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC-I ) under #000028 dated June 9 , 2000 and has a Public Health Services assurance renewal #A3261-01 dated February 5 , 2007 through February 28 , 2015 . The program is licensed by the USDA , #1-R-014 , and is in full compliance with Animal Welfare Regulations . E . chaffeensis Arkansas type strain was propagated in DH82 cells , and host cell–free E . chaffeensis was obtained by controlled sonication as described [53] , [54] . Human peripheral blood monocytes were derived from buffy coats; HEK293 , RF/6A and THP-1 cells were cultured as previously described [7] , [8] , [54] . BMDMs were established from wild-type and DNase X −/− mice as described [8] . Two groups of C3H/HeJ mice ( 4-week-old females; 4 mice per group ) ( Jackson Laboratories ) received either minced SDS-polyacrylamide gel containing 50 µg of rEtpE-C or minced gel alone , with Quil A ( Accurate Chemicals ) as adjuvant for a total of three times at 14-day intervals . E . chaffeensis challenge was performed 10 days following the last immunization as described [21] . DNase X−/− [41] and congenic wild-type C57BL/6 mice ( 5- to 6-week-old females; 5 mice per group ) were inoculated intraperitoneally with E . chaffeensis-infected THP-1 cells ( >90% cells infected; 6×105 cells/mouse ) . DNA was extracted from blood samples using a QIAamp blood kit ( Qiagen ) , and subjected to qPCR using E . chaffeensis 16S rDNA and mouse glyceraldehyde 3-phosphate dehydrogenase ( G3PDH ) gene primers . DNA fragments encoding EtpE-C and EtpE-N were amplified by PCR with Phusion high-fidelity DNA polymerase ( NEB ) using E . chaffeensis chromosomal DNA as template . The fragments were cloned into pET33b ( + ) vector ( Novagen ) ; recombinant proteins were expressed in E . coli BL21 ( DE3 ) and purified by Ni-affinity chromatography . The antibody against rEtpE-C was produced in ICR mice ( Harlan ) , and the antibody against rEtpE-N was produced in rabbits . Affinity-purified rEtpE-C and rEtpE-N ( 5 µg each ) were subjected to SDS-PAGE , transferred to a nitrocellulose membrane , and incubated with sera from E . chaffeensis-infected dogs ( CTUALJ , 3918815 , 1425 ) [28] , HME patients ( ID: MRL1-22 , MRL1-40 , 72088 ) [27] , or control sera . After washing , the membranes were incubated with horseradish peroxidase–conjugated goat anti-dog or anti-human IgG ( KPL ) . Reacting bands were visualized with enhanced chemiluminescence ( ECL ) , images were captured and densitometric analysis was performed using an LAS3000 image documentation system ( FUJIFILM Medical Systems ) . Yeast two-hybrid screening was performed using Matchmaker Two-Hybrid System ( Clontech ) according to manufacturer's instructions . The bait plasmid pGBKT7-EtpE-C was constructed by the fusion of EtpE-C with the GAL4 DNA-binding domain in pGBKT7 ( Clontech ) by PCR . EtpE-C coding sequence was amplified using the forward primer 5′-AATCCATGGAATTGTTGTCATTAGTTGGTGGGCATCG-3′ ( 5′ NcoI site underlined ) and reverse primer 5′-TCGACGGATCCAATCCCCTTCCAGCATTAATTTTATCAAAGG-3′ ( 5′ BamHI site underlined ) , and the product was ligated into pGBKT7 . pGBKT7-EtpE-C was transformed into Saccharomyces cerevisiae strain AH109 and selected by the ability to grow on SD agar plates lacking tryptophan . The expression of bait protein EtpE-C in yeast was examined by Western blotting . The human bone marrow MATCHMAKER cDNA library ( Clontech ) that was fused with GAL4-activating domain in pGADT7 was transformed in S . cerevisiae strain Y187 ( Clontech ) . Library clones expressing interacting prey proteins were screened with yeast mating . Positive clones were selected by their ability to grow on SD quadruple drop-out ( SD/QDO ) plates lacking adenine , histidine , leucine , and tryptophan , and verified on SD/QDO plates containing X-gal . Positive clones were then isolated , and the prey plasmids were purified and sequenced after they were transformed into E . coli TOP10F′ competent cells ( Invitrogen ) . The interaction was confirmed by re-shuttling the purified prey plasmid into S . cerevisiae AH109 transformed with bait plasmid and by nutritional selection in SD/QDO plates . Coverslip cultures of DH82 , HEK293 , RF/6A cells , or macrophages differentiated from human peripheral blood monocytes or established from bone-marrow of DNase X−/− or congenic wild-type mice and suspension culture of THP-1 cells were incubated with E . chaffeensis freshly isolated from infected cells at approximate multiplicity of infection ( MOI ) of 200 , unless otherwise noted , for 30 to 45 min for binding assays or 2 to 4 h for internalization assays at 37°C in 5% CO2/95% air . Cells were washed with phosphate-buffered saline ( PBS: 137 mM NaCl , 2 . 7 mM KCl , 8 . 1 mM Na2HPO4 , 1 mM KH2PO4 , pH 7 . 4 ) to remove unbound bacteria and labeled with antibodies as described [23] . For binding assay cells were fixed with 3% PFA and labeled with mouse monoclonal anti-DNase X ( Abcam ) , rabbit anti-E . chaffeensis P28 [21] , mouse anti-rEtpE-C , or dog anti-E . chaffeensis [55] as primary antibodies and Alexa Fluor ( AF ) 488–conjugated goat anti-mouse IgG , AF555–conjugated goat anti-rabbit IgG ( Invitrogen ) , or Texas Red–conjugated goat anti-dog IgG ( Jackson ImmunoLab ) as secondary antibodies . For internalization assays , two steps of labeling of fixed cells with anti-P28 were carried out as described: the first labeling step was performed without saponin permeabilization using AF488–conjugated goat anti-rabbit IgG , and the second labeling was performed with permeabilization using AF555–conjugated goat anti-rabbit IgG [56] . Fluorescent images were acquired using a Nikon Eclipse E400 fluorescence microscope with a xenon-mercury light source ( Nikon ) , Deltavision deconvolution microscope ( Applied Precision ) with 0 . 2 or 0 . 4-µm step size along the z-axis of the cells , or an LSM 510 laser-scanning confocal microscope ( Carl Zeiss ) . For immunostaining of live bacteria , host cell-free E . chaffeensis was incubated with anti-EtpE-C , anti-EtpE-N or E . chaffeensis P28 for 1 h at room temperature followed by fixing with 3% PFA and labeling with AF555-conjugated goat anti-mouse or anti-rabbit antibodies . To further demonstrate the surface exposure of EtpE , host cell-free E . chaffeensis was incubated with either pronase E ( Sigma ) at a concentration of 2 mg/ml in PBS or vehicle control for 15 min at 37°C [57]; pronase E was inactivated by adding 10% fetal bovine serum ( FBS ) , followed by washing in PBS twice . The bacteria were cytospun onto glass slides , fixed with 3% PFA , followed by quenching in PBS containing 0 . 1 M glycine , washed with PBS and labeled sequentially with anti-EtpE-C and anti-CtrA [18] with or without saponin permeabilization followed by AF488 or AF555-conjugated goat anti-mouse or anti-rabbit antibodies . Approximately 106 cells of E . chaffeensis-infected THP-1 cells/ml in 2 ml of methionine cysteine-deficient RPMI 1640 medium ( ICN Biomedicals ) supplemented with 10% FBS and 2 mM l-Gln were incubated with cycloheximide ( Sigma ) at 10 µg/ml at 37°C for 1 h . A metabolic labeling reagent ( Tran 35S-Label; 11 . 7 mCi/ml [1 , 100 Ci/mmol]; 100 µl; ICN Biomedicals ) was added and the mixture was incubated further at 37°C for 24 h . The radiolabeled E . chaffeensis was released by sonication and washed by centrifugation at 10 , 000×g for 10 min . To study the effect of rabbit anti-P28 on E . chaffeensis binding and entry , radiolabeled E . chaffeensis cells ( 40 , 000 cpm/200 µl ) preincubated with Fab fragment of anti-P28 IgG [0 . 5 mg/ml , prepared using Immobilized papain ( Pierce ) from IgG affinity purified with AffiPack Immobilized Protein A column ( Pierce ) ] or Fab fragment of normal rabbit IgG ( 0 . 5 mg/ml ) were added to 1×106 THP-1 cells in 0 . 4 ml of RPMI 1640 medium containing 10% FBS and 2 mM l-Gln and incubated at 4°C for 2 h . The uptake of E . chaffeensis was evaluated following removal of bound E . chaffeensis cells by incubation with pronase E at 2 mg/ml in PBS at 37°C for 10 min after incubation of E . chaffeensis with THP-1 cells at 37°C for 3 h . THP-1 cells were washed by centrifugation at 375×g for 5 min , the cells then were dissolved in 0 . 6 N NaOH and 0 . 5% SDS , and the radioactivity was measured in a scintillation counter . E . chaffeensis preincubated with 25 µg/ml of mouse anti-rEtpE-C , rabbit anti-rEtpE-N , or preimmune mouse or rabbit sera for 1 h at 4°C were used to infect THP-1 , RF/6A , or DH82 cells . Alternatively , E . chaffeensis was added to DH82 cells preincubated with 10 µg/ml of monoclonal anti-DNase X or control mouse monoclonal antibody for 30 min at 25°C in serum-free DMEM . Binding , internalization , and infection were determined at 30 min , 2 h and 48 h pi , respectively . HEK293 cells in 24-well plates were transfected with 50 nM DNase X siRNA ( Santa Cruz Biotechnology ) or scrambled control siRNA using Lipofectamine 2000 ( Invitrogen ) . A second transfection with 50 nM of DNase X and scrambled siRNAs was performed 30 h after the first transfection . An aliquot of cells were harvested at 24 h after the second transfection to determine the protein amount of DNase X by Western blotting and densitometry analysis with anti-DNase X and rabbit anti-actin ( Sigma ) . The other aliquot of cells were incubated with E . chaffeensis and incubated for an additional 48 h to evaluate infection . Infection was determined by qPCR of E . chaffeensis 16S rRNA gene relative to host cell G3PDH gene [18] . Far-Western blotting was performed using 5 µg of rEtpE-C and rECH0825 that were separated by SDS-PAGE , transferred to a nitrocellulose membrane and renatured with serial guanidinium-HCl treatment followed by incubation with THP-1 cell lysate in NP-40 lysis buffer ( 150 mM NaCl , 50 mM Tris-HCl pH 7 . 4 , 1% w/v NP-40 , supplemented with 1% protease inhibitor cocktail set III [Calbiochem] ) as described [58] . After stringent washing , the membrane was incubated with anti-DNase X and peroxidase-conjugated goat anti-mouse antibodies ( KPL ) . The membrane was stripped with Restore Western Blot Stripping Buffer ( Thermo scientific ) and reprobed with peroxidase-conjugated anti-histidine antibody ( Sigma ) . For protein pull-down , His-tagged rEtpE-C was bound to and renatured on the Ni-affinity matrix ( Promega ) . THP-1 cell lysate in NP-40 lysis buffer was applied to the matrix and incubated for 8 h at 4°C . After washing off the unbound or non-specifically bound proteins from the matrix , rEtpE-C and bound protein complex were eluted with 250 mM imidazole . The eluate and the post-elution Ni-matrix were resuspended in 2× SDS-sample buffer and subjected to Western blotting with anti-DNase X antibody . For co-immunoprecipitation assay , THP-1 cells were incubated with E . chaffeensis for 30 min and lysed in NP-40 lysis buffer . The lysate was immunoprecipitated with anti-EtpE-C ( 2 µg ) -bound protein A agarose or control mouse IgG ( 2 µg ) -bound agarose beads . The precipitate was resuspended in 2× SDS-sample buffer and subjected to Western blotting with anti-DNase X antibody . Sulfate-modified fluorescent red polystyrene beads ( 0 . 5 µm diameter; Sigma ) at 3–4×106 beads in 200 µl of 25 mM 2- ( N-morpholino ) ethanesulfonic acid ( MES ) buffer , pH 8 . 0 were incubated with 1 µg of rEtpE-C or rEtpE-N proteins in 5–7 µl 7 M urea in 50 mM sodium phosphate buffer , pH 7 . 2 at 4°C overnight with mixing at 20 rpm . MES buffer ( 150 µl ) was sequentially added to the mixture every 15 min and incubated at room temperature , rotating at 20 rpm eventually diluting to around 200 times the original volume of urea buffer . rECH0825 and rGroEL were treated similarly , but without urea . The coated beads were collected by low speed centrifugation , washed twice in MES buffer and resuspended in complete DMEM or advanced MEM media , then gently sonicated to disperse the beads . Protein coating of the beads were confirmed by dot blot assay and/or immunofluorescence labeling . Freshly prepared protein-coated or non-coated beads were added at a multiplicity of approximately 50 beads per cell for co-localization studies and 500 beads per cell for quantitation of binding and internalization studies . The beads were incubated with HEK293 or RF/6A cells for 1 h at 37°C . Unbound beads were removed by washing and cells were fixed for immunofluorescence labeling to detect the localization of DNase X or EEA1 ( anti-EEA1 , BD ) . To study the effect of MDC , genistein , or verapamil on bead internalization , RF/6A cells were incubated with these chemicals at a final concentration of 100 µM or 0 . 1% DMSO solvent control for 30 min , then with rEtpE-C-coated beads for 8 h at 37°C . BMDMs from wild-type mice were pre-incubated with MDC or genistein ( 100 µM ) , PI-PLC ( 5 U/ml ) or 0 . 1% DMSO for 30 min and then incubated with rEtpE-C-coated or non-coated beads for 45 min at 37°C . PI-PLC-treated cells were washed prior to addition of rEtpE-C-coated beads . The cells were washed and treated with 0 . 25% trypsin at 37°C for 10 min to remove surface-bound beads . The detached cells were further washed by low-speed centrifugation and later cytocentrifuged onto a glass slide and fixed with 3% PFA to observe internalized beads . To estimate the number of bound beads , a similar procedure for observing internalized beads was followed except that the beads were incubated with BMDM for 30 min at 4°C and following incubation the cells were washed to remove loosely bound beads and directly fixed with 3% PFA without trypsin treatment . For scanning electron microscopy , rEtpE-C-coated beads were incubated with RF/6A cells for 2 h at 37°C and processed as described previously [59] . For transmission electron microscopy , coated beads were incubated with RF/6A cells for 8 h at 37°C and processed as described previously [60] . The 3D orthogonal view of the cell to show spatial distribution of DNase X with beads was obtained by using the volume viewer function of SoftWoRx DeltaVision image acquisition software from Applied Precision . RF/6A cells were cultured in 35-mm glass bottom dishes ( Wilco ) , transfected with DNase X-GFP , and incubated with rEtpE-C-coated beads at 4°C for 1 h to facilitate bead binding , but prevent internalization . Unbound beads were washed off , cells were replenished with medium lacking phenol red , and the samples moved to a controlled environmental chamber at 37°C with under 5% CO2/95% air . Time-lapse images were acquired at an interval of 10 s for 5 to 20 min through a 60×1 . 42 NA oil immersion lens with an inverted Olympus IX-70 microscope , in 0 . 4-µm steps in the z-axis using the attached Applied Precision motorized stage ( DeltaVision deconvolution microscope ) . All stacks of images were deconvoluted using SoftWoRx software and the time-lapse images of a single focal plane of 0 . 4-µm focal depth at the cell surface were exported as a video . Statistical analysis was performed by unpaired two-tailed Student's t-test . P<0 . 05 was considered to be significant .
Human monocytic ehrlichiosis ( HME ) , discovered in 1986 , was designated as a nationally notifiable disease by Centers for Disease Control and Prevention in 1998 . HME is one of the most prevalent , life-threatening emerging infectious diseases in the United States . HME is caused by a bacterium , Ehrlichia chaffeensis and is transmitted by the bite of infected ticks . This bacterium has special ability to enter and replicate inside human white blood cells and this feature is very essential for the bacterial survival . How E . chaffeensis enters host cells has been a mystery . The present study revealed that E . chaffeensis outer-surface protein named EtpE binds a specific host cell-surface protein , DNase X , and this ligand-receptor interaction is required to induce bacterial entry into its host cells . In order to test whether E . chaffeensis infection can be prevented by EtpE immunization , mice were immunized with the recombinant EtpE protein , and challenged with live E . chaffeensis . Infection was significantly reduced in the EtpE protein-immunized mice compared to controls . Mice lacking DNase X were also resistant to infection . This study shows EtpE-mediated entry pathway of E . chaffeensis is important in infecting mammals and EtpE can be incorporated into a future HME vaccine design .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[]
2013
Ehrlichia chaffeensis Uses Its Surface Protein EtpE to Bind GPI-Anchored Protein DNase X and Trigger Entry into Mammalian Cells
Antibiotic susceptibility of bacterial pathogens is typically evaluated using in vitro assays that do not consider the complex host microenvironment . This may help explaining a significant discrepancy between antibiotic efficacy in vitro and in vivo , with some antibiotics being effective in vitro but not in vivo or vice versa . Nevertheless , it is well-known that antibiotic susceptibility of bacteria is driven by environmental factors . Lung epithelial cells enhance the activity of aminoglycoside antibiotics against the opportunistic pathogen Pseudomonas aeruginosa , yet the mechanism behind is unknown . The present study addresses this gap and provides mechanistic understanding on how lung epithelial cells stimulate aminoglycoside activity . To investigate the influence of the local host microenvironment on antibiotic activity , an in vivo-like three-dimensional ( 3-D ) lung epithelial cell model was used . We report that conditioned medium of 3-D lung cells , containing secreted but not cellular components , potentiated the bactericidal activity of aminoglycosides against P . aeruginosa , including resistant clinical isolates , and several other pathogens . In contrast , conditioned medium obtained from the same cell type , but grown as conventional ( 2-D ) monolayers did not influence antibiotic efficacy . We found that 3-D lung cells secreted endogenous metabolites ( including succinate and glutamate ) that enhanced aminoglycoside activity , and provide evidence that bacterial pyruvate metabolism is linked to the observed potentiation of antimicrobial activity . Biochemical and phenotypic assays indicated that 3-D cell conditioned medium stimulated the proton motive force ( PMF ) , resulting in increased bacterial intracellular pH . The latter stimulated antibiotic uptake , as determined using fluorescently labelled tobramycin in combination with flow cytometry analysis . Our findings reveal a cross-talk between host and bacterial metabolic pathways , that influence downstream activity of antibiotics . Understanding the underlying basis of the discrepancy between the activity of antibiotics in vitro and in vivo may lead to improved diagnostic approaches and pave the way towards novel means to stimulate antibiotic activity . While many biochemical substances that modulate antibiotic activity are known [1–10] , the influence of the local environment at the host-pathogen interface on bacterial responses to antibiotics is still poorly understood [4] . Supplementation of exogenous metabolites has been found to enhance the activity of certain antibiotics , including aminoglycosides . Combining various carbon sources , such as carbohydrates ( e . g . glucose , fructose ) , amino acids ( e . g . alanine ) , and metabolites from the tricarboxylic acid cycle ( e . g . pyruvate , citrate ) with aminoglycoside antibiotics rendered antibiotic resistant bacteria susceptible again , eradicated persister cells , and/or reduced biofilm viability [1 , 2 , 6–8] . Hence , metabolic adjuvants have raised excitement for therapeutic applications against multi-drug resistant bacteria . Yet , whether and how the complex host metabolic environment influences antibiotic activity is not fully understood . These insights are important to evaluate the potential role of the host in the clearance of bacterial pathogens during antibiotic treatment of an infection , and may help opening novel avenues to improve the correlation between antibiotic susceptibility profiles in vitro and in vivo . This is particularly relevant for infectious diseases for which antibiotic therapy chosen based on susceptibility assays frequently does not lead to clinical improvement [11–14] , including in respiratory tract infections in people with cystic fibrosis [11] . Indeed , these studies denote that some antimicrobial agents are effective in vitro but not in vivo or vice versa . We and others previously demonstrated that lung epithelial cells modulate the activity of antibiotics in vitro [15–19] . In particular , culturing biofilms of Pseudomonas aeruginosa on the surface of in vivo-like three-dimensional ( 3-D ) lung epithelial cells enhanced the activity of aminoglycosides as compared to when these same biofilms were cultured on a plastic surface [19] . Furthermore , Wu et al . recently demonstrated that P . aeruginosa isolated directly from mouse lungs was more susceptible to aminoglycosides as compared to laboratory-grown cultures , suggesting that host factors influence antibiotic activity in vivo [15] . In the present study we aimed at elucidating ( i ) whether the host microenvironment influences aminoglycoside activity against bacterial pathogens , ( ii ) if host metabolites can potentiate aminoglycoside activity , and ( iii ) what the underlying mode of action is . To this end , we assessed antibiotic activity against biofilms formed in the presence of conditioned medium of 3-D lung epithelial cells . The 3-D lung epithelial cell model has repeatedly been shown to better mimic physiological characteristics of in vivo lung epithelium as compared to conventional 2-D monolayers , especially relating to mucosal immunity ( barrier function , polarity , mucus production , and cytokine production ) [19–23] . We found that 3-D cell secretions potentiated the activity of aminoglycosides against several bacterial species . In contrast , secretions of the same lung epithelial cell type grown as monolayers did not influence antibiotic activity . Next , we revealed the mechanistic basis of the potentiating activity of 3-D cell secretions , by demonstrating a stimulation of the bacterial PMF by host cell factors . Finally , our data suggest that host metabolites improved activity of the aminoglycoside tobramycin . These findings highlight a significant contribution of the host metabolism to the activity of antibiotic treatment . To assess if the host microenvironment influences the activity of antibiotics , the ability of antibiotics to inhibit biofilm formation for 4h was evaluated in conditioned medium of in vivo-like 3-D lung epithelial cells ( 3-D CM ) and control medium ( GTSF-2 ) . 3-D CM was obtained by incubating differentiated 3-D A549 cells with fresh medium for 2h ( 1 x 106 cells/mL ) , and collecting the non-cellular medium fraction . These tests were initially performed with P . aeruginosa PAO1 , a well-studied biofilm-forming strain . 3-D CM increased the activity of gentamicin and tobramycin , and the level of potentiation varied with the antibiotic and concentration used , while the activity of colistin was not influenced by 3-D CM ( Fig 1A ) . In contrast , CM from A549 monolayers ( 2-D CM ) grown at the same cell density as the 3-D CM ( 1 x 106 cells/mL ) did not significantly influence the activity of any of the tested antibiotics ( Fig 1B ) . We subsequently investigated whether increasing the 3-D lung epithelial cell density further enhanced the observed changes in antibiotic activity . CM obtained from a higher cell density ( 4 x 106 cells/mL ) strongly potentiated all tested aminoglycosides , while leaving the activity of colistin unchanged ( Fig 1C ) . For all subsequent experiments , 3-D CM from high cell density ( 4 x 106 cells/mL ) cultures was used . 3-D CM did not influence the activity of antibiotics against established biofilms of P . aeruginosa PAO1 ( S1 Fig ) . We observed a minor effect of 3-D CM on the MIC of tobramycin ( 2-fold decrease ) , and a 4-fold decrease in the MIC of colistin ( S1 Table ) . Time-kill curves determined using a tobramycin concentration of 2 μg/mL and 8 μg/mL ( 2x and 8x MIC , respectively ) revealed a significantly increased bactericidal activity in the presence of 3-D CM ( Fig 1D ) . At 8 μg/mL tobramycin , bacterial regrowth was observed at the 24h time point for the control medium , while no culturable cells were detected ( i . e . <102 CFU/mL ) in the presence of 3-D CM ( p < 0 . 01 ) . Next , we evaluated whether the potentiating effect of 3-D CM is strain , genus or species-dependent . The majority of P . aeruginosa cystic fibrosis clinical and environmental isolates tested showed enhanced susceptibility to tobramycin or gentamicin ( the latter antibiotic was only used when ≤ 1 log biofilm inhibition was observed at ≥ 256 μg/mL tobramycin ) in the presence of 3-D CM ( 10/12 tested , 83 . 3% ) ( Table 1 ) . Only for strains DK2 and LES400 no significant potentiating effect could be observed . For all tobramycin resistant strains tested ( 1709–12 , Mi126 , AMT0023-34 ) increased susceptibility to tobramycin was observed in the presence of 3-D CM . All non-aeruginosa Pseudomonas species tested ( P . fluorescens , P . putida , P . stutzeri ) also exhibited a decreased tolerance to tobramycin in the presence of 3-D CM . The potentiating effect was also observed in a limited number of other bacterial species ( 3/10 tested , 30 . 0% ) , i . e . Staphylococcus aureus , Enterococcus faecium and Salmonella typhimurium . In contrast , the activity of aminoglycosides was inhibited by 3-D CM for Acinetobacter baumannii , Escherichia coli , and Achromobacter xylosoxidans ( Table 1 ) . To elucidate the mode of action behind the potentiating activity of 3-D CM , we evaluated whether intracellular tobramycin levels were increased upon exposure to 3-D CM , using fluorescent BODIPY-labelled tobramycin and flow cytometry . Flow cytometry settings and gates were determined using negative and positive controls , wherein the fraction of the bacterial population that did not contain detectable levels of BODIPY-tobramycin was set to be located in the “negative” gate , the fraction that was situated in the “positive” gate was saturated for BODIPY-tobramycin , and the fraction in between the positive and negative gates , was labelled as the “intermediate” gate ( S2 Fig ) . A concentration of 0 . 5 μg/mL or 0 . 75 μg/mL BODIPY-tobramycin was used in further experiments as these concentrations resulted in a partially saturated population under control conditions , hereby enabling to observe potential increases in BODIPY-tobramycin uptake upon exposure to 3-D CM ( S2 Fig ) . 3-D CM enhanced the fraction of positive cells ( p < 0 . 01 for 0 . 5 and 0 . 75 μg/mL BODIPY-tobramycin ) , while lowering the fraction of intermediate cells ( p < 0 . 05 for 0 . 5 μg/mL and p < 0 . 01 for 0 . 75 μg/mL BODIPY-tobramycin ) which is due to an increase in mean fluorescence intensity of the whole population ( Figs 2A and 2C and S3A ) . We also tested one of the few P . aeruginosa strains that showed a small ( non-significant ) enhanced susceptibility to tobramycin in the presence of 3-D CM , i . e . strain DK2 . This strain had the same MIC for tobramycin in the control medium as PAO1 ( S1 Table ) . The majority of P . aeruginosa DK2 cells exposed to BODIPY-tobramycin in control medium was already positive ( saturated ) for BODIPY-tobramycin at a concentration of 0 . 75 μg/mL , and no difference between control medium and 3-D CM could be observed ( p > 0 . 05 ) ( Fig 2B and 2D ) . A lower concentration of BODIPY-tobramycin ( 0 . 5 μg/mL ) resulted in a partially saturated P . aeruginosa DK2 population in control medium , and 3-D CM induced a smaller increase in tobramycin uptake ( 1 . 9 ± 0 . 2-fold ) compared to P . aeruginosa PAO1 tested at the same concentration ( 4 . 9 ± 1 . 4-fold ) ( p < 0 . 05 ) ( S3B Fig ) . These results are in line with the biofilm inhibition data , where 3-D CM exerted a stronger potentiating effect in PAO1 ( 606 . 0 ± 286 . 8-fold ) compared to DK2 ( 7 . 9 ± 4 . 3-fold ) ( p < 0 . 01 ) . To further assess whether tobramycin uptake was correlated with inhibition of biofilm formation , we used a standard series of the previously described tobramycin-potentiator succinate [7] . We observed that at concentrations ≥ 0 . 84 mM succinate , both tobramycin activity and uptake were significantly enhanced by this molecule ( p ≤ 0 . 05 ) ( Fig 3 ) . Higher intracellular levels of tobramycin could be due to higher uptake and/or lower efflux in response to 3-D CM . The uptake of aminoglycosides occurs in three phases: an energy-independent phase ( ionic binding and/or diffusion through outer membrane porins ) and two energy dependent phases ( EDPI and EDPII ) [24–26] . In order to find out which phase ( s ) of aminoglycoside uptake were influenced by 3-D CM , biofilm inhibition by tobramycin was studied in the presence of the proton ionophore CCCP , which enables inward transport of H+ across lipid membranes [27] , hereby dissipating the proton motive force ( PMF ) . The potentiating activity of 3-D CM was completely abolished by 100 μM CCCP ( p > 0 . 05 ) , while CCCP had no effect on tobramycin activity in control medium ( Fig 4A ) . To further elucidate the role of the bacterial PMF in the observed potentiating effect of 3-D CM , we determined intracellular pH levels using 2 , 7 -bis ( 2-carboxyethyl ) -5 ( 6 ) -carboxyfluorescein acetoxymethyl ( BCECF-AM ) . As a difference in pH was observed between 3-D CM ( pH 6 . 99 ± 0 . 28 ) and control medium ( pH 7 . 29 ± 0 . 10 ) , the pH of 3-D CM was increased to the control levels prior to determining the intracellular pH . The potentiating effect of 3-D CM was not affected by normalizing the pH between 3-D CM and control medium ( S5 Fig ) . We observed a consistent increase in intracellular pH of P . aeruginosa PAO1 in the presence of 3-D CM as compared to control medium , both in tobramycin treated ( pH 7 . 7 for 3-D CM , pH 7 . 2 for control ) and untreated samples ( pH 7 . 7 for 3-D CM , pH 7 . 1 for control ) ( Fig 4B ) . To confirm that the high intracellular pH induced by 3-D CM is responsible for the enhanced tobramycin activity in the presence of 3-D CM , we inhibited the 3-D CM-induced increase in intracellular pH . To this end , we used the K+ ionophore nigericin in combination with excess levels of K+ to equalize intra- and extracellular pH levels through promotion of K+/H+ exchange , hereby preventing 3-D CM-induced increases in intracellular pH . The biofilm inhibition assay was repeated with 3-D CM in the presence of nigericin/excess K+ , and this completely abolished the potentiating effect of 3-D CM ( Fig 4C ) . Accordingly , the 3-D CM-induced increase in intracellular tobramycin levels could not be observed in the presence of nigericin/excess K+ , and tobramycin activity of 3-D CM combined with nigericin/excess K+ equalled that of the control ( Figs 4D , 4E and 2C ) . Finally , as pH affects tobramycin stability , we also assessed whether higher pH on its own could potentiate tobramycin . To this end , 1 M NaOH was used to increase the pH of the control medium to the level of the intracellular pH in 3-D CM ( pH 7 . 7 ) , where after intra- and extracellular pH levels were equalized using nigericin/excess K+ . This approach did not result in tobramycin potentiation ( Fig 4F ) . We also determined the potentiating effect of 3-D CM in conditions that reduce bacterial metabolic activity by determining intracellular levels of tobramycin at 4°C . Also at 4°C , 3-D CM enhanced the fraction of bacteria that were positive for tobramycin , and lowered the fraction of intermediate and negative cells ( S4A Fig ) . The main difference with the experiment at 37°C was the distinct presence of a negative , intermediate and positive population in the control medium , with an influence of 3-D CM observed in each of these subpopulations , without affecting the mean fluorescence intensity as observed in the histogram ( S4B Fig ) . We evaluated whether alternative mechanisms could explain the aminoglycoside potentiating activity of 3-D CM . Firstly , the role of efflux was evaluated by testing whether 3-D CM could potentiate tobramycin activity in mutants , in which known efflux pumps associated with the export of aminoglycosides in P . aeruginosa were inactivated , i . e . MexXY and MexAB [28 , 29] . Also in these mutants , 3-D CM potentiated the activity of tobramycin against biofilm formation and increased tobramycin uptake ( Fig 5 ) . Secondly , to understand whether the induced uptake of aminoglycosides by 3-D CM is due to changes in membrane permeability/potential , we used DiBac4 ( 3 ) . DiBac4 ( 3 ) is a potential-sensitive dye that only enters depolarized cells , and can be used to evaluate changes in membrane potential , which in turn can be a result of increased membrane permeability . Exposure of P . aeruginosa PAO1 to tobramycin or gentamicin resulted in membrane depolarization at similar levels in both control medium or 3-D CM ( Fig 6A ) . We also performed a SYTO9/PI assay ( LIVE/DEAD ) ; staining of bacteria with SYTO9/PI after antibiotic exposure typically results in three subpopulations: live , dead and intermediate [30] . The intermediate population has been reported to represent bacteria with enhanced inner and outer membrane permeability [30] . 3-D CM did not significantly alter the proportion of live and dead bacteria in the tobramycin-exposed biofilm , but a small increase in the intermediate population was observed ( 1 . 7-fold , p < 0 . 01 ) ( Fig 6B ) . Stimulating bacterial metabolism by addition of various carbohydrates ( e . g . glucose , fructose ) or derivatives of glycolysis/TCA cycle ( e . g . pyruvate , succinate , glutamate , citrate ) has been found to enhance the activity of aminoglycosides through promotion of the PMF [1 , 2 , 6–8] . To evaluate whether 3-D CM promoted the PMF by stimulating bacterial metabolism , we blocked a main metabolic pathway of P . aeruginosa by supplementing 3-D CM with the pyruvate dehydrogenase inhibitor triphenylbismuth dichloride ( TPB ) [31] . At 64 μg/mL TPB ( i . e . 1/2 x MIC ) , the potentiating effect of 3-D CM was reduced ( p > 0 . 05 between TPB-treated 3-D CM and control ) ( Fig 7A ) . However , lowering the TPB concentration to 16 μg/mL ( 1/8 x MIC ) restored the effect of 3-D CM , indicating a concentration-dependent effect ( Fig 7A ) . To test whether host metabolites altered antibiotic activity , 3-D CM was filtered over a filter with a 3 kDa cut-off , and antibiotic-mediated biofilm inhibition in P . aeruginosa PAO1 was assessed . Potentiating activity for the three aminoglycosides tested was retained in the filtrate , which indicates that components that increase antibiotic activity are smaller than 3 kDa ( Fig 7B ) . To determine which host metabolites potentiated aminoglycoside activity , we quantified various central metabolites ( pyruvate , succinate and glutamate ) in 3-D CM ( derived from both 1 x 106 cells/mL and 4 x 106 cells/mL ) , 2-D CM ( 1 x 106 cells/mL ) and control . Control medium was found to contain 2 . 75 ± 0 . 26 mM pyruvate , and the pyruvate concentration was significantly lower in 3-D CM derived from a high cell number ( 1 . 37 ± 0 . 17 mM ) , indicating a consumption of approximately half of the pyruvate ( Fig 8A ) . The concentration of pyruvate in 3-D CM derived from a low cell number was also decreased , but to a lesser extent and this decrease was not statistically significant . No significant differences were observed between control medium and 2-D CM indicating no significant consumption of pyruvate by 2-D cells . Similarly , both glutamate ( 10 . 88 ± 3 . 12 mM ) and succinate ( 1 . 81 ± 0 . 51 mM ) were significantly higher than control medium in 3-D CM derived from a high cell number ( Fig 8B and 8C ) . However , 3-D CM derived from a low cell number contained higher levels of both glutamate and succinate than control and 2-D CM ( low cell number ) , yet statistical significance was not reached . Next , we supplied control medium with glutamate , succinate , or both , at concentrations detected in 3-D CM ( high cell number ) and evaluated the activity of tobramycin and tobramycin uptake . The combination of glutamate and succinate significantly potentiated the biofilm inhibitory activity of tobramycin and tobramycin uptake , indicating that these metabolites are contributing factors to the observed potentiating effect of 3-D CM ( Fig 9 ) . Next , we evaluated whether the culture conditions prior to the biofilm inhibition experiment played a role in the potentiating activity of host metabolites . Instead of using rich growth medium ( LB ) , overnight cultures were grown in M9 minimal medium supplemented with glucose , succinate or glutamate as sole carbon source , where after P . aeruginosa biofilm inhibition by tobramycin in control medium versus 3-D CM was evaluated . For both strains tested ( PAO1 , AA2 ) , prior culturing in M9 glucose and M9 glutamate also resulted in enhanced tobramycin activity in the presence of 3-D CM ( Fig 10 ) . Interestingly , culturing of P . aeruginosa PAO1 or AA2 in M9 supplemented with succinate abolished the potentiating activity of 3-D CM . These findings indicate the culture conditions preceding antibiotic treatment influence the potentiating activity of host metabolites . Finally , we ruled out a role for bicarbonate and host antimicrobial peptides in increasing aminoglycoside efficacy . Antimicrobial peptides ( e . g . β-defensins ) and proteins ( e . g . lysozyme ) are produced by epithelial tissues and have been shown to exert synergistic effects when combined with several classes of antibiotics , including aminoglycosides [32] . Conservation of the potentiating effect in the 3 kDa filtrate rules out all antimicrobial proteins and many known antimicrobial peptides with a molecular mass > 3 kDa ( e . g . CCL20 , HBD-2 ) ( Fig 7B ) . In addition , 3-D CM was treated with proteinase K or trypsin , where after the enzyme was removed by filtering over a 3 kDa filter and the activity of antibiotics to inhibit biofilm formation was assessed . Treatment of 3-D CM with proteinase K or trypsin did not affect the observed potentiating activity ( S6 Fig ) , confirming that the potentiating compound is not proteinaceous . Bicarbonate was previously found to improve aminoglycoside activity through interference with the pH gradient [5] , and to evaluate its role in the observed potentiating activity of 3-D CM , we tested whether 3-D CM could potentiate aminoglycosides in medium without bicarbonate . The potentiating activity of 3-D CM in the absence of bicarbonate was still observed ( S6 Fig ) , but to a lesser extent compared to the medium that contained bicarbonate ( p < 0 . 01 ) . During an infection , bacterial pathogens are exposed to a variety of host factors that influence the infection process and may affect their susceptibility to antimicrobial agents . These host factors are produced as part of normal tissue homeostasis ( e . g . products of metabolism ) or their production is triggered or enhanced upon sensing foreign microorganisms ( e . g . defensins ) . While interactions between the host microenvironment and the pathogen are known to play a role in the establishment and persistence of an infection , there is limited knowledge on how the host microenvironment modulates the activity of antimicrobial agents . In the present study , we provide mechanistic insights on how lung epithelial cells modulate the activity of antibiotics , using an in vivo-like 3-D lung epithelial cell model . The 3-D organotypic lung model simulates key components of the lung mucosa that are important for the innate defence against microorganisms , including barrier function , apical-basolateral polarity , mucus production , and cytokine secretion [19–23] . We previously found that 3-D lung cells potentiated the activity of aminoglycosides [19] . In the present study , we were able to demonstrate that the potentiator effect was due to metabolites secreted by the 3-D lung cells , since effects by 3-D CM on aminoglycoside efficacy against biofilm inhibition were comparable to our previously reported effects by the 3-D lung cells . Strikingly , 3-D CM was able to strongly enhance the bactericidal activity of tobramycin , and even prevent bacterial regrowth in a time-kill assay . Host cell secretions resulted in an increase of intracellular tobramycin levels in the bacterial population , which was the result of an enhanced intracellular pH . An increase in intracellular pH leads to a higher ΔpH , which is an important component of the PMF . Since tobramycin uptake depends on the PMF [25] , uptake of this antibiotic is promoted through a higher ΔpH . This is in line with recent reports where exogenous metabolites , including compounds derived from pyruvate metabolism ( e . g . succinate , glutamate ) , were found to increase the activity of aminoglycosides by stimulating tricarboxylic acid cycle ( TCA ) activity and/or the phosphoenolpyruvate ( PEP ) -pyruvate-AcCoA pathway [2 , 6 , 7 , 33] . This in turn increased cellular respiration ( through NADH production ) , PMF , antibiotic uptake and cell death . Inhibition of bacterial pyruvate dehydrogenase reduced the potentiating effect of 3-D CM , further supporting the involvement of the bacterial PMF . We examined whether host cells would secrete these previously reported metabolites at sufficient levels to influence tobramycin activity and found that 3-D cells consumed pyruvate and produced glutamate and succinate , which in turn led to tobramycin potentiation . These results suggest that host metabolites enhance tobramycin activity . Not only do our results fully support recent findings [2 , 7] , they demonstrate that host cells secrete these antibiotic-potentiating metabolites to levels that can actually exert a biological effect in the complex microenvironment of the host . Nevertheless , we found that the culturing conditions preceding antibiotic treatment may influence the potentiating activity of these host metabolites . The observation that prior culturing in culture medium with a high concentration of the most potent metabolite ( succinate ) precluded potentiating activity of 3-D CM might imply that the PMF is already strongly promoted at the start of treatment . Hence , the maximal capacity of the PMF might be reached which would prevent additional stimulation by host metabolites . Based on the Human Metabolome Database , concentrations of these metabolites in vivo are available for saliva ( fluid from relevant mucosal tissue ) and strongly vary within and between studies . For succinate , average concentrations of up to 2 . 3 mM ( ranging from 0 . 06–4 . 5 mM ) have been reported ( http://www . hmdb . ca/metabolites/HMDB0000254 ) , while for L-glutamate up to 13 . 6 ± 2 . 4 μM has been detected in saliva of healthy adults ( http://www . hmdb . ca/metabolites/HMDB0000148 ) . These levels approximate the in vitro concentration for succinate in our study , which was the metabolite contributing most to potentiation . In addition , the level of pyruvate present in the used cell culture medium ( GTSF-2 ) of 2 . 75 ± 0 . 26 mM ( corresponding to the concentration provided by the manufacturer of 3 mM ) is also in the range of reported studies—up to 5 . 4 mM on average in saliva ( ranging from 0 . 1–11 mM ) ( http://www . hmdb . ca/metabolites/HMDB0000243 ) . Based on this information , aminoglycoside potentiation appears possible in vivo , but might strongly vary for different body sites and between individuals . Therefore , future experiments will be needed to determine individual metabolite concentrations at relevant mucosal tissue locations , and to link metabolite ( s ) presence/concentration with antibiotic activity . Nevertheless , while we used a model system that reflects important traits of the parental lung epithelium , it should be noted that the in vivo environment at the host-pathogen interface is more complex . Hence , additional factors ( such as innate immune cells and other lung cells , paracrine signalling , oxygen levels , pH , and ionic content ) might generate a different metabolic environment than represented in this study , leading to a differential response to antibiotics . Wu et al . ( 2017 ) recently found an increased aminoglycoside activity against P . aeruginosa when A549 lung epithelial cells and neutrophils were co-cultured as 2-D monolayers . Neutrophils were found to be the main contributor to the observed effect , among others due to production of reactive oxygen species ( ROS ) . The observation that neutrophils and not A549 monolayer cells potentiated tobramycin is in line with our results , as we found that conditioned medium obtained from 2-D A549 cultures did not modify tobramycin activity . Culturing epithelial cells of the lung or other mucosal tissues ( such as the intestinal tract ) as 3-D aggregates has been shown to alter phenotypic and biochemical properties , as compared to culture of these same cell types as 2-D monolayers [19 , 20 , 22 , 23 , 34 , 35] . In our study , 2-D A549 cells did not consume pyruvate nor produced the aminoglycoside-potentiating metabolite in the 2h time frame used to generate conditioned medium , in contrast to when these same cells were grown as 3-D structures . Succinate was produced at a concentration of 0 . 02 mM by 2-D cells , which was well below the threshold of 0 . 84 mM needed for tobramycin potentiation in this study . While the metabolome of cell cultures grown in a rotating wall vessel ( RWV ) bioreactor compared to as a monolayer has not been reported , several studies described metabolic differences in culturing human cells as spheroids versus monolayers [36 , 37] . For example , differences in glucose uptake , cellular proliferation , or differentiation might explain a different metabolism between 2-D and 3-D cultures that could have downstream effects on antibiotic potentiation [38 , 39] . Furthermore , in differentiated cells , oxidative phosphorylation in combination with the TCA cycle is the main pathway for the generation of ATP , while tumorigenic cells convert most glucose to lactate–referred to as the Warburg effect [40] . Hence , the reduced tumorigenic properties of 3-D A549 cells compared to 2-D cells [21] might be associated with a differential metabolism , favouring the use of the TCA cycle with the generation of potentiating metabolites as a result . The aminoglycoside-potentiating activity of 3-D CM was confirmed for a broad range of P . aeruginosa strains and other Pseudomonas species . The observation that only few other bacterial species showed enhanced susceptibility to aminoglycosides was somewhat unexpected , especially since E . coli was reported to become more susceptible to aminoglycosides with supplementation of several TCA metabolites [2 , 7] . Strain- and species specific differences in intracellular pH ( basal or in response to 3-D CM ) , membrane permeability ( in particular for H+ ) , metabolism ( rate and type ) , and compensatory mechanisms might be responsible for this , and will be the subject of further study . In addition , while we chose antibiotic concentrations that resulted in at least 1 log biofilm inhibition in control conditions for all tested strains , concentration-dependent effects might also explain the observed differences in potentiation activity of 3-D CM between strains . Finally , we investigated possible alternative explanations for the potentiation of aminoglycosides by 3-D CM in this study , including the role of host-produced antimicrobial peptides ( such as defensins ) . Overall , the results do not support a major involvement of antimicrobial peptides in the observations , as we would expect pronounced effects on membrane depolarization and permeability . The small increase in membrane permeability observed in the presence of tobramycin and 3-D CM is most likely attributable to the higher antibiotic uptake and activity , which indirectly leads to the generation of aberrant polypeptides that damage the cell membrane [41] . It should be mentioned that while host metabolites potentiated aminoglycoside activity under the experimental test conditions of this study ( i . e . 4h biofilm inhibition assay , 24h time kill curve ) , other host-produced factors might influence activity of aminoglycosides and/or other antibiotics under different experimental conditions . For example , we observed a decrease in the MIC of colistin in 3-D CM compared to control ( S1 Table ) , which is unlikely due to increased PMF; future studies will be required to elucidate the mechanism behind this potentiation . In conclusion , our findings highlight the importance of tissue homeostasis in the innate defence against pathogens , through synergism between host metabolites and antibiotics . Our results support recent findings that the microenvironment of the host is a key player in determining antibiotic activity , and is important to consider when attempting to correlate antibiotic activity in vitro and in vivo . Our results also implicate that changes in metabolic activity of host cells , such as observed in lung epithelial cells of patients with cystic fibrosis [42] , may impact the potentiating activity of lung epithelial cells towards aminoglycosides . Hence , supplementation of specific metabolites and/or in vivo stimulation of host metabolism might be a relevant approach to treat or prevent bacterial infections . An overview of all strains used in this study is presented in Table 2 . All bacteria were cultured in Luria Bertani ( LB ) broth at 250 rpm and 37°C , with the exception of P . fluorescens which was cultured at 30°C and Gemella haemolysans which was grown in BHI broth . Bacteria were cultured until stationary phase for all experiments . When indicated , P . aeruginosa PAO1 and AA2 were cultured in M9 minimal medium supplemented with 10 mM glucose , 15 mM succinate or 12 mM glutamate ( total carbon content of 60 mM ) . All bacteria were grown aerobically , except for Streptococcus anginosus and G . haemolysans which were grown under low oxygen conditions ( ±5% O2 , ±15% CO2 ) using the CampyGen Compact system ( Oxoid , Thermo Fisher Scientific ) . An organotypic 3-D lung epithelial model was generated by culturing the human adenocarcinoma alveolar epithelial cell line A549 ( ATCC CCL-185 ) on porous microcarrier beads in the RWV as described previously [19 , 21] . A549 cells were cultured in GTSF-2 medium ( GE Healthcare ) supplemented with 2 . 5 mg/L insulin transferrin selenite ( ITS ) ( Sigma-Aldrich ) , 1 . 5 g/L sodium bicarbonate , and 10% heat-inactivated FBS ( Invitrogen ) . All cultures were grown at 37°C under 5% CO2 and >80% humidity conditions . 3-D lung epithelial cultures were used for generation of 3-D conditioned medium after 11 to 14 days of growth in the RWV . The cell concentration in the RWV bioreactor was evaluated by treating an aliquot of the 3-D culture with 0 . 25% trypsin-EDTA ( Life Technologies ) , followed by staining with 0 . 4% trypan blue ( Sigma-Aldrich ) and counting in a haemocytometer . Next , 2 . 5 x 105 or 1 x 106 viable cells/well were transferred in 48-well plates in a total volume of 250 μL fresh medium ( corresponding with 106 or 4 x 106 cells/mL ) for subsequent generation of 3-D CM . A549 cells were grown in T75 tissue culture flasks in GTSF-2 medium until reaching >70% confluence . The confluent monolayer was trypsinized with 0 . 25% trypsin-EDTA ( Life Technologies ) and 105 viable cells/well were transferred into 6-well plates and allowed to reach confluence ( 2–3 days ) . 3-D A549 lung epithelial cells distributed in 48-well plates at a concentration of 2 . 5 x 105 or 1 x 106 cells/well ( in 250 μL fresh GTSF-2 medium/well ) were incubated for 2h at 37°C , 5% CO2 , >80% humidity . Then , the cell culture medium was collected and filtered through a 0 . 22 μm low protein binding filter ( Millipore ) to remove cell debris , resulting in 3-D conditioned medium ( 3-D CM ) . A549 cells grown to confluence as monolayers in a 6-well plate were trypsinized to determine the total cell number per well , and fresh cell culture medium was added to intact monolayers to obtain a concentration of 1 x 106 cells/mL . After 2h incubation , conditioned medium of 2-D A549 cells ( 2-D CM ) was obtained using the same protocol as for 3-D CM . For experiments aiming to determine the role of bicarbonate in the potentiating effect of 3-D CM , GTSF-2 that was not supplemented with 1 . 5 g/L bicarbonate was used to generate 3-D CM . When indicated , 3-D CM was filtered over a 3 kDa filter ( Millipore ) , or treated with 2 μg/mL trypsin ( Promega ) or 476 μg/mL proteinase K ( Sigma ) following the manufacturer’s instructions . Inhibition of biofilm formation by antibiotics in 3-D CM , 2-D CM or control medium ( GTSF-2 ) on a plastic surface was determined as described previously [47] , with modifications . Where indicated , biofilm inhibition was evaluated in control medium supplemented with glutamate ( final concentration 10 . 88 mM ) and/or succinate ( final concentration 1 . 81 mM or concentration range ) or CM/control media were supplemented with triphenylbismuthdichloride ( 16 , 64 , 128 μg/mL ) , CCCP ( 100 μM ) or KCl ( 150 mM ) and nigericin ( 10 μM ) ( Sigma-Aldrich ) . Briefly , bacterial cultures grown to stationary phase were diluted to an OD of 0 . 05 and transferred to 96-well plates . Antimicrobial agents were added at a concentration that inhibited biofilm formation by at least one log unit in the control medium , i . e . for P . aeruginosa PAO1 2 μg/mL for tobramycin ( Sigma-Aldrich ) , 4 μg/mL for amikacin ( TCI ) , 8 μg/mL for gentamicin ( Sigma-Aldrich ) and 2 μg/mL for colistin ( TCI ) . Antibiotic concentrations for other P . aeruginosa strains , Pseudomonas species and other bacterial species were also chosen to obtain biofilm inhibition of at least one log unit in control medium and are listed in Table 1 . Plates were incubated for 4 h in a 37°C , 5% CO2 incubator , and the number of colony forming units ( CFU ) attached to the surface was determined by homogenizing the biofilm through two rounds of vortexing ( 900 rpm , 5 min ) and sonication ( 5 min; Branson Ultrasonic bath ) . The homogenized biofilms were serially diluted and plated on tryptic soy agar ( TSA ) for all bacteria ( detection limit = 102 CFU/mL ) , except G . haemolysans which was plated on Columbia agar with 6% sheep blood . Plates were incubated at 37°C overnight ( 16 h ) or until colonies could be counted . Biofilm eradication in 3-D CM or control medium ( GTSF-2 ) by antibiotics was performed as described previously [48] . Stationary phase cultures were diluted to an OD of 0 . 05 , transferred to 96-well plates and incubated at 37°C for 24h to allow biofilm formation . Then , biofilms were rinsed and subsequently treated with antibiotics dissolved in the 3-D CM or control medium . Antibiotic concentrations were chosen to obtain at least 1 log biofilm eradication in the control medium , i . e . 15 μg/mL for tobramycin ( Sigma-Aldrich ) , 35 and 50 μg/mL for amikacin ( TCI ) , 25 μg/mL for gentamicin ( Sigma-Aldrich ) and 500 μg/mL for colistin ( TCI ) . Biofilms were homogenized and CFU were determined as described for the biofilm inhibition assay . The minimal inhibitory concentration ( MIC ) was determined according to EUCAST guidelines . When indicated , the MIC was determined in control cell culture medium ( GTSF-2 ) or 3-D CM instead of Mueller Hinton ( MH ) Broth . Stationary phase cultures were diluted to an OD of 0 . 05 in 3-D CM or control medium , and tobramycin was added at concentrations 2-fold or 8-fold higher than the MIC determined according to the EUCAST guidelines . Cultures were incubated in a 37°C shaking incubator ( 250 rpm ) up to 24h . At the start of the experiment and at every indicated time point , an aliquot of the culture was serially diluted and plated on TSA or LB agar to determine the CFU/mL ( detection limit = 102 CFU/mL ) . BODIPY-labelled tobramycin ( S7 Fig ) was synthesized according to the protocol provided in the Supporting Information ( S1 Text ) . The biofilm formation assay was performed in the presence of varying levels of BODIPY-tobramycin ( as indicated ) in 3-D CM or control medium at 37°C , or at 4°C when indicated . Following 4h of biofilm formation , the biofilm was rinsed to remove extracellular tobramycin , homogenized and subjected to flow cytometry analysis ( Attune NxT , Life Technologies ) . The bacterial population was delineated based on the forward and side scatter signal , and a threshold was set to exclude non-cellular particles and cell debris . BODIPY-tobramycin that associated with bacterial cells was determined through excitation with a 488 nm laser . Fluorescence emission was detected through a 530/30 bandpass filter . Controls included bacterial biofilm cells that were not exposed to BODIPY-tobramycin ( negative control ) or to incremental levels of tobramycin to determine the concentration at which saturation was obtained . Based on the negative control and the concentration of tobramycin where maximal population saturation was obtained , negative and positive flow cytometry gates were determined respectively . The intermediate gate contained bacterial cells located in between the negative and positive gates ( S2 Fig ) . At least 10 , 000 bacteria were analysed per sample . Intracellular pH was measured by a 2 , 7 -Bis ( 2-carboxyethyl ) -5 ( 6 ) -carboxyfluorescein acetoxymethyl ( BCECF-AM ) assay as described previously [5] , with modifications . Briefly , stationary phase cultures were exposed to 25 μM BCECF-AM ( Sigma-Aldrich ) for 30 min at 30°C . Loaded cells were washed twice with 0 . 9% NaCl and resuspended in fresh LB medium . Next , the same protocol as described for biofilm inhibition in 3-D CM versus control medium ( in the presence and absence of 2 μg/mL tobramycin ) was used with BCECF-AM-loaded cultures . Fluorescence was measured every 20 s for 30 min as a ratio of emission at 535 nm with dual wavelength excitation at 480 nm and 450 nm , using a plate-reader spectrophotometer ( Envision , Perkin Elmer ) . Each experiment included an intracellular pH calibration curve using control medium at a pH range of 6 to 8 . To this end , bacteria were exposed to GTSF-2 medium supplemented with excess KCl ( 150 mM ) and nigericin ( 10 μM ) to equilibrate intracellular and extracellular pH . A fluorimetric assay was used to measure cell membrane depolarization of P . aeruginosa , using the membrane potential-sensitive dye DiBAC4 ( 3 ) ( Bis- ( 1 , 3-Dibarbituric acid ) -trimethine oxanol ) , as described previously with modifications [49] . Briefly , stationary phase cultures were diluted fifty times in fresh LB medium , and cultured until early logarithmic phase ( OD ~0 . 5 ) . Cultures were pelleted and resuspended in control medium or 3-D CM , and antibiotics were added at the indicated concentration . After 4h incubation , samples were taken and exposed to a final concentration of 10 μg/mL DiBAC4 ( 3 ) ( from a 10 mg/mL stock in DMSO ) ( Invitrogen ) for 5 min at 37°C in the dark . Bacteria were pelleted , resuspended in filtered 0 . 9% NaCl , and diluted 100x in filtered 0 . 9% NaCl . Samples were allowed to equilibrate for 15 min at room temperature prior to flow cytometry analysis . Fluorescence emission due to membrane depolarization was measured using a flow cytometer ( Attune NXT , Thermofisher ) equipped with a 530/30 bandpass filter and 488 nm light source . At least 10 , 000 bacteria were analyzed , which were delineated based on the forward scatter ( FSC ) and side scatter ( SSC ) signal . A threshold on the FSC and SSC was set to exclude debris and non-cellular particles . To evaluate membrane permeability , a LIVE/DEAD BacLight Bacterial Viability Kit was used ( Thermofisher ) , and manufacturer’s instructions were followed . Live , dead , and intermediate populations were determined based on the fluorescence emission detected with 530/30 and 695/40 bandpass filters , following excitation at 488 nm , as described previously [30] . Host metabolites were quantified using colorimetric or fluorometric assay kits for pyruvate , succinate , glutamate ( Abcam ) , according to the manufacturer’s protocols . All experiments were performed at least in biological triplicate , and with 1–3 technical replicates . For the graphs where standard error mean was used to present variability , the number of replicates per data point is provided in S2 Table . Statistical analysis of quantitative assays was done using SPSS statistics software version 25 . The Shapiro–Wilk test was used in combination with Q/Q plot analysis to verify the normal distribution of the data . For normally distributed data , assessment of equality of variances was performed using a Levene’s test , followed by an independent sample t-test . Data sets that were not normally distributed were analysed using a Mann–Whitney test . For data sets involving multiple sample comparisons of normally distributed data , ANOVA-testing was performed followed by a Dunnett’s post hoc analysis . When normality was not confirmed , a Kruskal-Wallis non-parametric test was done . P-values <0 . 05 were considered statistically significant .
There is a poor correlation between the activity of antibiotics in the laboratory and in patients , including in several infectious diseases of the respiratory tract . What may help explaining differences between antibiotic activity in vitro and in vivo is that current antibiotic susceptibility tests do not consider the in vivo lung environment . The lung environment contains many factors that may influence bacterial susceptibility to antibiotics . This includes lung epithelial cells , which have been shown to improve the activity of aminoglycoside antibiotics . Yet , how lung epithelial cells increase aminoglycoside activity is currently unknown . Here , we cultured lung epithelial cells in an in vivo-like model and found that they secrete metabolites that enhance the activity of aminoglycoside antibiotics . We found that host cell secretions increased antibiotic uptake through stimulation of bacterial metabolism , which in turn resulted in enhanced activity . Our findings highlight that cross-talk between host and bacterial metabolisms contributes to the efficacy of antibiotic treatment . Understanding how the host metabolism influences antibiotic activity may open up therapeutic avenues to exploit host metabolism for improving antibiotic activity and help explaining discrepancies between antibiotic efficacy in vitro and in vivo .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]
[ "bacteriology", "antimicrobials", "biofilms", "cell", "physiology", "medicine", "and", "health", "sciences", "neurochemistry", "pathology", "and", "laboratory", "medicine", "pathogens", "drugs", "microbiology", "cell", "metabolism", "neuroscience", "epithelial", "cells", "pseudomonas", "aeruginosa", "metabolites", "antibiotics", "pharmacology", "neurotransmitters", "bacteria", "bacterial", "pathogens", "pseudomonas", "animal", "cells", "medical", "microbiology", "microbial", "pathogens", "biological", "tissue", "glutamate", "biochemistry", "bacterial", "biofilms", "cell", "biology", "anatomy", "microbial", "control", "biology", "and", "life", "sciences", "cellular", "types", "epithelium", "metabolism", "organisms" ]
2019
Host metabolites stimulate the bacterial proton motive force to enhance the activity of aminoglycoside antibiotics
Dynamic models of large-scale brain activity have been used for reproducing many empirical findings on human brain functional connectivity . Features that have been shown to be reproducible by comparing modeled to empirical data include functional connectivity measured over several minutes of resting-state functional magnetic resonance imaging , as well as its time-resolved fluctuations on a time scale of tens of seconds . However , comparison of modeled and empirical data has not been conducted yet for fluctuations in global network topology of functional connectivity , such as fluctuations between segregated and integrated topology or between high and low modularity topology . Since these global network-level fluctuations have been shown to be related to human cognition and behavior , there is an emerging need for clarifying their reproducibility with computational models . To address this problem , we directly compared fluctuations in global network topology of functional connectivity between modeled and empirical data , and clarified the degree to which a stationary model of spontaneous brain dynamics can reproduce the empirically observed fluctuations . Modeled fluctuations were simulated using a system of coupled phase oscillators wired according to brain structural connectivity . By performing model parameter search , we found that modeled fluctuations in global metrics quantifying network integration and modularity had more than 80% of magnitudes of those observed in the empirical data . Temporal properties of network states determined based on fluctuations in these metrics were also found to be reproducible , although their spatial patterns in functional connectivity did not perfectly matched . These results suggest that stationary models simulating resting-state activity can reproduce the magnitude of empirical fluctuations in segregation and integration , whereas additional factors , such as active mechanisms controlling non-stationary dynamics and/or greater accuracy of mapping brain structural connectivity , would be necessary for fully reproducing the spatial patterning associated with these fluctuations . Neural elements in the brain are structurally connected and functionally coupled heterogeneously to form complex networks , in which neurons , neuronal populations , or brain regions can be viewed as nodes linked by edges of structural connectivity and functional connectivity [1 , 2] . Structural connectivity refers to a pattern of anatomical connections between neural elements [3] , defining the “wiring diagram . ” On the other hand , functional connectivity refers to a pattern of statistical dependence among activities of neural elements [4] , which , in human neuroimaging , has typically been assessed by the blood oxygenation level dependent ( BOLD ) signal measured over several minutes of resting-state functional magnetic resonance imaging ( rs-fMRI ) [5] . Recent advancements in measurement and analysis of rs-fMRI data allow tracking fluctuations in functional connectivity on a time scale of tens of seconds [6–9] . Fluctuations in such time-resolved functional connectivity have been found not only at the individual edge level , but also at the global network level; for example , fluctuations between segregated and integrated network topology [10] and fluctuations between high and low modularity topology [11 , 12] . Fluctuations in global network topology of time-resolved functional connectivity have been associated with various types of human behavior , e . g . , pupil dilation [10] and eyelid closures [13] during rest , as well as cognitive performance [10] and decoding accuracy [14] during tasks . Along with empirical studies , a number of efforts have been made to model collective neural behavior in large-scale cortical systems [15] , such as regional cortical activity during rest , and there is an increasing availability of computational tools to support these modeling practices [16 , 17] . Simulating resting-state cortical activity using a set of nonlinear dynamic models wired according to structural connectivity generates synthetic BOLD time series that can be processed to yield functional connectivity , illustrating relations between anatomy and brain dynamics [18 , 19] . Modeling of rs-fMRI-based functional connectivity has also been performed with other types of dynamic models of large-scale brain activity [20–23] . Furthermore , modeling efforts have recently been extended to fluctuations in time-resolved functional connectivity to reproduce salient empirical findings [9 , 24–27] . Features of functional connectivity being explained by model simulations are comprehensively reviewed in [28] . While fluctuations in modeled time-resolved functional connectivity have started to be investigated , the reproducibility of fluctuations in its global network topology has not been comprehensively evaluated . Existing studies have shown that model simulations can generate fluctuations in integrated topology [29] as well as in global network efficiency [8] ( a network metric closely related to modularity ) , but fluctuations in these global network configurations have not yet been directly compared to the empirical counterparts . Specifically , neither a detailed model parameter search using empirical data nor an examination of the degree to which empirical fluctuations can be predicted by the model has been performed . Moreover , no comparison has been conducted so far for many spatial and temporal features associated with network states determined based on fluctuations between segregated and integrated topology [10] or high and low modularity topology [12] . Examples of such features include the quantity of network metrics used for determining network states , temporal dynamics of transitions over network states , and spatial patterns of functional connectivity during each network state . In addition , it remains unclear if some or all aspects of empirical fluctuations can be accounted for by emerging properties of nonlinear stationary dynamics or require active physiological mechanisms , e . g . to trigger transitions between networks states . To address these gaps in the literature , we compared fluctuations in global network topology of modeled and empirical functional connectivity and examined the reproducibility of empirical findings . We modeled regional resting-state cortical activity using a variant of the Kuramoto model [30 , 31] , a coupled phase oscillator system in which each oscillator was linked to each other based on brain structural connectivity , and generated modeled BOLD signal using the Balloon/Windkessel hemodynamic model [32] . With this stationary model of spontaneous brain activity , we evaluated fluctuations in global network topology of modeled functional connectivity by comparing their magnitude to that derived from empirical functional connectivity and searched ( fitted ) model parameters , such as the global coupling constant and the conduction velocity , based on this evaluation . With the model parameters selected , we compared network states of functional connectivity ( segregated and integrated states [10]; high and low modularity periods [12] ) between the modeled and empirical data . We first checked the reproducibility of network metrics used for determining network states , as well as the reproducibility of temporal metrics characterizing the transition dynamics of network states . We then examined whether spatial patterns of functional connectivity during each network state are reproducible or not in the modeled data . We particularly focused on examining the reproducibility of empirical findings regarding spatial connectivity patterns in previous studies [12 , 33] , where we reported characteristic between-state changes in functional connectivity within/between task-positive and task-negative networks and in the similarity between structural connectivity and functional connectivity . Through these comparisons , we demonstrated which empirical features of fluctuations in segregation and integration can be reproduced by a stationary dynamic model typically used for simulating resting-state brain activity . Imaging data in this study are from the data sample labeled 100 Unrelated Subjects in ConnectomeDB ( https://db . humanconnectome . org ) , the database managed by the Washington University-University of Minnesota ( WU-Minn ) consortium of the Human Connectome Project ( HCP; http://www . humanconnectome . org ) . Participants were recruited by the WU-Minn HCP consortium and provided written informed consent prior to experiments [34] . All experimental procedures were approved by the Institutional Review Board ( IRB ) at Washington University ( IRB number 201204036; “Mapping the Human Connectome: Structure , Function , and Heritability” ) and no further IRB approval is required for our data analysis . From this data sample , we first discarded 15 subjects because of their large head movements during acquisitions of rs-fMRI data . Subjects were excluded when maximum translation exceeded 3 mm , maximum rotation exceeded 3° , or mean framewise displacement ( FD; the l2 norm version ) exceeded 0 . 2 mm [35] in at least one run of rs-fMRI acquisition . We additionally excluded one subject aged ≥ 36 years to obtain a sample of young adults aged ≥ 22 years and < 36 years . The final number of subjects in this sample was 84 ( male , 40; female , 44 ) . All MRI data in this data sample were acquired with a 32-channel head coil on a modified 3T Siemens Skyra . Scanning parameters of acquired T1-weighted structural images were: repetition time ( TR ) = 2 , 400 ms , echo time ( TE ) = 2 . 14 ms , flip angle = 8° , field of view ( FOV ) = 224 × 224 mm2 , 320 slices , and voxel size = 0 . 7 mm isotropic . The rs-fMRI data in this sample were acquired with the following parameters: TR = 720 ms , TE = 33 . 1 ms , flip angle = 52° , FOV = 208 × 180 mm2 , 72 slices , and voxel size = 2 mm isotropic . For all 84 subjects , four runs of rs-fMRI data were collected with an eyes open condition and the duration per run was around 14 min ( 1 , 200 time points ) . Scanning parameters of diffusion-weighted images ( DWI ) were: TR = 5 , 520 ms , TE = 89 . 5 ms , flip angle = 78° , FOV = 210 × 180 mm2 , 111 slices , and voxel size = 1 . 25 mm isotropic ( three shells , two repeats , and 36 b0 scans ) . The number of gradient directions of the acquired DWI data was 270 and the b-value was 1 , 000 , 2 , 000 , and 3 , 000 s/mm2 for each of the three shells . The data sample downloaded from the ConnectomeDB has already been preprocessed with the minimal preprocessing pipeline of the HCP [36] . Preprocessing steps for rs-fMRI data included in this pipeline were: correction of gradient distortion , motion correction , removal of bias fields , correction of spatial distortion , transformation to Montreal Neurological Institute ( MNI ) space , and normalization of the image intensity . Preprocessing steps for DWI data included normalization of the intensity and corrections of susceptibility distortion , eddy current distortion , motion-related artifact , and gradient nonlinearly . To further improve data quality , we additionally preprocessed the rs-fMRI data by taking the following steps: ( a ) removal of the first 10 s of volumes , ( b ) removal of outlier volumes and interpolation ( the percentage of interpolated volumes was 3 . 6 ± 0 . 1% [mean ± SD across all subjects and runs] ) , ( c ) regressing out the Friston-24 motion time series [37] and the global , white matter , and cerebrospinal fluid mean signals , and ( d ) detrending and band-pass filtering ( cutoff frequency: ( 66 TRs ) −1 = 0 . 021 Hz [low] , 0 . 1 Hz [high] ) . To exclude spurious fluctuations , we specified the low-cut frequency of the band-pass filtering to the reciprocal of the width of the sliding window for time-resolved functional connectivity [38 , 39] . The outlier removal and interpolation in step ( b ) were performed using 3dDespike in the AFNI package [40] as in [41] . The removal of outlier volumes is similar to motion scrubbing and censoring [42 , 43] , but we replaced outliers with interpolated volumes instead of discarding affected time points , in order to keep the original number of time points within a sliding window over the whole time course . We chose to include global signal regression in step ( c ) to remove global artifacts that are attributable to motion and/or respiration [44] . White matter fiber tracts were reconstructed from the DWI data using generalized q-sampling imaging [45] and deterministic streamline tractography . The use of the generalized q-sampling imaging method allows for the reconstruction of complex fiber configurations . Details of the procedure of tractography are presented in [46–48] . Connectivity analyses were performed in a region-wise manner within the cortex . Nodes of connectivity networks in this study were assigned to each of the 114 distinct cortical parcels , made by a subdivision of the Desikan-Killiany atlas [49] ( see S1 Fig ) . These subdivided parcels were obtained from the atlas files myatlas_60_lh . gcs and myatlas_60_rh . gcs in the Connectome Mapper package ( https://github . com/LTS5/cmp ) . In addition , we assigned every node ( parcel ) to one of the seven intrinsic connectivity networks defined in [50] by evaluating the area of overlap of cortical surface . These seven networks are named as follows: the control network ( CON ) , the default mode network ( DMN ) , the limbic system ( LIM ) , the dorsal attention network ( DAN ) , the saliency/ventral attention network ( VAN ) , the somatomotor network ( SMN ) , and the visual network ( VIS ) . Structural connectivity strength between a pair of cortical regions was measured using fiber density , defined as the streamline count between the two regions divided by the geometric mean of the surface areas of these regions . We used fiber density as a strength metric of structural connectivity in order to compensate for an effect of the size of regions on streamline counts [51] . From structural connectivity in individual participants , group-level structural connectivity was derived using a consensus approach that preserves the fiber length distributions of individual-level structural connectivity within and between hemispheres , respectively [52] . For the edges selected by this consensus approach , connectivity strength and fiber length were averaged across subjects to construct group-level matrices ( see Fig 1A , right ) , where averaging at such an edge was performed across subjects whose corresponding connectivity strength at this edge was non-zero . As a metric of functional connectivity , we used the Pearson correlation coefficient between regional BOLD time courses . The correlation coefficient was Fisher z-transformed except when it was shown in connectivity matrices in figures . We refer to the functional connectivity measured over the entire rs-fMRI run as long-timescale functional connectivity . With the metric of functional connectivity defined above , time-resolved functional connectivity was estimated using a tapered sliding window approach [7 , 53] . The shape and the size of tapered time window were specified in a similar way as in [41] . Specifically , a tapered time window was constructed by convolving a rectangle of width = 47 . 52 s ( 66 TRs ) with a Gaussian kernel of σ = 6 . 48 s ( 9 TRs ) . The tapered window was moved toward the end of the BOLD time series in steps of 2 . 16 s ( 3 TRs ) , which resulted in a total of 369 tapered windows . Communities or modules in networks of time-resolved functional connectivity were detected through modularity maximization [54] using the Louvain algorithm [55] . To cope with negative functional connectivity , we employed a modularity quality function Q generalized for networks containing both positive and negative edge weights [56] as follows: Q = 1 ν + ∑ i , j ( w i , j + - e i , j + ) δ M i , M j - 1 ν + + ν - ∑ i , j ( w i , j - - e i , j - ) δ M i , M j , ( 1 ) where w i , j + = w i , j and w i , j - = 0 if the edge weight wi , j between nodes i and j is positive , and w i , j + = 0 and w i , j - = - w i , j otherwise . δ in this equation is the Kronecker delta , where δ M i , M j = 1 if nodes i and j are within the same module and δ M i , M j = 0 otherwise . The term e i , j ± = s i ± s j ± / ν ± , where s i ± = ∑ j w i , j ± and ν ± = ∑ i , j w i , j ± , denotes the expected density of positive or negative weights in a network given a random null model . The first term in Eq ( 1 ) corresponds to a standard form of the quality function in which negative edge weights are not taken into account . Adding the second term allows to identify partitions in which negative edges are located between modules [56] . As was argued in [56] , we decided to add this term when identifying partitions in functional brain networks since negatively correlated pairs of nodes are indicative that these nodes reside in different communities . Maximization of the modularity quality function Q was performed using the Matlab function community_louvain . m in the Brain Connectivity Toolbox ( BCT; http://www . brain-connectivity-toolbox . net ) with the default setting of the resolution parameter γ = 1 . Modularity maximization was applied to the adjacency matrix of functional connectivity 100 times with random initial conditions for each time window of time-resolved functional connectivity . The maximum of the quality function across trials at time window t was regarded as the resulting modularity score Qt and its accompanying network partition as its community assignment . Based on detected communities in networks , within-module degree z-score and participation coefficient [57] were computed for each time window of time-resolved functional connectivity . Consistent with previous work , these two network metrics were used for estimating segregated and integrated states of functional connectivity [10] . In functional brain networks , the within-module degree z-score measures the extent to which a node is functionally coupled with the other nodes within the same module , relative to the weighted degrees of the other nodes within this module . The within-module degree z-score of node i at time t is computed as z i , t = κ i , M i , t , t - κ ¯ M i , t , t σ M i , t , t , ( 2 ) where κ i , M i , t , t denotes the weighted degree of node i at time t within its module Mi , t , and κ ¯ M i , t , t and σ M i , t , t are the mean and SD of the nodal weighted degrees at time t within module Mi , t . This network metric was computed using the function module_degree_zscore . m in the BCT toolbox . The participation coefficient for functional connectivity quantifies the extent to which a node is functionally coupled with other nodes across diverse modules . The participation coefficient of node i at time t is given by P i , t = 1 - ∑ m = 1 N t ( κ i , m , t + κ i , t + ) 2 , ( 3 ) where Nt is the number of detected modules at time t , κ i , m , t + is the weighted degree of the positive edge weights of node i at time t within module m , and κ i , t + is the weighted degree of the positive edge weights of node i at time t across all modules . This metric was computed using the BCT function participation_coef_sign . m . Regional resting-state cortical activity was simulated using a system of coupled phase oscillators , called the Kuramoto model [30 , 31 , 58 , 59] . We selected the Kuramoto model to simulate resting activity because it is simple enough to be tractable , while it can simulate synchronization behavior in neural dynamics by taking into account delays between oscillators [60] and , more importantly , can reproduce empirical findings about functional connectivity in the resting brain by linking oscillators based on structural connectivity [20 , 23 , 61–63] . While several recent studies directly model slow BOLD fluctuations using the Kuramoto model [9 , 64 , 65] , we adopted the approach taken by [20 , 23 , 62 , 63] , where the Kuramoto model is used for simulating fast oscillatory activity of neural populations in the gamma frequency band ( 30–90 Hz ) [66] and then the simulated neural activity is converted to modeled BOLD time series using a hemodynamic model . Experimental studies have shown that fluctuations in the gamma-band power of neural activity are closely related to spontaneous BOLD signal [67–70] . The periodical dynamic behavior of node i in the network of coupled oscillators is described using its phase θi ( t ) , and it obeys the following differential equation: d θ i d t = 2 π f + k ∑ j = 1 N C i , j sin ( θ j ( t - τ i , j ) - θ i ( t ) ) , ( 4 ) where f denotes the natural frequency , which was set to 60 Hz for all nodes [23 , 63] . The second term in this equation represents the influences from the other nodes that are structurally connected to node i . In this term , N denotes the number of nodes , k is the global coupling constant that controls the overall strength of the couplings , Ci , j is the strength of group-level structural connectivity between nodes i and j , where it was normalized so that the average of all non-zero edge weights equals one , and τi , j is the time delay of interactions between nodes i and j . The time delay was assumed to be proportional to the fiber length L between nodes i and j such that τi , j = Li , j/v , where v represents the conduction velocity in myelinated fibers [20 , 23 , 61–63] . The way to specify the model parameters k and v are described later in Model parameter search in Materials and methods . A schematic of model simulation is shown in Fig 1A . In Eq ( 4 ) , no noise was added to the system as in [61 , 71] . Qualitatively similar results were obtained even when the system noise was introduced to the model simulation . The main results of this study with noise added are presented in Supporting information ( S6 and S7 Figs ) . When the simulation was performed with noise , white Gaussian noise with σ = 1 . 25 rad/s was added to the system as in [23 , 63] . The differential equation in Eq ( 4 ) was numerically solved using the deterministic Heun method for the simulation without noise and the stochastic Heun method for the simulation with noise . The step size of the numerical integration was set to 0 . 2 ms . The initial value of the phase was randomly drawn from the uniform distribution of the range [0 , 2π] . The initial history of the phase , which must be specified due to the presence of delays , was generated by running simulations for a short duration without interactions [20 , 61] . To remove transient dynamics , the initial 20 s of data were discarded from the simulated phase time series . The global level of synchrony of the oscillator system can be evaluated using the order parameter R ( t ) : R ( t ) e i Φ ( t ) = 1 N ∑ n = 1 N e i θ n ( t ) , ( 5 ) where R ( t ) quantifies the phase uniformity , which varies between 0 ( fully incoherent ) and 1 ( fully synchronized ) , and Φ ( t ) describes the phase of the global ensemble of the oscillators . We characterized the global dynamics of the system using the mean and the SD of the order parameter R ( t ) , which measure respectively the level of global synchrony and the level of global metastability of the whole system [72 , 73] . After transforming the phase time courses θn ( t ) into the simulated regional activities rn ( t ) as rn ( t ) = sin ( θn ( t ) ) ( n = 1 , … , N ) [20] and downsampling them to the sampling frequency of 1 kHz , these regional activity data were converted to the BOLD time courses using the Balloon/Windkessel hemodynamic model [32] with the parameter setting used in [74] ( Fig 1B ) . The obtained modeled BOLD signal was preprocessed using the same band-pass filter that was applied to the empirical rs-fMRI data . Additionally , the modeled data were downsampled to make their TR identical to the empirical data , and the global signal was regressed out as was performed for the empirical data . The number of time points in a single simulation sample of the modeled data was set to be identical to that of a single run of the preprocessed empirical rs-fMRI data ( approximately 14 min ) . For each simulation sample of the modeled data , computation of long-timescale and time-resolved functional connectivity , community detection , calculation of the network metrics Qt , zt and Pt , and the estimation of the network states were conducted in the same manner as for the empirical data . In addition to relating to the empirical data , the magnitude of fluctuations in global network topology of modeled functional connectivity was compared to that obtained from the model simulations with surrogate structural connectivity . The surrogate connectivity data were constructed by randomly rewiring edges with preserving the degree and the weighted degree of each node . The rewiring of edges was performed using the BCT function null_model_und_sign . m with the default setting . We kept generating surrogates until we obtained 100 random samples whose correlation coefficient between weighted-degree sequences of actual and surrogate connection matrices was greater than 0 . 95 . We also controlled the level of global synchrony of simulated activity by performing model parameter search in each surrogate sample . Details of this procedure are presented in the last paragraph of the next section Model parameter search . The global coupling constant k and the conduction velocity v were searched so that long-timescale functional connectivity , time-resolved functional connectivity , and fluctuations in its global network topology of the modeled data become close to those obtained from the empirical data . As in [20 , 61 , 62] , the conduction velocity v was searched through changing the mean time delay τ ¯ = L ¯ / v , where L ¯ denotes the mean fiber length ( L ¯ = 84 . 5 mm in our data ) . The search for the model parameters k and τ ¯ was performed in two stages in the following manner . After the model parameter search , we compared network states of functional connectivity ( segregated and integrated states or high , middle , and low modularity periods ) between the modeled and empirical data ( Fig 1C , panel 3 ) . In particular , we compared network metrics that were used for determining network states ( i . e . , Qt , zt , and Pt ) , temporal metrics that characterize the dynamics of network states ( transition probability and mean dwell time ) , and spatial patterns of functional connectivity during each network state . When comparing spatial patterns of modeled and empirical functional connectivity , we investigated changes in functional connectivity across network states through the centroids of time-resolved functional connectivity during each network state . Edge weights of a centroid were computed as the median of time-resolved functional connectivity over time during the corresponding network state within each sample or run [41] . Centroids of the empirical data were averaged over all four rs-fMRI runs within each individual . With these centroids , we examined whether empirical findings about the following two characteristic changes in functional connectivity across network states , reported in [12 , 33] , are reproducible or not in the modeled connectivity data . The modeled data generated with noise in simulations yield main results similar to those obtained without noise . We confirmed that similar results were observed in the ratio of modeled to empirical fluctuations in global network topology ( Fig 3B and S6A Fig ) , network metrics used for determining network states ( Fig 4 and S6B and S6C Fig ) , temporal metrics of network states ( Fig 5 and S6D Fig ) , and spatial patterns of functional connectivity and their changes across network states ( Figs 6 , 7 and S7 Fig ) . The present study has several methodological limitations . First , while white matter tractography using DWI data is a primary technique for estimating human structural connectivity , it can be prone to inaccuracies [79 , 80] especially for estimating connections between hemispheres [63] . In our study , errors in structural connectivity may have influenced the simulations of resting-state cortical activity , as is suggested by our observation that the magnitude of fluctuations was not accurately reproduced in the modeled data simulated with surrogate structural connectivity . Furthermore , taking into account only intra-hemispheric functional connections for evaluating patterns in changes between network states exhibited within-system coherence , but failed to show between-system decoupling . Errors in estimating structural connections ( and thus in modeling functional connections ) between hemispheres may have contributed to this negative result . In spite of these limitations , we believe that the emergence of fluctuations in global network topology of modeled functional connectivity were not a consequence of inaccuracies inherent in tractography , since the fluctuations themselves have also been observed in modeled data simulated with tract-tracing-based macaque structural connectivity retrieved from the CoCoMac database [8 , 29] . Nevertheless , future research is needed to compare the modeled and empirical fluctuations without potential biases due to tractography , using e . g . the CoCoMac structural connectivity data and empirical macaque rs-fMRI data . Second , assessing the non-stationarity of coactivation patterns with rs-fMRI has its own limitations . Existing studies have shown that the non-stationarity of ( pairwise ) functional connectivity is difficult to be detected from an insufficient amount of rs-fMRI data [81] and is also subject to potential confounds such as head movements and/or physiological noise [82] . Especially the latter issue is needed to be carefully addressed when quantifying the magnitude of fluctuations in global network topology of empirical functional connectivity . We applied extensive artifact reduction methods to the empirical rs-fMRI data employed in this study . With this dataset , we previously demonstrated that no consistent relation was found in fluctuations in mean participation coefficient to either head motion or respiration [33] . Therefore , effects of artifacts on our evaluation of the magnitude of empirical fluctuations should be limited . Third , we modeled the dynamics of regional cortical activity using rather simple Kuramoto oscillators , in which the collective behavior of neural populations in each region was described by a single phase variable . The reason for choosing the Kuramoto model is computational efficiency to enable a systematic model parameter search with sufficient numbers of simulation samples per each parameter set , especially for the analysis of fluctuations in global network topology . Current computational resources do not allow replacing this model with more complex models , such as a neural mass model employed in [8 , 18 , 19] , in which local neural dynamics are described by multiple nonlinear differential equations . In support of our model choice , previous studies have demonstrated that the prediction accuracy of long-timescale functional connectivity of the Kuramoto model is comparable to that of other major computational models including the above-mentioned neural mass model [23 , 63] . Fourth , we used group-level structural connectivity for generating modeled resting-state cortical activity , although it would be ideal to use individual structural connectivity instead to conduct comparison of the modeled and empirical data within each individual . Resting-state activity has recently been simulated using individual structural connectivity in e . g . [83] for the purpose of exploring individual differences in cognition . Nevertheless , conducting all the analyses in this study using individual structural connectivity is infeasible due to excessive demands on computation time , especially for searching model parameters of every single subject . Performing the analyses in this study using individual structural connectivity for tens of subjects may become feasible as computing power increases . Future work is needed to gain further insights into the generative mechanism of fluctuations in global network topology of functional connectivity . An interesting direction of future research is to try to uncover local features of transient activity propagations underlying these global network-level fluctuations . Revealing such local features may allow us to understand how the global fluctuations in network integration and modularity emerge from sequences of time-resolved regional activities over the entire brain . Propagations of regional activity can be investigated using point-process analysis [84 , 85] . Another important future direction is to provide a mechanistic understanding of the global network-level fluctuations by manipulating elements in the network model , for example , introducing lesions in structural connectivity that wires local dynamic models [62 , 86] . Examining the effects of manipulations on the outcomes would contribute to elucidate model elements necessary for the emergence of fluctuations in network integration and modularity . We will pursue these avenues of research in future work , with continued emphasis on the generative aspects of fluctuations in global network topology .
In human neuroscience , there is growing interest in temporal fluctuations in coactivation patterns of resting-state brain activity . To elucidate generative mechanisms of these fluctuations , theoretical studies try to reproduce their empirical properties by simulations using dynamic models of large-scale spontaneous brain activity . However , evaluations of the reproducibility have not been extended so far to the fluctuations in global network topology of coactivation patterns , recently shown to be related to human cognition and behavior . Here we examine the extent to which a stationary model typically used for simulating resting-state activity can reproduce spatial and temporal patterns of the empirically observed fluctuations in global network topology . We found that such a model successfully reproduced the magnitude of empirical fluctuations as well as their temporal dynamics , whereas their spatial patterning was not fully accounted for by the simulation . Our results suggest that stationary models can explain many empirical properties in the fluctuations in global network topology , while modeling of non-stationary dynamics and/or greater estimation accuracy of anatomical connections underlying the simulation would be required for complete replication . This finding provides new insights into how fluctuations in global network topology of coactivation patterns emerge in the human brain .
[ "Abstract", "Introduction", "Materials", "and", "methods", "Results", "Discussion" ]
[ "data", "acquisition", "neural", "networks", "social", "sciences", "neuroscience", "simulation", "and", "modeling", "systems", "science", "mathematics", "network", "analysis", "research", "and", "analysis", "methods", "computer", "and", "information", "sciences", "behavior", "dynamical", "systems", "research", "assessment", "psychology", "biology", "and", "life", "sciences", "physical", "sciences", "reproducibility", "dwell", "time" ]
2018
Comparison of fluctuations in global network topology of modeled and empirical brain functional connectivity
As plans to expand mass drug treatment campaigns to fight schistosomiasis form , worries about reliance on praziquantel as the sole available treatment motivate the investigation for novel antischistosomal compounds . Drug repurposing might be an inexpensive and effective source of novel antischistosomal leads . 1600 FDA approved compounds were first assayed against Schistosoma mansoni schistosomula at a concentration of 10 µM . Active compounds identified from this screen were advanced to the adult worm screen at 33 . 33 µM , followed by hit characterization . Leads with complementary pharmacokinetic and toxicity profiles were then selected for in vivo studies . The in vitro screen identified 121 and 36 compounds active against the schistosomula and adult stage , respectively . Further , in vitro characterization and comparison with already available pharmacokinetic and toxicity data identified 11 in vivo candidates . Doramectin ( 10 mg/kg ) and clofazimine ( 400 mg/kg ) were found to be active in vivo with worm burden reductions of 60 . 1% and 82 . 7% , respectively . The work presented here expands the knowledge of antischistosomal properties of already approved compounds and underscores variations observed between target-based and phenotypic approaches and among laboratories . The two in vivo-active drugs identified in this study , doramectin and clofazimine are widely available and present as novel drug classes as starting points for further investigation . Worldwide , schistosomiasis continues to affect the health and quality of life of millions , causing 3 . 3 million Disability-Adjusted Life-Years lost [1] . Most of the burden is contained in the tropics , mostly in Sub-Saharan Africa , where it disproportionally affects children in poor rural areas [2] . The three principal causative agents are Schistosoma mansoni , Schistosoma haematobium and Schistosoma japonicum . Infection with any of these three species , when left untreated , results in chronic inflammation which slowly develops into swelling , fibrosis and necrosis of the tissues of intestinal organs , the liver or the bladder , as well as a range of other symptoms which gradually impair the host physiologically and even cognitively [3 , 4] . The World Health Organisation ( WHO ) places morbidity control as a priority for treating schistosomiasis via preventative chemotherapy in the form of mass drug administration campaigns . Treatment targeted at high risk groups , mainly school-aged children , interrupts advancement to the cumulative damage of chronic stages , which causes most of the disease burden [5] . To date , this is seen as the most cost-effective strategy , as interruption of transmission is very difficult , costly and subject to many factors , and vaccine development is still far out of reach [6 , 7] . Yet of the 207 million people infected annually , in 2012 only 35 million received treatment at a given time [4] . Therefore , it has become essential to expand mass treatment campaigns . Indeed , as many as 235 million children are targeted to receive treatment by 2018 [8] . Nonetheless , we still rely on praziquantel as the sole treatment and the expanded use of this drug , while positively reducing morbidity , would also increase the potential for praziquantel resistance [9 , 10] . Regardless of expansion plans , reliance on one single drug for mass treating a population is dangerous . The international community has repeatedly stated the need for new medication , since the drug discovery and development pipeline is dry [11] . Earlier , we reviewed the ways in which drug repurposing is aiding helminth drug discovery [12] and highlighted several clinical success stories such as antimalarials for the treatment of schistosomiasis . Drug repurposing ( or repositioning ) is the development of new indications from existing , failed or abandoned drugs and offers some obvious benefits: researchers can piggy-back off the availability of pre-clinical data , saving time and costs , making more informed decisions on hit-to-lead identification and ultimately decreasing the time it takes to bring a drug to market [13 , 14] . In the framework of a Gates-funded drug discovery project , overseen by the Drugs for Neglected Diseases initiative ( DNDi ) , different libraries , including a library of 1600 FDA approved compounds with diverse classes and initial applications , were screened on Schistosoma mansoni . Abdulla and colleagues had previously evaluated a similar library of 2160 compounds on a Puerto Rican strain of S . mansoni , finding many in vitro-active compounds but no strong in vivo-active candidates [15] . Considering our past experience with strain and hit cut-off differences [16] , we were encouraged to screen the above-mentioned 1600 compound FDA library in the hopes of identifying strong candidates to test in vivo and to compare our findings . In more detail , the full 1600 compound library was initially assayed on newly transformed schistosomula ( NTS- the larval stage ) . Compounds that reduced NTS viability by 75% were further tested on adult worms and their activity was compared to their existing pharmacokinetic and toxicity profiles before initiating studies in a mouse-S . mansoni infection model . Finally , we compare our results with findings reported from the above-mentioned screen by Abdulla et al . and a recent target-based chemogenomics screen of a dataset of 2 , 114 proteins by Neves et al . [17] , and discuss overlaps and contradictions . The FDA Pharmakon compound library was purchased from MicroSource Discovery Systems , Inc . ( USA ) . Compounds were delivered in microplates ( 10 mM , dissolved in DMSO ) and kept at -80°C until use . For in vivo studies , flunarizine hydrochloride , pimozide , nicardipine hydrochloride , oxethazaine , menadione , clofazimine , doramectin and metitepine mesylate were purchased from Sigma-Aldrich ( Buchs , Switzerland ) and fendiline hydrochloride , manidipine hydrochloride and lomerizine hydrochloride were purchased from Santa-Cruz Biotechnology ( California , USA ) . Hanks Balanced Salt Solution ( HBSS ) was obtained from Gibco ( Lucerne , Switzerland ) . Culture medium components for NTS and adult worms were obtained as follows: Medium 199 RPMI 1640 and penicillin ( 100 U/ml ) and streptomycin ( 100 μg/ml ) were purchased from Lubioscience ( Lucerne , Switzerland ) whereas inactivated fetal calf serum ( iFCS ) was purchased from Connectorate AG ( Dietikon , Switzerland ) . NTS were obtained using a transformation method described previously [18] . Briefly , cercariae ( Liberian strain ) were harvested from infected intermediate host snails ( Biomphalaria glabrata ) after several hours’ exposure to light . The collected cercarial suspension was cooled , centrifuged and pipetted , and vortexed vigorously in HBSS to remove the tails . The suspension was rinsed in cool HBSS to remove the tails and the resulting NTS suspension was adjusted to a concentration of 100 NTS per 50 μl in NTS culture medium ( Medium 199 supplemented with 5% iFCS and 1% penicillin/streptomycin ) . The NTS suspension was then incubated at 37°C , 5% CO2 in ambient air for 24 hours . Drugs were first tested at a concentration of 10 μM on NTS . The worms were incubated in culture medium and the test compounds in a 96-well plate in triplicate for 72 hours . Thereafter , they were assessed microscopically using a viability scale previously described [16] , which scores the morphology and motility of the NTS ( 3 = motile , no changes to morphology; 2 = reduced motility and/or some damage to tegument noted; 1 = severe reduction to motility and/or damage to tegument observed; 0 = dead ) . Hits were characterized as compounds that achieved an average viability score of 0 . 5 or less ( corresponds to NTS viability of ≤ 25% ) . Compounds identified as hits in the NTS drug assay were further tested on adult worms . Mice were infected as detailed in the in vivo studies section below and the infection was allowed to mature for 7 weeks . Mice were then euthanized with CO2 and their intestinal apparatus was dissected . Worms were collected from the hepatic portal and mesenteric veins and subsequently rinsed and stored in culture medium ( RPMI supplemented with 5% iFCS and 1% penicillin/streptomycin ) at 37°C , 5% CO2 until use . In a 24-well plate , 2–4 worm pairs were placed in culture medium and 33 . 33 μM of the test compound for 72 hours , 2 wells per compound . Effects were assessed microscopically with the same viability scale used for NTS and again , compounds that achieved an average score of 0 . 5 or less but after 24 hours were considered as hits . For further hit characterization , the IC50 values were determined in an adult worm dose-response assay ( 33 . 33 , 11 . 11 , 3 . 70 , 1 . 43 and 0 . 41 μM drug concentration ) at 1 , 2 , 4 , 7 , 10 , 24 , 48 and 72 hours post-drug incubation . For in vivo studies , female 3-week old NMRI mice were used . Mice were purchased from Charles River ( Sulzfeld , Germany ) and allowed to adapt under controlled conditions ( temperature ca . 22°C; humidity ca . 50%; 12-hour light and dark cycle; free access to rodent diet and water ) for one week . Thereafter , they were infected subcutaneously with approximately 100 S . mansoni cercariae ( obtained as described above ) . Seven weeks post-infection , 4 mice were assigned to each drug treatment , while 8 mice were left untreated to serve as controls . Compounds were prepared in a 70:30 Tween/EtOH mixture dissolved in dH2O ( 10% ) . Available compound toxicity data was used to guide the dosing regimen . Compound doses were adjusted to the mouse weight and were administered orally . Three weeks post-treatment , mice were killed by the CO2 method and dissected , and the worms were sexed and counted . Mean worm burdens of treated mice were compared to the mean worm burden of untreated animals and worm burden reductions were calculated . In vivo studies were conducted at the Swiss TPH , Basel , and approved by the veterinary authorities of the Canton Basel-Stadt ( permit no . 2070 ) based on Swiss cantonal ( Verordnung Veterinäramt Basel-Stadt ) and national regulations ( the Swiss animal protection law ( Tierschutzgesetz ) . For in vitro assays , the viability scores were averaged across replicates and normalized to control-well viability scores using Microsoft Office Excel ( 2010 ) . IC50 values were computed using CompuSyn2 ( ComboSyn Inc . , 2007 ) by converting viability scores into effect scores for each drug concentration . The worm burden ( WB ) of treated mice was calculated and compared with the worm burden of control mice in order to obtain the worm burden reduction ( WBR ) , calculated as follows: WBR ( % ) =100%- ( 100%/WBcontrol×WBtreatment ) Statistical comparison was done using the Kruskal Wallis Test and the Mann Whitney U test at a significance level of p < 0 . 05 . The overall screening cascade is presented in Fig 1 . Of the 1600 compounds screened on NTS , 121 compounds ( summarized by indication in Table 1 ) showed activity at a concentration of 10 μM after 72 hours . Of these , 57 compounds killed the NTS completely within 72 hours of exposure and 64 compounds damaged the NTS severely ( viability score ≤ 0 . 5 , corresponding to a viability of ≤ 25% ) within the same time frame . After a quick scan of the hits , 20 compounds were excluded due to their known high toxicity ( e . g . colchicine ) or because their activity against S . mansoni has already been described ( e . g . mefloquine ) ( Table 1 ) . From the NTS screen , therefore 101 compounds qualified for testing on S . mansoni adult worms , at a single high concentration of 33 . 33 μM . Of these , 36 compounds were found to be active 24 hours post-incubation: the compounds induced death of the worms or a 75% reduction in their viability ( a final viability score of ≤ 0 . 5 ) . However , of the 36 active compounds , 25 were excluded following a closer review for the following reasons: 8 were excluded due to known toxicity in humans , 9 were indicated for topical use only , 5 had been described to have toxicity in rodents ( low LD50 values ) , 2 were excluded due to past or current studies conducted on S . mansoni in vivo models ( niclosamide studied by Abdulla et al . ( 15 ) ; tamoxifen studied by Cowan et al , submitted for publication ) , and 1 was rejected due to its poor absorption ( Table 2 ) . The remaining 11 compounds were further characterized with an IC50 determination assay at various time-points ( Fig 2 and S1 Table ) . Already after 2 hours incubation , most compounds ( except pimozide , doramectin , clofazimine and flunarazine hydrochloride ) exhibited IC50 values below 10 μM , and by the 10 hour time-point , IC50 values for these compounds ranged from 1 . 73–7 . 80 μM . The fastest acting compounds were nicardipine hydrochloride and oxethazaine , exhibiting IC50 values of 2 . 67 and 2 . 95 μM respectively already at 1 hour post-incubation . Meanwhile , doramectin and clofazimine were the slowest acting , with IC50 values of 16 . 92 and 20 . 72 μM respectively at 4 hours post-exposure . Between 24 and 72 hours post-exposure , IC50 values did not vary greatly between drugs , ranging between 1 . 34 to 4 . 17 μM , except for pimozide which jumped to 8 . 78 μM at 24 hours and declined to 3 . 46 μM by the 72-hour time-point . These timed IC50 values were compared to available pharmacokinetic data , and all 11 compounds were selected as good in vivo candidates . As previously mentioned , the in vitro data and available pharmacokinetic and rodent toxicity data were also used to guide the maximum possible single oral dose regimens . Dosing , worm burden and worm burden reductions of the 11 compounds tested are presented in Table 3 . Metitepine mesylate proved to be toxic to mice at each dose tested ( 400 , 200 , 100 and 50 mg/kg , one mouse tested per dose and observed ) and further investigation with this compound was ceased . Doramectin exerted a moderate worm burden reduction ( 60 . 1% ) and clofazimine caused a high , however not statistically significant worm burden reduction ( 82 . 7% ) . In a follow-up in vivo study , a dose 200 mg/kg clofazimine was tested in 4 S . mansoni-infected NMRI mice . Lowering the dose , however , resulted in a complete lack of efficacy ( 0% female and total WBR ) . Pimozide and nicardipine hydrochloride also demonstrated some efficacy ( 49 . 5% WBR for both ) , whereas mild WBRs were observed for flunarizine hydrochloride ( 26 . 7% WBR ) , oxethazaine ( 35 . 5% WBR ) and manidipine hydrochloride ( 27 . 7% WBR ) . Lomerizine hydrochloride , fendeline hydrochloride and menadione lacked in vivo activity . The advent of praziquantel in the 1970s was a great milestone for the control of schistosomiasis in that finally , a safe , cheap and effective drug became available that could be used to treat millions in cost-effective preventive chemotherapy campaigns . Unfortunately , the success of praziquantel also resulted in many labs and firms choosing to drop further investigations on their leads [19] . This , along with inadequate attention and funding has rendered the antischistosomal arsenal dangerously dependent on a single drug [10] . As de novo drug discovery becomes increasingly expensive , drug repurposing , on the other hand , has shown to bear fruit in the antischistosomal drug discovery field as well as others with fewer resources involved [12 , 20 , 21] . By screening a library of well-characterized compounds , it was our hope to identify new drugs or drug classes that could be explored further in pre-clinical development . An initial screen against NTS revealed a hit rate of ~7 . 6% and encompassed a wide range of compound indications including antipsychotics , antibiotics , antifungals , antihistamines , antihypertensives and even vitamin precursors and metabolites ( Table 1 ) . Results of this work in part mirrored the screen conducted by Abdulla and colleagues ( mentioned earlier ) , in that the variety of active compounds also ranged across a large spectrum of indications [15] . However , in comparing our NTS hits , it was interesting to note that although there was some overlap , there were numerous incongruences as well ( S2 Table and Fig 3A ) . Of the 121 NTS hits we identified in our library , 69 of the compounds were also found in their library , but of these 69 , only 25 were identified as hits ( 36% overlap ) . Conversely , of the 105 hits identified in the library of Abdulla and colleagues , 55 were found in our library and of those , 25 were characterized as hits ( 45% ) . These inconsistencies are likely a combination of differences in drug concentrations used , time of evaluation post-drug exposure and screen cut-off filters for hit identification , which indicates that these factors can greatly influence the outcome of a screen . Indeed , a closer inspection of hits identified by Abdulla et al . and missed in our library revealed that most of these “missed compounds” had some effect on our NTS but not enough to reach the cut-off threshold . Nonetheless , it may also be possible that strain differences result in differing drug susceptibilities ( Abdulla et al . used a Puerto Rican strain , whereas ours was a Liberian strain ) . Indeed , Ingram-Sieber et al . observed similar differential sensitivities from their screen of MMV Malaria Box compounds conducted by two independent labs using exactly these two strains [16] . Bearing these incongruences in mind , it might be useful to start a discussion on a possible need for standardization and replication . Recently , Neves et al , took advantage of the published genome and transcriptome of S . mansoni [22] as well as public drug databases to conduct an in silico screen of compounds with known targets that theoretically match targets found in the S . mansoni genome and transcriptome [17] . When we compared their hits to our NTS hits , a moderate overlap was observed ( S2 Table and Fig 3B ) . In more detail , of the 162 compounds described to match to an S . mansoni target in silico ( 115 compounds they describe as novel along 47 with compounds for which some activity has already been described ) , 102 were present in our library , and of these 102 , 19 were deemed as active , corresponding to a 19% overlap . The fact that the in silico prediction did not strongly match our in vitro hits , doesn’t necessarily mean the in silico hits are incorrect: they could be differentially active on other life stages ( ex . juvenile ) , active in vivo , at a higher concentration , or , as we saw with Abdulla and colleagues , in a different screen with a different strain . Nonetheless , it does hint that target-based approaches still require further development . In this screen we identified doramectin and clofazimine as two moderately active compounds against S . mansoni in an NMRI mouse infection model ( Fig 4 ) . The activity of doramectin , though not statistically significant , is nonetheless surprising: studies in S . mansoni-infected mice with the highly related ivermectin showed minimal efficacy when administered as a single oral dose of 25 mg/kg [23] . Moreover , clinical trials with ivermectin showed little efficacy against intestinal and urinary schistosomiasis [24] . However , doramectin is reported to have more favorable pharmacokinetic properties , which could account for its higher in vivo efficacy in our study [25] . Doramectin has not been previously studied in humans and therefore would need substantial efforts to register the drug for human medicine . However , the closely related moxidectin has shown a moderate effect against S . mansoni in preliminary clinical studies [26] . Moxidectin was active against NTS and moderately active against the adult stage worm in our screen , and hence may be worthy of further investigation . Clofazimine is originally indicated for treatment against leprosy and is on the WHO Model List of Essential Medicines [27] . It is a fat-soluble iminophenazine dye that has demonstrated immunosuppressive properties , including inhibition of macrophages , neutrophil motility , lymphocyte transformation and mitogen-induced PBMC formation [28–30] . Recently , clofazimine was also identified as a promising preclinical anti-trypanosomal agent in an in silico screen of marketed drugs [31] . The authors noted that the compound was also effective in inhibiting epimastigote proliferation in vitro and in reducing parasitaemia levels in a murine infection model at a dose of 20 mg/kg . In our study , the initial dose of 400 mg/kg clofazimine showed a moderately high WBR ( 82 . 7% ) , while lowering the dose to 200 mg/kg was not effective . Considering the long half-life of clofazimine ( 12–15 hours ) as well as its reportedly good absorption ( 60–100% ) , we deemed it unnecessary to study the effects of multiple dosing . Currently , off-label use of clofazimine is highly discouraged by the WHO , as it is the first line of treatment against leprosy and there are legitimate fears of drug resistance [32] . Nonetheless , it may be worth exploring the antischistosomal activities of related structures . With the advent of the S . mansoni genome , there have been attempts to incorporate rational drug screening in the antischistosomal drug discovery process . Recently , the observation that fatty acids play a major role in schistosome development , fecundity and tegument construction has spurred researches to investigate the potentials of cholesterol-lowering statins [33–36] . Consequently , Rojo-Arreola and colleagues evaluated six statin compounds , atorvastatin , fluvastatin , lovastatin , pravastatin , rosuvastatin and simvastatin , all targeting 3-hydroxy-3-methylglutaryl coenzyme A reductase ( HMGR ) of the eukaryotic mevalonate pathway on S . mansoni NTS and adult worms . All drugs were found to be active in vitro and targeted SmHMGR [37] . In our own screen , fluvastatin and lovastatin were found to be active on NTS but inactive on adult worms . Atorvastatin and simvastatin were inactive already at the NTS stage and pravastatin and rosuvastatin were not present in our library . The contrasting results could be partly attributed to the stricter cut-off parameters used in our screen ( shorter drug incubation time , only severe reduction in viability considered ) or could also be due to strain differences ( the use a Puerto Rican strain ) . In vivo tests would be required to say anything substantial about the potential of statins as antischistosomals . Considering rational S . mansoni drug targets , it was interesting to note that none of the calcium channel blockers such as fendeline hydrochloride or flunarizine hydrochloride showed a potent in vivo effect . In light of the notion that praziquantel’s mode of action is highly suspected to be due its disruption of Ca2+ homeostasis , the idea that known calcium channel blockers could be effective against S . mansoni in vivo is not too far-fetched [38 , 39] . That being said , these drugs are often used to treat chronic human disorders such as hypertension , migraines or allergies , meaning they also bind to human receptors . The fact that these drugs were very potent in vitro but not in vivo could be attributable to many factors such as drug metabolism or protein binding , but competition between host and parasite receptors might play a role . This could be a general drawback to repurposing drugs with known human receptor targets for use against parasite infections . Indeed , while drug repurposing can potentially reduce the time and costs of the drug discovery process , its limitations should also be carefully considered and are already observable in our study . Although compound libraries intended for new indication screens often contain already marketed drugs , their safety window may not necessarily be acceptable for schistosomiasis treatment and preventative chemotherapy . Drug repurposing is a popular strategy for an array of diseases , some of which a narrow safety window is acceptable due to the nature of the disease [40 , 41] . Compounds chosen for development against schistosomiasis , however , must have an excellent safety profile , as they will be very widely used in preventive chemotherapy campaigns mainly targeted towards children [42] . Indeed , of the 36 adult worm hits , it was disappointing to note that 25 of these compounds were unsuitable for testing , notably due to documented severe side-effects , restriction to topical use or low LD50 values in mice . Some compounds , for example terfenadine , had even been withdrawn from FDA approval or were no longer marketed [43] . It would be favorable if further libraries of already known compounds would be strictly composed of drugs currently on the market with a good safety profile , in order for a real drug repurposing effort to be possible . A further major challenge to drug repurposing might be the difficulty to develop a dose regimen in humans that provides plasma exposure in the range of the in vitro IC50 concentration , which tends to be high for helminths . Hence the chance that safety , pharmacokinetics and pharmacological action will match for a very different indication is uncertain . With these limitations in mind , the NTD community should not rely on drug repurposing alone as a drug discovery strategy . Nonetheless , it is a very worthy venture: as previously stated , many of the anthelmintics used today were repurposed from veterinary applications [12] . Moreover , 90% of the drugs available today have secondary indications , showing that repurposing continues to be a popular strategy both for academia and industry [44] . Importantly , each marketed drug very likely has a library of analogues behind it , with which structure activity relationship for a hit expansion program can certainly be envisaged . Access to these analogues would facilitate an optimization program aiming at the identification of preclinical candidates . In conclusion , by screening a library of 1600 well characterized compounds , we have identified dozens of compounds active against S . mansoni in vitro . Many of the compounds safety or pharmacokinetic profiles rendered them unfavorable for further exploration . Nonetheless , of the 11 compounds screened in vivo , we identified two compounds with moderate to high activities with which further investigations may result in novel compound class treatments .
For a disease of large global health importance , schistosomiasis has a disproportionally small treatment tool box- only praziquantel is used to treat all 3 major forms of the disease . While drug discovery can be a long , laborious and expensive process , especially for an under-funded neglected disease such as schistosomiasis , drug recycling ( also termed repositioning or repurposing ) can bypass some of the development processes and offset the costs . We conducted a drug screening project of 1600 FDA-approved compounds from a very diverse set of indications against Schistosoma mansoni . The full 1600 compounds were first screened in vitro against the larval stage of the worm , of which 121 drugs were identified as active . These hits were then screened on the adult stages of the worm in vitro where 36 of these hits were also found to be active on the adult stage . The safety and pharmacokinetic profiles of hit compounds were then compared to their in vitro activity and 11 compounds were chosen for studies in mice . Of these , clofazimine and doramectin were found to be moderately active , and present new antischistosomal scaffolds with which further investigations can be pursued . Our findings are placed in context with results obtained from previous in vitro and in silico chemogenomics work and agreements and disagreements discussed .
[ "Abstract", "Introduction", "Materials", "and", "Methods", "Results", "Discussion" ]
[]
2015
Activity Profile of an FDA-Approved Compound Library against Schistosoma mansoni
Central abdominal fat is a strong risk factor for diabetes and cardiovascular disease . To identify common variants influencing central abdominal fat , we conducted a two-stage genome-wide association analysis for waist circumference ( WC ) . In total , three loci reached genome-wide significance . In stage 1 , 31 , 373 individuals of Caucasian descent from eight cohort studies confirmed the role of FTO and MC4R and identified one novel locus associated with WC in the neurexin 3 gene [NRXN3 ( rs10146997 , p = 6 . 4×10−7 ) ] . The association with NRXN3 was confirmed in stage 2 by combining stage 1 results with those from 38 , 641 participants in the GIANT consortium ( p = 0 . 009 in GIANT only , p = 5 . 3×10−8 for combined analysis , n = 70 , 014 ) . Mean WC increase per copy of the G allele was 0 . 0498 z-score units ( 0 . 65 cm ) . This SNP was also associated with body mass index ( BMI ) [p = 7 . 4×10−6 , 0 . 024 z-score units ( 0 . 10 kg/m2 ) per copy of the G allele] and the risk of obesity ( odds ratio 1 . 13 , 95% CI 1 . 07–1 . 19; p = 3 . 2×10−5 per copy of the G allele ) . The NRXN3 gene has been previously implicated in addiction and reward behavior , lending further evidence that common forms of obesity may be a central nervous system-mediated disorder . Our findings establish that common variants in NRXN3 are associated with WC , BMI , and obesity . Body mass index ( BMI ) is a commonly used measure of overall adiposity . However , specific fat depots may confer differential metabolic risk . In particular , central abdominal fat , as measured by waist circumference ( WC ) , may be more strongly associated with the development of metabolic risk factors and cardiovascular disease as compared with BMI [1]–[4] . Therefore , understanding the pathogenesis of central fat distribution may provide further insight into the relationship between adiposity , cardiometabolic risk , and cardiovascular disease . Both genetic and environmental factors have been linked to obesity [5] . Heritability estimates for BMI and WC range from 30 to 70% in family and twin studies [6] , and multiple quantitative trait loci and candidate genes have been mapped to genes for central adiposity [5] . Despite strong evidence for an underlying genetic component , genes for obesity-related traits , particularly central obesity , have been difficult to identify and replicate . Early genome-wide association studies ( GWAS ) identified both FTO and MC4R as genes related to BMI and WC [7]–[10] . Many new loci have been identified in recent obesity related GWAS studies [11]–[13] . However , collectively these variants explain only a small proportion of the variation in adiposity [7]–[13] . In addition , no GWAS exist exclusively to identify genes for central fat . Thus , to identify new variants , we carried out a large-scale meta-analysis of GWAS from eight studies to detect variants associated with central body fat distribution . Participants for the current analysis were drawn from 8 cohort studies , including the Age , Gene/Environment Susceptibility-Reykjavik Study ( AGES- Reykjavik Study ) , the Atherosclerosis Risk in Communities Study ( ARIC ) , the Cardiovascular Health Study ( CHS ) , the European Special Population Network consortium ( EUROSPAN ) , the Family Heart Study , the Framingham Heart Study , Old Order Amish ( OOA ) , and the Rotterdam Study ( RS ) . These groups comprise the CHARGE ( Cohorts for Heart and Aging Research in Genome Epidemiology ) Consortium . All participants provided informed consent . Local ethical committees at each institution approved the individual study protocols . Text S1 contains details regarding all participating cohorts . Common to all analyses were use of the raw WC measures and the assumption of an additive model; study specific details follow . Each study reported an effect allele which was meta-analyzed consistently across all studies . Results are currently presented relative to the minor G allele for the NRXN3 SNP . In all studies except CHS , MACH ( version 1 . 0 . 15 in Family Heart , Framingham , EUROSPAN and RS; version 1 . 0 . 16 in ARIC , AGES , and OOA ) was used to impute all autosomal SNPs on the HapMap , using the publicly available phased haplotypes ( release 22 , build 36 , CEU population ) as a reference panel . In CHS , the program BIMBAM was used [14] . Details are provided in Table S1 regarding covariates and trait creation . In ARIC , Framingham , and RS , sex- and either cohort-specific or study center-specific residuals were created after adjustment for age , age-squared , and smoking status . In CHS and Family Heart , linear regression models were used to adjust for age , age-squared , sex , smoking , and study center . In AGES , linear regression models using PLINK v1 . 04 [15] were used to adjust for age , age-squared , sex , and smoking . In the OOA the measured genotype mixed effects model was used adjusting for age , age-squared , sex and family structure based on the complete 14-generation pedigree as implemented in ITSNBN [16] . Framingham employed the linear mixed effect model for continuous traits and the generalized estimating equations for dichotomous traits in R [17] to account for family relatedness . In RS , linear regression models were run using MACH2QTL [18] . In ARIC and EUROSPAN , all regression models were run using the ProbABEL package from the ABEL set of programs [19] and in EUROSPAN genomic control [20] was used to correct standard errors of the effect estimates for relatedness among individuals . The Family Heart Study determined the effect of each SNP using linear mixed effects models to account for the siblings present in the data using SAS . Principal components calculated using EIGENSTRAT [21] were adjusted for in the individual studies when significant in order to account for population substructure . A weighted z-score approach was used to conduct meta-analyses with METAL ( www . sph . umich . edu/csg/abecasis/metal/ ) . Genomic control correction was applied to each study prior to the full meta-analysis . P-values less than 4 . 4×10−7 were considered genome-wide significant [22] . In stage 2 of our study , we conducted an in silico exchange of the results of 48 SNPs with the GIANT consortium . To create our list of SNPs to exchange , we first selected the top 34 SNPs from independent loci ( defined as SNPs with R2<0 . 2 ) from our meta-analysis of WC , excluding SNPs in known loci for adiposity . An additional 14 SNPs of independent loci with a p-value<1 . 0×10−5 from a secondary list that focused on SNPs for WC with corresponding BMI p-values>0 . 01 were also included in an attempt to isolate genes that might be specifically associated with central fat deposition . Our a priori threshold for replication was a p-value<0 . 001 ( 0 . 05/48 SNPs ) and/or reaching genome-wide significance in a combined meta-analysis . CHARGE and GIANT results were then meta-analyzed using METAL . Table 1 presents descriptive statistics across the 8 cohorts providing data for the meta-analysis . We had a total sample size of 31 , 373 individuals of Caucasian descent . Participants were mostly middle-aged with ages ranging from a mean of 45 to 76 years of age . Figure S1 shows the genome-wide association results for WC in the stage 1 CHARGE-only analysis . The top SNPs for WC were in the FTO and MC4R genes ( Table S3 ) . Figure S2 shows the QQ plot for our results excluding SNPs in FTO and MC4R . For FTO , the top SNP was rs1558902 ( p = 4 . 6×10−19 ) . For MC4R , the top SNP was rs489693 ( p = 3 . 5×10−7 ) . The top results excluding SNPs in FTO and MC4R from our stage 1 meta-analysis are shown in Table 2 along with the stage 2 in silico replication results from the GIANT consortium; additional meta-analysis results from CHARGE are presented in Table S3 . The lowest p-value on our list , for SNP rs10146997 in the NRXN3 gene , had a stage 1 meta-analysis p-value of 6 . 4×10−7 and was confirmed in 38 , 641 participants from the GIANT consortium with a p-value of 0 . 009 and a combined p-value of 5 . 3×10−8 . The NRXN3 SNP was derived from the list of SNPs associated with WC irrespective of association with BMI . None of the other SNPs that were exchanged were confirmed in GIANT . We do note that while rs10857809 ( proxy for rs10857810 ) in the FAM40A gene had a p-value of 0 . 003 in GIANT , the results were not direction-consistent with CHARGE and therefore did not replicate in the combined analysis . Figure 1 presents the genomic region for SNP rs10146997 ( intronic ) in NRXN3 . Table 3 shows detailed results of rs10146997 in the NRXN3 gene by contributing CHARGE study and corresponding results appear in the forest plot in Figure S3; there was no evidence for heterogeneity across the stage 1 studies ( p = 0 . 64 ) . The minor allele ( G ) frequency ( MAF ) for rs10146997 in our sample ranged from 0 . 14 in the OOA to 0 . 24 in the Croatians; the frequency of the NRXN3 SNP G allele is 0 . 275 , 1 . 0 , 1 . 0 , and 0 . 35 , in Hapmap CEPH , Han Chinese , Japanese , and Yoruba populations , respectively . This SNP was genotyped in AGES , CHS , Family Heart Study , Rotterdam and all EUROSPAN studies , and imputation scores for the other studies indicated very high quality . Overall , per copy of the G allele , mean WC was increased 0 . 0498 z-score units ( 0 . 65 cm ) . Beta coefficients ( in z-score units ) were consistently positive in all samples except the ERF study ( β = −0 . 0098; p = 0 . 86 ) , which is most likely due to chance . Due to overlap in participants from the Framingham Heart Study and ARIC with those from the Family Heart Study , the CHARGE meta-analysis was re-run for the NRXN3 SNP without the Family Heart Study; results were essentially unchanged ( p = 6 . 6×10−7 ) . Individual study-specific results for rs10146997 from the studies comprising the GIANT consortium can be found in Table S2 . Within CHARGE we also observed an association of rs10146997 with BMI ( p = 7 . 4×10−6 ) . Overall , mean BMI was increased 0 . 024 z-score units per G allele ( 0 . 10 kg/m2 ) . When WC was additionally adjusted for BMI , the signal was completely attenuated ( 0 . 0065 z-score units per G allele; p = 0 . 32 ) . The association of rs10146997 with WC was similar in women and men and in older and younger individuals ( Table 4 ) . After excluding smoking from the covariate adjustment list , results were essentially similar . Per copy of the G allele , the odds ratio of having high WC ( ≥88 cm in women; ≥102 cm in men ) was 1 . 07 ( 95% CI 1 . 02–1 . 11; Table 4 ) . Similarly , the odds ratio of obesity was 1 . 13 ( 95% CI 1 . 07–1 . 19 ) . We calculated a risk score of FTO ( rs9939609 ) , MC4R ( rs17782313 ) , and NRXN3 with possible scores ranging from 0–6 risk alleles ( Figure 2 ) . Across this range , mean WC increased from 92 . 4 cm among those with 0 risk alleles , to 95 . 7 cm among those with 4 or more risk alleles . To put our findings in perspective , per copy of the effect allele , the NRXN3 SNP resulted in a WC difference of 0 . 65 cm; FTO 0 . 73 cm , and MC4R 0 . 37 cm . CHARGE consortium meta-analysis results for BMI can be found in Table S4; Manhattan and QQ plots for BMI can be found in Figure S4 and Figure S5 , respectively . In a discovery sample of more than 30 , 000 individuals from several cohort studies , we identified a novel locus in the NRXN3 gene associated with WC . In combination with data from the GIANT consortium , the p-value for this finding exceeded our pre-defined threshold for genome-wide statistical significance . This SNP was also significantly associated with BMI and obesity . This gene has previously been associated with addiction and reward behavior , and is a compelling biologic candidate for obesity . We also confirmed the significant associations with FTO and MC4R that have previously been reported . Although our genome-wide scan was performed for WC , the NRXN3 SNP was also significantly associated with BMI . In secondary analyses , the signal for WC was attenuated after additionally adjusting for BMI , suggesting that this locus is most likely involved in overall adiposity and not specific to central fat deposition . Similar observations have been made for FTO [10] and MC4R [7] , highlighting the inter-dependence between different measures of adiposity and the importance of performing GWAS on multiple adiposity-related traits . The small magnitude of the effect size of the NRXN3 variant on WC is consistent with what has previously been reported for FTO and MC4R . These findings highlight the need for large sample sizes in order to facilitate continued gene discovery for obesity-related traits . In particular , genes that emerge for waist circumference will most likely be genes for overall adiposity because of the strong correlation between the two measurements [22] . More specific measures of visceral abdominal fat depots may make it possible to isolate genes involved in regional body composition . NRXN3 is part of a family of central nervous adhesion molecules and is highly expressed in the central nervous system . Prior studies of NRNX3 point towards an important role in alcohol dependence , cocaine addiction , and illegal substance abuse [23]–[26] . In addition , opioid dependence has been linked to the chromosome 14q region [23] . In mice , NRXN3 beta expression was observed in the globus pallidus when exposed to cocaine [24] . Many of the neuronal pathways in these sub-cortical regions of the brain in which NRXN3 is expressed are involved with learning and reward training [25] . Obesity and addiction may share common neurologic underpinnings [26] . Other well-replicated obesity loci , including MC4R , have also been shown to be associated with centrally-mediated phenomena including binge eating behavior [11] , [12] , [27] . Studies in mice indicate that FTO expression is particularly pronounced in regions of the brain known to regulate energy balance [28] , and recent data suggest that variants in the FTO gene may regulate food intake and selection [29] . Additional research is needed to understand the association of rs10146997 with the NRXN3 gene and to identify a causal variant . Since there are no other genes within a distance of more than several hundred kilobases of this SNP , it is unlikely that a different gene accounts for this finding . A search of publically available databases [30]–[32] did not identify an association between SNPs in NRXN3 and gene expression . A relationship between WC and causal variants in the NRXN3 gene may have clinical implications . Obesity is a multifactorial trait that results from a complex interaction between genes and environment . The identification of an association between obesity and variants in a gene that has been associated with substance abuse suggests that further exploration of the role of this gene in vulnerability to addiction to food substances should be undertaken . The strengths of this work include the large discovery sample size . The effect size was small , and achieving conventional levels of genome-wide significance required combining data from more than 70 , 000 participants in two large consortia . Although the confirmation with the GIANT consortium is promising , the joint p-value based on more than 70 , 000 participants achieved only borderline genome-wide significance . Our findings warrant the need for further replication in other ethnic groups . We identified a SNP at a novel locus in the NRXN3 gene associated with WC . This gene has previously been implicated in addiction and reward behavior , lending further support to the concept that obesity , in part , is a centrally-mediated disorder .
Obesity is a major health concern worldwide . In the past two years , genome-wide association studies of DNA markers known as SNPs ( single nucleotide polymorphisms ) have identified two novel genetic factors that may help scientists better understand why some people may be more susceptible to obesity . Similarly , this paper describes results from a large scale genome-wide association analysis for obesity susceptibility genes that includes 31 , 373 individuals from 8 separate studies . We uncovered a new gene influencing waist circumference , the neurexin 3 gene ( NRXN3 ) , which has been previously implicated in studies of addiction and reward behavior . These findings lend further evidence that our genes may influence our desire and consumption of food and , in turn , our susceptibility to obesity .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "genetics", "and", "genomics/complex", "traits", "nutrition/obesity" ]
2009
NRXN3 Is a Novel Locus for Waist Circumference: A Genome-Wide Association Study from the CHARGE Consortium
Successful replication within an infected host and successful transmission between hosts are key to the continued spread of most pathogens . Competing selection pressures exerted at these different scales can lead to evolutionary trade-offs between the determinants of fitness within and between hosts . Here , we examine such a trade-off in the context of influenza A viruses and the differential pressures exerted by temperature-dependent virus persistence . For a panel of avian influenza A virus strains , we find evidence for a trade-off between the persistence at high versus low temperatures . Combining a within-host model of influenza infection dynamics with a between-host transmission model , we study how such a trade-off affects virus fitness on the host population level . We show that conclusions regarding overall fitness are affected by the type of link assumed between the within- and between-host levels and the main route of transmission ( direct or environmental ) . The relative importance of virulence and immune response mediated virus clearance are also found to influence the fitness impacts of virus persistence at low versus high temperatures . Based on our results , we predict that if transmission occurs mainly directly and scales linearly with virus load , and virulence or immune responses are negligible , the evolutionary pressure for influenza viruses to evolve toward good persistence at high within-host temperatures dominates . For all other scenarios , influenza viruses with good environmental persistence at low temperatures seem to be favored . Influenza A viruses infect both humans and animals , causing frequent outbreaks [1] , [2] . In humans , the infection can be life-threatening for individuals with weak immune systems , leading to an estimated annual worldwide mortality burden of [3] , [4] . Due to its zoonotic nature , and frequent spillover from wild and livestock populations , eradication of the virus is virtually impossible [1] , [5] . Further , the danger that a novel influenza strain with high virulence and pandemic potential will start to spread in the human population is always present [6]–[8] . The 2009 H1N1 pandemic demonstrated that the emergence of novel pandemic strains is still largely unpredictable . Improvement of our surveillance , prediction and control capabilities requires that we obtain a better understanding of the whole transmission cycle of the virus and the mechanisms governing the complex processes of infection and spread . One useful approach for studying the whole infection and transmission process is through the use of multiscale studies , wich have seen increased general development and use in recent years ( see e . g . [9] , [10] for reviews and [11] for a recent application to influenza ) . A multiscale approach allows one to address the question of how different selection pressures on the within- and between-host levels interact to impact overall fitness . This is important if we want to better understand and predict the infection and transmission dynamics and evolution of the virus . Here , we use such a multiscale framework and focus on one specific aspect , namely evolutionary pressures shaped by temperature-dependent virus persistence . The importance of temperature on influenza virus fitness is well established . For instance , the attenuated live influenza vaccine is cold-adapted , which leads to reduced fitness in human hosts , making it safe for vaccination purposes [12] , [13] . Temperature has also been shown to impact within-host dynamics and transmission in laboratory studies [14] , [15] . Recent theoretical and experimental evidence suggests that persistence in the environment is an important factor of transmission for avian influenza [16]–[24] . Transmission through an environmental stage ( e . g . long-lasting droplets , fomites ) seems to also play a role for influenza transmission in humans [25]–[29] . Since temperatures in the environment and within a host can be markedly different , it is possible that the virus faces a trade-off: It can either optimize its ability to persist within a host , or optimize its ability to persist outside a host . It is well known that the decay rate of most viruses depends on temperature , with faster virion decay occurring at higher temperature [30]–[32] . Interestingly , recent data [33] suggest that temperature-dependent decay rates differ between influenza strains . Some strains are very stable at environmental temperatures ( ) but rapidly decay at higher within-host temperatures ( ) , while others persist less well at low temperatures but also have a less rapid decay as temperature increases [33] . These data suggest that some virus strains might optimize persistence within a host , while others might optimize persistence outside a host , with a possible trade-off between the two . This in turn can affect both within-host and between-host dynamics . The dynamics on these two levels interact to determine overall fitness . ( Note that the data presented in [33] – which we will analyze below – is for different HA-NA serotypes . However , the phenomenon of temperature-dependent decay we discuss is not specific to distinct serotypes . We will therefore use the generic term “strain” throughout this study ) . To analyze the impact that such a temperature-dependent trade-off can have on virus fitness , we build a multi-scale model that embeds a within-host infection process within a population transmission framework . A number of theoretical studies have previously considered trade-offs between environmental persistence and within-host performance , see e . g . [34]–[38] . Those studies considered generic trade-offs and models without direct relation to a specific pathogen or fitting to data . A few notable studies that involved data looked at environmental survival and virulence of human pathogens [39] and environmental survival and growth in phages [40] . Here , we focus on avian influenza A and combine experimental data with models to explicitly consider temperature-dependent virus decay as the mediator of trade-offs . We find that for direct transmission scenarios , viruses with long within-host persistence perform overall best . For environmental transmission scenarios , the balance was shifted toward viruses with good environmental persistence . This was especially true if shedding or infection rates were assumed to be proportional to the logarithm of the virus load . We further show that the addition of an immune response or pathogen virulence reduced the importance of differences in the within-host decay rate between strains , and lead to an increased importance of good environmental persistence . We consider a simple model for an acute viral infection . These types of models have been used in several recent analyses of influenza A virus within-host infection dynamics ( see e . g . [41] , [42] for reviews ) . Our model tracks uninfected cells , , infected cells , , and infectious virus , . Cells become infected at rate , infected cells produce virus at rate and die at rate . Infectious virus decays at rate . The model equations are given by ( 1 ) ( 2 ) ( 3 ) The model is illustrated in figure 1 , table 1 summarizes the model variables and parameters . This simple model can describe most data for influenza virus infections rather well [41] , [42] . After an initial rise in virus load , uninfected target cells become depleted , leading to a subsequent virus decline and resolution of the infection . This so-called target-cell limited model is basically equivalent to a simple epidemic model , which produces a single infectious disease outbreak in a susceptible population . However , it is also known that influenza infections stimulate an immune response , which likely plays some role in viral clearance , though the exact contributions of various components of the immune response to virus clearance are still not fully understood . We consider an alternative model with an immune response in the supplementary materials . To describe influenza transmission dynamics on the between-host level , we use a framework that takes into account both direct and environmental transmission routes , as has been recently advocated [16] , [17] . Similar models – not specific to influenza – that explicitly include an environmental stage have been designed and analyzed previously [35] , [36] , [38] , [43]–[46] . We use coupled partial differential equations to allow for explicit tracking of the age of infection within the infected population . This allows for convenient linking of the within- and between-host scales as described below . The model is shown and explained in figure 2 and legend , table 2 summarizes model quantities . The model equations are given by ( 4 ) ( 5 ) ( 6 ) ( 7 ) Time indicates the usual “system time” , while indicates the time since infection of a host . The parameters , and , i . e . the rate of transmission between hosts , the rate of shedding and the rate of recovery all depend on the time since infection . We will choose specific forms for those parameters in the next section . Note that we do not actually simulate the between-host dynamical process . The reason for specifying the between-host model is to compute the basic reproductive number , , which is our measure of between-host fitness ( see next section ) . Analysis of other fitness measures that would require simulating the between-host dynamical process ( e . g . probability of extinction over multiple outbreaks ) is a suitable subject of future studies but will not be considered here . Our main quantity of interest is fitness of the virus at the host population level . One way to quantify fitness is through the basic reproductive number , , which is defined as the expected number of new infections caused by one infected host in a fully susceptible population [47]–[49] . For our model , one can split into two components , namely direct transmission from host to host ( ) , and indirect transmission through the environmental route ( ) , such that [17] , [36] . For direct transmission , we have ( 8 ) where is the susceptible population at time 0 , is fraction of hosts that are still infectious at time after infection started , and denotes the rate at which an infectious individual at infection age infects new individuals . If we assume that all infected hosts are infectious for a fixed duration , , and non-infectious afterwards , we can write ( 9 ) Mathematically , this corresponds to choosing the proportion of host infectious after time , , as a Heaviside function , and the recovery rate , , in the between-host model equations as a Dirac delta-function . While the infectious period could end either due to resolution of the infection ( recovery ) or host death , for the low pathogenic influenza strains we consider here , mortality is negligible [50]–[52] . Therefore , for the main part of this study , the end of the infectious period should be interpreted biologically as recovery . In the supplementary materials we briefly consider virus-associated mortality ( i . e . virulence ) and how it might alter the results presented in the main part of the manuscript . We can define the duration of infectiousness in terms of the within-host model , as the time from the start until the end of the infection , which we define as the time virus levels drop below a given level , ( in our simulations chosen to be one virion ) . Mathematically , this can be written as ( 10 ) The rate at which direct transmission between hosts occurs , , also likely depends on the within-host dynamics . One possible assumption is that is directly proportional to virus load: ( 11 ) where is the virus load at time after infection and is some constant of proportionality . This assumption corresponds to the “flu like infection regime” in [53] , and seems to be a reasonable approximation [54]–[57] . Defining ( 12 ) as the total infectious virus during the infection ( area under the curve ) , and substituting equations ( 12 ) and ( 11 ) into ( 9 ) , we obtain as expression for the directly transmitted virus fitness ( 13 ) While a linear relationship between transmission and virus load , as described by equation ( 12 ) , is plausible , it is certainly not the only possibility . For instance , we previously showed that a sigmoid function of the form ( 14 ) provides a good description of the total amount of nasal discharge as function of virus load for human influenza A infections [58] . Here , the coefficients describe the shape of the sigmoid curve . While the hosts in the present study are ducks , not humans , we submit that representing the total amount of discharge by a sigmoid curve makes inherent biological sense for any host . Multiplying virus load by the amount of discharge and integrating over the duration of infection gives ( 15 ) For our numerical analysis below , we set , , , which are values close to those previously determined by fit of this sigmoid curve to shedding data for humans [58] . The exact values for those coefficients matter little for the results we present in this study . Using the equation for instead of the equation for in equation ( 13 ) is an alternative for linking within-host dynamics to between-host fitness . Another plausible scenario is one where the rate of transmission is proportional to the logarithm of the virus load , giving ( 16 ) We can use this expression in equation ( 13 ) instead of . Such a logarithmic dependence of transmission on virus load makes especially good sense given that and therefore are a measure for the number of new infections produced , which not only includes the shedding and transmission process , but also includes the probability that a subsequent infection in a new host is started . A logarithmic dependence between pathogen dose and the probability of infection occurring appears to be common [53] , [59]–[63] . Since it is not known which assumption for the link from within-host virus load to between-host transmission is most applicable to the host-pathogen system we study here , we will investigate all three possible functions ( ) and their impact on host population level fitness as measured by . The environmental transmission component of fitness , , can be linked to the within-host model in the same way as just described for the direct component , . Specifically , we can write ( 17 ) The rate of viral shedding into the environment , , again depends on the within-host dynamics . If we assume that depends on the within-host virus load in the same way as the direct transmission rate , we obtain ( 18 ) where the terms represent the different link functions described in equations ( 12 ) , ( 15 ) and ( 16 ) , and is another constant of proportionality . Table 3 summarizes the important quantities we introduced in this section . All statistical analyses and simulations were done in the R programming environment [64] . The scripts are available from the corresponding author's webpage ( http://ahandel . myweb . uga . edu/resources . htm ) . In a recent study [33] we found that for a panel of different avian influenza A strains , the decay rate of infectious virus varies as a function of temperature . We can quantify the virus decay rate , , as a function of temperature , . The data suggest that a simple exponential function of the form fits each strain well . Figure 3 shows the data and best-fit exponential curves , with the estimated values for and provided in table 4 . The simple equation allows us to compute decay rates at a within-host temperature of around corresponding to the body temperature of a duck [15] , [65] and at a between-host environmental temperature assumed to be cold lake water at around . Those quantities correspond to and in our within-host and between-host models . Table 4 lists their values for the different strains . Figure 3 and table 4 suggest that while some strains have a relatively low ( e . g . H3N2 ) or high ( e . g . H5N2 ) decay rate irrespective of temperature , others appear to specialize . Some strains ( e . g . H6N4 , H11N6 ) decay relatively slowly at low temperatures but persist poorly at high temperatures , while others ( e . g . H8N4 , H7N6 ) do relatively better at high versus low temperature . Thus , some strains are able to persist for a long time at low temperatures , but as temperature increases , their rate of decay also rapidly increases . In contrast , other strains are not able to persist for quite as long at low temperatures , but increases in temperature leads to a slower rise in atrophy . As we illustrate in figure 4A , this can lead to a cross-over in decay rates as function of temperature . In figure 4B , we regress the strain-specific values for the intercept of the decay rate curve , , ( quantifying virus persistence at low temperature , specifically at ) against the value for the temperature-dependence of the decay rate , , ( quantifying virus persistence at high temperature ) . In figure 4C , we provide the same information , but for the rank of those parameters . These plots demonstrate a negative correlation between persistence at low and high temperatures . Since the center panel indicates a linear relation for the logarithm of and , we fitted a regression line to the data . We find for the regression fit , ( , ) . Similarly , computing a correlation coefficient for the rank-transformed data , we find a negative correlation of ( ) . The analysis of this dataset can be taken as suggestion for the presence of a trade-off between stability at low and high temperatures – at least for the panel of strains we investigated here . Since this is a small sample of strains , we do not want to over-emphasize the finding . However it seemed real and interesting enough to ask the questio: “How would such a potential trade-off lead to interactions on the within-host and between-host levels and affect overall virus fitness ? ” . We address this question in the remainder of the paper . As a potentially interesting side question – not further considered in the remainder of this paper – we wondered whether there are systematic differences between strains belonging to different groups . Based on amino acid differences , strains with different HA types can be clustered into two groups , as indicated in Table 4 ( see e . g . [66]–[68] ) . We were curious to see if systematic differences in the decay behavior between the two groups could be observed . However , statistical tests applied to both the absolute and rank-transformed values of and did not identify significant differences between groups , suggesting that – based on the available data – differences in HA sequences between the two groups do not express themselves phenotypically as differences in temperature-dependent decay characteristics . To simulate a within-host infection , we need to specify parameter values for the within-host model . While parameter estimates are available for influenza infections in humans and mice [41] , [42] , they have not been previously estimated for ducks . We therefore fitted the model to recent data from influenza infections with H3N8 in mallards ( Anas platyrhynchos ) [69] . This virus strain was not used in the decay experiments shown in table 4 , therefore , we do not have a direct estimate for the within-host clearance rate , . The straightforward approach would be to obtain together with the other parameters by fitting to the data , but this approach is problematic . As has been shown previously , it is impossible to use the within-host model ( equations 1–3 ) to accurately estimate both and death rate of infected cells , , from virus titer data alone [42] , . Because of this , we instead set per day , which is the mean value of for the 12 strains reported in table 4 . We also tried to fit , and as expected , the fit did not improve and could not be properly estimated . To perform the fit , we assume that the infection was started by a ( is the viral dose that results in a 50% chance of infecting an embryonated egg , assumed to correspond to 1 infectious virion ) and that the initial number of uninfected target cells is [71] ( while this estimate is for chickens rather than ducks , the exact value is not qualitatively important: changes in the target cell numbers only re-scale the model parameter and otherwise produce the same dynamics ) . In figure 5 , we show the best fit to the data , with parameter values presented in Table 1 . We want to point out that while these parameter estimates are useful and accurate enough for the purpose of our study , they come with caveats . Most importantly , estimates are based on the validity of the model used . A model that does not include an immune response is likely an over-simplification , albeit a necessary one since adding additional immune response components and trying to fit such a model to virus load data only would lead to over-fitting . See e . g . [41] , [42] and references therein for further discussions of this and related points concerning fitting influenza data to models . For each strain listed in table 4 , we can use and the parameters determined in the previous section and simulate the within-host infection dynamics . This allows us to numerically determine the duration of infection , , and the total virus load , which in turn specifies the different link functions , . We also have estimates for for each strain . To determine fitness as measured by , we also need to know the population size , and several constants of proportionality , namely and describing the linkage between within-host virus load and shedding and infection rates , and the environmental transmission rate , . Those quantities are not well known and will likely differ for different environments . Therefore , absolute values of and are hard to estimate . However , for any strain we can consider its fitness relative to some reference strain , . If we make the assumption that for a given scenario , , , and do not differ between strains; and consider the two extreme cases of either only direct ( ) or only environmental ( ) transmission , relative fitness for strain and link-function ( ) , relative to some reference strain , , is given by ( 19 ) for direct transmission and ( 20 ) for indirect , environmental transmission . As expected , if we consider only direct transmission ( equation 19 ) , the ability of the virus to persist at low temperatures ( low ) does not impact its fitness and therefore the strain that optimizes persistence at high temperatures ( low ) and therefore optimizes within-host dynamics ( large ) performs best . In the presence of environmental transmission ( equation 20 ) , fitness is influenced by persistence both inside the host ( low , leading to high ) and in the environment ( low ) . The two cases , environmental only and direct only transmission represent extremes in terms of potential trade-offs . For direct transmission alone , there is no trade-off; optimizing within-host fitness is always the best strategy . The environmental transmission only scenario represents the case where the importance of the environmental stage is as large as it can possibly be . Mixture of the two transmission routes leads to values with intermediate importance of environmental persistence . While it is certainly possible to consider the general case with both direct and environmental transmission and compute absolute and relative fitness values , this would require making rather arbitrary assumptions about values for some of the unknown parameters of proportionality . Since considering such a general mixed transmission scenario would not add much beyond the results for the two simpler extreme cases , we focus on these two extreme cases in the following . In figure 6 , we show the relative fitness of the 12 different strains , for exclusively direct or environmental transmission scenarios . We plot relative fitness for the three different link-functions between within-host virus load and transmission/shedding described above ( , and given by equations ( 12 ) , ( 15 ) and ( 16 ) ) . Strains are sorted according to within-host performance ( i . e . with increasing values of ) . We arbitrarily chose H1N1 as the reference strain , which therefore always has a fitness of 1 . As expected , for direct transmission ( figure 6A ) , better within host persistence at high temperatures leads overall to higher fitness . Results differ little between the link function based on the simple linear assumption , , and the additional inclusion of total discharge , . However , assuming that the amount of shedding is proportional to the logarithm of virus load , , reduces the relative importance of within-host dynamics . Put another way , since “counts” instead of , the fitness impacts of differences in within-host virus load between strains are diminished and , consequently , the relative fitness advantage of strains with high within-host persistence is reduced . This therefore increases the relative fitness of the strains with high . In fact , for the three strains with the lowest within-host fitness ( H5N2 , H11N6 and H6N4 ) , the somewhat reduced within-host fitness due to higher leads to lower virus load but a longer duration of infection , and because virus load factors into shedding only in a logarithmic fashion , a longer duration of infection leads to a slightly increased fitness despite higher . See also the next section for another appearance of this phenomenon . Note that it is unclear how biologically reasonable sustained within-host virus load ( i . e . a long duration of infection ) is . In most immunocompentent hosts , the immune response usually clears influenza relatively rapidly [72]–[77] . In the supplementary materials , we investigate an extended within-host model which includes an antibody-mediated immune response . Not surprisingly , for the environmental transmission scenario ( figure 6B ) , the trend of higher overall fitness for the strains with better within-host persistence is less pronounced . For instance , the H7N6 strain is the second fittest strain for the direct transmission scenario , but is surpassed in fitness for the environmental transmission scenario by several other strains with better low-temperature persistence . Again , results for the different link functions are rather similar . The one outlier is H6N4 , which has the best low-temperature and worst high-temperature persistence . For this strain and link function , the reduction in relative importance of the high-temperature within-host dynamics compared to the low-temperature between-host persistence strongly increases this strain's relative fitness ( see top left corner of figure 6B ) . So far , we analyzed decay data for specific influenza strains and documented differences in their ability to persist well at low and high temperatures . We can go one step further and study the hypothetical fitness of strains that we did not measure . To do so , we can vary ( i . e . clearance rate at ) over a wide range of values , and for each value we can compute a corresponding according to the regression equation estimated above . We then use the values of and to compute virus decay rate , ( specifically , and at 5 and 40 degrees Celsius ) . These values for both the actual virus isolates and the theoretical model are shown in figure 7 . The figure shows that not surprisingly , as ( clearance rate at ) increases , clearance rate at a close-by low temperature ( ) also increases . In contrast , as increases ( worse low-temperature persistence ) , the trade-off leads to a decrease of the within-host clearance rate , , ( better high-temperature persistence ) – at least initially: At high enough , within-host clearance rate starts to increase again . Mathematically , this is due to the fact that at large and small , the linear term in the decay equation dominates . Biologically , this indicates a strain with poor persistence largely independent of the temperature ( i . e . both large and ) . In our dataset , H5N2 seems to fit this description . To determine between-host fitness for a generic strain with given and values , we use for every value of and simulate the within-host infection model , compute duration of infection and total virus load , determine the link functions , and finally compute fitness as quantified by and . We normalize fitness to 1 to cancel out the different constants of proportionality , as done previously . Figure 8 shows normalized fitness for direct and environmental transmission for the different link functions . For and , results are virtually indistinguishable . For both and , an intermediate level of leads to optimal fitness . For direct transmission , with fitness measured by , the maximum fitness directly corresponds to the value of at which is lowest ( see figure 7B ) . For environmental transmission , the maximum fitness is shifted towards lower ( i . e . lower ) values , meaning better persistence at low temperatures becomes important . For the scenario where shedding and infection rate are proportional to the logarithm of virus load ( ) one finds that for environmental transmission , within-host dynamics plays a minor role and persistence at low temperatures ( low ) is the dominating component for fitness . Direct transmission for the link function produces the most interesting pattern . While this scenario shows a local maximum at medium like those seen for and link functions , fitness is highest for either low or high , close to the edge at which within-host infection becomes impossible . This is because at those values , virus load is low but the duration of infection is rather long . As explained in the previous section , the long duration of infection can more than make up for the reduced virus levels , leading to an overall increase in and therefore explaining the high fitness at the edges . The pronounced fitness peaks resulting from long-lasting infections are not seen in a within-host model that includes an immune response ( see supplementary materials ) , and are therefore likely not relevant for influenza in immunocompetent hosts . However , such a long-lasting infection might have relevance for immunocompromised hosts , and is likely important for other pathogens ( see e . g . the “sexually transmitted infection regime” in [53] ) . Trade-offs between different traits or phenotypes acting at different scales are likely common and have been explored previously ( see e . g . [9] , [10] , [40] , [53] , [58] , [78]–[88] for some recent work ) . In this study , we focused on trade-offs in temperature-dependent virus decay and analyzed how the interaction of within- and between-host scales determines overall fitness . Taking a panel of influenza A strains , we found evidence that a trade-off exists between the ability to persist at low temperatures versus high temperatures . Of course , the negative correlation found in the data should by no means be taken as proof of the existence of such a trade-off . Further , more detailed studies are needed to investigate this potential trade-off more carefully . If the finding holds up , it would also be very interesting to elucidate the mechanism responsible for this trade-off . As it currently stands , we consider the observed pattern as an interesting suggestion that made it worthwhile to investigate how – given such a trade-off – the within-host and between-host scales interact to impact overall fitness . By linking the within-host dynamics to the population level , we were able to estimate population level-fitness as measured by the reproductive number for both the cases where transmission is through direct contact between birds and where transmission occurs through an environmental stage ( i . e . virus persistence in water ) . We found that if direct transmission is dominant , viruses that persist well at high temperatures and therefore perform well within a host also had the best between-host fitness . This trend was most pronounced if transmission or shedding was directly proportional to the total within-host virus load . For the environmental transmission scenario , the balance was somewhat shifted toward viruses with good environmental , low-temperature , persistence . This was especially true if shedding and infection rate were assumed to be proportional to the logarithm of the virus load . In the supplementary materials , we also explored the impacts of taking into account an immune response . We found a somewhat diminished importance of differences in the between-host decay rate between strains . This , in turn , leads to greater emphasis on the fitness contribution of environmental persistence . Along similar lines , a brief analysis of a model including virulence suggests that if high within-host fitness leads to host death and thereby interruption of transmission , the balance would be further tipped toward strains that have good environmental persistence . Both more detailed within-host models including further aspects of the immune response and more detailed virulence models are worthwhile avenues for further studies . So are models with more detailed links of the within-host and between-host scales . However , to go beyond qualitative results , the right kind of data would need to be available to allow proper specification and parameterization of such more complex models . In addition , it will be worthwhile to follow up with studies that look at virus fitness beyond the reproductive number . Specifically , given the epidemic behavior of influenza , a model that would explicitly simulate multiple rounds of seasonal between-host outbreaks ( along the lines of [16] ) and track persistence and extinction of strains with different temperature-dependent persistence strategies might be insightful . Similarly , a more detailed model of environmental persistence , e . g . through inclusion of seasonal variation and other dynamical features , and its effect on fitness as measured by the reproductive number or some other suitable quantity might be of interest . Another fruitful topic for future studies is to investigate additional potential trade-offs . It is known that temperature has an effect on other phenotypes , such as virus binding efficiency or the performance of polymerase . This could be included in a model by making other model parameters temperature-dependent . Provided the right kind of data were available , one could then study how temperature impacts these additional parameters and thereby overall fitness . In summary , our results show that differences in fitness can at times be substantial and strongly depend on transmission route and how within-host and between-host models are linked . Based on our findings , we predict that if shedding and infection rates are proportional to virus load , virulence is negligible , and within-host virus clearance is primarily determined by temperature-dependent virus decay , there is strong evolutionary pressure for influenza viruses to increase persistence at high temperatures . Conversely , if virus shedding and direct transmission rates scale with the logarithm of virus load , if virulence plays an important role , or if within-host virus clearance is essentially via the immune response or other non-temperature dependent mechanisms , influenza viruses with good environmental persistence at low temperatures should be favored .
It has recently been suggested that for avian influenza viruses , prolonged persistence in the environment plays an important role in the transmission between birds . In such situations , influenza virus strains may face a trade-off: they need to persist well in the environment at low temperatures , but they also need to do well inside an infected bird at higher temperatures . Here , we analyze how potential trade-offs on these two scales interact to determine overall fitness of the virus . We find that the link between infection dynamics within a host and virus shedding and transmission is crucial in determining the relative advantage of good low-temperature versus high-temperature persistence . We also find that the role of virus-induced mortality , the immune response and the route of transmission affect the balance between optimal low-temperature and high-temperature persistence .
[ "Abstract", "Introduction", "Models", "Results", "Discussion" ]
[ "population", "modeling", "biology", "computational", "biology", "infectious", "disease", "modeling" ]
2013
A Multi-scale Analysis of Influenza A Virus Fitness Trade-offs due to Temperature-dependent Virus Persistence
A simple biochemical method to isolate mRNAs pulled down with a transfected , biotinylated microRNA was used to identify direct target genes of miR-34a , a tumor suppressor gene . The method reidentified most of the known miR-34a regulated genes expressed in K562 and HCT116 cancer cell lines . Transcripts for 982 genes were enriched in the pull-down with miR-34a in both cell lines . Despite this large number , validation experiments suggested that ∼90% of the genes identified in both cell lines can be directly regulated by miR-34a . Thus miR-34a is capable of regulating hundreds of genes . The transcripts pulled down with miR-34a were highly enriched for their roles in growth factor signaling and cell cycle progression . These genes form a dense network of interacting gene products that regulate multiple signal transduction pathways that orchestrate the proliferative response to external growth stimuli . Multiple candidate miR-34a–regulated genes participate in RAS-RAF-MAPK signaling . Ectopic miR-34a expression reduced basal ERK and AKT phosphorylation and enhanced sensitivity to serum growth factor withdrawal , while cells genetically deficient in miR-34a were less sensitive . Fourteen new direct targets of miR-34a were experimentally validated , including genes that participate in growth factor signaling ( ARAF and PIK3R2 ) as well as genes that regulate cell cycle progression at various phases of the cell cycle ( cyclins D3 and G2 , MCM2 and MCM5 , PLK1 and SMAD4 ) . Thus miR-34a tempers the proliferative and pro-survival effect of growth factor stimulation by interfering with growth factor signal transduction and downstream pathways required for cell division . microRNAs ( miRNAs ) that promote cell differentiation , inhibit cell proliferation , or enhance DNA damage or stress-induced cell cycle arrest or death , and whose expression is reduced in some cancers , are candidate tumor suppressor genes [1] . One of the most well studied tumor suppressor miRNAs is miR-34a . Depending on cellular context [2] , ectopic over-expression of miR-34a induces cell cycle arrest [3] , senescence [4] or apoptosis [5] . miR-34a is up-regulated by p53 in response to DNA damage [6]–[8] , but can also be transcriptionally activated independently of p53 [9] , [10] . miR-34a is located on chromosome 1p36 , a locus deleted in neuroblastoma , breast , thyroid , and cervical cancer [11] , [12] . In other cancers , miR-34a expression is epigenetically reduced by hypermethylation [13] . miR-34a administration can inhibit tumor outgrowth in mice [4] . Thus miR-34a satisfies the criteria for a tumor suppressor gene . The best way to understand the function of a miRNA is to identify the genes it regulates . In this study we sought to understand how miR-34a acts as a tumor suppressor by identifying its direct target genes . However , target gene identification is not straightforward because of the partial complementarity of the short ∼22 nt miRNA sequence with the miRNA recognition element ( MRE ) of the target gene [14] . MRE pairing to the miRNA seed region ( nt 2–7 ) contributes significantly to target gene recognition and is the basis for the most successful target gene prediction algorithms [15] , [16] . However , a perfect seed match is not necessary [17] , [18] and does not guarantee targeting [19] . miRNA target prediction algorithms typically predict hundreds to thousands of putative miRNA target genes , but most predicted target genes are not bona fide targets and the best algorithms sometimes miss key targets [17] , [19]–[21] . It is unclear how many target genes are in fact regulated by a given miRNA in any physiological context . Analysis of genes whose mRNA or protein expression decreases when a miRNA is overexpressed or increases when it is antagonized identifies genes that may be either direct targets or indirectly regulated [22] . Biochemical methods to capture RNA-induced silencing complex ( RISC ) -bound mRNAs potentially provide a more direct way to identify miRNA-regulated target genes [23]–[25] . However , immunoprecipitation has mostly been used to define the general features of miRNA-regulated mRNAs and their MREs , rather than to identify the targets of a particular miRNA . Already 36 putative miR-34a targets have been validated by luciferase reporter assays . These targets strongly support miR-34a's role as a tumor suppressor . They include genes that promote cell cycle progression through the G1/S transition ( CCND1 , CCNE2 , CDK4 , CDK6 , MYC , MYCN and E2F3 ) [3] , [8] , [10] , [12] , [26] , enhance transcription ( MYB , HNF4A and FOXP1 ) [9] , [27] , [28] or growth factor signaling ( MET , MEK1 , AXL and RRAS ) [8] , [29]–[32] , inhibit apoptosis ( BCL2 ) [33] or p53 activity ( YY1 , MTA2 , SIRT1 and MAGE-A ) [5] , [32] , [34] , [35] , and promote stem cell survival ( NOTCH1 , NOTCH2 , LEF1 WNT1 , DLL1 , JAG1 and CD44 ) [32] , [36]–[40] . The diversity of direct miR-34a targets suggests that miR-34a acts pleiotropically by regulating many genes . To identify additional direct target genes of miR-34a without bias and understand better how miR-34a functions , we optimized a simple biochemical method to isolate mRNAs that bind to transfected biotinylated ( Bi- ) miR-34a [41] , [42] . mRNAs significantly enriched in the Bi-miRNA pull-down with streptavidin relative to their cellular expression were candidate targets . The pull-down was performed in two unrelated cancer cell lines , K562 erythroleukemia cells and HCT116 colon carcinoma cells . p53 activates transcription of miR-34a [8] . Under basal conditions , p53-sufficient HCT116 cells highly express miR-34a , while p53-null K562 cells do not express it above background ( data not shown ) . We selected disparate cell lines to identify genes that may be regulated in multiple cell types or more specifically in a particular context . Several thousand genes were significantly enriched in the miR-34a pull-down in each cell line and 982 were significantly enriched in both cell lines . Most known miR-34a target mRNAs expressed in these cells were pulled down with miR-34a . Despite the large number of genes significantly enriched in the miR-34a pull-down , 91% of a random list of 11 genes enriched in both cell lines contained miR-34a-regulated 3′UTR sequences . These results suggest that the pull-down is quite specific and that miR-34a potentially directly regulates hundreds of genes . Bioinformatic analysis of the pulled down genes or of genes down-regulated after miR-34a transfection suggested that miR-34a regulates a dense network of genes that transduce proliferative signals arising from growth factor stimulation . Multiple candidate target genes participate in RAS-RAF-MAPK signaling . In fact miR-34a knockout reduced sensitivity to growth factor withdrawal by serum starvation , while miR-34a transfection led to increased vulnerability . Fourteen novel miR-34a targets identified by the pull-down in both cell lines were experimentally verified , including ARAF and PIK3R2 in the RAS-RAF-MAPK pathway , and additional target genes required for cell cycle progression , including cyclins D3 and G2 , MAD2L2 , MCM2 , MCM5 and PLK1 . We modified a method [5] for capturing miRNA-mRNA complexes using streptavidin-coated beads from cells transfected with miR-34a biotinylated at the 3′-end of the mature strand . Control samples were transfected with a biotinylated C . elegans miRNA ( Bi-cel-miR-67 ) ( Figure 1A ) . Biotinylation did not interfere with miRNA-mediated gene suppression as measured by luciferase reporter assay ( Figure 1B ) . Over-expressing Bi-miR-34a or miR-34a in K562 cells also similarly suppressed expression of known miR-34a target genes ( Figure 1C ) . Moreover , immunoprecipitation of HA-tagged Ago1 or Ago2 in K562 cells cotransfected with Bi-miR-34a specifically enriched for miR-34a by ∼4-fold and ∼6-fold , respectively ( Figure 1D ) . Thus the Bi-miRNA is incorporated into the RISC and functions like the unbiotinylated miRNA . We next optimized conditions to capture known target gene mRNAs . In the Bi-miR-34a pull-down of K562 cells , known miR-34a target transcripts CDK4 and CDK6 , but not UBC ( a housekeeping gene ) , were enriched 12 hr after transfection , and their capture plateaued at 24–48 hr ( Figure 1E ) . Therefore , 24 hr was chosen for subsequent experiments . The specificity of the pull-down and applicability to other cell types was verified since CDK4 , CDK6 and MYB mRNAs were consistently enriched by transfection of Bi-miR-34a , but not Bi-cel-miR-67 , in K562 ( Figure 1F ) and HCT116 ( Figure S1A ) cells . Streptavidin beads did not enrich for non-target SDHA and UBC mRNAs , and the specific target mRNAs were not pulled down in cells transfected with unbiotinylated miR-34a ( data not shown ) . miR-34a was specifically enriched >40-fold in the Bi-miR-34a pull-down compared to the input lysate ( Figure S1B ) . Modifications of the pull-down to include formaldehyde cross-linking and/or pre-isolation of RNAs in high molecular weight cellular fractions reduced the amount of captured RNA , but did not improve the relative enrichment for known target gene mRNAs ( data not shown ) . To confirm that association of Bi-miRNAs with target mRNAs was not a post-lysis artifact , we performed streptavidin pull-downs after adding Bi-miR-34a or Bi-cel-miR-67 to cytoplasmic extracts of untransfected K562 cells . CDK4 , CDK6 and MYB mRNAs were not enriched when Bi-miR-34a was added post-lysis ( Figure S1C ) . The general applicability of the pull-downs to enrich for miRNA target genes was also verified for another miRNA , miR-24 in HepG2 cells . Bi-miR-24 capture enriched for 3 known miR-24 targets ( H2AFX , E2F2 and MYC [43] ) by 2–5-fold ( Figure 1G ) . We next used gene expression microarrays to identify putative miR-34a targets captured by Bi-miR-34a in duplicate experiments from K562 ( p53 deficient ) and HCT116 cells ( p53 proficient ) ( Table S1 ) . mRNA abundance in the streptavidin pull-down and input in Bi-miR-34a-transfected cells were separately normalized to their levels in Bi-cel-miR-67-transfected cells . For each biological replicate , the ratio of the abundance of the pull-down mRNA compared to the input mRNA for cells transfected with Bi-miR-34a versus Bi-cel-miR-67 was calculated , averaged and used to define the enrichment ratio {Bi-miR-34a PD/Bi-cel-miR-67 PD}/{Bi-miR-34a input/Bi-cel-miR-67 input} . Normalizing to the input improved identification of true targets in 2 ways – by reducing the background caused by highly abundant mRNAs that associate with streptavidin beads nonspecifically and by incorporating a measure of mRNA knockdown into the denominator of the ratio . The miR-34a pull-downs enriched for 2416 genes in HCT116 cells ( by ≥1 standard deviation ( SD ) , enrichment ratio ≥2 . 5 ) and for 2816 genes in K562 cells ( ≥1 SD , enrichment ratio ≥3 . 3 ) ( Figure 2A ) . The overlap of genes enriched ≥1 SD in both of these unrelated cell lines was 982 genes . To determine the sensitivity of the pull-down , we first looked at how many of the 36 published targets of miR-34a were captured in the K562 or HCT116 pull-downs ( Figure 2B ) . Of the known expressed targets , 22 of 31 mRNAs ( 71% ) were enriched in HCT116 cells and 14 of 29 ( 48% ) were enriched in K562 cells . It should be noted that the choice of cut-off is somewhat arbitrary . Two additional known targets had enrichment ratios of 2 . 5–3 . 2 in K562 cells . The enrichment ratio ranged from 2 . 7–85 . 12 genes were identified in both pull-downs . The enrichment ratio for the shared hits was not significantly different in K562 cells , which do not express miR-34a , compared to HCT116 cells , which do , suggesting that the pull-downs efficiently captured miR-34a targets even in cells that express endogenous miR-34a . To compare the mRNAs that associate with miR-34a to mRNAs that decrease with miR-34a over-expression , we measured mRNA abundance in cells transfected with miR-34a or cel-miR-67 by gene expression microarrays ( Table S1 ) . Genes whose mean mRNA level ratio decreased by at least 20% after miR-34a transfection were considered to be down-regulated either directly or indirectly by miR-34a . With this arbitrary cut-off ( ∼1 SD ) , 2087 genes were down-regulated in HCT116 cells and 945 genes were down-regulated in K562 cells ( Figure 2C ) . About a third of these transcripts in both cell lines were also pulled down with Bi-miR-34a ( 30% in HCT116 , 36% in K562 ) . Many miRNA targets contain a perfect match to the miRNA seed region in their 3′UTR . We examined the frequency of 3′UTR matches to all hexamer sequences in miR-34a in the pull-down and down-regulated gene sets relative to all genes probed on the microarray ( Figure S2A ) . Hexamer matches to nt 2–7 in the miR-34a seed region were significantly enriched in the pull-down ( HCT116 p = 1 . 8E-95; K562 p = 2 . 4E-11 ) and down-regulated ( HCT116 p = 1 . 7E-24; K562 p = 1 . 0E-11 ) datasets . There was also significant enrichment in the HCT116 pull-down genes for nt 13–19 exact matches , suggesting that base-pairing there enhances miRNA binding , as has previously been shown [44] . In both cell lines , seed enrichment was greater for the overlapping set of genes that was both pulled down and down-regulated by miR-34a . For genes in this overlap , exact matches to nt 2–7 were 1 . 8–2 . 0-fold more frequent per kb of 3′UTR than for all genes on the microarray . These data suggest that genes in the overlap may be more likely to be direct targets than genes identified by only one method or that a perfect seed match might enhance miRNA-mediated mRNA decay . We next examined hexamer enrichment in the 982 genes enriched ≥1 SD in pull-downs from both HCT116 and K562 cells ( Figure S2B ) . Seed matches were most enriched in the 3′UTRs of these genes , with the nt 2–7 match being the most abundant ( 1 . 7 fold more abundant than in all genes on the microarray ( p = 8 . 4E-39 ) . The coding region ( CDS ) of these genes also contained a highly significant enrichment for hexamer seed matches ( p = 6 . 1E-13 ) . These results are consistent with recent cross-linked RISC pull-downs that suggest that 25–50% of MREs may be in the CDS [23] , [25] . There was also a modest enrichment of hexamers matching the seed in the 5′UTR ( p = 0 . 005 ) . Thus the pull-down and down-regulated mRNAs were enriched for expected miRNA target sequence features . We next analyzed whether mRNA expression of the enriched genes was reduced by miR-34a transfection in HCT116 cells ( Figure 2D ) . The mRNAs of the 982 genes enriched in the miR-34a pull-down by ≥1 SD in both cell lines were significantly down-regulated after miR-34a transfection compared to the set of all genes expressed in the cell ( p = 4 . 7E-80 ) . The extent of down-regulation was comparable to the set of 469 TargetScan-predicted , evolutionarily conserved targets of miR-34a and significantly greater than in the larger list of 2904 poorly conserved , TargetScan-predicted genes ( p = 1 . 6E-20 ) . Increasing the cutoff for the enrichment ratio in the pull-down led to a greater proportion of highly down-regulated genes , indicating that a higher enrichment ratio correlates with more effective mRNA degradation and/or that highly enriched mRNAs are more likely to be miR-34a targets . Thus , the Bi-miR-34a pull-down enriches for known sequence and gene expression characteristics of bona fide miRNA targets . To determine the specificity of the pull-down , we generated a random list ( Table S2 ) of 11 genes enriched >2 . 5 fold in both pull-downs ( median enrichment 3 . 5-fold , range 2 . 5–17 . 3 ) . The random list contained 3 known target genes ( AXL , CDK4 and FOXP1; AXL and FOXP1 were not known when the list was generated ) . First , qRT-PCR analysis verified that the random gene mRNAs are pulled down by Bi-miR-34a and not Bi-cel-miR-67 . All 11 mRNAs were enriched ( ∼4–10 fold ) by Bi-miR-34a pull-down in K562 cells , validating the microarray results ( Figure 2E ) . miR-34a over-expression significantly down-regulated mRNA levels of 9 of 11 genes by 25–90% ( Figure 2F ) . PCYOX1L expression declined by 20% , but the change was not significant . To test whether the 3′UTR of each gene could be regulated by miR-34a , the full 3′ UTR of each gene was cloned into a dual luciferase reporter plasmid . miR-34a repressed the 3′UTRs of 10 of 11 genes by ∼20–80% ( Figure 2G ) . Thus , miR-34a could regulate the 3′UTR of 91% of a random set of genes enriched in both miR-34a pull-downs . These results suggest that the Bi-miRNA pull-down is highly specific for identifying direct miRNA targets . An important implication of the large number of genes in the overlapping target list and the low false positive rate is that miR-34a is capable of regulating hundreds of genes . To understand miR-34a's biological functions , we analyzed the cellular pathways whose genes were most enriched in the Bi-miR-34a pull-downs ( Figure 3A ) . In both K562 and HCT116 cells , Bi-miR-34a pull-downs enriched for genes in pathways related to growth factor signaling and cell cycle control . Bi-miR-34a pull-downs enriched significantly for genes in the EGFR , TGF-β , interleukin , estrogen , and androgen receptor signaling pathways ( Figure 3A ) . Many of these pathways utilize common downstream signaling molecules and have a well-established link to cancer . Genes in the MAPK pathway , activated by most growth factors , were highly enriched in the pull-downs for both cell lines . Growth factor signaling also activates cell proliferation . Genes involved in cell cycle regulation , especially the G1/S transition , and the p53 response were enriched in both pull-downs , consistent with previously described targets and roles of miR-34a [3] , [4] , [7] , [8] . We performed a similar pathway enrichment analysis for genes down-regulated by miR-34a ( Figure 3B ) , which includes both direct and indirect miR-34a targets . The downstream effects of growth factor signaling on cell proliferation and p53 activation were more prominent in the down-regulated genes than in the pulled-down gene set , especially in p53-sufficient HCT116 cells . Cell cycle and DNA repair pathways were enriched in genes down-regulated by miR-34a in both K562 and HCT116 cells . These results suggest that miR-34a directly inhibits growth factor signal transduction and cell cycle progression pathways , culminating in reduced expression of genes needed for cell proliferation . A pathway enrichment analysis of the TargetScan-predicted targets of miR-34a ( Figure S3 ) also highlighted the most significantly enriched pathways in the experimental pull-down and down-regulated gene sets , notably TGFβ and MAPK signaling and cell cycle and G1/S transition . However , the significance of the enrichment was weaker and the strong role of miR-34a in growth factor signaling was less obvious . To begin to understand regulation of growth factor signaling and cell proliferation at the gene level by miR-34a , an interactome of pulled down or down-regulated genes in HCT116 cells that participate in the significantly enriched pathways was generated ( Figure 4 ) . miR-34a potentially regulates the expression of critical genes involved in virtually every step and branch of growth factor signal transduction from ligand binding to downstream growth-promoting transcription factors . The putative direct targets included genes encoding multiple TGFβ and FGF isoforms , receptors for EGF , FGF , and insulin , and several oncogenic receptor tyrosine kinases , including MET and AXL . Several genes operating proximally in signal transduction , including SRC , PLCG1 and VAV2 , were selectively pulled down . miR-34a targets also included protein kinase subunits that activate downstream signaling , including subunits of protein kinase A and C . In the RAS-RAF-MAPK signal transduction pathway , putative directly regulated genes included RRAS and RASA2 , ARAF and BRAF , JAK2 , and 11 MAPK genes . Although knockdown of most of the targets would be expected to inhibit cellular activation by diverse growth factors , the genes also encode for some important inhibitors , including the ubiquitin ligase CBLC , RASA2 , and 5 DUSP genes ( MAPK phosphatases ) . The pull-down also captured 76 transcripts of transcription factors , including some that orchestrate the transcriptional response to signal transduction ( including STAT3 , CREB1 and CREB3 , SP1 , ELK1 and SMAD4 ) . A major downstream effect of growth factor signaling and its activated transcription factors is to stimulate cell proliferation . miR-34a is already known to suppress E2F3 and some key cyclins and cyclin-dependent kinases that regulate the G1/S transition . The miR-34a pull-down enriched for additional cyclins ( CCND3 , CCNG2 ) , but also for transcripts of genes that inhibit the kinases that promote exit from G1 ( CDKN1C that encodes p57 ( KIP2 ) , CDKN2A ( p14 ( ARF ) ) . Other enriched transcripts include MCM5 , whose product is required to initiate DNA replication , and several genes required for mitosis ( PLK1 , MAD2L2 and CDC23 ) . Ectopic miR-34a expression led to down-regulation of mRNAs for many genes needed to replicate DNA , including 2 members of the initiating complex that assembles at origins of DNA replication , 7 components of the MCM complex , 4 DNA polymerases , and 5 components of the RFC complex , a cofactor for DNA polymerase . These results suggest that miR-34a not only interferes with the signaling that transduces the growth factor response , but also directly and indirectly suppresses the expression of numerous genes needed for cell proliferation . The Ras–extracellular signal-regulated kinase ( ERK ) and phosphoinositide 3-kinase ( PI3K ) –AKT pathways are key transducers of the cellular response to growth factors . Since many candidate miR-34a target gene products act in pathways converging on ERK and AKT activation , we analyzed the effect of miR-34a over-expression on ERK and AKT phosphorylation . miR-34a transfection reduced basal phosphorylation of ERK and AKT in HCT116 and HeLa cells ( Figure 5A , 5B ) , but not in A549 cells ( Figure S4A ) . miR-34a over-expression both reduced basal proliferation in the absence of serum and blunted the ability of HCT116 ( Figure 5C ) , HeLa ( Figure 5D ) and A549 ( Figure S4B ) cells to proliferate in response to serum growth factors . Conversely , immortalized mouse embryonic fibroblasts ( MEFs ) genetically deficient in miR-34a were more resistant to serum starvation than WT MEFs ( Figure 5E ) . Apoptosis measured by annexin V and propidium iodide staining was also significantly reduced in miR-34a−/− MEFs compared to wild-type MEFs after 24 hours of serum starvation ( Figure 5F ) . Despite the strong difference in cell survival in cells deficient in miR-34a , expression of several known miR-34a targets did not differ significantly between wild-type and miR-34a−/− MEFs ( data not shown ) . The lack of a notable difference may be due in part to compensatory up-regulation of miR-34b and miR-34c in miR-34a−/− MEFs ( Figure 5G ) . These data suggest that miR-34a dampens the basal state of activation of proliferative and pro-survival pathways mediated by AKT and ERK by down-modulating multiple genes whose products contribute to their phosphorylation . To determine whether some of the candidate miR-34a target genes identified in the pull-down that participate in growth factor signaling are bona fide targets , we next tested miR-34a targeting of selected receptor-proximal ( AXL , MET and PIK3R2 ) and more downstream ( ARAF and MEK1 ) components of ERK and AKT signal transduction pathways . These 5 genes were both pulled down with Bi-miR-34a and down-regulated by miR-34a in HCT116 cells . ARAF is a serine/threonine protein kinase that phosphorylates and activates MEK1 , which in turn phosphorylates ERK [45] . AXL is a receptor tyrosine kinase that stimulates cell proliferation and also promotes metastasis [46] , [47] . PIK3R2 is a regulatory subunit of PI3K [48] and MET is a tyrosine kinase receptor that activates both PI3K and RAS [49] . AXL , MET and MEK1 are described miR-34a targets [8] , [29] , [31] , although AXL and MEK1 were not known when these studies were performed . The transcripts of all 5 genes were enriched 3–15-fold in the Bi-miR-34a pull-down by qRT-PCR , validating the microarray results ( Figure 6A ) . Furthermore , over-expression of miR-34a down-regulated both the mRNA and protein levels of all 5 genes ( Figure 6B , 6C ) . All but ARAF are also predicted miR-34a targets by TargetScan . To determine whether these genes are direct miR-34a targets , we tested the 3′UTRs for 4 of the genes ( ARAF , AXL , MEK1 and MET ) by luciferase assay . miR-34a reduced reporter activity of these 3′UTRs by ∼40–75% ( Figure 6D ) . Using the PITA algorithm [50] to identify potential MREs in their 3′UTRs , we found 1 potential MRE in AXL , 2 in ARAF , 3 in MEK1 , 4 in PIK3R2 and 5 in MET ( Figure S4 ) . We tested repression of these MREs by miR-34a using luciferase assays . All 5 genes contained at least one miR-34a-responsive MRE ( Figure 6E ) . Point mutations that disrupt the MRE-miR-34a interaction restored luciferase activity , validating their regulation by miR-34a . Therefore , these 5 important genes in PI3K and MAPK signaling are all directly regulated by miR-34a . Ectopic expression of miR-34a reduces expression of multiple direct target genes whose products facilitate the G1/S transition ( CDK4 , CDK6 , CCND1 , CCNE2 and E2F3 ) . The pull-down identified novel genes acting at the G1/S transition and genes involved in DNA replication and mitosis . Two cell cycle-regulating genes enriched in the miR-34a pull-down are in the random gene list and were already shown ( Figure 2C–2E ) to be miR-34a-regulated - CCNG2 , which is most highly expressed in late S phase , and MAD2L2 , a component of the mitotic spindle assembly checkpoint complex . To examine whether some of the other putative targets that participate in cell cycle progression are direct miR-34a targets , we focused on genes that were both pulled down and down-regulated by miR-34a in HCT116 cells ( Table S2 ) . Fourteen cell cycle-regulating genes ( CDK4 , CDK6 , CCNE2 , E2F2 , E2F3 , E2F5 , HDAC1 , CDKN2A , MCM5 , PKMYT1 , PLK1 , SMAD4 , MAD2L2 and CCND3 ) met these criteria . Four of these ( CDK4 , CDK6 , CCNE2 and E2F3 ) are known miR-34a targets . We experimentally tested 5 of the 9 putative novel targets . These genes were CCND3 , a cyclin that binds to CDK4 or CDK6 and regulates Rb phosphorylation; MCM5 , a mini-chromosome maintenance ( MCM ) protein involved in initiating DNA replication , MYT1 , a serine/threonine protein kinase that phosphorylates and inactivates CDC2 , thereby negatively regulating cell cycle progression at the G2/M transition; PLK1 , a serine/threonine protein kinase required for mitotic spindle maturation; and SMAD4 , a TGFβ-activated transcription factor that induces G1 arrest and apoptosis . To determine whether these miR-34a pull-down genes are bona fide miR-34a target genes , we first verified that their transcripts associate with Bi-miR-34a ( Figure 7A ) . After miR-34a over-expression , 3 of the 5 genes ( MCM5 , PLK1 and MYT1 ) had reduced mRNA by at least 2-fold ( Figure 7B ) and all 5 had significantly reduced protein ( Figure 7C ) . Two other MCM genes , MCM2 and MCM4 , also demonstrated a significant miR-34a-dependent reduction in mRNA , and their protein levels became undetectable in miR-34a-transfected cells . To investigate whether these 5 genes are directly regulated , we measured changes in luciferase activity in HeLa cells after miR-34a co-transfection with reporters containing their 3′UTRs . The 3′UTRs of 4 of 5 of these genes ( CCND3 , MCM5 , PLK1 and SMAD4 ) were significantly repressed 30–60% by miR-34a ( Figure 7D ) . The 3′UTR of MYT1 , which bound to Bi-miR-34a and was down-regulated by miR-34a over-expression ( Figure 7A , 7B ) , was not regulated by miR-34a . MYT1 expression could be regulated by MREs outside the 3′UTR or indirectly . PITA and TargetScan were used to identify miR-34a MREs in the 3′UTRs of CCND3 , SMAD4 , MCM5 , and PLK1 ( Figure 7E , Figure S5 ) . CCND3 MRE1 , SMAD4 MRE1 and MCM5 MRE5 were significantly suppressed by miR-34a ( Figure 7E , Figure S5 ) . The CCND3 and SMAD4 MREs were predicted by TargetScan , while MCM5 MRE5 contains a miR-34a hexamer seed match . Mutations that disrupt base pairing with miR-34a rescued luciferase expression , further confirming that these genes are direct miR-34a targets . Because the enrichment ratios for MCM2 and MCM4 in the pull-down ( ∼2 . 3 ) were close to our cut-off , we also evaluated whether MCM2 and MCM4 might be direct targets . MCM2 is a direct target as verified by mRNA enrichment in the pull-down , decrease in mRNA and protein following miR-34 over-expression , miR-34a regulation of its 3′UTR by luciferase activity and MRE identification ( Figure 7A–7E ) . However , the MCM4 3′UTR was not active in luciferase assays . Collectively , these findings suggest that miR-34a acts as a master regulator of cell proliferation , directly suppressing many key genes that control cell cycle progression . Despite improvements in bioinformatic and experimental tools , distinguishing the direct targets of a miRNA from indirectly regulated genes remains challenging [14] . Here we describe a simple biochemical method to isolate candidate miRNA targets by streptavidin pull-down of mRNAs that associate with a transfected Bi-miRNA , and apply it to study miR-34a . Comparison of the set of mRNAs that directly associate with the Bi-miRNA with mRNAs down-regulated by miRNA over-expression makes it possible to distinguish the direct and indirect effects of a miRNA . Candidates identified by Bi-miR-34a pull-down have properties of validated miRNA targets: they are enriched for sequences complementary to the miR-34a seed and tend to decrease in expression with miR-34a over-expression . Genes that both decrease in mRNA abundance after over-expression and are isolated by Bi-miR-34a pull-down are further enriched for seed matches , indicating that either they are more likely true miR-34a targets or that a perfect seed match might enhance target mRNA degradation . In our analysis we defined candidate direct targets using an arbitrary enrichment ratio cut-off of 1 SD , which corresponded to an enrichment of ≥2 . 5-fold for HCT116 cells and ≥3 . 3-fold for K562 cells . As the enrichment ratio cut-off was increased , mRNA suppression after ectopic miR-34a expression increased in tandem ( Figure 2D ) . A more stringent cut-off would reduce the already low false positive rate , but also reduce the sensitivity to detect direct targets ( Figure 2B ) . With this cut-off , we identify 71% of the known miR-34a targets expressed in HCT116 cells as “hits” , but only 48% of the known expressed targets in K562 cells . If we had also chosen a 2 . 5-fold cut-off for K562 cells , our sensitivity for picking targets would have increased to 55% , while a 2-fold cut-off would have increased it to 69% . Since 10 of 11 genes in the random list of genes enriched by ≥2 . 5 fold by Bi-miR-34a pull-downs in both cells have 3′UTRs regulated directly by miR-34a by luciferase assay , a lower cut-off for the enrichment ratio might have increased sensitivity without an unacceptable false discovery rate . Some bona fide target genes are only enriched in the pull-down by ∼2-fold; one of the novel genes we validated by identifying its MRE ( MCM2 ) was only enriched by 2 . 3-fold in the pull-down of both cell lines . The low false positive rate of target identification demonstrated with the random gene list was also supported by the high degree of experimental validation of the growth factor signaling and cell cycle regulatory genes we chose to examine experimentally ( Table S2 ) . In all , we provided experimental evidence for 14 novel direct targets of miR-34a and identified 14 miR-34a MREs , of which 11 had a perfect hexamer seed match and the 3 others had perfect matches if G∶U wobbles were allowed . Thus , the majority of genes we identified as regulated by miR-34a contain canonical 3′UTR MREs with good seed pairing . In the setting of over-expression by transfection , protein levels of all 11 genes we analyzed by immunoblot declined substantially . The few target genes that we tested for which we did not find miR-34a regulation of the 3′UTR might be false positives or might be direct targets , regulated by sequences in the 5′UTR or CDS . In fact we found enrichment for hexamer seed matches in these regions in the mRNAs pulled down with miR-34a , consistent with MRE properties in recent cross-linking-RISC immunoprecipitation experiments [23] , [25] . Known targets may not have been identified by the pull-down for a variety of reasons . First , not all of the targets in the literature may be correctly assigned . Second , some known targets , such as CD44 , are only modestly regulated by miR-34a [40] . The ratio that defines a “hit” is arbitrary . We set a relatively high threshold for identifying “hits” to maximize the specificity of the method ( especially given the large numbers of enriched mRNAs in the pull-down ) , which came at the cost of sensitivity . Some known targets , which we did not designate hits with our 1 S . D . threshold of the enrichment ratio ( which corresponded to >3 . 3 in K562 cells ) had enrichment ratios of 2 . 5–3 . 2 in K562 cells . Other bona fide targets may have low , but detectable expression levels , and could have been missed due to the low sensitivity and inter-assay variability of microarray experiments . In addition to cellular variation in endogenous miRNA expression and RISC abundance , other context-dependent biological factors , such as target site accessibility , might vary due to the expression of RNA binding proteins , which could influence the efficiency of miRNA target site binding and the mechanism of targeting [51] , [52] . Cell-type specific expression of other MRE-containing genes that compete for miRNA binding could also influence the pull-down enrichment ratio [53] . Finally , some missed targets are likely to be false negatives . Normalizing the pulled down mRNAs to their abundance in the input cellular mRNA was critical to eliminate from consideration highly abundant housekeeping mRNAs . Our pull-down method modified a previously developed protocol [41] , [42] , which did not normalize the pull-down mRNAs to the input RNA . Many of the “hits” pulled down with Bi-miR-10a included ribosomal mRNAs , which may represent background binding of very abundant transcripts . Moreover , the miR-10a “hits” were not enriched for mRNAs containing miR-10a 3′UTR seed matches and were not down-regulated by miR-10a over-expression . In other work to be presented elsewhere , the pull-down method was used to identify genome-wide targets of miR-200c and miR-21 . Importantly , the miR-200c and miR-21 pulled down mRNAs are also enriched for known targets and for 3′UTR seed sequences . An advantage to the Bi-miRNA pull-down method described here is its simplicity . In contrast to mRNA expression-based target identification methods , Bi-miRNA pull-downs should identify only direct targets , excluding genes whose expression is indirectly modulated by changes in miRNA expression . Because the degree of mRNA suppression mediated by miRNAs is often small relative to changes in protein , methods that rely on changes in mRNA expression in response to manipulation of miRNA levels will necessarily miss some direct targets . Although the enrichment ratio takes into account a reduction in target gene mRNA in its denominator , the pull-down should not only identify target genes whose mRNA levels decline , but also those that are regulated primarily by inhibiting translation . Unlike approaches based on Ago pull-downs , the Bi-miRNA pull-down identifies the mRNAs directly associated with a specific miRNA , simplifying analysis of biological processes regulated by the miRNA . The method described here without cross-linking does not directly identify MREs . The streptavidin pull-down method might , however , readily be modified to include cross-linking , RNase digestion of unbound mRNA segments and sequencing , similar to the HITS-CLIP protocol [23] , [24] , to capture not only direct targets , but also identify MREs of an individual Bi-miRNA . Isolating RNAs associated with an individual miRNA rather than all RISC-associated RNAs in cells over-expressing the miRNA of interest might be a more direct way to define specific target sequences . Future bioinformatic studies of Bi-miRNA pull-down datasets could be used to better define in an unbiased manner the sequence features that dictate miRNA targeting , and could reveal non-canonical modes of targeting , such as those that contain only partial seed complementarity [17] or pairing to the central region of the miRNA [18] or that lie outside the 3′UTR . Indeed , in this work , we enriched for mRNAs with 5′UTR and CDS seed matches , indicating that some direct miR-34a targets may be regulated outside of their 3′UTR . Only 29% of the 2416 enriched genes in the HCT116 pull-down had down-regulated mRNA levels by mRNA microarray analysis after over-expressing miR-34a for one day , while 10 of 11 randomly chosen genes in the pull-down had significantly decreased mRNA by qRT-PCR analyzed 72 hr after transfection . Thus although miRNAs may commonly lead to mRNA degradation , the degree of mRNA down-regulation of most genes is slight if cells are harvested within a day of transfection . mRNA microarrays may be too noisy to detect subtle changes in expression , unless the analysis is performed on many replicates . Our data also suggest that the kinetics of mRNA degradation may be slow . The early 24 hr time point used for the assay may have fortuitously enhanced our ability to capture miRNA-bound transcripts before too many had been degraded . Indirect effects of the miRNA are also likely to increase over time . The set of genes enriched in the miR-34a pull-down of both HCT116 and K562 cells contains 76 transcription factors or co-factors , whose suppression would reduce many mRNAs . One important corollary of our results is that miR-34a likely directly regulates hundreds of genes . However , further experimental work is needed to assess how many of the hundreds to thousands of genes whose mRNAs associated with ectopic miR-34a are actually directly regulated by endogenous miR-34a . Possibly only a minority of potential targets is indeed directly regulated in an individual cell at any time . Based on our analysis ( Figure 2D ) , the genes whose transcripts are most enriched in the pull-down may be the most significant targets in a given context . Additional experiments are needed to probe the functional consequences of miR-34a regulation of the genes we identified as targets . The directly regulated genes might vary considerably from cell type to cell type or even in the same cell lineage depending on differentiation state or environmental conditions . For this study we focused on the shared targets identified in two very different types of cells , rather than the ones that were unique to each cell-type . The pull-down method could be used in the future to compare miRNA target genes in different cellular contexts . Notably , the effect of miR-34a on cell signaling differed in the cancer cells we examined . Basal phosphorylation of AKT and ERK was reduced by miR-34a over-expression in HCT116 and HeLa cells ( Figure 5 ) , but not in A549 cells ( Figure S4 ) . Constitutively active RAS in A549 cells may override the effect of miR-34a in that context . Our results suggest that a dense network of genes that participate in common pathways , sometimes with opposing functions , is capable of being regulated by one miRNA . Although we observed a clear effect of genetic loss of miR-34a on the ability to cells to survive growth factor withdrawal , we did not see reduced expression in miR-34a−/− compared to wild-type cells of some of the key miR-34a target genes we identified . Since growth factor signaling is so central to cell survival and proliferation , the permanent loss of miR-34a expression likely led to myriad compensatory changes . This seeming paradox supports the conclusions of our study – namely that a single miRNA may exert its biological effect by regulating expression of hundreds of genes . The capacity of miR-34a to potentially regulate so many genes that affect growth factor signaling may enable it to exert an effect in diverse contexts . The numbers of genes that are actually regulated by miR-34a in any setting will likely depend on how strongly miR-34a is expressed . In our pull-down , we greatly over-expressed miR-34a . However , the level of over-expression throughout this study was not greater than endogenous miR-34a expression in some physiological settings , i . e . in K562 cells stimulated with phorbol ester where miR-34a increases 1000-fold [9] . There may be a target gene hierarchy – some genes regulated by low levels of miR-34a , others regulated only by high levels . The dense network of cell signaling genes captured in the pull-downs suggests that an important function of miR-34a is to regulate the proliferative and activation responses to extracellular growth factors . Despite its function in regulating growth factor signaling and cell proliferation , we did not find a significant variation in miR-34a expression after serum starvation or when cells were synchronized in different phases of the cell cycle ( data not shown ) . In this study we experimentally verified as direct miR-34a targets 5 growth factor signaling genes ( ARAF , AXL , MEK1 , MET and PIK3R2 ) . miR-34a was previously shown to inhibit the G1/S transition [3] , [8] . Here we identified 7 novel cell cycle-regulating direct targets that included genes also required for DNA replication and mitosis . The ultimate anti-proliferative effect of miR-34a integrates both direct consequences of suppressing expression of genes required for progression through the G1/S transition and at other steps of the cell cycle as well as indirect anti-proliferative effects from repressing the growth factor signaling pathways that activate cell cycle progression . Consistent with our genome-wide target gene analysis , miR-34a expression resets the basal state of ERK and AKT phosphorylation in several cell lines , rendering cells less responsive to growth factor signaling ( Figure 5 ) . This was shown both by miR-34a overexpression as well as by genetic deletion . miR-34a may reduce cellular sensitivity to growth factor signaling by suppressing many genes in multiple signal transduction pathways . miR-34a candidate targets include genes that are universally involved in transmitting growth factor activation signals as well as some that participate in specific pathways . The particular signaling genes that are suppressed in a given cell line will likely vary from cell to cell , depending on the growth factors to which the cell responds . These types of differences likely contribute to the incomplete overlap between the enriched pathways captured in the two hematopoietic and colon cancer cell lines examined here . HCT116 , K562 , A549 and HeLa cells were from ATCC . miR-34a+/+ and miR-34a−/− MEFs were generated from E14 . 5 littermate embryos . A full description of the mice will be published elsewhere . MEFs were transformed by infecting the cells with retroviruses encoding H-RAS-V12 and E1A and by selection with puromycin ( 1 µg/ml ) and hygromycin ( 50 µg/ml ) . The plasmids for expression of H-RAS-V12 ( plasmid 9051 ) and E1A ( plasmid 18748 ) were obtained from Addgene . The VSV-G pseudotyped viruses were produced in 293T cells using the standard protocol . MEFs , HCT116 , A549 and HeLa cells were grown in DMEM with 10% fetal bovine serum and supplemented with penicillin , streptomycin , HEPES , L-glutamine and β-mercaptoethanol , K562 cells were grown in RPMI containing 10% fetal bovine serum and the same supplements . For most experiments , 2×106 HCT116 or K562 cells were transfected with 200 pmol hsa-miR-34a or cel-miR-67 miRNA mimics ( Dharmacon ) , using Amaxa nucleofection according to the manufacturer's protocol . Biotin was attached to the 3′-end of the active strand . HeLa and A549 cells were transfected with Lipofectamine 2000 and miRNA mimics at a final concentration of 50 nM ( Invitrogen ) . To study the association of Bi-miRNAs with HA-Ago1 or HA-Ago2 , pIRESNeo ( Clontech ) or pIRESNeo-HA-Ago1 or pIRESNeo-HA-Ago2 ( Addgene ) plasmids were co-transfected in six-well plates ( 2 µg/well , 1×106 cells/well ) with 200 pmol Bi-miR-34a or Bi-cel-miR-67 using Amaxa as per the manufacturer's instructions . Total RNA was isolated using Trizol reagent ( Invitrogen ) , treated with DNase I ( Ambion ) and reverse transcribed using random hexamers and superscript III reverse transcriptase ( Invitrogen ) . qRT-PCR was performed in triplicate samples using SYBR Green FastMix ( Quanta ) on a BioRad CFX96 . mRNA levels were normalized to housekeeping genes GAPDH , UBC or SDHA . miRNA was quantified in triplicate using the TaqMan MicroRNA Assay ( Applied Biosystems ) as per the manufacturer's instructions and normalized to U6 . Primer sequences are listed in Table S3 . Whole cell lysates from transfected K562 or HCT116 cells were prepared using RIPA buffer . Proteins were analyzed by SDS-PAGE , transferred to nitrocellulose membranes and probed with the following antibodies: AXL [4566] , ARAF [4432] , MEK1 [9124] , CDK4 [2906] , MCM2 [3619] , PKMYT1 [4282] , PLK1 [4513] , SMAD4 [9515] , FOXP1 [2005] , RBBP4 [4633] , AKT [9272] , pAKT ser-473 [4051] , ERK [4370] , pERK [9107] from Cell Signaling; MET [sc-161] , MCM5 [sc-165995] , E2F1 [sc-251] , E2F3 [sc-879] , CHEK1 [sc-8408] from Santa Cruz; ACSM3 [SAB1400253] , MAD2L2 [SAB1400387] , AGBL5 [AV53752] , CCNG2 [AV03032] , PSMD5 [WH0005711M1] from Sigma; MCM4 [06-1296] from Millipore; and PI3KR [610045] , BD Biosciences . Western Blots were quantified by densitometry . HCT116 or K562 cells ( 1×106 ) were transfected in triplicate with Bi-miR-34a or Bi-cel-miR-67 ( Dharmacon ) as described above and then cultured in six-well plates . Twenty-four hours later , the cells from 3 wells were pelleted at 500×g . After washing twice with PBS , cell pellets were resuspended in 0 . 7 ml lysis buffer ( 20 mM Tris ( pH 7 . 5 ) , 100 mM KCl , 5 mM MgCl2 , 0 . 3% NP-40 , 50 U of RNase OUT ( Invitrogen ) , complete mini-protease inhibitor cocktail ( Roche Applied Science ) ) , and incubated on ice for 5 min . The cytoplasmic lysate was isolated by centrifugation at 10 , 000×g for 10 min . Streptavidin-coated magnetic beads ( Invitrogen ) were blocked for 2 hr at 4°C in lysis buffer containing 1 mg/ml yeast tRNA and 1 mg/ml BSA ( Ambion ) and washed twice with 1 ml lysis buffer . Cytoplasmic lysate was added to the beads and incubated for 4 h at 4°C before the beads were washed five times with 1 ml lysis buffer . RNA bound to the beads ( pull-down RNA ) or from 10% of the extract ( input RNA ) , was isolated using Trizol LS reagent ( Invitrogen ) . The level of mRNA in the Bi-miR-34a or Bi-cel-miR-67 control pull-down was quantified by qRT-PCR or mRNA microarray . For qRT-PCR , mRNA levels were normalized to a housekeeping gene ( GAPDH , SDHA or UBC ) . The enrichment ratio of the control-normalized pull-down RNA to the control-normalized input levels was then calculated . Total RNA ( independently in two experiments ) was amplified , labeled and hybridized to Affymetrix U133 plus 2 . 0 mRNA microarrays . The quality of the RNA was assessed before performing the microarray and the quality of the microarray data was assessed using affyPLM and Affy software . The replicate data sets for the 4 sets of samples ( pull-down and input for miR-34a and cel-miR-67 ) were compared using an unsupervised hierarchical clustering algorithm , which verified the similarity of the duplicates . The microarray data were normalized using RMA [7] to reduce interarray variation . The enrichment ratio {Bi-miR-34a PD/Bi-cel-miR-67 PD}/{Bi-miR-34a input/Bi-cel-miR-67 input} was calculated for each probe . For genes represented by multiple probes , the mean ratio for all the probes was calculated . Genes for which none of the probe hybridization signals exceeded the background were considered not expressed and were disregarded in the analysis . For informatic analysis of the PD data , genes whose enrichment ratio were ≥1 SD above background based on a log-normal distribution were considered “hits” . HCT116 or K562 cells were transfected in independent duplicate experiments as above with unbiotinylated miR-34a or cel-miR-67 ( Dharmacon ) and total RNA was harvested 24 hr later and analyzed as above by gene expression microarrays . After normalization , fold changes for each probe were calculated as the ratio of input RNA from miR-34a-transfected cells to the ratio of input RNA from cel-miR-67-transfected cells . Genes were considered down-regulated if the ratio decreased by at least 20% , which corresponded to ∼1 SD . To test the expression levels of putative target sets , each gene list was plotted in a cumulative distribution function ( CDF ) plot , and the Kolmogorov-Smirnov [KS] test was used for statistical comparisons between gene sets . To determine whether a gene was also a predicted target of miR-34a , the presence of miR-34a binding sites was analyzed using TargetScan 4 . 2 ( http://www . targetscan . org/ ) [39] , [54] , [55] or PITA ( http://132 . 77 . 150 . 113/pubs/mir07/mir07_prediction . html ) [50] . The mature hsa-miR-34a sequence was obtained from miRBase ( http://mirbase . org/ ) . All RefSeq human mRNA sequences were downloaded from NCBI in July 2009 ( http://ftp . ncbi . nih . gov/ ) . mRNAs were indexed by Entrez Gene ID; in cases where multiple sequences matched a gene ID , the sequence with the longest 3′UTR was selected . For each test gene list and miR-34a hexamer , the miR-34a hexamer frequency ( hexamer matches per kb of sequence ) was calculated . The frequency of hexamer matches for all genes on the microarray ( the background set ) was also determined . Gene IDs with no corresponding sequence in the database were excluded from analysis . Monte Carlo simulations of equally sized random gene sets ( without replacement ) were used to generate an empirical 2-tailed p-value for each gene set/hexamer combination . When p<1E-4 , the p-value was calculated from curve fitting relative to the random background distribution . For each of the lists of down-regulated and pull-down-enriched genes , the p-value of over-representation in a suite of canonical pathways ( KEGG [56] and Wikipathways [57] ) was determined using the hypergeometric distribution . A visualization of the relationship between the enriched pathways ( p<0 . 001 ) based on the number of overlapping genes was rendered using Cytoscape [58] . The network of gene-gene interactions underlying these relationships was constructed based on interactions supplied by MetaCore ( GeneGo Inc ) . Physical , predicted and genetic interactions were used to connect the down-regulated and pull-down enriched genes within the significant signaling , cell cycle or DNA repair pathways . Signaling pathway genes with no connection to any other node were removed and the network was arranged according to predicted sub-cellular localization . HeLa cells were cotransfected in 24 well plates using Lipofectamine 2000 ( Invitrogen ) with 50 nM miR-34a mimic or control miRNA mimic and 50 ng of psiCHECK2 ( Promega ) vector containing the MRE or 3′UTR of indicated genes cloned into the multiple cloning site of Renilla luciferase . After 48 hr of transfection ( unless otherwise indicated ) luciferase activities were measured using the Dual Luciferase Assay System ( Promega ) and Top count NXT microplate reader ( Perkin Elmer ) per manufacturer's instructions . All experiments were performed at least in triplicate . Results were normalized to those obtained in cells transfected with an empty vector . For some experiments , a perfectly complementary antisense sequence to the active strand of miR-34a was inserted into the multiple cloning site for use as a positive control . Data were normalized to Firefly luciferase and results from 3 independent experiments were compared . Sequence of primers used for cloning 3′UTRs for miR-34a target genes are listed in Table S4 . MREs sequences were cloned into psiCHECK-2 by annealing complementary oligomers matching each MRE sequence ( Figures S4 , S5 ) with overhanging ends complementary to the XhoI and NotI sites of psiCHECK-2 . HCT116 , HeLa and A549 cells were transfected as described above . One day after transfection , cells were placed in serum-free medium or medium containing 10% fetal calf serum . 48 hours after the medium was changed , total cell numbers were counted . MEFs were plated at a density of 2 . 5×105 or 5×105 cells per well of a 6-well plate . The medium was changed to vary serum concentration 24 hr after plating . The MEFs were harvested 24 hr later and counted using Trypan blue staining or stained in PBS+0 . 4% BSA with annexinV-APC ( Invitrogen ) at a 1∶30 dilution , then washed once and stained with propidium iodide ( 4 µg/ml ) ( Sigma-Aldrich ) .
microRNAs ( miRNAs ) are small RNAs that regulate gene expression by binding to mRNAs bearing a partially complementary sequence . miRNAs decrease the stability or translation of mRNA targets , leading to reduced protein expression . Understanding the biological function of a miRNA requires identifying its targets . Here we developed a sensitive and specific biochemical method to identify candidate microRNA targets that are enriched by pull-down with a tagged , transfected microRNA mimic . The method was applied to miR-34a , a miRNA that inhibits cell proliferation . We found that miR-34a can potentially regulate hundreds of genes . Computational analysis of these genes suggested a novel function for miR-34a—suppression of the pro-proliferative response to diverse growth factors . This function complements the previously known role of miR-34a in blocking cell cycle progression . Thus , by reducing the expression of an extensive network of genes , miR-34a dampens growth factor signaling as well as its downstream consequences , promotion of cell survival and proliferation .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "rna", "molecular", "cell", "biology", "rna", "interference", "cell", "biology", "nucleic", "acids", "cell", "growth", "gene", "expression", "regulatory", "networks", "biology", "computational", "biology", "signaling", "networks", "molecular", "biology" ]
2011
Capture of MicroRNA–Bound mRNAs Identifies the Tumor Suppressor miR-34a as a Regulator of Growth Factor Signaling
We report the genome of the facultative intracellular parasite Rhodococcus equi , the only animal pathogen within the biotechnologically important actinobacterial genus Rhodococcus . The 5 . 0-Mb R . equi 103S genome is significantly smaller than those of environmental rhodococci . This is due to genome expansion in nonpathogenic species , via a linear gain of paralogous genes and an accelerated genetic flux , rather than reductive evolution in R . equi . The 103S genome lacks the extensive catabolic and secondary metabolic complement of environmental rhodococci , and it displays unique adaptations for host colonization and competition in the short-chain fatty acid–rich intestine and manure of herbivores—two main R . equi reservoirs . Except for a few horizontally acquired ( HGT ) pathogenicity loci , including a cytoadhesive pilus determinant ( rpl ) and the virulence plasmid vap pathogenicity island ( PAI ) required for intramacrophage survival , most of the potential virulence-associated genes identified in R . equi are conserved in environmental rhodococci or have homologs in nonpathogenic Actinobacteria . This suggests a mechanism of virulence evolution based on the cooption of existing core actinobacterial traits , triggered by key host niche–adaptive HGT events . We tested this hypothesis by investigating R . equi virulence plasmid-chromosome crosstalk , by global transcription profiling and expression network analysis . Two chromosomal genes conserved in environmental rhodococci , encoding putative chorismate mutase and anthranilate synthase enzymes involved in aromatic amino acid biosynthesis , were strongly coregulated with vap PAI virulence genes and required for optimal proliferation in macrophages . The regulatory integration of chromosomal metabolic genes under the control of the HGT–acquired plasmid PAI is thus an important element in the cooptive virulence of R . equi . Rhodococcus bacteria belong to the mycolic acid-containing group of actinomycetes together with other major genera such as Corynebacterium , Mycobacterium and Nocardia [1] . The genus Rhodococcus comprises more than 40 species widely distributed in the environment , many with biotechnological applications as diverse as the biodegradation of hydrophobic compounds and xenobiotics , the production of acrylates and bioactive steroids , and fossil fuel desulfurization [2] . The rhodococci also include an animal pathogen , Rhodococcus equi , the genome of which we report here . R . equi , a strictly aerobic coccobacillus , is a multihost pathogen that causes purulent infections in various animal species . In horses , it is the etiological agent of “rattles” , a lung disease with a high mortality in foals [3] . R . equi lives in soil , uses manure as growth substrate , and is transmitted by the inhalation of contaminated soil dust or the breath of infected animals . Pathogen ingestion may result in mesenteric lymphadenitis and typhlocolitis , and multiplication in the fecal content of the intestine contributes to dissemination in the environment . R . equi causes chronic pyogranulomatous adenitis in pigs and cattle and severe opportunistic infections in humans , often in HIV-infected and immunosuppressed patients . Human rhodococcal lung infection resembles pulmonary tuberculosis and has a high case-fatality rate [3] , [4] . R . equi parasitizes macrophages and , like Mycobacterium tuberculosis ( Mtb ) , replicates within a membrane-bound vacuole . A 80–90 kb virulence plasmid confers the ability to arrest phagosome maturation , survive and proliferate in macrophages in vitro and mouse tissues in vivo , and to cause disease in horses . Virulence-associated protein A ( VapA ) , a major plasmid-encoded surface antigen , is thought to mediate these effects [5]–[7] . The vapA gene is located within a horizontally-acquired pathogenicity island ( PAI ) together with several other vap genes [8] . Equine , porcine and bovine isolates carry specific virulence plasmid types differing in PAI structure and vap multigene complement , suggesting a role for vap PAI components in R . equi host tropism [8] , [9] . Apart from the key role of the plasmid vap PAI , little is known about the pathogenic mechanisms of R . equi . We investigated the biology and virulence of this pathogenic actinomycete by sequencing an analysing the genome of strain 103S , a prototypic clinical isolate . With its dual lifestyle as a soil saprotroph and intracellular parasite , R . equi offers an attractive model for evolutionary genomics studies of niche breadth in Actinobacteria . The comparative genomic analysis of R . equi and closely related environmental rhodococi reported here provides insight into the mechanisms of niche-adaptive genome plasticity and evolution in this bacterial group . The R . equi genome also provides fundamental clues to the shaping of virulence in Actinobacteria . The genome of R . equi 103S consists of a circular chromosome of 5 , 043 , 170 bp with 4 , 525 predicted genes ( Figure S1 ) and a circular virulence plasmid of 80 , 610 bp containing 73 predicted genes [8] . Overall G+C content is 68 . 76% . Table 1 summarizes the main features of the R . equi genome . The 5 . 0 Mb R . equi chromosome contains relatively few pseudogenes ( n = 14 , Table 1 ) , most associated with horizontally acquired regions ( n = 10 , including two degenerate DNA mobility genes ) , consistent with a slow “core” gene decay rate . This suggests that the differences in chromosome size between rhodococci result mainly from genome expansion in environmental species rather than contraction in R . equi . Potential virulence-associated determinants were identified in silico based on ( i ) homology with known microbial virulence factors , ( ii ) literature mining for Mtb virulence mechanisms , ( iii ) automated genome-wide screening for virulence-associated motifs [41] and ( iv ) systematic inspection of HGT genes , the secretome , and of genes shared with pathogenic actinomycetes but absent from nonpathogenic species . Based on the well-established principle that coexpression with pathogenicity determinants is a strong indicator of involvement in virulence [70] , [71] , we sought to identify novel R . equi virulence-associated chromosomal factors through their coregulation with the plasmid virulence genes . The expression profiles of 103S and an isogenic plasmid-free derivative ( 103SP− ) were compared , using a custom-designed genomic microarray and in vitro conditions known to activate ( 37°C pH 6 . 5 ) or downregulate ( 30°C pH 8 . 0 ) the virulence genes of the plasmid vap PAI [72] , [73] . The plasmid had little effect on the chromosome in vap gene-downregulating conditions , but significantly altered expression was observed for numerous genes in vap gene-activating conditions ( n = 88 with ≥2 fold change ) ( Table S10 ) . Most of the differentially expressed genes ( 68% ) were upregulated in the presence of the plasmid . These data suggest that the virulence plasmid activates the expression of a number of chromosomal genes , but whether this upregulation involves direct , specific ( potentially virulence related ) interactions or incidental pleiotropic effects is unclear . Somewhat counterintuitively for an organism with a dual lifestyle as a soil saprotroph and intracellular parasite , the R . equi genome is significantly smaller than those of environmental rhodococci . This may reflect that the main R . equi habitats –herbivore intestine , manure and animal tissues– provide a richer and more stable environment than the chemically diverse and probably nutrient-scarce environments of the nonpathogenic species . In nutrient-poor conditions , the simultaneous use of all available compounds as sources of carbon and energy may offer a competitive advantage , driving the selection of expanded genomes with greater metabolic versatility [10] , [68] . Indeed , the much larger genome of the polychlorinated biphenyl-biodegrading R . jostii RHA1 encodes a disproportionately large metabolic network [10] , with a wider diversity of paralogous families , unique metabolic genes and catabolic pathways . The relatively small number of pseudogenes and virtual lack of DNA mobilization genes in R . equi suggests that this species has not experienced a sudden evolutionary bottleneck with a concomitant relaxation of selective pressure and increase in mutation fixation [82] . The “coprophilic” and parasitic lifestyle specialization of R . equi seems to result from a “non-traumatic” adaptive process in an organism that , despite having suffered some specific functional losses ( e . g . sugar utilization , thiamine synthesis ) , remains an “average” soil actinomycete with a normal-sized genome under strong selection . The greater genomic complexity of the environmental Rhodococcus spp . may reflect a “multi-substrate” niche specialization necessarily linked to the strict selection criteria —for unusual metabolic versatility— under which these species are generally isolated , [10] . Our analyses show that genome expansion in the environmental rhodococci has involved a linear gain of paralogous genes and an accelerated pattern of gene acquisition through HGT and extrachromosomal replicons , which evolve more rapidly and clearly play a critical role in rhodococcal niche specialization . The lipophilic , asaccharolytic metabolic profile and capacity for assimilating inorganic nitrogen may be key traits for proliferation in herbivore intestine and feces , which are rich in volatile fatty acids [3] , and in the macrophage vacuole and chronic pyogranulome , presumably poor in amino acids and rich in membrane-derived lipids [20] , [23] . The potential for anaerobic respiration via denitrification may be critical for survival in the anoxic intestine or , as suggested for Mtb [83] , [84] , in necrotic granulomatous tissue . The inability to use sugars , unique among related actinomycetes , may confer a competitive advantage in the intestine and feces , dominated by carbohydrate-fermenting microbiota generating large amounts of short-chain fatty acids , which R . equi use as main carbon source . Alkalophily is probably an advantage in fresh manure , a major R . equi reservoir . R . equi is also well equipped to survive desiccation , important for dustborne dissemination in hot , dry weather , when rhodococcal foal pneumonia is transmitted [3] , [4] . R . equi infections are notoriously difficult to treat due to the intracellular localization of the pathogen , compounded by a lack of susceptibility to antibiotics ( e . g . penicillins , cephalosporins , sulfamides , quinolones , tetracyclines , clindamycin , and chloramphenicol ) ( Table S7 and refs . therein ) . With its panoply of drug resistance determinants , the 103S genome illustrates how naturally selected resistance traits , typically abundant in soil organisms , may have an important impact on the clinical management of microbial infections [40] . Finally , our analyses suggest that the appropriation of preexisting core actinobacterial components and functions are key events in the evolution of rhodococcal virulence . Although the underlying notion may be intuitively apparent when considering , for example , the contribution of housekeeping genes to bacterial virulence [85] , here we are identifying it specifically as “gene cooption” , a key mechanism enabling rapid adaptive evolution and the emergence of new traits [65]–[67] . Underpinned by a few critical “host niche-accessing” HGT events , such as acquisition of the “intracellular survival” plasmid vap PAI or the “cytoadhesion” chromosomal rpl locus , this evolutionary mechanism is likely to have facilitated the rapid conversion of what was probably an animal-associated commensal into the pathogenic R . equi . Given the pervasive distribution of the “virulence-associated” gene pool among nonpathogenic species ( Tables S8 , S9 ) , the notion of cooptive virulence is possibly applicable to all pathogenic actinomycetes and , indeed , universally to bacterial pathogens . The incorporation of adaptive changes in the regulation of the “appropriated” genes is a key mechanism in genetic cooption [65] . Our genome-wide microarray experiments and transcription network analyses indicate that the plasmid vap PAI , essential for intracellular survival and pathogenicity , has recruited housekeeping genes from the rhodococcal core genome under its regulatory influence . Among these are two chromosomal genes encoding key metabolic enzymes involved in aromatic amino-acid biosynthesis , coexpressed with the virulence genes of the vap PAI in response to an increase in temperature to 37°C ( the body temperature of the warm-blooded host ) . These two metabolic genes are required by R . equi for full proliferation capacity in macrophages , providing supporting experimental evidence for the cooptive nature of R . equi virulence . A cooptive virulence model is consistent with the sporadic isolation of “nonpathogenic” ( pre-parasitic ) Actinobacteria , including environmental rhodococci ( e . g . R . erythropolis [86] ) , as causal agents of opportunistic infections . An appreciation of the importance of gene cooption in the acquisition of pathogenicity provides a conceptual framework for better understanding and guiding research into bacterial virulence evolution . We sequenced the original stock of the foal clinical isolate 103 , designated clone 103S , to avoid mutations associated with prolonged subculturing in vitro . Strain 103 belongs to one of the two major R . equi genogroups ( DNA macrorestriction analysis , unpublished data ) , is genetically manipulable , and is regularly used for virulence studies [25] , [56] . Random genomic libraries in pUC19 were pair-end sequenced using dye terminator chemistry on ABI3700 instruments , with subsequent manual gap closure of shotgun assemblies and sequence finishing , as previously described [8] . The 103S genome sequence was manually curated and annotated with the software and databases listed in Table S12 . A conservative annotation approach was used to limit informational noise [8] . For phylogenomic analyses , putative core ortholog genes were identified by reciprocal FASTA using a minimum cutoff of 50% amino acid similarity over 80% or more of the sequence . A similarity distance matrix was built with the average percentage amino acid sequence identity obtained by pairwise BLASTP comparisons ( distance = 100 − average percent identity of 665 loci ) and used to infer a neighbor-joining tree with the Phylip package [87] . The accession numbers of the genome sequences used in comparative analyses are listed in Table S13 . The sequence from the R . equi 103S genome has been deposited in the EMBL/GenBank database under accession no . FN563149 . The nutritional and metabolic profile of R . equi 103S and its susceptibility to various drugs were analysed in Phenotype MicroArray screens ( Biolog Inc . , http://www . biolog . com ) [15] . Substrate utilization was validated in supplemented mineral medium ( MM ) containing salts , trace elements , and ammonium chloride as the sole nitrogen source [19] ( see Figure S7 ) . For electron microscopy , a bacterial cell suspension in 0 . 1 M Tris-HCl ( pH 7 . 5 ) was negatively stained with 1% uranyl acetate and observed at 80 . 0 kV in a Phillips CM120 BioTwin instrument ( University of Edinburgh ) . Fluorescence microscopy was carried out on paraformaldehyde-fixed bacteria with an R . equi whole-cell rabbit polyclonal antiserum and Alexa Fluor 488-conjugated secondary antibodies ( both diluted 1∶1000 in 0 . 1% BSA ) . Total RNA was obtained from logarithmically growing R . equi bacteria ( OD600 = 0 . 8 ) in Luria-Bertani ( LB ) medium , by homogenization in guanidinium thiocyanate-phenol-chloroform ( Tri reagent , Sigma ) with FastPrep-24 lysing matrix and a FastPrep apparatus ( MP bio ) , followed by chloroform-isopropanol extraction , DNAase treatment ( Turbo DNA-free , Ambion ) and purification with RNeasy kit ( Qiagen ) . RNA quantity and quality were determined with a Nanodrop ( Thermo Scientific ) and 2100 Bioanalyzer with RNA 6000 Nano assay ( Agilent ) . RNA samples ( 500 ng ) were amplified with the MessageAmp II-bacteria kit and 5- ( 3-amionallyl ) -UTP ( Ambion ) , labeled with Cy3 or Cy5 NHS-ester reactive dyes ( GE Healthcare ) , and purified with RNeasy MinElute ( Qiagen ) . Whole-genome 8×15K custom microarrays with up to four different 60-mer oligonucleotides per CDS ( 13 , 823 probes for the chromosome , 201 for the virulence plasmid ) ( Agilent ) were hybridized in Surehyb DNA chambers ( Agilent ) with 300 ng of Cy3/Cy5-labeled aRNA , using Gene Expression Hybridisation and Wash Buffer kits ( Agilent ) . Three experimental replicates per condition were analyzed , one with dye swap . The hybridization signals were captured and linear intensity-normalized , with Agilent's DNA microarray scanner and Feature Extraction software . Data were subsequently LOESS-normalized by intensity and probe location and analyzed with Genespring GX 10 software ( Agilent ) . Network analysis of microarray expression data was carried out with Biolayout Express3D 3 . 0 software [74] , using log base 2 normalized ratios of Cy3/Cy5 signals and methods described in detail elsewhere [75] . Biolayout Express3D is freely available at http://www . biolayout . org/ . In-frame deletion mutants of REQ23860 and REQ23850 were constructed by homologous recombination [56] , using the suicide vector pSelAct for positive selection of double recombinants on 5-fluorocytosine ( 5-FC ) [43] . Briefly , oligonucleotide primer pairs CMDEL1/CMDEL2 and CMDEL3/CMDEL4 were used for PCR amplification of two DNA fragments of ≈1 . 5 Kb corresponding to the seven 3′- and six 5′-terminal codons plus adjacent downstream and upstream regions of REQ23860 . The CMDEL2 and CMDEL3 primers are complementary and were used to join the two amplicons by overlap extension . The PCR product carrying the ΔREQ23860 allele was inserted into pSelAct , using SpeI and XbaI restriction sites; the resulting plasmid was introduced into 103S by electroporation and transformants were selected on LB agar supplemented with 80 µg/ml apramycin . The same procedure was followed for ΔREQ23860 , with primers ASDEL 1 to 4 . Allelic exchange double recombinants were selected as previously described [43] , [56] . For complementation , the REQ23860-50 genes plus the entire upstream intergenic region were amplified by PCR with CACOMP1 and 2 primers and stably inserted into the R . equi chromosome , using the integrative vector pSET152 [88] . PCR was carried out with high-fidelity PfuUltra II fusion HS DNA polymerase ( Stratagene ) . The primers used are shown in Table S14 . Low-passage ( <20 ) J774A . 1 macrophages ( ATCC ) were cultured in 24-well plates at 37°C , under 5% CO2 atmosphere , in DMEM supplemented with 2mM L-glutamine ( Gibco ) and 10% fetal bovine serum ( Lonza ) until confluence ( ≈2×105 cells/well ) . J774A . 1 monolayers were inoculated at 10∶1 MOI with washed R . equi from an exponential culture at 37°C in brain-heart infusion ( BHI , OD600≈1 . 0 ) . Infected cell monolayers were immediately centrifuged for 3 min at 172×g and room temperature , incubated for 45 min at 37°C , washed three times with Dulbecco's PBS to remove nonadherent bacteria , and incubated in DMEM supplemented with 5µg/µl vancomycin to prevent extracellular growth . After 1 h of incubation with vancomycin ( t = 0 ) and at specified time points thereafter , cell monolayers were washed twice with PBS , detached with a rubber policeman and lysed by incutation for 3 min with 0 . 1% Triton X-100 . Intracellular bacterial counts were determined by plating appropriate dilutions of cell lysates onto BHI . The presence of the virulence plasmid was checked by PCR on a random selection of colonies , using traA- and vapA- specific primers [9] to exclude the possibility of intracellular growth defects being due to plasmid loss . As the intracellular bacterial population at a given time point depends on initial numbers , bacterial intracellular kinetics data are expressed as a normalized “Intracellular Growth Coefficient” [89] according to the formula IGC = ( IBt = n−IBt = 0 ) /IBt = 0 , where IBt = n and IBt = 0 are the intracellular bacterial numbers at a specific time point , t = n , and t = 0 , respectively .
Rhodococcus is a prototypic genus within the Actinobacteria , one of the largest microbial groups on Earth . Many of the ubiquitous rhodococcal species are biotechnologically useful due to their metabolic versatility and biodegradative properties . We have deciphered the genome of a facultatively parasitic Rhodococcus , the animal and human pathogen R . equi . Comparative genomic analyses of related species provide a unique opportunity to increase our understanding of niche-adaptive genome evolution and specialization . The environmental rhodococci have much larger genomes , richer in metabolic and degradative pathways , due to gene duplication and acquisition , not genome contraction in R . equi . This probably reflects that the host-associated R . equi habitat is more stable and favorable than the chemically diverse but nutrient-poor environmental niches of nonpathogenic rhodococci , necessitating metabolically more complex , expanded genomes . Our work also highlights that the recruitment or cooption of core microbial traits , following the horizontal acquistion of a few critical genes that provide access to the host niche , is an important mechanism in actinobacterial virulence evolution . Gene cooption is a key evolutionary mechanism allowing rapid adaptive change and novel trait acquisition . Recognizing the contribution of cooption to virulence provides a rational framework for understanding and interpreting the emergence and evolution of microbial pathogenicity .
[ "Abstract", "Introduction", "Results/Discussion", "Materials", "and", "Methods" ]
[ "microbiology/microbial", "evolution", "and", "genomics" ]
2010
The Genome of a Pathogenic Rhodococcus: Cooptive Virulence Underpinned by Key Gene Acquisitions
Avian leukosis virus ( ALV ) is a simple retrovirus that causes a wide range of tumors in chickens , the most common of which are B-cell lymphomas . The viral genome integrates into the host genome and uses its strong promoter and enhancer sequences to alter the expression of nearby genes , frequently inducing tumors . In this study , we compare the preferences for ALV integration sites in cultured cells and in tumors , by analysis of over 87 , 000 unique integration sites . In tissue culture we observed integration was relatively random with slight preferences for genes , transcription start sites and CpG islands . We also observed a preference for integrations in or near expressed and spliced genes . The integration pattern in cultured cells changed over the course of selection for oncogenic characteristics in tumors . In comparison to tissue culture , ALV integrations are more highly selected for proximity to transcription start sites in tumors . There is also a significant selection of ALV integrations away from CpG islands in the highly clonally expanded cells in tumors . Additionally , we utilized a high throughput method to quantify the magnitude of clonality in different stages of tumorigenesis . An ALV-induced tumor carries between 700 and 3000 unique integrations , with an average of 2 . 3 to 4 copies of proviral DNA per infected cell . We observed increasing tumor clonality during progression of B-cell lymphomas and identified gene players ( especially TERT and MYB ) and biological processes involved in tumor progression . Avian leukosis virus ( ALV ) is a simple retrovirus that causes cancer , primarily B-cell lymphomas in chickens [1–3] . The ALV genome does not contain a viral oncogene and induces aberrant host gene expression via use of strong viral enhancer and promoter elements . Relative to other well studied retroviruses like HIV-1 and MLV , ALV was shown to integrate relatively randomly into the host genomic DNA , with little bias for genomic features [4–7] . The FACT ( facilitates chromatin transcription ) complex , a chromatin remodeler , was recently reported to promote ALV integration [8] . ALV-induced lymphomas develop in a multistage process , appearing initially as neoplastic follicles in the bursa , some of which develop into primary bursal tumors . Primary tumors can then metastasize to distant organs and form secondary tumors [9] . We are studying rapid-onset lymphomas , which develop in less than 3 months after infection of chicken embryos with ALV [10 , 11] . Cellular transformation occurs through multiple genetic changes in oncogenes and tumor suppressor genes , as well as noncoding RNAs [12] . These oncogenic changes can occur via different genetic mechanisms; insertional mutagenesis by retroviruses is one such mechanism . Viral integration into the host genome can alter host gene expression and induce cancer development [1–3] . In turn , common viral integration targets observed in multiple tumors can help identify oncogenes [2 , 13–19] . Consequently , retroviral-mediated lymphomagenesis in chickens provides an excellent experimental model system for analysis of neoplastic change in tumors of B-cell lineage [20] . We have previously identified common proviral integrations in ALV-induced B-cell lymphomas , notably in the TERT promoter region , and in hemangiomas [15–17] . Since selection in tumorigenesis alters the pattern of viral integration sites , we also analyze integrations in cultured cells , to identify preferences of ALV integrations in an unbiased way . We investigate how ALV integrations in tissue culture correlate with previously unreported genomic features . We observe an enrichment of ALV integrations in the 5’ end of gene bodies , proximal to CpG islands and transcription start sites , as well as a preference for expressed and highly spliced genes . No association was observed with levels of alternative splicing . In order to determine the effects of selection for oncogenic characteristics , we compare integration sites in cultured cells with those in ALV-induced B-cell lymphomas . We observe that ALV integrations are more strongly selected for proximity to transcription start sites in tumors . In addition , there are fewer integrations near CpG islands in tumors . ALV tumors are thought to be clonal , as determined by previous work [9 , 15] . However , the clonality of these neoplasms has not been empirically defined . We analyze ALV infection in tumors by quantifying the clonal abundance and distribution of integrations during progression of tumors . Using the statistical Gini index , we calculate the empirical degree of oligoclonality and extent of clonal expansion in different stages of tumorigenesis [21 , 22] . Quantifying the clonality index and average number of integrations per cell within individual tumors helps determine the clonal architecture and hierarchies of lymphomagenesis . Furthermore , the gene ontology analysis of host genes most proximal to proviral sites provides insight into underlying gene players and their contribution to oncogenic transformation . Thus , our work helps unravel how the integration sites of ALV are selected for in oncogenesis and play a role in the clonal progression of tumors . This is the most in depth analysis for ALV integration sites to date and is novel in terms of being able to follow the patterns of integration from early infection ( in tissue culture ) through to early and late tumor development . Via deep sequencing , we analyzed approximately 87 , 000 unique ALV integration sites ( UISs ) in tissue culture cells and in tumors , as summarized in S1 Table . Randomly generated integration sites were used as a control for all subsequent analysis . ALV integrations were analyzed for different ALV subgroups ( A , C and J ) in different infected cell types , including primary chicken embryo fibroblasts ( CEF ) , DT-40 , a chicken B-cell lymphoma cell line , and the human HeLa tumor cell line . After mapping the UISs to the host genome , we used the HOMER bioinformatics tool [23] to associate integrations with specific annotated genomic sites ( Table 1 ) . Upon analysis of ALV integrations in CEFs , using the ensembl Gallus gallus 4 genome , we observed a significant bias for ALV integration into genes ( approximately 40% ) relative to random events ( 27% ) ( t test , p-value 0 . 006 ) ( Fig 1A ) . We also observed a slight enrichment for integrations near LINE sequences , gene promoters , simple repeat and satellite DNA sequences; however , these were not statistically significant ( Table 1 ) . Independent analysis of all these features was consistent for infections with ALV in CEFs , DT-40 chicken B cells and in HeLa cells , suggesting that these preferences are not cell type specific ( Table 1 ) . To study selection of ALV integration sites in tumors , we sequenced 72 tissue samples from 41 different birds ( S2 Table ) . We obtained 17 . 2 million reads , originating from viral integrations in neoplasms and non-tumor tissues , which were mapped to 71 , 368 UISs . Similar to the integration pattern in cultured cells , integrations in the ALV-induced tumors , at the primary sites in the bursa or secondary metastases , showed a significant enrichment for genes ( approximately 38% ) , relative to random ( 27% ) ( t test , p-value 0 . 022 ) ( Fig 1A , Table 1 ) . We analyzed the distribution of ALV integrations within transcriptional units by dividing the gene bodies into 10 equal segments or bins . Then , we calculated the number of integrations within each bin to determine the density of ALV integrations within a given part of a transcriptional unit . Relative to the matched control set ( 11 . 99% ) , there was a significant enrichment of ALV integrations towards the 5’ end of the gene body . This bias is most distinct within the first 10% of the gene body , i . e . in proximity to the transcriptional start site , in both tissue culture ( 16 . 25% ) ( t test , p-value 0 . 031 ) , and in tumors ( 16 . 22% ) ( t test , p-value 0 . 004 ) ( Fig 1B ) . To determine the pattern of integrations surrounding transcription start sites ( TSS ) , we plotted the observed ALV integrations in cultured cells and in tumors with respect to the nearest TSS , extending over 15 kb on either end ( Fig 2A ) . Relative to the random integration sites within 5 kb of the TSS , we observed a nearly 2-fold enrichment of ALV integration sites in tissue culture ( t test , p-value 0 . 042 ) ( Fig 2B ) . We observed an even greater frequency of integrations near TSS in tumors , with nearly 3-fold enrichment relative to random events within the 5 kb window ( t test , p-value 0 . 006 ) . This suggests that clonal selection of integration sites in tumors has a strong bias for proximity to TSS , probably to promote induction of aberrant gene expression in tumorigenesis . In addition , consistent with previously reported data from our lab [17] , we observed a pronounced drop in the integration frequency in the vicinity of the TSS in tumors ( Fig 2A ) . Interestingly , we also observed this drop in tissue culture ( Fig 2A ) , suggesting it is a result of integration preference and not due to selection in tumors . Based on our initial HOMER analysis , we observed a 3-fold preference for integrations within CpG islands ( approximately 3% ) relative to random sites ( 1 . 1% ) in cultured cells ( t test , p-value 0 . 041 ) ( Fig 3A ) . In contrast to tissue culture , the percentage of integrations within CpG islands was not enriched in tumors ( 1 . 4% ) , and appeared near random . The same was true for the most clonally expanded integrations ( 0 . 96% ) , with 10 or more breakpoints ( see Materials and methods ) . To further investigate this , we determined the frequency of ALV integrations in the area immediately surrounding CpG islands ( Fig 3B ) . Analyzing ALV integrations in tissue culture , we found that in the 1 kb region surrounding CpG islands , there was a 1 . 5 fold enrichment of integration relative to random ( t test , p-value 0 . 047 ) . If the window is expanded to 5 kb flanking the CpG island , the enrichment becomes more pronounced . Nearly 33% of all integrations are observed within 5kb of CpG islands , which is a 1 . 7 fold enrichment relative to the matched random control ( 21% ) ( t test , p-value 0 . 043 ) . In contrast , this enrichment for integration near CpG islands in cultured cells was not observed in tumors ( Fig 3B ) . Frequency of integration within 1 or 5 kb of CpG islands in tumors was not significant relative to random events . When the same analysis was done for only the most clonally expanded integrations , there is a striking depletion of integrations within 5kb of CpG islands ( 11 . 8% ) relative to the total integration set in tumors ( 27 . 7% ) as well as a matched random control ( 21% ) ( Fig 3B ) . This suggests that in tumors , there is an enrichment of integrations away from CpG islands ( t test , p-value 0 . 045 ) . We next investigated ALV integration as a function of expression levels of the most proximal transcriptional units . In order to determine the background expression levels of host genes , we analyzed RNA-seq data for CEF , DT-40 and HeLa cells . We divided the whole transcriptome into 13 bins and determined the percentage of integrations within each bin , to observe any enrichment above background . While nearly 22 . 5% of the chicken genes are not expressed in CEFs , only 8 . 2% of integrations occur in this expression bin . Thus , we observe a significant depletion in the percentage of integrations within or in proximity to unexpressed genes ( t test , p-value 0 . 006 ) ( Fig 4 ) . For expressed genes , there was no preference observed for ALV integration with the level of gene expression , relative to random events . Thus , ALV integrates randomly in proximity to genes with low , intermediate or high levels of expression . Since integrations can occur in intergenic regions at distant loci from transcriptional units , we asked whether ALV integrations within the transcriptional unit might exhibit a bias towards expressed genes . To address this , we repeated our analysis for only those integrations that occur within the gene body ( between the transcription start and termination sites ) . Overall , we observed a similar integration pattern relative to random , with a more pronounced depletion of integrations within unexpressed genes . In contrast to random events ( 25 . 4% ) , there was a nearly 6-fold decrease in preference for ALV integrations in genes with no expression ( 3 . 9% ) ( t test , p-value 0 . 003 ) ( S1 Fig ) . Therefore , while ALV preferentially integrates near or within expressed genes , there is no preference for the level of gene expression . We also correlated ALV integrations with the gene expression levels via analysis of RNA-seq data for a subset of the ALV-induced lymphomas . Similar to our findings in cultured cells , we observed that in tumors ALV integrations are selected for proximity to or within expressed genes , but there is no distinct bias for varying gene expression levels ( S2 Fig ) . HIV has been previously reported to preferentially integrate into genes that are highly spliced , i . e . have a greater number of introns [24] . In order to determine whether ALV might display any similar preferences , we correlated ALV integration with the levels of mRNA splicing and alternative splicing . We associated the percent of integrations with the number of introns of the most proximal transcriptional unit , using the ensembl Gallus gallus 4 genome . While by random chance 12 . 1% of integrations are predicted to occur in unspliced transcriptional units , only 7 . 7% ( t test , p-value 0 . 004 ) and 6 . 4% ( t test , p-value 0 . 002 ) of ALV integrations in cultured cells and tumors , respectively , occurred within this range . Therefore , there is a significant lack of integration into unspliced genes in tumors . By random chance , the vast majority of the integrations are predicted to occur in transcriptional units with 1–19 introns ( 71 . 1% ) . For this window of splicing , ALV integrations in tissue culture ( 68 . 3% ) appear close to random as well . On the other hand , nearly 22 . 1% of ALV integrations in tissue culture fall into highly spliced genes with 20 or more introns , which is a significant enrichment above random events ( 16 . 6% ) ( t test , p-value 0 . 012 ) . Furthermore , ALV integrations in tumors ( 47 . 1% ) have a greater enrichment for integration within or proximity to transcriptional units with 10 to 39 introns , relative to random sites ( 39 . 3% ) ( t test , p-value 0 . 026 ) . Thus , we observe that ALV has a bias for integration into spliced genes with enrichment for higher levels of gene splicing ( Fig 5 ) . We also asked whether the levels of ALV integration are associated with the number of spliced isoforms of the proximal transcription unit . The chicken genome is not well characterized for reporting alternative spliced variants of genes . Since the human genome is better characterized than the chicken genome , we repeated our analysis for ALV integrations in a HeLa cell line ( S3 Fig ) . However , our analysis did not identify any preference for integrations relative to the level of alternative splicing of proximal transcriptional units . In order to measure the relative abundance , or clonal expansion , of UISs within a tissue , we quantified the number of sonication breakpoints for each site , as described previously [16] . The number of breakpoints reveals the extent of clonal expansion of the UIS . In tissue culture ( 2–5 days after infection ) we identified 16 , 978 unique sonication breakpoints , i . e . an average of 1 . 1 breakpoints per integration site ( Fig 6A ) . The vast majority of these integrations ( 82 . 9% ) had a single breakpoint , suggesting that these integrations were not clonally expanded . On the other hand , in tumors we identified 92 , 951 unique sonication breakpoints . The average number of breakpoints per integration was 1 . 3 , with the vast majority of integrations ( 67 . 6% ) showing only a single sonication breakpoint . In contrast to 17 . 1% of integrations in tissue culture , 32 . 3% of integrations in tumors had two or more breakpoints , revealing that a large fraction of the infected cells are from expanded clones . Moreover , a large number of integrations ( approximately 13 , 000 ) in tumors had a very high number ( 10 or more ) of breakpoints . The distribution of viral integration sites in the neoplasms is depicted in a composite pie chart ( Fig 6B ) . The most highly expanded clones , each with 70 or more breakpoints , are highlighted in a table . The table depicts 28 UISs , indicating the gene most proximal to the integration site , respective tumor , and its corresponding number of breakpoints . The maximum observable number of breakpoints is limited by the length of deep sequencing reads and , in the case of highly abundant integration sites , the probability of repeated sonication at the same genomic position . Thus , it is important to note that our standard breakpoint analysis is an underestimate of the fraction of the infected cells with expanded clones [21] . Cancers exist in a number of stages , characterized by a spectrum of divergent cells and genetic changes . Each cancer is unique , and in any given tumor the clonal structure shifts over time , which involves the clonal selection and expansion of cells . [25] . In cases of ALV-induced B-cell lymphomas , the bursa serves as the primary organ of malignant transformation and site of tumorigenesis [9 , 26] . Infected chickens typically develop multiple primary neoplastic follicles in the bursa , some of which may eventually form primary tumors . The development of these neoplasms is a multi-stage process [26–28] . In order to examine the clonality of lymphomagenesis , we studied different stages of cancer progression . These include inflammation , neoplastic follicles , primary tumors in the bursa , and metastatic tumors at secondary sites . The stage of neoplastic follicles in the bursa is an early step in tumor progression towards transformation and malignancy [26] . Metastases of primary tumors are observed from the bursa to secondary sites in the liver , spleen , and kidneys . Analyzing the different neoplasms , we observed an increasing extent of clonality with the advancing stages of tumorigenesis ( S4 Fig ) . This clonal expansion can be represented by a pie chart , where each pie represents an individual tumor . A given slice of a pie represents a UIS , and the size of the slice corresponds to its relative clonal abundance , denoted by the respective number of sonication breakpoints for that UIS . The most clonally expanded integrations in secondary tumors can be investigated by comparison of metastasized versus non-metastasized neoplasms within an individual bird ( Fig 6C ) . For example , the different tumors in bird A2 , all harbor overlapping UISs with an increasing level of clonal expansion in the metastasized neoplasms . For example , the expanded clones of UISs at TAB2 , BTBD1 and mir-30a integrations appear in a mix of other clonally expanded integrations in the primary bursa tumor , whereas the secondary liver , kidney and spleen tumors are more homogenous . This suggests increasing clonal homogeneity of the metastasized tumors relative to the bursa . Additional examples of comparisons between primary and secondary neoplasms within an individual bird are depicted in S5 Fig . In order to empirically estimate the clonality of different neoplasms we made use of an objective parameter called the oligoclonality index ( OCI ) as defined by the Gini co-efficient index [21 , 29] . The OCI defines the clonal abundance of a tissue on an objective scale of 0 to 1 . In theory , a tissue with perfect monoclonality would have an OCI value of 1 . Conversely , an entirely polyclonal tissue would have an OCI value of 0 . The OCI values of the neoplastic subtypes , in representative stages of lymphomagenesis , are depicted in Fig 7A . The plot represents the OCI values in ascending order with tumor progression , suggesting an increased magnitude of clonal expansion within these neoplasms . As was previously depicted by the pie charts of bursal tumors ( Fig 6C , S4 Fig ) , the OCI value is lower in these samples compared to the metastases . The OCI for the metastasized tumors was significantly greater , in some cases close to 1 . Additionally , in order to further validate the OCI values , we investigated the ALV integrations and their extent of clonal expansion in different slices of representative neoplasms ( Fig 7B ) . A sample with high clonal homogeneity would exhibit a highly uniform pattern of integrations and corresponding clonal abundance in different slices of the tumor , as depicted for tumor D2L . Conversely , a sample with lower OCI value such as a bursa tumor exhibited more clonal heterogeneity in different portions of the neoplasm ( S6 Fig ) . Additional data for integration patterns in slices of other neoplasms is summarized in S6 Fig . In addition to the extent of clonal expansion , we also wanted to investigate the average number of proviral integrations within tumors , defined as its proviral load ( PVL ) . Twenty randomly selected tumors across different stages of lymphomagenesis were isolated for this analysis . The PVL varies widely between the tissues , ranging on an average from approximately 2 . 3 to 4 copies per infected cell of a tumor ( Fig 8 ) . We observed a correlation between the PVL and different stages of tumor progression , suggesting that a higher PVL is associated with later stages of tumorigenesis . ALV env genes are more distinct and offer greater specificity to distinguish ALV proviral genomes from endogenous retrovirus genomes in the host chicken genome . However , proviruses undergo varying amounts of genome rearrangements and deletions during cellular transformation and oncogenesis , especially in the env region , probably to evade cellular immune surveillance . Therefore , we utilized LTR specific primers to estimate the PVL . We assume each provirus contains 2-LTRs therefore , the PVL values described here may be underestimates of the actual PVL among infected neoplasms due to the possibility of solo LTRs . Additional analyses of PVL estimates , via use of gag or env regions of ALV , exhibit similar association of PVL with the progression of tumors ( S7 Fig ) . Cancers acquire , via mutational and epigenetic changes , a variety of traits that trigger clonal expansion , via proliferation , migration and invasion . These properties are alterations to normal developmental and physiological cellular processes [25] . We wanted to further investigate how ALV integrations near host genes , in certain functional categories , confer oncogenic advantages on the infected B-cell clone . To test this , we used the G:profiler analysis software to analyze the ontology of the nearest host gene upstream or downstream of each integration site . G:profiler allows functional profiling of genes within a neoplasm , in a quantitative fashion [30] . Queries are ordered with more importance given to the more expanded clones within tumors . The results showed a significant overrepresentation of genes in five cellular pathways: cell differentiation , phosphorylation , immune response signaling , proliferation and regulation of apoptosis ( Fig 9 ) . These biological processes show a greater enrichment in the secondary tumors ( malignant ) than in neoplasms with low or intermediate clonality . These changes reflect the temporal order of genetic alterations and underlying cellular processes acquired in the progression of B-cell lymphomagenesis . These processes were associated with the host gene nearest to the viral integration sites , regardless of its transcriptional orientation relative to the provirus . The genes associated with these processes are listed in S3 Table . Furthermore , when we analyzed the GO terms associated with the most clonally expanded integrations in individual tumors , the above mentioned biological processes appeared repeatedly in many tumors ( S4 Fig ) . This suggests that the gene players involved in these biological processes exhibit a degree of cooperativity to trigger oncogenic transformation . For example , among the most clonally expanded UISs in Fig 6C , TAB2 is known to be involved in immune response signaling , BTBD1 plays a role in cellular differentiation and mir-30a has known roles in regulating cell proliferation , migration and invasion [31–33] . Furthermore , we identified TERT and MYB among the most clonally expanded integrations in 15 independent tumors from 9 different birds , and in 15 independent tumors from 11 different birds , respectively ( S4 Fig , S4 Table ) . TERT , the catalytic component of telomerase , has known roles in immortalization , senescence and apoptotic signaling [34] . MYB , a transcription factor , functions in regulation of cellular differentiation and proliferation [35] . Clonally expanded MYB integrations co-occur in half of the birds with TERT tumors . Additionally , we also observed up regulated MYB expression in TERT tumors without any ALV integrations near or within MYB [15] . This suggests a possible cooperation between MYB and TERT . Genes proximal to other clonally expanded integrations , which occur in the TERT or MYB tumors , might also cooperate with them for inducing oncogenic transformation . Of particular interest , integrations in CTDSPL and CTDSPL2 are frequently clonally expanded , along with TERT and MYB [36] . miR-155 integrations were also frequently seen with MYB in tumors ( S4 Table ) . These putative cooperating gene players are involved in varying biological processes such as differentiation , proliferation , apoptosis , phosphorylation , immune response signaling , immortalization and DNA damage repair ( S4 Table ) . In order to determine common pathways activated in multiple individual tumors , we also analyzed genes near the clonally expanded integrations within individual tumors . A number of common transcription factor target gene networks were identified as common integration sites in various tumors . Among the common targets of ALV integration , the most enriched are the genes targeted by the E2F , EGR , WT1 and SP families of transcription factors ( S5 Table ) . E2F is a well-characterized protein family that mediates both cell proliferation and apoptosis [37] . EGR ( early growth response ) is a family of nuclear proteins that function as transcriptional regulators and target genes required for regulating differentiation and mitogenesis [38] . The SP ( specificity protein ) and WT ( Wilms tumor ) family of transcription factors are involved in many cellular processes , including cell differentiation , cell growth , apoptosis , immune responses , and response to DNA damage [39–41] . This suggests that the ALV integrations in these genes are at the intersection of events of tumorigenic transformation . These gene players might cooperate to trigger oncogenic characteristics , thus resulting in tumor formation . We report the analysis of more than 87 , 000 UISs , leading from early infections ( in tissue culture ) through to early and late tumor development . With only slight preferences for some genome features , we show that ALV exhibits a relatively random integration pattern . Due to this relatively minimal discrimination , ALV serves as a good insertional mutagenesis tool to study tumorigenesis . We utilize the OCI to empirically define the magnitude of clonality in different stages of tumorigenesis . Consistent with the clonal expansion hypothesis [25] , we observe that ALV clonality increases with progressing stages of tumorigenesis We also identify putative cooperating gene players ( especially TERT and MYB ) and the underlying biological processes of cell differentiation , phosphorylation , immune response signaling , proliferation and regulation of apoptosis involved in tumor progression . We observed a semi-random integration pattern for ALV . In contrast , MLV and FV exhibit a strong preference for integrations within TSS or CpG islands [42–44] . HIV-1 , on the other hand , has a strong preference for integrating into transcriptional units with higher expression levels [5 , 6] . ALV shows a more random distribution of integration sites , similar to HTLV and MMTV [45 , 46] . Similar trends in the integration sites of different ALV subgroups were observed via an independent analysis in CEF , DT-40 and HeLa cells , suggesting that the ALV integration preference is not cell type specific . We report a significant enrichment of ALV integrations within gene bodies as nearly 40% of integrations are found in genes relative to 27% at random integration sites . Barr et al . ( 2005 ) reported a similar bias for ALV integrations into transcriptional units relative to matched random sites [5] . They also reported that ALV favors integrations in transcriptional units with higher expression levels [5]; however , our data does not support this . This difference could be explained by our use of different methods to measure gene expression . Since we use RNA-seq data , in lieu of microarrays used previously ( with 249 probe sets ) , our gene expression values might be different for the host genome . Moreover , we analyzed 15 , 416 proviral integration sites in this study compared to their analysis of 658 integrations , which should offer a more comprehensive analysis . ALV displays a slight preference for integration near TSSs in tissue culture infections . We observe a further enrichment of integrations near the TSSs in tumors , as a consequence of tumorigenic selection . ALV integrations are also enriched within CpG islands as well as near spliced and expressed genes . There is a significant selection of ALV integrations away from CpG islands in the highly clonally expanded tumor cells ( 10 or more breakpoints ) . Since DNA methylation is often observed in CpG islands , ALV integrations near CpG islands may be more susceptible to repression by methylation [47] . Thus , cancer cells are likely enriched for integrations away from CpG islands , where the ALV proviruses are more likely to remain transcriptionally active . In an emerging picture of B-cell malignancy , understanding tumor progression is an important piece of the puzzle . Here , we show that clonal expansion of ALV-infected B-cells is a key feature of malignant transformation in tumors . Approximately 100 to 500 UISs have been observed for HIV in in peripheral blood lymphocytes [48 , 49] . On the other hand , nearly 500–5000 UISs have been observed for a typical HTLV-1 host [21] . We observe approximately 700 to 3000 UISs in individual tumors induced by ALV mutagenesis . The most clonally expanded viral integrations appear to be early events in tumorigenesis and are expanded during progression of tumors . Therefore , this pattern of selection and expansion defines the clonal evolution of this cancer . The distribution of the clone abundances can be quantified by an OCI value . Late stage B-cell neoplasms are associated with higher OCI values than earlier stages , and the PVL is also observed to correlate with the progressing stages of tumorigenesis . Interestingly , while the gag and env ratios appear very similar , LTR ratios are elevated for some individual tumor samples . This suggests that over the course of tumorigenesis , there are likely more deletions and rearrangements acquired in the gag and env regions of the viral genome . Further work will be necessary to identify the epigenetic factors that may influence proviral expression and tumorigenesis . We observed a correlation between the PVL and different stages of tumor progression . As determined by the PVL analysis , a single cell in a tumor has multiple ( 2 . 3 to 4 ) copies of integrated ALV proviruses , suggesting multiple UISs contribute to oncogenic transformation . Therefore , loss of super-infection resistance could be involved in tumorigenesis . Alternatively , deletions in the env region of proviruses or mutations affecting env expression , identified in some tumors in previous work in our lab [18] , might allow cells to overcome super-infection resistance . Analysis of the ontology of genes flanking integration sites demonstrated a functional overrepresentation of certain pathways that are deregulated in many lymphomas [17] . Consistent with present concepts of oncogenesis and lymphomagenesis , GO analysis revealed that five major gene functions contribute to clonal dominance: regulation of proliferation , differentiation , immune response , apoptosis , and phosphorylation . Of these , cell differentiation and phosphorylation appear to be significantly altered in earlier stages of tumor progression . Interestingly , we also observed possible cooperativity between TERT and MYB , which might function together to induce oncogenic transformation . Further analysis via single cell sequencing would be useful to investigate this cooperativity . Our work depicts a comprehensive investigation into the role of ALV integrations in lymphomas in chickens . The value of our work can be extended to mammalian systems . B-cell development in chicken and mammals is a very similar process [50] . These similarities are evident at levels of molecular changes and gene regulatory networks [51–53] . Although mice serve as a good mammalian model , in terms of oncogenesis they differ in some fundamental ways from humans . Unlike humans , the mouse telomerase enzyme is active in normal somatic cells [54] . This difference between humans and mice is important because telomerase activation is a critical step in the human oncogenic process , with telomerase activation seen in approximately 90% of human cancers [55 , 56] . Similar to human expression , chicken telomerase expression is down regulated in most somatic tissues [57] . Furthermore , chicken telomeres shorten with age , and telomerase activity is important for oncogenesis [58] . Therefore , chicken serves as an advantageous model over mouse , to study oncogenic events . Limited information is available about the molecular mechanisms of lymphomagenesis , and the role of selective clonal expansion . Cells containing certain integration sites can undergo selective expansion in tumors , resulting in abundant clonal populations . We observed that in the course of tumor progression , the more transformed neoplasms contained integrations with a high number of breakpoints [25] . Via our genomic analysis of ALV integrations across progression of B-cell lymphomas , we are able to provide insights into the biological processes associated with initiation , progression , and metastasis of tumors . Chicken embryo fibroblasts ( CEFs ) were cultured in medium 199 ( Thermo Fisher Scientific ) supplemented with 2% tryptose phosphate , 1% fetal calf serum , 1% chicken serum , and 1% antibiotic at 39°C and 5% CO2 . Viruses were generated by transfecting CEFs via electroporation , with vectors RCASBP ( A ) and RCASBP ( C ) to generate viral titers of subgroups A and C respectively [59] . ALV-J virus [60] was generated from homogenates of tumors with ALV-J integrations , by passing it through a 0 . 22 micrometer pore size filter . The collected supernatant from tumors was in turn used to infect CEFs . CEFs were grown at approximately 40% confluency and were infected with ALV subgroup A , C or J at an MOI of 1–2 . The cells were collected at 48 hours and 120 hours post infection for DNA isolation . DT-40 cells were cultured in Dulbecco’s modified eagle medium ( Thermo Fisher Scientific ) , 10% fetal calf serum , 5% chicken serum , 5% tryptose phosphate , and 1% antibiotic at 37°C and 5% CO2 . DT-40s were grown at approximately 40% confluency and were infected with ALV subgroup C at an MOI of 1–2 . The cells were collected at 48 hours post infection for DNA isolation . HeLa cells ( ATCC ) were cultured in Dulbecco’s modified eagle medium , 10% fetal bovine serum ( FBS ) , and 1% antibiotic at 37°C and 5% CO2 . To generate ALV pseudo-typed with vesicular stomatitis virus glycoprotein ( VSV-G ) , CEFs were co-transfected via electroporation , with pMD . G ( VSV-G envelope plasmid ) and RCASBP ( C ) plasmid [61] . Viral supernatant was collected after 48 h , filtered through a 0 . 22-micrometer filter , and concentrated by polyethylene glycol ( PEG ) precipitation ( 10% PEG8000 ) [62] . This concentrate of pseudo-typed ALV was used to infect HeLa cells and cells were collected 48 hours post infection for DNA isolation . 5 or 10-day-old chicken embryos were injected with ALV-LR9 , ALV- ΔLR9 , ALV-G919A , or ALV-U916A as described previously [10 , 17] . Chickens were observed daily and were euthanized when apparently ill or at 10–12 weeks after hatching . A total of 72 tissues were selected for characterization by high-throughput sequencing ( S2 Table ) . Three uninfected tissues and several non-tumor tissues from infected birds were sequenced to serve as controls . All of the B-cell lymphomas included in the study were rapid-onset lymphomas , arising within 10–12 weeks . LR-9 is an ALV subgroup A recombinant virus consisting of gag , pol , and env genes derived from UR2-associated virus and LTRs derived from ring-necked pheasant virus [63] . ALV-ΔLR-9 contains a deletion in the gag gene , causing increased splicing to downstream genes [11] . ALV-G919A contains a silent mutation in the NRS [10] . Tumors were collected from primary bursal ( B ) tissue or metastasized liver ( L ) , kidney ( K ) or spleen ( S ) tissues . Five- and ten-day old chicken embryos were injected with virus . Chickens injected include inbred SC White Leghorn line embryos ( Hy-Line International , Dallas Center , IA ) , and SPAFAS embryos ( Charles River ) . Chickens were euthanized at 10–12 weeks post hatching . Institutional Animal Care and Use Committee ( IACUC ) approval was obtained at the University of Delaware and the Fred Hutchinson Cancer Research Center . DNA from ALV infected cultured cells or tumor samples were isolated . The sequencing libraries were prepared as described previously [16] . Five micrograms of purified genomic DNA was sonicated with a Bioruptor UCD-200 . End repair , A-tailing , and adapter ligation were performed as described previously [21] ( adapter short arm , P-GATCGGAAGAGCAAAAAAAAAAAAAAAA , and adapter long arm , CAAGCAGAAGACGGCATACGAGATXXXXXXGTGACTGGAGTTCAGACGTGTGCTCTTCCGATC*T , where “X’s” denote the barcode sequence , “P” denotes phosphorylation , and “*” denotes a phosphorothioate bond ) . Nested PCR was performed to enrich the library for proviral junctions . The first PCR step had 23 cycles and employed an ALV-specific primer ( CGCGAGGAGCGTAAGAAATTTCAGG ) between the 3’ LTR and env and a primer ( CAAGCAGAAGACGGCATACGAGAT ) within the adapter that was attached by ligation in the previous step . In the second round of PCR , a primer ( AATGATACGGCGACCACCGAGATCTACACTCGACGACTACGAGCACATGCATGAAG ) near the 3’ end of the LTR was used . This primer ended 12 nucleotides short of the junction between viral and genomic DNA . This primer was paired with an adapter-specific primer on the opposite side of the fragment , which overlapped the adaptor’s bar code sequence ( CAAGCAGAAGACGGCATACGAGATXXXXXX ) . Libraries were quantified by quantitative PCR ( qPCR ) and then under- went single-end 100-bp multiplexed sequencing on the Illumina Hi-Seq 2000 . A custom sequencing primer ( ACGACTACGAGCACATGCATGAAGCAGAAGG ) was used , which hybridized near the end of the viral 3’ LTR , 5 nucleotides short of the proviral/genomic DNA junction . The resulting reads could be validated as genuine integrations by verifying that they began with the last 5 nucleotides of the proviral DNA , CTTCA . The last two nucleotides of the unintegrated proviral DNA , TT , are cleaved by ALV integrase upon integration , so the lack of these 2 nucleotides in the read acted as further validation of a true viral integration . Reads were first curated with a custom python script to remove sequences that did not begin with the last five nucleotides of viral DNA , “CTTCA” [16 , 17] . The files were then uploaded to Galaxy [64–66] , which was used to perform downstream analyses . In Galaxy , first the quality scores were converted to Sanger format with FastQ Groomer v1 . 0 . 4 [67] . CTTCA and adapter sequences were then trimmed using the Galaxy Clip tool v1 . 0 . 1 . This tool also removed reads containing an N and reads less than 20 nucleotides in length after adapter removal . The remaining reads were mapped with bowtie [66] using the Gallus gallus 4 . 0 genome ( Nov . 2011 ) . Sequences were aligned using a seed length of 28 nucleotides , with a maximum of 2 mismatches permitted in the seed . All alignments for a read were suppressed if more than one reportable alignment existed . This was done to prevent multiple mapping and ensure that reads correspond to only unique integration sites . 100 , 000 random mapped reads were selected from each sample to be used for further analysis . If less than 100 , 000 reads were present for a sample , all available reads were used . A custom Perl pipeline developed in the lab was used to analyze the aligned reads output from bowtie [16 , 17] . This custom pipeline identified unique integration locations , and calculated the number of reads and sonication breakpoints for each integration site . It also identified hotspots of integration and common integration sites among multiple samples . Integrations from two unrelated barcodes on the same sequencing lanes were omitted via our pipeline . The pipeline source code is available upon request . The integration sites identified in our work are deposited at the NCI Retrovirus Integration Database ( RID ) ( https://rid . ncifcrf . gov/ ) [68] . Reads for the junctions of proviral integration and genomic DNA were mapped with Bowtie [69] . Only reads that mapped uniquely to the genome were utilized for further analysis . This step filtered out reads that originate from repetitive elements . Mapped reads from all samples were then combined into a single file and analyzed with HOMER [23] . HOMER calculates the enriched features at each integration locus as well the proximity to closest transcription start site . A random integration control data set was generated with Bedtools Random [70] . The genomic DNA sequences corresponding to the genomic coordinates obtained from Bedtools Random were extracted from the Gallus gallus 4 genome using the Galaxy tool Extract Genomic DNA . Control sequences were mapped with Bowtie and analyzed with HOMER using the same parameters as for ALV integrations . Proximity to CpG islands was determined using the WindowBed tool in Galaxy [66] . We note that our calculations are subject to certain biases . This includes , but is not limited to , an underestimate of the chicken or human genome sizes due to unsequenced gaps or overlapping sequences . Furthermore , an aberrant karyotype , which might exist in the transformed HeLa [71] or the DT-40 cells [72] , was not taken into account for our analysis . However , as previously determined by Narezkina et al . ( 2004 ) , despite the aberrant karyotype in HeLa cells , the ratio between the genome size and the gene number in HeLa cells is equivalent to that of the normal human genome [4] . The ensembl Gallus gallus 4 genome was utilized to obtain reference information for the transcript count and number of introns for all transcriptional units in the chicken genome . If an integration occurs within a gene , then the corresponding gene is used for all subsequent analysis . If an integration occurs in an intergenic region , then the nearest gene is used for all subsequent analysis . RNA-seq data for analysis of CEFs was downloaded from the public Sequence Read Archive ( SRA ) database ( SRA accession no . SRP107761 ) [73] . A custom Python script was utilized to associate the expression , transcript count and number of introns of a gene with the number of ALV integrations proximal to or within the given gene . A matched random control set , generated as mentioned above , was used as a control . The Python source code is available upon request . PVL was measured by quantitative polymerase chain reaction ( qPCR ) of ALV-LR9 for env ( primers CCTGAAACCCAGTGCATAAGG and CTAGCTGTGCAGTTCACCGT ) , gag ( primers GTTTAGAGAGGTTGCCCGAC and GTCAATGATCACCGGAGCCC ) and LTR ( CGAACCACTGAATTCCGCAT and GAATCAACGGTCCGGCCATC ) ; and HMG14b ( primers ACTGAAGAGACAAACCAAGAGC and CCAGCTGTTTTAGACCAAAGAATAC ) using Q SYBR green Supermix ( Bio-Rad ) according to the manufacturer’s protocol on a Bio-Rad C1000 thermal cycler/CFX96 Real-Time System . We assumed a single copy of env and gag and 2 copies each of HMG14b and the LTR per cell . HMG14b is a known single copy gene in the chicken genome and thus , was used as a housekeeping reference gene [74] . Thermal cycling conditions were 95°C for 20 seconds and 40 cycles each of 95°C for 1 second followed by 53°C for 30 seconds . Quantitative PCR ( qPCR ) was performed in duplicate , with each sample present in technical duplicate during each run . The results were normalized to those for normal bursa using the comparative threshold cycle ( CT ) method . Statistical analysis for clonality index was carried out using R version 2 . 15 . 2 ( http://www . R-project . org/ ) . The oligoclonality index ( OCI; Gini coefficient ) was calculated using the R package sonicLength ( http://soniclength . r-forge . r-project . org/ ) as described previously [21 , 22] . Functional profiling of genes and ontology analysis for the clonally expanded integrations was conducted with g:profiler , using an ordered query option ( http://biit . cs . ut . ee/gprofiler/ ) [30] .
The Avian Leukosis Virus ( ALV ) is a simple retrovirus that causes cancer in chickens . The virus integrates its genome into the host genome and induces changes in expression of nearby genes . Here , we determine the sites of viral integrations and their role in the progression of tumors . We report pathways and novel gene players that might cooperate and play a role in the progression of B-cell lymphomas . Our study provides new insights into the changes during lymphoma initiation , progression , and metastasis , as a result of selection for specific ALV integration sites .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]
[ "blood", "cells", "medicine", "and", "health", "sciences", "immune", "cells", "biological", "cultures", "immunology", "cancers", "and", "neoplasms", "vertebrates", "animals", "dna", "transcription", "oncology", "epigenetics", "dna", "dna", "methylation", "chromatin", "research", "and", "analysis", "methods", "white", "blood", "cells", "birds", "chromosome", "biology", "animal", "cells", "gene", "expression", "gamefowl", "chromatin", "modification", "tissue", "cultures", "dna", "modification", "fowl", "poultry", "carcinogenesis", "biochemistry", "antibody-producing", "cells", "eukaryota", "cell", "biology", "nucleic", "acids", "b", "cells", "genetics", "biology", "and", "life", "sciences", "cellular", "types", "chickens", "amniotes", "organisms", "neoplasms" ]
2017
Selection for avian leukosis virus integration sites determines the clonal progression of B-cell lymphomas
Antifilarial antibody testing has been established as a sensitive and specific method of diagnosing lymphatic filariasis . However , the development of serological responses to specific filarial antigens and their relationship to acquisition of infection is poorly understood . In order to evaluate whether the development of antigen specific antifilarial antibodies precedes microfilaremia and antigenemia , we compared the antibody responses of serum samples collected between 1990 and 1999 from a cohort of 142 Haitian children followed longitudinally . Antigen status was determined using the Og4C3 ELISA and the presence of microfilaremia was detected using microscopy . Antibody responses to Wb123 , a Wuchereria bancrofti L3 antigen , were measured using a Luciferase Immunoprecipitation System ( LIPS ) assay . Antibody responses to Bm14 and Bm33 , Brugia malayi antigens and to a major surface protein ( WSP ) from Wolbachia were analyzed using a multiplex bead assay . Over follow-up , 80 ( 56% ) of the children became antigen-positive and 30 ( 21% ) developed microfilaremia . Detectable antibody responses to Bm14 , Bm33 , Wb123 , and WSP developed in 95% , 100% , 92% , and 29% of children , respectively . With the exception of WSP , the development of antibody responses generally preceded detection of filarial antigen . Our results show that antifilarial antibody responses can serve as an important epidemiological indicator in a sentinel population of young children and thus , may be valuable as tool for surveillance in the context of lymphatic filariasis elimination programs . Lymphatic filariasis ( LF ) is a significant cause of global morbidity and is responsible for causing lymphedema , elephantiasis , and hydrocele . Research focusing on the pathogenesis of LF has historically neglected children , both because the onset of clinical disease tends to occur in adults and due to the logistical and ethical issues involved with including children in studies; however , surveys in areas of intense transmission demonstrate that children acquire infections early in life [1] , [2] . In addition , recent studies have demonstrated that lymphangiectasia generally starts in early childhood and have documented the presence of significant subclinical pathology in children [3] , [4] , dispelling the belief that disease manifestations are restricted to adulthood . More important from the public health perspective , there is now evidence that early disease in children is reversible following treatment [5] . These observations reinforce the argument for using community-based treatment strategies for the control and elimination of LF as such efforts will prevent the development of morbidity in children residing in LF-endemic areas as well as in future generations [6] . The World Health Organization estimates that there are 120 million people living in 72 countries that are infected with the filarial parasite which causes LF and 1 . 34 billion people worldwide who live in filariasis-endemic areas and are at risk of developing the infection [7] , [8] . Mass drug administration ( MDA ) programs have now been developed in more than 50 countries and more than ten countries have stopped MDA in all or part of the country after carrying out 5 or more rounds of annual MDA [8] . These successes in the efforts to eliminate LF have highlighted the need for more sensitive , standardized tools to help programs define MDA endpoints and to conduct surveillance [9] , [10] . Currently , WHO guidelines are based on the monitoring of antigenemia in children; however , since antibody responses generally develop before patent infection , their detection in a serum-based assay could be used to provide an early measure of filarial exposure and ongoing transmission [10] , [11] . Monitoring the natural history of LF is important in defining the relationship between the development of antibody responses to specific filarial antigens and the acquisition of infection . Longitudinal studies of the development of antifilarial antibody responses in a population of children provide an opportunity to compare the performance of different diagnostic tools relative to the first detection of microfilaremia and antigenemia . Such studies can help inform our choices of tools best suited for monitoring transmission and conducting post-MDA surveillance . In this study , we monitored the development of antifilarial immunity in a cohort of Haitian children living in a highly endemic area before the onset of MDA campaigns . The study population and design have been previously described [1] . In brief , children were followed longitudinally to investigate risk factors for filarial infection . The children were residents of Leogane , Haiti , a coastal community with a population of approximately 10 , 000–15 , 000 people that is known to be highly endemic for lymphatic filariasis [12] , [13] . Although a small number of persons were treated as part of drug studies [14] , no MDA programs were conducted in the community over the period of follow-up ( 1990–1999 ) nor was diethylcarbamazine available except through Ste . Croix Hospital . Neighborhoods that were known to have a prevalence of microfilaremia greater than 20% were targeted for the longitudinal study . The protocol for this study was reviewed and approved by the Centers for Disease Control and Prevention Institutional Review Board and the Ethical Committee of Ste . Croix Hospital ( Leogane , Haiti ) . Mothers of children less than 24 months of age were approached for participation in the study on a rolling basis . After explaining the purpose of the study in Creole , the mothers were asked to provide consent for themselves and for their children to be enrolled in the study . Consent was documented verbally at enrollment and during follow up for all study participants; children seven years of age or older provided assent . Consent and assent were documented by project staff on enrollment forms . Verbal consent was approved by the IRB and Ethical Committee because of the low rate of literacy in the communities that were being monitored . Children who were enrolled were assessed annually over the period of follow-up ( 1990–1999 ) for microfilaremia , antigenemia , and intestinal parasite burden . Serum samples from each study visit were stored for lab analysis . The frequency of follow-up was influenced by political events which limited field work . Those children with 5 or more serum samples at the conclusion of the study were included in the current cohort and their serum samples were selected for antigen and antifilarial antibody testing . Samples with incomplete information and duplicate samples were excluded from the analyses . Parasitologic examinations were performed as previously described [1] . Briefly , a nocturnal blood exam ( Giemsa-stained 20 ul-thick film ) was performed on the children and their mothers in order to determine the baseline microfilaremia infection status [15] . Follow-up examinations for microfilaremia occurred every 9–12 months , and stool examinations were performed at regular intervals to monitor intestinal parasite burdens in the children . Stools were preserved in 10% formalin and were examined for ova and parasite following concentration by the formalin/ethyl acetate technique . When infections were detected , microfilaremic persons were treated with a single dose of diethylcarbamazine ( DEC; 6 mg/kg ) and children with Ascaris , Trichuris , or hookworm infections were provided treatment with mebendazole ( 100 mg×3 days ) . Serum samples ( 100 ul ) collected during follow-up were used for antigen and antifilarial antibody assays . All blood specimens were collected by finger prick; venipuncture was not acceptable to the mothers of the children . Filarial antigen status was determined by the commercial Og4C3 ELISA kit ( TropBio , Townsville , Australia ) . Serum samples were diluted 1∶10 in sample buffer and then assayed in duplicate according to the manufacturer's instructions as previously described [1] . Samples with antigen levels ≥128 units were considered to be positive . Antibody responses to Wb123 , a Wuchereria bancrofti L3-specific antigen selected based upon its lack of cross reactivity with other filarial species , were measured using the highly sensitive LIPS ( Luciferase Immunoprecipitation System ) assay [16] , [17] . A detailed description of the antigen is provided in the companion paper [17] . Serum samples were run in duplicate against a standard curve in order to control for plate to plate variability . Positive values were interpreted from the data based on a cutoff value , 10968 LU/ml , which was based on the responses of sera from 50 nonendemic persons using receiver operating characteristic ( ROC ) analysis . Bm33 , also known as Bm-AP-1 , was included in the study based on previous reports that it was frequently recognized by sera from persons in LF-endemic areas [18]–[20] . The cloning and purification of recombinant Bm33 protein containing both an amino-terminal GST fusion and a carboxy-terminal 6× His tag have been previously reported [20] . Similarly , the cloning and purification of a recombinant major surface protein ( WSP ) from Wolbachia have been previously reported [21] . WSP was expressed as a 6× His-tagged dihydrofolate reductase ( DHFR ) fusion protein and was cleaved from the fusion partner using thrombin [21] . Bm14 , also known as SXP-1 has been used extensively both as a diagnostic antigen and for monitoring LF programs [9]–[11] , [22] , [23] . For this work , the Brugia malayi Bm14 antigen coding sequence [22] was PCR amplified from an adult female cDNA library in Lambda Uni-Zap XR ( National Institutes of Health/National Institute of Allergy and Infectious Diseases Filariasis Research Reagent Repository Center , Molecular Resources Division , Smith College , Northampton , MA ) and was cloned into the BamHI and EcoRI restriction endonuclease sites of pGEX 4T-2 expression vector ( GE Healthcare , Piscataway , NJ ) using previously described techniques [19] . The deoxyoligonucleotides used for PCR amplification were: 5′-CGC GGA TCC CAA AGA GAA GCA CAA TTA CCT CAG-3′ and 5′-GCG GAA TTC TTA TTG TGA ATT AAA TCC TTC CAA GAT-3′ . Recombinant Bm14/GST protein was purified on a GST affinity column as directed by the manufacturer ( GE Healthcare ) . Purity of the Bm14/GST recombinant protein was estimated to be >99% by polyacrylamide gel electrophoresis with Coomassie Blue staining . Purified Bm14 , Bm33 , and WSP recombinant proteins ( 120 µg of protein for 12 . 5×106 beads ) were coupled at pH 7 . 2 to SeroMap beads ( Luminex Corp . , Austin , TX ) as previously described [20] . Antibody responses to Bm14 and Bm33 , and to WSP from Wolbachia were analyzed using a multiplex bead assay that incorporated 28 antigens , including malaria and vaccine antigens as well as antigens from a number of waterborne pathogens [20] , [24] . Inclusion of nonfilarial antigens provided additional controls for sample integrity; i . e , responses to certain antigens ( e . g . , enterotoxigenic E . coli heat labile toxin Beta subunit and SAG2 of Toxoplasma ) when positive , were consistent across samples from a given child . Thus , the absence of expected responses was considered evidence that samples were degraded or had been misnumbered . Data from these samples ( 13 from a total of 785 serum samples ) were deleted . Responses to nonfilarial antigens will be reported elsewhere . All serum samples for the multiplex assay were diluted in PBS containing 0 . 05% BSA , 0 . 05% Tween 20 , 0 . 02% sodium azide , 0 . 5% polyvinyl alcohol ( PVA ) , 0 . 8% polyvinylpyrrolidone ( PVP ) to reduce the background reactivity [25] . Crude E . coli extract was added to the dilution at a final concentration of 3 µg/ml to decrease potential nonspecific binding of antibodies to residual E . coli proteins in purified recombinant proteins [20] . Samples were run in duplicate at a final serum dilution of 1∶400; antigen-coated beads were incubated with the samples for 90 minutes . Data are reported as the average of the median fluorescence intensity from duplicate wells minus the background from a serum blank run in parallel on each plate ( MFI-bg ) . Thresholds for positive responses were defined based on the mean plus three standard deviations of the antifilarial antibody response of serum samples from nonendemic persons . Using the Kaplan-Meier ( KM ) method , failure rate probabilities were computed for age at time of initial response for each antibody . Cox proportional hazards model was used to investigate factors that influenced the age at which the initial antibody response occurred . Gender , infection status ( both mother's and child's ) , neighborhood in which child resides , and study year were considered . Children with no response by the time of their last follow-up visit were considered censored in both the KM survival analysis and proportional hazards model . Poisson regression was used to estimate seroconversion rates and their related confidence intervals . The nonparametric Kruskal Wallis test was utilized to test for differences in age at time of first sample and number of years of follow-up between the three neighborhoods . Longitudinal studies were set up in Leogane neighborhoods to monitor antifilarial immune responses associated with exposure to LF and development of patent filarial infection . The median period of follow-up for the children in this study was 4 . 7 years , with the first sample collected at a median age of 1 . 4 years . Ninety-six ( 67 . 6% ) of the one hundred and forty-two children were from the Bino neighborhood of Leogane , and boys ( 57 . 7% ) outnumbered girls ( 42 . 3% ) in the study population ( Table 1 ) . Median age at time of first sample was 0 . 9 years in Cada , 1 . 4 years in Bino , and 2 . 2 years in “other” locales . The difference was significant at p = 0 . 006 . There was no difference , however , in the median number of years of follow up between children from different areas ( p = 0 . 83 ) . Children were monitored periodically by nocturnal blood exam for microfilaremia and by microscopic examination of stool samples for intestinal parasites . Early infection and re-infection with intestinal helminths was a common occurrence throughout the study . The prevalence of Trichuris , Ascaris , hookworm and Strongyloides infection in children under the age of 5 is shown in Figure 1 . By the age of 3 , more than 59% and 30% of children were infected with Trichuris and Ascaris , respectively . Over the period of follow-up , hookworm prevalence increased dramatically in the community [26] . Nocturnal blood smears were prepared for microfilaremia assessment during each sampling period . The cumulative prevalence of microfilaremia in the children was 23 . 2% by the end of the study ( Table 2 ) ; this prevalence should be considered a minimum estimate because of the small volume of blood ( 20 µl ) examined . Serum samples were assessed for circulating filarial antigen using the Og4C3 ELISA to determine when children first became antigen positive; cumulative antigen prevalence was 56 . 3% . All microfilaria-positive children were also antigen-positive . The mean age at which children acquired W . bancrofti infection as assessed by microfilaremia and antigenemia was 6 . 3 and 4 . 3 years , respectively ( Table 2 ) . Antibody responses to Bm14 , Bm33 , and WSP antigens were measured using a multiplex assay platform . A novel antigen , Wb123 , was measured using LIPS technology . Positive antibody responses for both techniques were defined based on cutoff values determined from nonendemic control samples . Representative plots from two children are shown in Fig . 2 . Increases in antifilarial antibody to Wb123 , Bm33 and Bm14 were noted in conjunction with ( e . g . , panel A ) or prior to the detection of filarial antigen ( panel B ) . Antibody to WSP was not detected in most children . Children were treated with DEC when microfilariae were detected and as previously reported [20] , treatment often led to declines in levels of antibody against all the filarial antigens as seen in Figure 2 , panel B . Age prevalence curves showing the profiles of circulating filarial antigen , microfilaria , and the antibody responses to Bm14 , Bm33 , Wb123 , and WSP filarial antigens with age are shown in Figure 3 . Responses to Bm14 , Bm33 , and Wb123 increased markedly between one and three years of age . Bm33 was the first antibody response to be detected in children with a mean age of incidence occurring at 2 . 8 years , followed by Bm14 ( 3 . 4 years ) , Wb123 ( 3 . 7 years ) , and WSP ( 4 . 3 years ) ( Table 2 ) . The longitudinal nature of the study provided an opportunity to analyze serocoversion rates . Over the course of the study , 55 . 6 , 47 . 8 , and 61 . 9% of children seroconverted to Bm14 , Bm33 , and Wb123 , respectively . Only 23 . 9% of children developed responses to WSP and these responses were often transient in nature and unrelated to changes in antibody to the other filarial antigens . The rate of seroconversion was measured using total person years and was highest for Bm33 , with 49 . 9 seroconversions per 100 person-years , followed by Bm14 , Wb123 , and WSP with seroconversion rates of 34 . 7 , 31 . 0 , and 5 . 5 per 100 person-years , respectively ( Table 3 ) . The correlation of children's infection status and antibody status at the end of the study is represented in Table 4 . All of the children who were microfilaria-positive at the end of the study had antibody responses to Bm14 and Bm33 antigens , and 96 . 7% of these children were found to have a Wb123 response . Of the 80 children determined to be antigen-positive , 100 , 100 , and 97 . 5% had responses to Bm14 , Bm33 , and Wb123 , respectively . Of children who were considered uninfected by antigen tests ( Og4C3-negative ) and microscopy ( microfilaria-negative ) , 88 . 7 , 100 , and 83 . 9% had filarial antibody responses to Bm14 , Bm33 , and Wb123 , respectively . The antibody prevalence was not significantly different by infection status for any of the filarial antigens . Quantitative analyses of the antibody responses of antigen-positive and antigen-negative children are shown in in Figures 4 and 5 , respectively . Antigen-positive children had levels of anti-Bm33 and anti-Bm14 antibody that were at or near the maximum level of the assay at the serum dilution tested across all ages; however , Wb123 responses were lower than the peak responses . Among antigen-negative children , anti-Bm33 responses increased with age , reaching maximal values by age 5 . Similar increases in antibody levels with age were noted for Bm14 and Wb123 , but antibody levels did not reach assay maximums for either antigen . A Cox proportional hazards model was generated to analyze factors influencing antibody responsiveness , including gender , child's infection status , maternal infection status , community of residence , and study year . The analysis for Bm14 is shown in Table 5 . Bm14 responses were influenced by gender with females responding at an earlier age than males , but not by antigen status , maternal infection status , or community of residence . Children who were sampled during the early study period of 1990–1995 were significantly more likely to develop a Bm14 antibody response than children sampled during the later study period ( 1996–1999 ) ( p = 0 . 0036 ) . A similar result was seen for Wb123 and WSP , but not Bm33 ( p = 0 . 37 ) . Use of trade names is for identification only and does not imply endorsement by the Public Health Service or by the U . S . Department of Health and Human Services . The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention .
Programs to eliminate lymphatic filariasis ( LF ) are designed to interrupt transmission of the parasite by treating the human reservoir of infection . As infection levels decline , assessing infection and transmission levels becomes more and more challenging . In principle , measuring the level of antibody to filarial antigens in children may provide a sensitive measure of transmission intensity . Here , we used samples collected over time from 142 Haitian children living in an area of intense transmission of LF to determine when they first developed antibody responses to defined filarial antigens compared to when they became infected . Antibody responses were measured to several filarial antigens using sensitive assays based on multiplex and LIPS assay methods . Our results show that antibody responses developed before infection could be detected by conventional tests for the presence of microfilariae or antigen in the blood . These results support the idea that antibody tests can be used to monitor the impact of mass drug administration programs on transmission of LF and to carry out surveillance for LF after drug treatments have stopped .
[ "Abstract", "Introduction", "Materials", "and", "Methods", "Results", "Discussion" ]
[ "biology", "microbiology", "parasitology" ]
2012
Longitudinal Monitoring of the Development of Antifilarial Antibodies and Acquisition of Wuchereria bancrofti in a Highly Endemic Area of Haiti
In light of multinational efforts to reduce helminthiasis , we evaluated whether there exist high-risk subpopulations for helminth infection . Such individuals are not only at risk of morbidity , but may be important parasite reservoirs and appropriate targets for disease control interventions . We followed two longitudinal cohorts in Sichuan , China to determine whether there exist persistent human reservoirs for the water-borne helminth , Schistosoma japonicum , in areas where treatment is ongoing . Participants were tested for S . japonicum infection at enrollment and two follow-up points . All infections were promptly treated with praziquantel . We estimated the ratio of the observed to expected proportion of the population with two consecutive infections at follow-up . The expected proportion was estimated using a prevalence-based model and , as highly exposed individuals may be most likely to be repeatedly infected , a second model that accounted for exposure using a data adaptive , machine learning algorithm . Using the prevalence-based model , there were 1 . 5 and 5 . 8 times more individuals with two consecutive infections than expected in cohorts 1 and 2 , respectively ( p<0 . 001 in both cohorts ) . When we accounted for exposure , the ratio was 1 . 3 ( p = 0 . 013 ) and 2 . 1 ( p<0 . 001 ) in cohorts 1 and 2 , respectively . We found clustering of infections within a limited number of hosts that was not fully explained by host exposure . This suggests some hosts may be particularly susceptible to S . japonicum infection , or that uncured infections persist despite treatment . We propose an explanatory model that suggests that as cercarial exposure declines , so too does the size of the vulnerable subpopulation . In low-prevalence settings , interventions targeting individuals with a history of S . japonicum infection may efficiently advance disease control efforts . Recent multinational efforts to control and eliminate helminthiasis have the potential to dramatically reduce morbidity among the rural poor [1] , [2] . Approximately one billion people are infected with one or more helminthes and the health impacts of these infections , including impaired growth , cognitive development and work capacity are substantial and poverty reinforcing [3]–[5] . Population-level interventions are the recommended strategy in areas where infection prevalence and morbidity are high [6] , but as infections decline , how should limited disease control resources be allocated in order to sustain disease control achievements ? We are interested in whether there exist high-risk subpopulations for helminth infection , as such individuals may not only be particularly vulnerable to morbidity , they may also play a key role in sustaining transmission in regions where control efforts have reduced but not eliminated helminthiasis [7] . In many infectious disease transmission systems , a few individuals are responsible for a disproportionate number of future infections: control efforts targeting such superspreaders can efficiently reduce disease transmission compared to randomly allocated or population-based control efforts [8] , [9] . In the case of helminthiasis , helminthes typically are aggregated in a population such that at any point in time , a few individuals harbor a large number of worms and therefore may be responsible for a large number of future infections [9] . If the same individuals are repeatedly infected , this suggests the presence of high-risk groups for helminthiasis – groups that may serve as persistent parasite reservoirs in the presence of on-going treatment and control efforts . Prior research suggests such high-risks groups may exist: for example , past infection with the water-borne helminth , Schistosoma sp . is a positive predictor of subsequent infection [10]–[13] . What mechanisms might promote the aggregation of infections in a few individuals ? The cross-sectional clustering of helminthes in a population has largely been attributed to differential pathogen exposure – highly exposed individuals are most likely to harbor greater pathogen loads [9] , [14] . If we assume an individual's exposure is relatively constant over time , we expect the same , highly exposed individuals will be repeatedly infected over time . Host susceptibility to infection may also favor repeated infections in a particular subpopulation . Host genetics play a role in susceptibility to soil-transmitted helminthiases and schistosomiasis , likely via variations in genes regulating immune function , including , in the case of Schistosoma sp . , Th2 response [15]–[18] . In contrast to exposure and host-susceptibility , exposure-dependent immunity should protect highly infected individuals at a given time point from subsequent infection , resulting in a disaggregation of infections across the population over time . Age-dependent immunity should concentrate infections in vulnerable age groups , leading to time-limited membership in high-infection subpopulations . We examined longitudinal patterns of infection with the water-borne helminth , S . japonicum , in two cohorts in order to assess the aggregation of infections in the same individuals over time and , if present , the extent to which aggregation can be attributed to exposure vs . host-susceptibility . In fall 2000 , we conducted S . japonicum exposure and infection surveys in 20 villages in Xichang County [20] . All residents were invited to participate in S . japonicum infection surveys ( individuals age 4–60 were targeted , but infection testing was open to people of any age ) . A 25% random sample of residents , stratified by village and occupation , was interviewed about water contact behaviors at the same time as the infection surveys . Individuals were asked to report the frequency and duration of contact with surface water sources while conducting the following activities: washing clothes or vegetables , washing agricultural tools , washing hands and feet , playing or swimming , irrigation ditch operation or maintenance , rice planting , rice harvesting , and fishing; for each month from April to October ( Supporting Information S1 in Text S1 ) . Infection surveys were repeated in 2002 and 2006 in ten villages with high infection prevalence in 2000 ( range 12 . 9 to 72 . 3% ) . This cohort includes all individuals from the 10 follow-up villages who completed the water contact interview and were tested for infection all three years . Infection status and intensity at enrollment did not differ between cohort members that were lost to follow-up and those with complete data , but those who were lost to follow-up reported less water contact and were younger , on average . Details of cohort selection and retention are provided in Figure S1 and Table S1 in Text S1 . In fall 2007 , a cross sectional survey was conducted in 53 villages in three counties where S . japonicum reemerged following attainment of national transmission control criteria [21] . All residents age 6 to 65 were invited to participate in S . japonicum infection surveys . In May 2008 , a magnitude 7 . 9 earthquake in Sichuan severely impacted one of the three selected counties , forcing us to limit follow-up studies to the two other counties . For efficiency , water contact behaviors were assessed using a stratified random sample of individuals based on 2007 infection status . All individuals who tested positive for S . japonicum in 2007 , and , for each infected person , five people randomly drawn from the same village who tested negative for S . japonicum in 2007 , were selected for participation in a survey of water contact behaviors . Interviews about water contact patterns were conducted monthly , from June to October 2008 . At each interview , participants were asked to report the frequency and duration of water contact activities in the past two weeks including washing laundry , washing vegetables , washing agricultural tools , washing hands or feet , playing or swimming , ditch cleaning and repair , rice planting , rice harvesting , fishing , and collecting water for drinking or cooking . During the first interview , participants were also asked to report water contact behaviors during the May rice planting season , as , due to earthquake relief efforts , no interviews were conducted in May . Nobody reported water contact while collecting water for drinking and cooking , and this behavior was excluded from analyses . For comparability with cohort 1 , washing laundry and washing vegetables were combined into a single water contact measure . Participants were tested for S . japonicum infection again in 2008 and 2010 . This cohort includes all individuals who were tested for infection all three years and completed the water contact interview . As was the case for cohort 1 , baseline infection status and intensity did not differ between cohort 2 members that were lost to follow-up and those with complete data , but those who were lost to follow-up reported less water contact , were more likely to be male and were younger , on average . Details of cohort selection and retention are provided in Figure S2 and Table S1 in Text S1 . As some members of cohort 2 did not complete all monthly water contact interviews , missing water contact measures were imputed using multiple imputation by chained equations [22] , [23] . Multiple imputation avoids bias presented by the exclusion of incomplete cases . Imputation is based on the assumption that data are missing at random , and that missing data can be explained by other measured variables [24] . We imputed water contact minutes by month and activity using all other water contact measures , as well as age , sex , and village of residence . During the monthly interviews , participants were also asked to report the number of days they spent outside of their village and distance traveled in the past month . As travel may influence water contact patterns , travel was also included in the set of existing data used to impute missing values . The duration of water contact was imputed using predictive mean matching . Because nobody reported water contact from fishing in October or rice harvesting in June , all individuals missing these variables were assumed to have zero water contact for this exposure . Participants with one or more missing values were more likely to be younger and live in county 1 , but did not otherwise differ substantially from participants with complete data ( Table S2 in Text S1 ) . Ten imputed datasets were generated . We calculated the mean of each imputed value for use in the predictive models described below . Before imputation , 5 . 0% of the water contact measures were missing: 71 participants ( 18% ) did not complete all monthly interviews and 13 participants ( 3% ) were interviewed each month but did not answer all survey questions . All questionnaires in both cohorts were administered in the local dialect by trained staff at the Institute of Parasitic Diseases ( IPD ) , Sichuan Center for Disease Control and Prevention and the county Anti-schistosomiasis Control stations . During each infection survey , participants were asked to submit three stool samples , one each from three consecutive days . Each sample was analyzed using the miracidia hatching test: approximately 30 grams of stool was filtered , suspended in aqueous solution and examined for miracidia according to Chinese Ministry of Health protocols [25] . In addition , one sample from each participant was analyzed using the Kato-Katz thick smear procedure: three slides were prepared using 41 . 7 mg homogenized stool per slide and examined for S . japonicum eggs by trained technicians [26] . Infection intensity , in eggs per gram of stool ( EPG ) , was calculated as the total number of S . japonicum eggs divided by the total sample weight . In 2002 , only one stool sample was collected per person in cohort 1 , and this sample was analyzed using both the miracidia hatching test and the Kato-Katz thick smear procedure . After each infection survey , all individuals testing positive for S . japonicum were promptly notified and provided treatment with 40 mg/kg praziquantel by health workers at the county anti-schistosomiasis control stations . The research protocols and informed consent procedures and were approved by the Sichuan Institutional Review Board and the University of California , Berkeley , Committee for the Protection of Human Subjects . In cohort 1 , all participants provided oral informed consent , documented by IPD staff , before participating in this study . Oral consent was obtained due to the high prevalence of illiteracy , and because the survey procedures used were similar to those used by IPD for schistosomiasis surveillance . In cohort 2 , all participants provided written , informed consent before participating in this study . Minors provided assent and their parents or guardians provided written , informed permission for them to participate in this study . We examined the extent to which S . japonicum infections repeatedly occur in the same individuals in regions where schistosomiasis case detection and treatment is ongoing . For each cohort we defined three time points: baseline ( T0 ) , the first follow-up infection survey ( T1 ) and the second follow-up infection survey ( T2 ) . We estimated the ratio of the observed proportion of the population with of two consecutive infections at T1 and T2 ( ODI ) , to the predicted proportion of the population with two consecutive infections at T1 and T2 ( PDI ) . The simplest model of PDI is based solely on the probability of infection at T1 and T2 , such that where indicates S . japonicum infection status at time point x . Because all infections were treated at each time point , the probability of infection at Tx is the incidence of infection from T ( x-1 ) to Tx multiplied by the elapsed time between T ( x-1 ) and Tx , which is equal to the prevalence of infection at Tx . Note that at T0 , we know the prevalence , but not the time elapsed since last treatment , which may vary by individual , and therefore can only estimate the probability of infection at T1 and T2 . Our estimates of infection probability assume all infections , defined as the presence of adult S . japonicum worm pairs , are detected and successfully treated at each time point . Using this prediction model , if , this suggests that there exists a subset of individuals that are repeatedly infected with S . japonicum . A more complex model of PDI accounts for exposure , as individuals who are repeatedly infected may be those who are most highly exposed to S . japonicum cercariae . In this case , we estimate where is S . japonicum cercarial exposure . Using this exposure-based prediction model , if , this suggests that S . japonicum infections repeatedly occur in a subset of individuals in the population for reasons not attributable to the exposure variables in the statistical model . S . japonicum cercarial exposure is determined by human behaviors that put people in contact with potentially contaminated water sources ( primarily irrigation ditches and ponds ) , and by cercarial concentrations at the site of contact . We accounted for human behavior using questionnaire derived estimates of month- and activity-specific water contact duration . Cercarial concentration can vary over space and time due to the non-uniform distribution of the intermediate snail host and because cercarial shedding is affected by temperature , diurnal patterns and reservoir host species [27]–[29] . Currently , practical , field deployable methods for measuring cercarial concentrations are lacking . A mouse bioassay exists , in which sentinel mice are dermally exposed to surface water , then sacrificed and examined for S . japonicum worms , approximately 45 days post-exposure ( allowing time for the parasite to mature inside the host ) . The mouse bioassay is not only resource intensive but , in low-prevalence settings , has limited sensitivity and , while new molecular methods offer promise , they have yet to be widely deployed [30] , [31] . We used several proxies for cercarial concentration in our infection prediction models . We included village infection prevalence at T0 , based on the assumption that villages with more infected individuals at enrollment have the potential for greater cercarial concentrations . In cohort 2 , we also included county in the infection prediction model , as control measures which may impact cercarial concentration such as application of moluscicides are administered at the county level ( all participants in cohort 1 are from a single county ) . To account for temporal variation in cercarial concentration , we included the year of infection testing . Additionally , we included age and sex to account for potential differences in the location of water contact ( concentration ) and the reporting of water contact activities ( behavior ) by age and sex . The first step in estimating PDI requires a model that predicts infection status at a given time point based on exposure: . However , given the large number of predictor variables and the potentially complex , nonlinear relationships between exposure and infection , any single arbitrary parametric model one might choose will lead to an unknown degree of bias in the estimate of and , ultimately , PDI [32] . To minimize this problem we used a machine-learning algorithm , known as the Super learner as implemented in R [33] . In essence , this procedure estimates based on a convex combination of a number of different modeling algorithms ( some simple parametric models , some highly data adaptive , generically called learners ) . In this case , the learners include random forests [34] , k-nearest neighbor classification [35] , elastic net regression [35] , generalized linear models , stepwise regression and generalized boosted regression [36] . Cross-validation is used to determine the optimal combination of learners , that is the combination that maximizes the cross-validated fit . It has been shown that the Super learner estimate is asymptotically equivalent to the estimator that would come closest to the truth if the truth were known ( called the Oracle selector ) , even if a very large number of competing models were used . In addition , in the unlikely case that the true model is a simple parametric model , then Super learner achieves nearly the same performance as a simple parametric estimation procedure ( a parametric Oracle ) . From a practical point of view , Super learner replaces the usual ad hoc exploration of the adequacy and fit of various candidate models with a machine-based procedure that produces a robust , replicable , and theoretically defensible estimate . We excluded from the set of exposure variables water contact variables for which <20% of the population reported any water contact . Models were fit separately for each cohort . An individual's infection probability was calculated for each year ( T1 and T2 ) using the selected model , and the probability of two consecutive infections was calculated as the product of the infection probabilities at T1 and T2 . All estimates of observed and predicted infections were weighted to account for the stratified sampling used to assess water contact behavior . Each individual in the cohort was assigned a weight equal to the inverse probability of being sampled . Inference was estimated by calculating the probability of the observed number of consecutively infected individuals in the reweighted population ( ) . We assumed follows a binomial distribution where is equal to the number of individuals in the reweighted population and is the probability of two consecutive S . japonicum infections in an individual . Statistical analyses were conducted using Stata12 . 0 and R 2 . 14 . 1 software . The demographic characteristics of the two cohorts , reported water contact behaviors and the distribution of S . japonicum infections at enrollment are presented in Table 1 . In the 10 villages from which cohort 1 was drawn , mean S . japonicum infection prevalence among all 1 , 801 residents surveyed was 46 . 9% ( 12 . 9 to 72 . 3% by village ) and intensity , 46 . 0 EPG ( 1 . 1 to 107 . 9 EPG by village ) at enrollment ( T0 ) . In the 27 villages from which cohort 2 was drawn , mean infection prevalence among all 1 , 608 individuals surveyed was 10 . 6% ( 1 . 5 to 42 . 9% by village ) and intensity 2 . 6 EPG ( 0 to 10 . 6 EPG by village ) . Note that in 3 villages , infections were detected by the miracidia hatching test only , no eggs were detected by the Kato-Katz method , resulting in mean village infection intensities of 0 EPG . In both cohorts , adults were generally farmers with limited formal schooling . The percent of people reporting water contact , and the average duration of water contact varied by month , activity and cohort . Infection prevalence and intensity at follow-up was low in both cohorts ( Table 2 ) . Notably , many individuals who tested positive for S . japonicum had no detectable eggs through the Kato-Katz examination: these individuals were positive via the miracidia hatching test only . In cohort 1 , 30% and 27% of the individuals that tested positive for S . japonicum infection at T1 and T2 , respectively , had no detectable S . japonicum eggs on Kato-Katz examination . In cohort 2 , 55% and 65% of infected individuals at T1 and T2 , respectively , had no detectable S . japonicum eggs on Kato-Katz examination . There were 21 and 20 individuals infected with S . japonicum at both T1 and T2 in cohorts 1 and 2 , respectively ( Table 3 ) . Consecutive S . japonicum infections at follow-up were 3 and 7 times more common among those who were infected with S . japonicum at T0 than those who were uninfected at T0 in cohorts 1 and 2 , respectively . Individuals that were infected at T1 and T2 were not demographically distinct from the cohorts as a whole . The age distributions of individuals with two consecutive infections to those with one or no infections at follow-up are similar ( Figure 1 ) . Among those with infections at T1 and T2 , mean age at enrollment was 30 . 1 ( range 5–56 ) and 48 . 2 ( 18–63 ) in cohorts 1 and 2 , respectively . In cohort 1 , 11 of the 21 twice-infected individuals at follow-up were female and in cohort 2 , 8 of 20 were female . The observed fraction of the population with two consecutive S . japonicum infections was 1 . 48 times greater than expected in cohort 1 , and 5 . 82 times greater than expected in cohort 2 ( Table 4 ) . This concentration of repeated S . japonicum infections in the same individuals is very unlikely due to chance ( p = 0 . 00051 and p = 6 . 6×10−12 in cohorts 1 and 2 , respectively ) . When we accounted for S . japonicum cercarial exposure , the ratios declined to 1 . 30 and 2 . 06 in cohorts 1 and 2 , respectively . The excess of individuals with repeated S . japonicum infection , even when accounting for exposure , is highly unlikely due to chance in cohort 2 ( p = 0 . 00056 ) and unlikely due to chance in cohort 1 ( p = 0 . 013 ) . In two cohorts from two geographically distinct environments , S . japonicum infections repeatedly occurred in the same individuals over time , following treatment with praziquantel . This clustering of infections occurred even when accounting for exposure , and clustering was particularly strong in cohort 2 , a population with low overall infection prevalence and intensity . These findings suggest there exists a subset of individuals within the general population that is particularly vulnerable to S . japonicum infection . Alternatively , this subset of individuals may have uncured infections due to non-compliance or treatment failure . This has important implications for disease surveillance: individuals with a history of S . japonicum infection may serve as appropriate targets for infection monitoring and treatment in low-prevalence environments . In addition , our findings provide evidence for host susceptibility to helminth infections – suggesting some individuals may be more vulnerable to infection given equivalent exposures . It is possible that individuals who are repeatedly infected with helminthes are simply the most highly exposed individuals in the population . Cercarial exposure is a well-documented determinant of S . japonicum infection [37]–[40] . We found that the ratio of observed to expected prevalence of consecutive infections exceeded unity using an exposure-blind prediction model . This ratio was lower when we included exposure in the prediction models , but still exceeded unity . This suggests some individuals may be repeatedly infected due to their high cercarial exposure , but exposure does not fully explain this phenomenon . S . japonicum exposure is challenging to assess due to the difficulties in quantifying daily human behaviors and the absence of practical methods for directly measuring cercarial concentration , and our prediction models are limited by our ability to accurately measure cercarial exposure . However , the imperfections of our exposure measures are likely offset by the use of an aggressive , data adaptive algorithm to predict S . japonicum infection using over 25 exposure variables . Over-fitting is possible when using such methods , which , in this case , would have a conservative impact on our estimates , pushing observed to expected ratios closer to unity . Therefore , exposure alone is unlikely to explain the observed concentration of repeated schistosomiasis infections in a subset of the population . More likely , individuals who are repeatedly infected with helminthes may be those who have a sufficiently elevated combination of susceptibility and exposure . We explored the clustering of infections within certain individuals from a mechanistic perspective by postulating that an individual's worm burden , w , accumulated from exposures subsequent to successful praziquantel treatment , can be described at the end of one or more infection seasons as a result of that individual's cumulative exposure to cercariae , E , and the subsequent penetration and development of a fraction of these cercarial hits , α , into adult parasites . That is , w = αE where E is composed of two elements , water contact , S , and cercarial concentration , C . The parameter α , reflecting host susceptibility , is assumed to be a stable property of each individual in the village population and the distribution of C is assumed to be a village property shared by all inhabitants . The water contact measurements described above and cercarial bioassay data collected in conjunction with the prior studies of cohort 1 [27] , [37]suggest that the population distribution of E is strongly right skewed as is generally observed to be the case for distributions of w . Assuming that the distributions of exposure and susceptibility in a population are independent , their joint distribution is depicted in Figure 2 . The marginal distribution of exposures , f ( E ) , is for illustrative purposes shown as a negative exponential distribution since multiple cercarial hits are thought to be necessary to lead to a single adult worm . Also for illustration , the marginal distribution of susceptibility , h ( α ) , is shown as symmetric . The line wT = αE is the threshold of infections that are epidemiologically visible which we define here as the minimum worm burden necessary to produce eggs at the lower limit of detection by a combination of the miracidia hatching test and the Kato-Katz method . The fraction of the population susceptible to infection at or above this threshold is that lying to the right of the line α = α* . That is , the probability of an exposure leading to a diagnosis of infection for an individual with an α less than α* is essentially zero given the maximum cercarial exposure in this hypothetical environment . The shaded area depicts the set of exposure-susceptibility combinations that produce detectable infections . Specification of the two marginal distributions allows the calculation of the distribution of their product , that is , the distribution of worm burden in the population . However , the point here is that , at least in this generic example , the proportion of the population at risk for infection is less than the entire population . That is , the number of individuals susceptible to infection , , in this environment is:Where is the total population size . Hence , if is the observed number of infections , the ratio of prevalence of infection in the susceptible population to the total population is:which is always equal to or greater than unity . Returning to the re-infection issue , suppose the population is exposed in an unchanging environment , treated annually with praziquantel at T = 0 , T = 1 , and T = 2 , and infection assessed at the end of year 1 and year 2 . Since the same population is at risk of infection with the same marginal distribution of exposure in both years , and this population is less than the entire population , the observed number of repeated infections will be greater than that expected based on infections occurring randomly in the entire population . It follows that the ratio of observed re-infections to the expected number , if distributed randomly in the entire population , is simply the square of the foregoing equation:Moreover , as the fraction of exposure-susceptibility combinations that produce infection decreases , α* and this ratio both increase . Hence , the simple model of the infection process with individual differences in susceptibility to infection , depicted in Figure 2 , provides a heuristic explanation of the epidemiological finding that the ratio of observed to expected re-infections increases as prevalence of infection decreases . Clearly , more refined analyses are possible that address a more rigorous definition of α* , take distributional assumptions into account , or explore the effect of variability in individual water contact . We will further address these and related determinants of transmission in the low-risk environment via an individually-based stochastic model which will be the subject of a future report . In addition , it is possible to estimate the proportion of susceptibles in a population via a statistical innovation using a model selection procedure like SuperLearner in the context of a latent mixture model , where the susceptibility status is latent – an approach that we will pursue in the future . The factors that govern α are not fully characterized for schistosomiasis or other helminthiases . However , there is substantial evidence that immune function , particularly the ability to mount antigen-specific IgE response , can confer host resistance to schistosomiasis as well as other helminthiases [10] , [11] , [41]–[43] . Immune response is likely attributable to a combination of past exposure , treatment and host genetics [16] , [44]–[46] . Physical characteristics such as skin thickness may also play a role in determining host resistance or susceptibility . As these genetic and immunological pathways are further elucidated , the definition of α may be further refined . Alternatively , it is possible that the individuals who appear to be repeatedly infected with S . japonicum do not have new infections , but instead have residual , uncured infections that persist despite treatment . Praziquantel is the primary drug used to treat schistosomiasis infections , and resistance is an ongoing concern , particularly in areas where the drug has been used extensively . In China , praziquantel has been widely administered since the 1990s through mass and targeted treatment campaigns . Currently , there is no evidence of population-level resistance to S . japonicum , S . haematobium or S . mansoni , but praziquantel resistant laboratory isolates have been identified [47]–[51] . It is possible that praziquantel kills some but not all parasites , resulting in an incomplete cure . Repeated dosing with praziquantel may enhance treatment efficacy , particularly for individuals with high infection intensities [52] . While infection intensities in our two cohorts were generally low , we cannot rule out the possibility that what appear to be repeated infections are , in fact , infections that were not cured by praziquantel treatment . Uncured S . japonicum infection may also be the result of poor adherence to drug treatment . As schistosomiasis morbidity declines , it is possible that so too do the perceived risks of infection and willingness to take praziquantel . Praziquantel has an excellent safety record and is appropriate for mass drug distribution , even in very young populations [53] but the drug has a bitter taste and can cause transient side effects , including nausea and dizziness . In a recent survey , 33% of people said such side effects impacted their ability to work [49] . We have found a high degree of self-reported treatment adherence ( >90% ) in surveys of 236 people drawn from the same villages as cohort 1 ( surveyed in 2007 ) and 686 people drawn from the same villages as cohort 2 ( surveyed in 2008 ) , but other studies have documented poor compliance with mass-treatment campaigns for helminthiasis [54] , [55] . Our findings underscore the importance of continued monitoring of treatment effectiveness , including both drug resistance and population perceptions of the risks and benefits of treatment . Methods capable of distinguishing new from residual infections could advance our understanding of treatment efficacy and drug adherence . Our findings underscore surveillance challenges in areas where worm burdens are low . While individuals with high worm burdens have the potential to contribute a large number of future infections , our prior work suggests that even modest parasite inputs are sufficient to sustain schistosomiasis transmission [7] . In China , surveillance and elimination efforts are made more complex as there are at least 40 competent mammalian host species for S . japonicum , and bovines are suspected to be key reservoirs in some areas [56] . Thus the ability to identify humans and , in the case of S . japonicum , other mammalian hosts with low-intensity helminth infections may be crucial to efforts to prevent the reemergence of helminth infections in areas where disease control efforts have successfully lowered infections and morbidity . Many of the individuals who tested positive for S . japonicum in our study had worm burdens below the limit of detection of the Kato-Katz assay , the schistosomiasis diagnostic method recommended by the World Health Organization [6] . Immunoassays generally have high sensitivity , but it can be difficult to distinguish past from current infections , which is particularly problematic when attempting to identify residual infections in regions with previously high infection prevalence and intensity [57] , [58] . While new methods offer promise [59] , the current lack of practical , highly sensitive diagnostics is a barrier to the long-term control of helminthiases [1] , [60] . As China aims to eliminate schistosomiasis and global efforts are launched to eliminate a number of helminthiases , the success of such efforts may hinge , in part , on the ability to identify reservoirs of infection and reduce the potential of such reservoirs to generate future infections . Our findings suggest that there exist an identifiable , high-risk subpopulation for S . japonicum infection . Due to high exposure , host susceptibility or treatment failure , these individuals are potential future reservoirs of S . japonicum . Further , as infection prevalence declines , and with it , cercarial exposure , we expect the fraction of the population that is susceptible to S . japonicum infection to decline . Thus , as regions approach disease control goals , targeted interventions may prove efficient and effective . In low-prevalence regions , individuals who test positive for S . japonicum should be tested regularly and provided pharmaceutical treatment and transmission-blocking interventions such as improved household latrines [56] , [61] .
Approximately 1 billion people are infected with one or more helminthes – a class of parasites that can impair physical , mental and economic development . We are interested in whether there exist groups who are repeatedly infected with helminthes over time in areas where treatment is ongoing . Such individuals may be at risk of morbidity and may also serve as parasite reservoirs , making them appropriate targets for disease control programs . We followed two cohorts in rural Sichuan , China in order to evaluate whether the same individuals were repeatedly infected with the water-borne helminth , Schistosoma japonicum . Each participant was tested for infection at enrollment and two follow-up points – all infections were promptly treated . We conducted detailed interviews to assess exposures to S . japonicum . We found infections repeatedly occurred in a subgroup of individuals and this clustering of infections was only partly explained by differences in exposure . This suggests some individuals may be particularly susceptible to S . japonicum infection . Further exploration of the interplay of exposure and susceptibility suggest that as exposure declines , so too does the fraction of the population vulnerable to infection . Helminth control programs that target people with a history of infection may efficiently reduce helminth infections and morbidity .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "medicine", "infectious", "diseases", "schistosomiasis", "public", "health", "and", "epidemiology", "epidemiology", "infectious", "disease", "epidemiology", "global", "health", "neglected", "tropical", "diseases", "infectious", "disease", "control", "infectious", "disease", "modeling", "parasitic", "diseases" ]
2013
Repeated Schistosoma japonicum Infection Following Treatment in Two Cohorts: Evidence for Host Susceptibility to Helminthiasis?
The World Health Organization ( WHO ) aims at eliminating onchocerciasis by 2020 in selected African countries . Current control focuses on community-directed treatment with ivermectin ( CDTI ) . In Ghana , persistent transmission has been reported despite long-term control . We present spatial and temporal patterns of onchocerciasis transmission in relation to ivermectin treatment history . Host-seeking and ovipositing blackflies were collected from seven villages in four regions of Ghana with 3–24 years of CDTI at the time of sampling . A total of 16 , 443 flies was analysed for infection; 5 , 812 ( 35 . 3% ) were dissected for parity ( 26 . 9% parous ) . Heads and thoraces of 12 , 196 flies were dissected for Onchocerca spp . and DNA from 11 , 122 abdomens was amplified using Onchocerca primers . A total of 463 larvae ( 0 . 03 larvae/fly ) from 97 ( 0 . 6% ) infected and 62 ( 0 . 4% ) infective flies was recorded; 258 abdomens ( 2 . 3% ) were positive for Onchocerca DNA . Infections ( all were O . volvulus ) were more likely to be detected in ovipositing flies . Transmission occurred , mostly in the wet season , at Gyankobaa and Bosomase , with transmission potentials of , respectively , 86 and 422 L3/person/month after 3 and 6 years of CDTI . The numbers of L3/1 , 000 parous flies at these villages were over 100 times the WHO threshold of one L3/1 , 000 for transmission control . Vector species influenced transmission parameters . At Asubende , the number of L3/1 , 000 ovipositing flies ( 1 . 4 , 95% CI = 0–4 ) also just exceeded the threshold despite extensive vector control and 24 years of ivermectin distribution , but there were no infective larvae in host-seeking flies . Despite repeated ivermectin treatment , evidence of O . volvulus transmission was documented in all seven villages and above the WHO threshold in two . Vector species influences transmission through biting and parous rates and vector competence , and should be included in transmission models . Oviposition traps could augment vector collector methods for monitoring and surveillance . The London Declaration on Neglected Tropical Diseases ( NTDs ) [1] and the World Health Organization’s ( WHO ) road map to accelerate progress for overcoming the impact of NTDs [2] have set goals for the elimination of human onchocerciasis by 2020 in selected African countries . Based on the results of epidemiological studies conducted in some foci of Mali , Senegal and Nigeria [3 , 4 , 5] , it has been suggested that 14–17 years of annual ( or biannual ) ivermectin treatment may lead to local elimination of the infection reservoir in the absence of vector control . The repeatability of these achievements depends , in part , on the initial level of onchocerciasis endemicity , geographical and therapeutic coverage , treatment compliance and frequency , parasite susceptibility to ivermectin , and the intensity and seasonality of transmission , including the species composition of the simuliid vectors [6] . Previous reports assessing the feasibility of onchocerciasis elimination have concluded that although ivermectin mass drug administration ( MDA ) alone would help to eliminate the public health burden of onchocerciasis , it would not lead to elimination of infection in most foci , with the possible exception of areas of low endemicity [7] . However , more recent and encouraging results in areas of moderate to higher endemicity [3 , 4 , 5] , have spurred the African Programme for Onchocerciasis Control ( APOC ) to shift its goals from morbidity control to local elimination of Onchocerca volvulus where possible [8] . Recognising the need to understand the nature and extent of transmission zones , APOC and WHO have emphasized the importance of conducting entomological studies on the determinants and feasibility of elimination [8 , 9 , 10] . Current WHO guidelines state that parasite levels within the vector need to be below a threshold of one L3 larva per 1 , 000 parous flies [11] . However , understanding how this measurement relates to the rate of transmission assessed via the biting rate , the infectious biting rate , the parous rate and the transmission potential , and importantly , how it varies with vector species composition and season , is vital for accurate monitoring and interpretation of this threshold [8] . Ghana was originally a country under the umbrella of the Onchocerciasis Control Programme in West Africa ( OCP ) , which operated between 1974 and 2002 , and was initially a vector control programme [12 , 13] . Vector control activities started in 1975 in the onchocerciasis savannah foci of northern and central Ghana , but the southern forest foci were not part of the programme [9] . When the microfilaricidal drug ivermectin was licensed for human use in 1987 [14 , 15] , Ghana was one of the first countries to commence MDA . In particular , community trials were conducted in the then highly hyperendemic focus of Asubende ( initial microfilarial prevalence of 80% ) [16] , where vector control had taken place but was suspended during the ivermectin distribution pilot study in the late 1980s . When the OCP ceased operations in 2002 , the persistence of onchocerciasis at Asubende required this focus to be part of the so-called Special Intervention Zones , which maintained extensive coverage with ivermectin leading to dramatic reductions in infection intensity and prevalence [17] . In 2007 , Osei-Atweneboana and co-workers [18] reported on the epidemiological situation in Ghana after the closure of the OCP and observed that despite vector control , and 19 years of annual ivermectin treatment , some communities exhibited high microfilarial prevalence and intensity ( measured as the community microfilarial load ) [19] . This was subsequently attributed to adult female worms being less responsive to the anti-fecundity effects of multiple treatments with ivermectin in some communities [20] , but others pointed out the possibility of programmatic causes such as poor coverage permitting significant residual transmission [21 , 22 , 23] . Concerned by these findings , the NTD Programme of the Ghana Health Service initiated biannual ivermectin distribution in some communities in 2009 [6 , 24] . From 2003 , ivermectin distribution was also extended to include endemic areas in Ghana which had not previously been included in the OCP . Motivated by the need to understand the feasibility of elimination in Ghana , and in particular the entomological determinants of transmission persistence despite prolonged control , we conducted a study on the transmission of onchocerciasis in areas both within and outside the original OCP area . We have already reported on the spatial and temporal distribution of species within the Simulium damnosum complex found at breeding sites in southern Ghana from 1971 to 2011 [25] , and on the biting and parous rates of host-seeking females [26] . In this paper , we present the spatial and temporal patterns of infection with Onchocerca spp . larvae of host-seeking and ovipositing flies in communities that have experienced different durations ( and frequency ) of ivermectin treatment . We relate our findings to the therapeutic coverage recorded in each study village and discuss the potential of fly trapping techniques , not based on the traditional OCP vector collector method , for the monitoring of transmission prior to or after the initiation of post-MDA surveillance . Ethical clearance was obtained from the Imperial College Research Ethics Committee ( ICREC_9_1_7 ) and the Institutional Review Board of the Noguchi Memorial Institute for Medical Research , University of Ghana ( IRB:0001276 , 006/08-09 ) . No tissue samples were taken from human subjects; however , local villagers and elders assisted with blackfly collections . Signed informed consent was obtained from all individuals involved after detailed explanations in their local languages about the study . Participating individuals were not at an increased risk of exposure , nor were human samples obtained for diagnosis , therefore , no treatments were offered . However , all participants were receiving ivermectin as part of the national programme following appropriate ( annual or biannual ) schedules according to the Ghana Health Service strategy [24] . Site selection , geography and key simuliid species are described elsewhere [26] , but , in brief , blackfly collection was conducted in seven villages within four regions of Ghana: Asubende ( 08°01'01 . 4"N , 00°58'53 . 8"W ) and Agborlekame ( 08°14'04 . 0"N , 2°12'23 . 2"W ) in the Brong-Ahafo Region; Asukawkaw Ferry ( 07°40'55 . 9"N , 00°22'16 . 0"E ) , Dodi Papase ( 07°43'22 . 5"N , 00°30'38 . 3"E ) and Pillar 83 ( 07°42'20 . 3"N , 00°35'21 . 5"E ) in the Volta Region ( Pillar 83 is the village on the Ghanaian side of the river Wawa , which forms the border and is known as the Gban-Houa in Togo , opposite the former OCP catching site of Djodji in Togo ) ; Bosomase ( 05°10'44 . 7"N , 01°36'23 . 1"W ) in the Western Region and Gyankobaa ( 06°20'12 . 4"N , 01°16'11 . 3"W ) in the Ashanti Region ( Fig 1 ) . A pilot study was conducted at Bosomase in January–February 2006 to assess the efficacy of Bellec traps ( see below ) as a fly collection method , and to test the performance of DNA amplification methods for the determination of blackfly species , infection status and blood meal origin . The main sample collection took place during one wet season , 23rd July–5th September 2009 , and two dry seasons , 14th February–28th March 2010 and 30th January–5th March 2011 . Villages were visited and samples were collected for up to five consecutive days per site per trip . Not all sites were successfully sampled during each period due to weather conditions and variability in blackfly population abundance . In Ghana , six main species are known to contribute to the transmission of O . volvulus . These are S . damnosum sensu stricto ( s . s . ) Vajime and Dunbar; S . sirbanum Vajime and Dunbar; S . sanctipauli Vajime and Dunbar; S . yahense Vajime and Dunbar; the Beffa form of S . soubrense Vajime and Dunbar [32] and S . squamosum ( Enderlein ) ( of which both C and E forms occur ) [25] . Morphological identifications , parity status and molecular fly identifications have been described in detail previously [26] and were carried out using standard methods [32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41] . The colour of the fore-coxae used by some authors [33 , 34] to separate S . damnosum s . s . from S . sirbanum is unreliable since many individuals of both species with either dark or pale fore-coxae have been noted , especially in the eastern parts of the former OCP , and therefore these two species were not split by definitive identification and are termed S . damnosum s . s . /S . sirbanum . Morphological identifications and parity status of the host-seeking blackflies were performed the day after being caught . Parous females’ abdomens were separated from the head and thorax , which were preserved individually in corresponding wells of two 96-well PCR plates ( one for heads plus thoraces , one for abdomens ) in absolute ethanol for subsequent molecular analysis . When catch numbers were manageable ( up to 300 flies per day ) , all host-seeking flies were first dissected for parity in the field . When parity of some blackflies was not assessed due to high catch numbers and time constraints ( >300 per day ) , all remaining host-seeking flies were only morphologically identified and their heads and thoraces separated from their abdomens and stored as above . Simulium squamosum shares many morphological traits with other sympatric species , causing difficulties when identifying some adult blackflies [33] . Therefore , DNA from all abdomens was extracted and used for definitive molecular identification of S . squamosum and for Onchocerca spp . infections as described below . Flies caught in Bellec and Monk’s Wood traps were morphologically identified using the same techniques [35 , 36 , 37 , 38 , 39 , 40 , 41] , and the heads , thoraces and abdomens separated and stored individually as for the host-seeking flies [26] . The heads and thoraces of all the known parous and unknown parous ( physiological age not determined ) host-seeking blackflies were dissected for Onchocerca infection . Flies caught in Bellec and Monk’s Wood traps were in the process of ovipositing and hence were not dissected for parity , as their gravid status made parity assessment impossible without counting their ova [42] . Although the flies coming to lay eggs in breeding sites would comprise both nulliparous ( laying eggs for the first time ) and parous flies ( having laid eggs before ) , it was assumed that they would have all taken at least one blood meal ( as S . damnosum s . l . is obligatorily anautogenous [43] ) and , therefore , capable of ingesting Onchocerca microfilariae if feeding on infected hosts 2–3 days previously . By the time of oviposition , some of these microfilariae could have migrated out of the abdomen and established in the thorax as L1 larvae . In parous flies , infections picked up 2–3 gonotrophic cycles earlier , could have developed into pre-infective ( L2 ) in the thorax , or infective ( L3 ) larvae , found in heads or thoraces . Therefore , the heads and thoraces of all ovipositing flies were dissected for infection with Onchocerca larvae . Heads and thoraces were soaked in distilled water for one hour , stained with a solution of 7% lactopropionic orcein in distilled water for a further hour [44] , and examined in a drop of the staining solution under a dissecting microscope . The numbers , developmental stage ( L1 , L2 , L3 ) , and location within the fly ( head or thorax ) of any Onchocerca spp . larvae were recorded . Larvae were transferred to steel-frame 0 . 9μm POL-membrane slides ( Microdissect , Leica , Germany ) [45] for subsequent individual DNA-based identification of parasite species ( such as O . volvulus , O . ochengi , O . ramachandrini , O . dukei , O . denkei and the Siisa-clade of O . ochengi ) [46 , 47 , 48] . In the field , during the morphological identification and parity dissection , any Onchocerca larvae which emerged were also recorded and transferred to a POL-membrane slide . Since S . damnosum s . l . is also involved in the transmission of other Onchocerca species [46 , 49] , parasite larvae were identified by molecular methods to ensure that transmission of human onchocerciasis would be accurately recorded . POL-membrane slides with the Onchocerca spp . L1 , L2 and/or L3 were placed on a Leica LMD6000 laser dissection microscope , viewed on a computer screen , and any Onchocerca larvae were cut out individually using an ultraviolet laser , with the sample falling into a PCR tube cap below [45] . Larvae were stored in 15μl Qiagen ATL buffer and frozen until DNA extraction . DNA extraction was performed using the QIAamp DNA Micro kit ( QIAGEN ) following the ‘isolation of genomic DNA from laser-microdissected tissues’ protocol , with DNA eluted into 30μl sterile distilled water . DNA was amplified using general Onchocerca ( primer O-150 ) [47 , 50] and the O . volvulus specific ( C1A1-2 ) [47] primers and the results run on agarose gels for species identification through presence or absence of the O . volvulus specific amplicon , when the Onchocerca general PCR had been successful . In addition , PCR amplifications were performed using three further pairs of primers 12SOvB and C , 16SOvB and C , and ND5OvA and C amplifying 12S rRNA , 16S rRNA , and ND5 mitochondrial genes respectively [51 , 52] . PCR clean-up , quantification and sequencing was performed on these 12S , 16S , and ND5 amplicons . Sequences were then individually run through BLAST and Onchocerca species identification scored when successful matches occurred . Sequences were also compared to known sequences of Onchocerca on ClustalW for additional clarification of any species identification . PCR plates contained negative water controls , O . ochengi ( adult worm DNA ) positive controls , and O . volvulus ( microfilarial DNA ) positive controls . Presence of Onchocerca ( most likely microfilariae or infective larvae ) in the abdomens was detected using the same 16S protocol [51] mentioned above for dissected Onchocerca larvae; any positive amplicons were then also sequenced and run through BLAST and ClustalW . The study communities currently receive community-directed treatment with ivermectin ( CDTI ) but with varying treatment histories in terms of number of years of MDA and treatment frequency , as well as having experienced a range of historical vector control activities , summarised in Table 1 . Community drug distributors were interviewed regarding recent drug administrations in each village , as well as village , regional and national treatment records checked for historical treatments . Dates of historical vector control are indicated in Fig 1 and previously discussed in [26] . Data on yearly therapeutic coverage of ivermectin for each study village for annual or biannual treatment rounds were provided by the Ghana Health Service . Except where specified as PCR results on the blackfly abdomens , all data presented are from dissections of heads and thoraces only . Data are reported as per fly , per parous fly , per infected fly or infective fly throughout . The proportion infected is taken as the number of flies of each species with any larval stage ( L1 , L2 or L3 ) divided by the total number of flies of that species dissected and are presented with 95% exact confidence intervals ( 95% CI ) , determined using the method of Clopper-Pearson [53] . Because Onchocerca L3s can migrate from other parts of the body to the head during a blood meal , a fly with L3s in any body part is counted as infective [54 , 55 , 56] . ( Infective larvae develop in the fly’s thoracic muscles and typically migrate to the head capsule and the fly’s proboscis , but they have also been detected in the halteres and abdomen . ) Therefore , the proportion infective is the number of flies of each species with L3 larvae ( in head and/or thorax ) divided by the total number of flies of that species dissected and is presented with 95% CIs . In addition we also present the number of flies with L3s in the head only for comparison with published literature . We calculated monthly infective biting rates , which take into account the number of infective flies that ( come to ) bite a host per month , but not their parasite burden . These were calculated by multiplying the proportion of infective flies ( with L3 larvae in head and/or thorax ) by the monthly biting ( landing ) rates as reported elsewhere [26] , but summarised in S1 Table . Monthly parous biting rates , the monthly rate at which a host would be bitten by parous flies , have been presented and analysed by species elsewhere [26] . We calculated the arithmetic mean number of L3 per infective fly per species ( L3s/infective fly ) as the total number of L3 larvae divided by the number of flies which contained any L3 larvae . The monthly transmission potential is the mean number of L3 larvae to which a host is exposed per month . These were calculated by multiplying monthly infective biting rates by the number of L3s/infective fly . We report transmission potentials for given months in the wet and dry seasons , but as we did not collect data throughout the whole year we do not extrapolate these results to annual transmission potentials . As fly survival rates have been shown to affect variations in transmission rates [57 , 58] , we also present the number of L3 larvae per 1 , 000 parous flies as recommended by the WHO [11] . These values are reported , separately , for parous host-seeking flies and ovipositing flies for each location and season . The mean number of L3s/1 , 000 parous ( or ovipositing ) flies was calculated as the total number of L3 larvae divided by the total number of parous ( or ovipositing ) flies dissected for Onchocerca multiplied by 1 , 000 . We did not assume that the same parity rates determined in samples of host-seeking flies would apply to the ovipositing flies caught near ( by light traps ) or in breeding sites ( by Bellec traps ) because a phenomenon of differential dispersal of nulliparous and parous flies along rivers and inland from rivers has been documented in S . damnosum s . l . , which varies between the savannah and forest members of the species complex [59] . The transmission indices described above were calculated from flies captured by vector collectors ( and therefore relate to human exposure and the potential of transmission from flies to humans ) unless stated otherwise . Host-seeking infective flies collected in the cow tents—had they been able to bite cattle and shed their entire L3 larval load—would not have contributed effectively to the transmission of O . volvulus . However , these flies indicate occurrence of active transmission from humans to flies , as they have become infected and survived the incubation period of the parasite . Therefore , these transmission parameters are presented for each host-seeking catching technique . Also , our results indicate that flies that bite cattle may also bite humans ( blood meal results to be presented elsewhere ) and so , if able to survive further gonotrophic cycles , infected and infective flies attracted to cattle could subsequently feed on humans and transmit their remaining infective larval load as , on average , only 50 to 80% of L3 larvae are shed per bite [55 , 60] . The proportion infected , proportion infective , the mean number of L3s/infective fly and the number of L3s/1 , 000 ( parous or ovipositing ) flies are reported , separately , for host-seeking and ovipositing flies . Statistical analyses were performed on SPSS version 22 ( SPSS , Inc . , Chicago , IL , USA ) or R [61] . Numbers of infected and infective flies , for all catches , and per species , were compared among villages , seasons and trapping methods using chi-squared ( χ2 ) tests . Ninety five percent CIs for the number of L3/1 , 000 parous , L3 per 1 , 000 ovipositing and L3 per infective flies were determined using a percentile bootstrap method [62] . A correlation between the number of years since the start of ivermectin treatment and the proportion of infected and infective flies was tested using Spearman’s Rank correlation coefficient ( rS ) . Variation in infection intensities among different species was compared using Kruskal Wallis and Mann–Whitney U tests . Numbers of infected versus uninfected flies as measured by PCR of the abdomens were compared between catching techniques using the chi-squared ( χ2 ) test . Therapeutic coverage of ivermectin distribution was plotted against time since each village commenced treatment , with a best fit polynomial plotted for each village . DNA was extracted and amplified from all 463 larvae of all stages , from the 97 infected flies ( on average , 4 . 8 larvae per infected fly and 0 . 03 per fly ) . The PCR product using the ND5 primers was consistently of poor quality and therefore only the 12S and 16S amplicons [51] were used for Onchocerca spp . identification with BLAST and ClustalW . Of all individual L1 to L3 larvae , 76% ( 352/463 ) were positively identified as O . volvulus using either 12SOv , 16SOv primers and/or O . volvulus specific ( O-150 versus C1A1-2 ) amplicons in the agarose gels . The remaining 24% were not successfully amplified . No O . ochengi was observed in the field-caught flies , but the positive O . ochengi controls were successfully identified by BLAST and/or ClustalW and did not have O . volvulus specific amplicons in the agarose gels . There were no ambiguous results for the species identification . Of the 111 non-identifiable larvae , 107 ( 96% ) came from flies in which other larvae of the same stage had been successfully identified as O . volvulus . Blackflies infected with O . volvulus larvae were recorded at Asubende , Asukawkaw Ferry , Bosomase and Gyankobaa , and infective flies ( with L3s in head and/or thorax ) were recorded at Asubende , Bosomase and Gyankobaa ( Tables 1 and S2 ) . No infected or infective flies were observed at Agborlekame , Dodi Papase or Pillar 83 during our study from the heads and thoraces; however , O . volvulus DNA was amplified in flies from all seven villages from the abdomens ( see below ) . There was no statistically significant difference in the proportion of infected ( χ2 = 5 . 06 , d . f . = 3 , p = 0 . 168 ) and infective ( χ2 = 2 . 79 , d . f . = 3 , p = 0 . 425 ) flies caught at Gyankobaa or Bosomase by the different trapping methods . A higher but , not statistically significant , proportion of infected and infective parous flies were caught in the cow-baited tents ( infected = 2 . 54% , infective = 1 . 34% ) than the other trapping methods ( Fig 3 ) , with infected and infective levels of 1 . 53% and 0 . 77% in the human-baited tents , 2 . 30% and 0 . 73% by the vector collectors and 1 . 06% and 0 . 97% in the oviposition traps , respectively . Twenty seven percent of the infected flies were caught in the cow-baited tents , 19% in the human-baited tents , 34% in the vector collector caught flies and 20% by the oviposition traps . There was no statistically significant difference between the proportion of infected ( χ2 = 0 . 90 , d . f . = 1 , p = 0 . 353 ) or infective ( χ2 = 2 . 09 , d . f . = 1 , p = 0 . 148 ) flies caught by the oviposition and host-seeking methods combined , nor between the two most successful catching techniques , namely the Bellec traps and the vector collector method ( infected: χ2 = 3 . 08 , d . f . = 1 , p = 0 . 079; infective: χ2 = 0 . 30 , d . f . = 1 , p = 0 . 584 ) . In contrast , in the abdomens , statistically significantly more flies had O . volvulus infections , as recorded by PCR , in the ovipositing flies than in the host-seeking flies ( χ2 = 19 . 58 , d . f . = 1 , p<0 . 001 ) , as well as in just the Bellec-caught flies in comparison with the vector collector-caught flies ( χ2 = 8 . 51 , d . f . = 1 , p = 0 . 004 ) . There was a negative correlation between the number of years since the start of ivermectin treatment and the proportion of infected and infective flies as measured from all those dissected , including the nullipars ( Table 1 ) ( infected: rs = –0 . 717 , p = 0 . 045; infective: rs = –0 . 654 , p = 0 . 078 ) and parous flies ( infected: rs = –0 . 700 , p = 0 . 188; infective: rs = –0 . 667 , p = 0 . 219 ) ( Fig 4 ) , but this reached statistical significance only for the overall proportion infected . Therapeutic coverage ( the proportion of the overall population treated with ivermectin ) for all villages was rarely below 60% . Coverage in Asubende , Pillar 83 and Gyankobaa had steadily increased since the beginning of mass treatment implementation , whilst Agborlekame , Dodi Papase , and Bosomase appeared to experience a recent decreasing trend in treatment coverage ( Fig 5 ) . Monthly infective biting rates and monthly transmission potentials calculated from host-seeking flies only were zero in all villages except for Bosomase and Gyankobaa , the villages most recently incorporated into the CDTI programme . These transmission indices were also negative for Asubende , as the only fly identified as infective was an ovipositing blackfly caught using a Bellec trap , rather than a host-seeking fly . Monthly infective biting rates varied greatly between villages , seasons , catching techniques and vector species ( Table 2 ) . In Bosomase , for human and cattle-seeking catching methods , these rates ranged from 0 to 42 . 2 infective bites/host/month , with higher values in the wet season than in the dry season . In the wet season of 2009 , the forest form of S . sanctipauli was the main vector recorded in Bosomase and the only species with infective larvae , whilst in the dry season of 2010 , S . yahense was the main vector species harbouring infective larvae ( Table 2 ) . At Gyankobaa , only 11 flies were collected in the dry seasons of 2010 and 2011 ( S2 Table ) , all from Bellec traps , but in the wet season of 2009 , the infective biting rates ranged from 38 . 9 infective bites/person/month , caught by vector collectors , to 50 . 4 infective bites/cow/month , for flies collected in the cow-baited tents ( Table 2 ) . Simulium sanctipauli flies harboured infective larval stages across all catching techniques at Gyankobaa indicating that this species was able to pick up infections from humans ( although they would later attempt to feed on a non-human host ) , whereas infective S . damnosum s . s . /S . sirbanum were only caught in the man-baited tents or by vector collectors , contributing both to transmission from humans to flies and from flies to humans . However , the overall sample sizes of S . damnosum s . s . /S . sirbanum at Gyankobaa from the wet season of 2009 were low , with only 35 , 4 and 13 S . damnosum s . s . /S . sirbanum caught and dissected for infection from the vector collectors , human-baited and cow-baited tents , respectively . The number of L3 larvae recorded varied between villages , seasons and catching techniques ( S2 Table ) . The WHO states that a level of less than one L3 per 1 , 000 parous flies is required to control onchocerciasis transmission [11] . Gyankobaa in the wet season had levels of over 100 L3s per 1 , 000 parous flies whilst at Bosomase in the dry and wet season these were more than 350 and 250 L3s per 1 , 000 parous flies respectively ( Fig 6A ) . Both these villages had not been included in the former OCP and were incorporated into the CDTI programme more recently in 2006 for Gyankobaa and 2003 for Bosomase . Asubende was just above this level , with 1 . 35 L3/1 , 000 ( 95% CI: 0–4 . 0 ) ovipositing ( of which not all would be parous ) flies ( Fig 6B ) . This is despite 24 years of ivermectin treatment at the time of sampling , but the infection leading to this result was detected in an ovipositing rather than in a host-seeking fly , with 0 L3/1 , 000 host-seeking parous flies . Combining L3 numbers and infective biting rates for the different vector species across trapping techniques for Gyankobaa and Bosomase resulted in transmission potentials ranging from 0 to 422 . 1 L3/host/month ( Table 3 ) . All flies at Asubende were S . damnosum s . s . /S . sirbanum , but at Bosomase and Gyankobaa vector composition varied between seasons and catching techniques ( S1 Table ) [26] . Simulium sanctipauli was the most important vector species at both Bosomase and Gyankobaa in the wet seasons , whilst S . yahense played a more important role in transmission at Bosomase in the dry season of 2010 . The importance of vector species at Bosomase in the dry season also differed between catching techniques , with S . sanctipauli having higher transmission potentials by flies caught in the human-baited tents , and S . yahense having higher transmission potentials by flies caught in the cow-baited tents ( Table 3 ) . For all infected flies successfully identified to species ( 95 out of 97 ) , the arithmetic mean number of O . volvulus larvae per infected fly varied greatly and statistically significantly among species , with S . damnosum s . s . /S . sirbanum harbouring 1 . 33 larvae per infected fly ± 0 . 33 SE; S . sanctipauli 3 . 61 ± 0 . 40 and S . yahense 17 . 86 ± 4 . 32 ( Kruskal Wallis χ2 = 15 . 50 , d . f . = 2 , p<0 . 001 ) . The mean number of L3s per infective fly also differed statistically significantly among vector species , with S . damnosum s . s . /S . sirbanum harbouring 1 . 33 L3s per infective fly ± 0 . 33 SE; S . sanctipauli 2 . 73 ± 0 . 50 and S . yahense 17 . 67 ± 9 . 23 ( Kruskal Wallis χ2 = 6 . 83 , d . f . = 2 , p = 0 . 033 ) . These differences were also observed when analysed at the village level , controlling for variations in local transmission levels , with S . yahense having significantly higher infection intensities at Bosomase in the infected flies ( Mann Whitney U = 8 . 50 , d . f . = 46 , p<0 . 001 ) . The difference had only borderline significance in the infective flies ( U = 0 . 00 , d . f . = 33 , p = 0 . 057 ) , as there was only one infective S . yahense with 17 L3s , despite the large difference between this and the mean in S . sanctipauli of 2 . 12 ± 0 . 56 L3/infective fly . Overall , 258 of the 11 , 122 ( 2 . 3% ) abdomens tested for Onchocerca infections were positive . The majority of these ( 240 ) were from Gyankobaa or Bosomase; however , there was also one positive result from each of Agborlekame , Dodi Papase and Pillar 83 , which had been negative by dissection of heads and thoraces . The number of infected abdomens in vector collector flies was lower ( 0 . 8% ) than that in the flies caught using all other methods combined ( 2 . 3% , χ2 = 58 . 0 , d . f . = 1 , p<0 . 001 ) , suggesting that the infections did not originate from the flies acquiring an infectious blood meal with microfilariae at the point of collection . We have documented active onchocerciasis transmission , raising questions regarding the potential for CDTI alone to interrupt transmission under the treatment frequency and coverage levels commonly achieved in Africa . We report high monthly infectious biting rates and transmission potentials ( measuring transmission from vectors to humans ) for the communities most recently incorporated into the CDTI strategy . We also report infections in fly abdomens from all study villages , providing evidence of transmission from humans to flies . These infections were identified molecularly as O . volvulus . Infection levels above the WHO threshold of one L3 larva per 1 , 000 parous flies were recorded in the villages of Bosomase and Gyankobaa which started receiving treatment , respectively , in 2003 and 2006 , i . e . 6 and 3 years prior to our entomological study . The WHO’s value forms part of the criteria for achieving the operational elimination thresholds for treatment cessation and commencement of surveillance [8] , which in some West African foci have been reached after 14–17 years of annual ( or biannual ) ivermectin distribution [3 , 4] . This threshold was also exceeded in Asubende , which by the time of our study had received 24 years of ivermectin . Clear interpretation of this result is difficult since it is based on one infective fly caught in a Bellec trap , and flies using local breeding sites may originate from afar . However , there is also evidence from other studies that transmission in Asubende is continuing at a rate of >40 L3/person/month in some months ( F . D . B . Veriegh , pers . comm . ) . Similarly , after 15 [64] and 17 [65] years of CDTI in Cameroon , or 20 years in the Central African Republic [66] have not resulted in interruption of transmission . Due to these and similar studies , there is a strong call for introducing more frequent ( e . g . biannual ) ivermectin treatments ( or other strategies ) if elimination is to be attained [67] . In regions in North Cameroon , approximately 70–90% of the filarial larvae in S . damnosum s . l . caught biting man were O . ochengi [68 , 69] . Given that cattle are present in some of our study villages ( e . g . Agborlekame ( ~300 cows ) and Asukawkaw Ferry ( ~500 cows ) , that S . damnosum s . l . flies feed on a range of blood hosts , and that 20% of the infective flies were caught using cattle-baited tents , we anticipated that we might have identified cattle-borne Onchocerca species such as O . ochengi but we only found O . volvulus . Over three quarters of the larvae had definitive O . volvulus identifications , and 96% of the unidentified larvae were from blackflies which had also contained known O . volvulus ( of the same larval stage ) . No other species were identified and we are therefore confident that all of the Onchocerca larvae originated from flies infected with O . volvulus . This indicates active onchocerciasis transmission from humans to flies ( early larval stages or infective flies attempting to feed on cattle ) and from flies to humans ( infective larvae in flies attempting to feed on humans ) . During the OCP , transmission potentials had been initially calculated on the assumption that all larvae would be O . volvulus; these ‘crude’ transmission potentials were subsequently corrected when tools for molecular identification of parasite larvae became available revealing that a geographically variable proportion of infective flies harboured non-volvulus Onchocerca spp . of zoonotic origin [70] . In 1980 ( pre-ivermectin and pre-vector control ) , over 75% of the Asubende population were infected with microfilariae , and in 1987 , prior to the ivermectin community trials , an infection prevalence of 80% was recorded [16] , only slightly higher than that of Agborlekame ( both in the Brong-Ahafo region ) . These communities were highly hyperendemic at baseline . The absence of infective flies observed at Agborlekame may be attributable to our low sample sizes , and/or recent treatment , rather than true lack of transmission . This conjecture is supported by on-going entomological studies ( F . B . D . Veriegh pers . comm . ) indicating high levels of L3 infections in flies from Agborlekame reaching 68 L3/person/month . This is further supported by our molecular analyses of fly abdomens , which revealed one infected fly in 83 flies analysed . At Asubende , biting rates have returned to pre-vector control levels [26] , suggesting ecological conditions propitious for continuing transmission . Asubende has received regular annual treatment since 1987 , and bi-annual treatment since 2009 , with the most recent treatment round just 2 months before our sample collection . The village had a population of only 88 inhabitants at the time of sampling , and inspection of the community distributor’s notebooks and district records indicated a high therapeutic coverage . Therefore , in addition to the return of high biting rates and the possibility of infective flies migrating into the area [71] , the potential for sub-optimal responses to ivermectin , perhaps suggesting decreased drug susceptibility , cannot be ignored . After 20 years of annual ivermectin administration , epidemiological assessments in 19 communities in Ghana , including Asubende , indicated a persistent reservoir of microfilarial infection [18 , 20] . In contrast , in the three Volta Region villages , transmission was low , despite a shorter history of vector control and ivermectin treatment than in Brong-Ahafo . The lack of infections may be attributable to the success of the OCP vector control strategy , which eliminated the Djodji form of S . sanctipauli [72] , one of the S . damnosum complex species with the highest vector competence . Previous studies had shown that the Djodji form of S . sanctipauli carried , on average , three times as many L3 larvae per 1 , 000 biting flies as S . squamosum [73] . The reduction in biting rates associated with the disappearance of the Djodji form of S . sanctipauli [26] may also explain the reduction in transmission . Ivermectin treatment records also indicate that Pillar 83 had repeated ivermectin treatments in the years from 1993 to 1997 ( potentially rapidly reducing levels of transmission in this community at the early stages of ivermectin control ) , followed by annual CDTI . At Bosomase and Gyankobaa , which never received vector control and were incorporated into CDTI only recently , high levels of active transmission are still occurring , despite their lower baseline levels of infection intensity and prevalence , and current biannual or annual ivermectin treatment . In Gyankobaa , the most recent round of ivermectin distribution had taken place over a year before our sample collection date , providing ample opportunity for the reappearance of microfilariae in the hosts’ skin and their ensuing transmission [74 , 75] . In Bosomase , the high infection levels observed in the wet season in August 2009 are probably explicable by the missed annual treatment in that year , highlighting the importance of understanding the programmatic determinants of persistent transmission . The transmission in the dry season of 2010 at Bosomase is of concern , with flies collected just one month after ivermectin treatment . However , seasonal variations ( transmission levels in the 2009 wet season were higher than in the 2010 dry season ) , and in vector species composition and competence may also play a role in explaining the reported transmission patterns . In the dry season , monthly transmission potentials were driven by S . yahense , with a higher number of L3s per infective fly than the extant form of S . sanctipauli . In contrast , the higher monthly infective biting rates in the wet season were driven by higher numbers of infective S . sanctipauli flies , despite their lower numbers of L3s per infective fly . Although not as anthropophagic and efficient a vector as the eliminated Djodji form , the forest form of S . sanctipauli has previously been demonstrated to be a highly efficient vector . In an area environmentally similar to , and just north of , Bosomase , a mean of 377 L3 in 1 , 000 parous flies , and 122 L3 per 1 , 000 biting flies ( with 44% of parous flies infected ) were recorded [76] . Even higher values , of 616 L3 per 1 , 000 parous flies have been reported in other African localities [56] . Overall , we observed lower infection rates than these , potentially due to the high therapeutic coverage of annual CDTI in this community . However , some reductions attributed to CDTI may actually be due to river pollution , lowering fly breeding success and associated transmission , particularly for S . sanctipauli [77] , further supporting our previous biting rate findings and potential factors involved [26] . The influence of vector competence on transmission observed in Bosomase was also seen in Gyankobaa , where S . yahense , and to a lesser extent S . squamosum , were responsible for lower monthly transmission potentials due to lower biting rates and parous biting rates . In contrast , the forest form of S . sanctipauli , contributed to high numbers of L3/person/month due to high biting rates . Consequently , although both Bosomase and Gyankobaa have a shorter history of CDTI , the high transmission parameters recorded here for the vector species prevailing in this area must be emphasised . In Gyankobaa , infection levels ( numbers of L3/1 , 000 parous flies ) were 129 times as high , and in Bosomase , 291 to 365 times as high , as the WHO threshold . In both localities , the greatest proportion of L3 were found in S . sanctipauli , a species poorly or not at all represented in current transmission models . Transmission models for African onchocerciasis have been mostly parameterised using S . damnosum s . s . /S . sirbanum data [6 , 63 , 78 , 79 , 80 , 81 , 82 , 83] to reflect transmission dynamics in savannah areas suffering from severe ocular sequelae due to onchocerciasis . Exceptions to these models are the studies by Davies ( 1993 ) [84] , based on transmission of forest onchocerciasis by S . soubrense B sensu Post; some quantitative analyses on other S . damnosum complex species , including S . leonense and S . squamosum B [85 , 86] , and the recent modelling study of the effect of climate change on onchocerciasis transmission in Ghana and Liberia , including S . soubrense [87] . Our findings highlight that data on vector competence and vectorial capacity for O . volvulus for other important vector species are crucially needed , particularly as regions with diverse and seasonally varying simuliid vector composition strive towards elimination . Approximately 40% of the flies were caught on Bellec traps , a similar proportion to that caught by the traditional OCP vector collector method , resulting in roughly equal numbers caught by host-independent and host-dependent methods . Light traps performed poorly , despite previous success at trapping S . squamosum [29] and other members of the S . damnosum complex [30] in Ghana . The prevalence of infected and infective flies , assessed by dissection , was similar among our host-dependent and host-independent catching techniques . Bellec-caught flies had higher infection prevalence , measured by DNA analyses of the abdomens , than the vector collector-caught flies . Positive abdomens in ovipositing flies could originate from microfilariae ingested with the blood meal ( that did not escape the peritrophic matrix ) —indicating transmission from humans to flies , and/or from L3 larvae migrating out of the thorax—indicating potential transmission from flies to humans . These results suggest that using oviposition ( Bellec ) traps in breeding sites along rivers close to villages , could augment ( and perhaps replace ) the more labour-intensive methods of human vector collection for monitoring vector infection levels . Large numbers of flies are required by techniques such as pool-screening [88] , and with decreasing infection rates , the numbers to power transmission studies seeking to quantify reductions in transmission may need to be even larger [89] . Potential replacements for human landing catches , such as the Esperanza Window Trap , have been developed for S . ochraceum s . l . ( the vector in Mexico and Guatemala ) [90 , 91] and evaluated for host-seeking flies in Africa [92] . Oviposition traps have the added advantage that even nulliparous flies could contribute to the quantification of infection in thoraces , as sufficient time between an infected bite and oviposition elapses allowing any potential microfilariae to establish as L1s within the flies . The O . volvulus larvae thus collected could also be tested for ivermectin resistance markers once field probes are developed , helping in the monitoring and evaluation of transmission and of the potential spread of decreased ivermectin efficacy . This will become particularly pertinent with the increasing need for large-scale entomological evaluation of interventions as programmes strive for elimination , which will raise ethical concerns surrounding the widespread use of human landing catches . The host-independent Bellec traps could also be used in wider geographical perimeters during the post-MDA surveillance phase to complement more human exposure-focused methods in sentinel sites . As vector competence is known to vary between seasons [93] , blackfly collection was performed in both wet and dry seasons at five of the seven locations . ( Due to incorporation at a later stage in the study of Asubende and Agborlekame , data were only collected during the dry season in these communities . ) However , due to low blackfly catches at four of the study locations in one or the other of the seasons , Bosomase was the only location where substantial data were collected during both seasons . Although this reflects a lack of biting or ovipositing blackflies at the sampling times in these localities during these seasons , our results may not reflect true absence of simuliids and of any associated transmission for the whole season . This is particularly highlighted by our inability to detect Onchocerca larvae at Agborlekame , despite recent observations of on-going transmission ( F . B . D . Veriegh , pers . comm . ) . Indeed , when blackfly abdomens were analysed , at least one positive result was obtained for O . volvulus infection in each of the villages assessed , indicating some level of active transmission . A potential limitation of analysing fly abdomens by molecular means is that higher levels of infection in vector collector-caught flies might be expected if any of the vector collectors caught the flies after the start of feeding and were themselves infected with microfilariae . There was no evidence that O . volvulus-positive abdomens were caused by microfilariae from the vector collectors as proportions of infected blackfly abdomens were significantly lower in the vector collector-caught flies than in those obtained by the remaining trapping methods .
The World Health Organization ( WHO ) aims at eliminating onchocerciasis by 2020 in selected African countries . The success of elimination using ivermectin treatment alone will depend on several interacting factors including baseline endemicity , treatment coverage and vector species mix . In Ghana , transmission persists despite prolonged control . We investigated entomological determinants of this persistence . Blackflies were collected from seven villages with 3–24 years of ivermectin treatment . A total of 12 , 196 flies was dissected , with 463 larvae ( all Onchocerca volvulus ) in 97 infected and 62 infective flies . Transmission indices in the wet season , at Gyankobaa and Bosomase , amounted to , respectively , 86 and 422 infective larvae/person/month after 3 and 6 years of ivermectin treatment . Infection levels at these villages were over 100 times the WHO threshold of one L3/1 , 000 parous flies . At Asubende , an infective fly was caught among ovipositing flies in nearby breeding sites , indicating that infection was just over the WHO threshold despite extensive ivermectin and vector control . Spatial and seasonal vector species composition influences the magnitude of transmission indices through variations in biting and parous rates , and vectorial competence and capacity , and should be reflected in transmission models . Oviposition traps could enhance vector collection for transmission monitoring and surveillance .
[ "Abstract", "Introduction", "Materials", "and", "Methods", "Results", "Discussion" ]
[]
2015
Onchocerciasis Transmission in Ghana: Persistence under Different Control Strategies and the Role of the Simuliid Vectors
Emerging evidences exhibit that mitogen-activated protein kinase ( MAPK/MPK ) signaling pathways are connected with many aspects of plant development . The complexity of MAPK cascades raises challenges not only to identify the MAPK module in planta but also to define the specific role of an individual module . So far , our knowledge of MAPK signaling has been largely restricted to a small subset of MAPK cascades . Our previous study has characterized an Arabidopsis bushy and dwarf1 ( bud1 ) mutant , in which the MAP Kinase Kinase 7 ( MKK7 ) was constitutively activated , resulting in multiple phenotypic alterations . In this study , we found that MPK3 and MPK6 are the substrates for phosphorylation by MKK7 in planta . Genetic analysis showed that MKK7-MPK6 cascade is specifically responsible for the regulation of shoot branching , hypocotyl gravitropism , filament elongation , and lateral root formation , while MKK7-MPK3 cascade is mainly involved in leaf morphology . We further demonstrated that the MKK7-MPK6 cascade controls shoot branching by phosphorylating Ser 337 on PIN1 , which affects the basal localization of PIN1 in xylem parenchyma cells and polar auxin transport in the primary stem . Our results not only specify the functions of the MKK7-MPK6 cascade but also reveal a novel mechanism for PIN1 phosphorylation , establishing a molecular link between the MAPK cascade and auxin-regulated plant development . Mitogen-activated protein kinase ( MAPK/MPK ) cascades play important roles in a broad spectrum of signals , including biotic and abiotic stresses and hormone-mediated development in higher plants [1] . The basic MAPK module is composed of three sequentially activated kinases: MAPK kinase kinase ( MAPKKK ) , MAPK kinase ( MKK ) , and MPK . MAPK could phosphorylate the downstream substrates to elicit biological responses to various developmental requirements and environmental stimuli [1] . The Arabidopsis genome encodes a large number of MAPK cascade components with more than 60 MAPKKKs , 10 MKKs , and 20 MPKs , which participate in regulating many essential biological processes [1 , 2] . In the MAPK signaling module , MKKs are of particular importance because they serve as the convergence and divergence points in the MAPK signal transduction [3 , 4] . Based on the protein microarray data , an overview of interactions between the MKKs and MPKs as well as between MPKs and the downstream substrates has been proposed [5 , 6] , showing that an individual MKK could target multiple MPKs , and an individual MPK could be a substrate of multiple MKKs . Moreover , downstream substrates of MAPK cascades also determine the function of MAPK signaling . The complexity of MAPK cascades raises challenges not only to identify the MAPK module in planta but also to define the specific role of an individual module . As there are 10 MKKs and 20 MPKs in the Arabidopsis genome , the signaling specificity of the MAPK modules should partially rely on the diversity of the MPKs and their downstream signaling events . MPK3 and MPK6 , the most intensively studied MPKs in Arabidopsis , have overlapping functions in diverse development and stress-related adaptation processes [7–16] . Although the different roles of MPK3 and MPK6 in certain biological events have been recently reported [17–23] , the signaling specificity of the two MPKs in more diverse biological processes remains to be elucidated . Plant growth regulator auxin is synthesized in the shoot apical and flows down through the vasculature of the primary stem to mediate plant development [24] . A subset of the Arabidopsis PIN-FORMED proteins ( PINs ) with long hydrophilic loop ( HL ) , namely PIN1–PIN4 , PIN6 , and PIN7 , localizes predominantly to the plasma membrane in diverse tissues and displays distinct subcellular polarity depending on PIN species and tissue types , determining the direction of auxin flow [24–26] . Among these PIN proteins , PIN1 is the major member that regulates shoot development in Arabidopsis [27] . Recent studies suggested that the PIN1 polar localization is related to its phosphorylation status [28–32] . The Ser/Thr ( S/T ) protein kinase PINOID ( PID ) and protein phosphatase 2A ( PP2A ) have been reported to mediate PIN1 apical-basal polarity by regulating PIN1 phosphorylation in an antagonistic manner [31 , 33] . The residues S231 , S252 , and S290 of PIN1 are directly phosphorylated by PID [29] . Although S337 and T340 were also shown to be essential for both PIN1 polar localization and auxin flow , they are not substrates of PID , implying that there might exist other protein kinases involved in PIN1 phosphorylation [32] . Our previous studies isolated a semidominant bushy and dwarf 1 ( bud1 ) mutant in Arabidopsis , which results from the overexpression of MAP Kinase Kinase 7 ( MKK7 ) [34] . The constitutively increased expression of MKK7 leads to multiple phenotypic changes , including enhanced gravitropism of dark-grown seedlings , fewer lateral roots , abnormal filament elongation , more branches , dwarfism , and smaller and curled leaves [34] . These diverse phenotypes in the bud1 mutant imply that multiple MAPK signaling pathways may be activated by MKK7 . Here , we demonstrated that MPK3 and MPK6 were two major downstream substrates of MKK7 in vitro and in vivo . Genetic analysis showed that MKK7-MPK6 and MKK7-MPK3 signaling pathways play distinct roles in plant development . The MKK7-MPK6 signaling pathway specifically regulates shoot branching , plant height , lateral roots development , flower filament elongation , hypocotyl gravitropism , and basipetal polar auxin transport in seedlings and main roots , whereas the MKK7-MPK3 signaling pathway specifically regulates leaf development . We further showed that PIN1 is the substrate of the MKK7-MPK6 cascade for phosphorylating at S337 , which determines PIN1 polarization and regulates shoot branching in Arabidopsis . Although MKK7 was predicted to have the ability to activate multiple MPKs by different screening systems [6 , 35] , there is no in vivo experimental evidence showing the relationships between MKK7 and its downstream MPKs . Using myelin basic protein ( MBP ) as the substrate of MPKs , we found that constitutively activated MKK7 ( cMKK7 ) could activate MPK3 and MPK6 under tested experimental conditions ( Fig 1A and S1 Fig ) , which is consistent with the previous report by Yoo et al . [9] . The activated MAPK system is a common approach to characterize downstream targets of MAPK cascade [13 , 36–38] . To verify that the phenotypes in bud1 are not ectopic effects of MKK7 overexpression , we identified a MKK7 knockout mutant ( S2A Fig ) . Compared with the wild type , the mkk7 mutant exhibited significantly reduced shoot branch number , increased plant height and lateral root number ( S2B–S2D Fig ) , enhanced polar auxin transport in inflorescence stems ( S2E Fig ) , and longer hypocotyls at high temperature under light ( S2F and S2G Fig ) . All of these phenotypes are opposite to those observed in bud1 , which further supports our conclusion that MKK7 plays an important role in the regulation of shoot branching and other auxin-related developmental events , indicating that bud1 can be used for characterizing MKK7 downstream targets . To confirm the activation of MPK3 and MPK6 by MKK7 in planta , we analyzed the activation of MPKs in the bud1 mutant using the phospho-p44/p42 antibody , which specifically recognizes the phosphorylated MPK3 and MPK6 [39] . The result showed that both MPK3 and MPK6 were phosphorylated in bud1 , indicating that MKK7 could activate MPK3 and MPK6 in vivo ( Fig 1B ) . MPK3 and MPK6 belong to Group A MPKs [2] and share 68 . 69% sequence identity at the amino acid level ( S3 Fig ) . To dissect the specific roles of MPK3 and MPK6 mediated by MKK7 , we generated the double mutants of mpk3bud1 and mpk6bud1 by crossing the bud1 mutant with the mpk3 or mpk6 single mutant , respectively . The mpk3 mutant is a deletion mutant caused by fast neutron mutagenesis [40] , and the mpk6 mutant is a T-DNA insertion line ( SALK_073907 ) . Homozygous mutant plants were identified by PCR with MPK3- , MPK6- , or MKK7-specific primers ( S4A–S4C Fig and S1 Table ) . In vivo kinase assays were unable to detect phosphorylated MPK3 and MPK6 in their respective homozygous double mutants ( Fig 1B ) . In addition , the expression levels of MKK7 in the double mutants of mpk3bud1 and mpk6bud1 were as high as those in bud1 ( S4D Fig ) . Therefore , these two homozygous double mutants were used for further studies . To determine the contribution of MKK7-MPK3 and MKK7-MPK6 cascades for the multiple phenotypes in bud1 , we first compared the phenotypes among the wild-type , bud1 , mpk3 , mpk6 , mpk3bud1 , and mpk6bud1 plants . The results showed that the leaf venation pattern ( Fig 2A ) , filament elongation ( Fig 2B ) , gravitropism of dark-grown seedlings ( Fig 2C and 2D ) , lateral root number ( Fig 2E and S5 Fig ) , and branch number in the mpk6bud1double mutant were restored to the wild-type , whereas these phenotypes in the mpk3bud1 double mutant were similar to those in the bud1 mutant ( Fig 3 ) . On the other hand , the curled leaves in the bud1 mutant were largely rescued in mpk3bud1 plants and partially rescued in mpk6bud1 plants ( S6 Fig ) . Genetic analysis indicated that the MKK7-MPK6 cascade is specifically involved in multiple aspects of plant development including the regulation of leaf venation architecture , gravitropism , filament elongation , lateral root formation , and shoot branching , whereas the MKK7-MPK3 and MKK7-MPK6 cascades function redundantly in leaf morphology . The bud1 mutant has been shown to exhibit the polar auxin transport ( PAT ) deficiency due to the overexpression of MKK7 [34] . Because PAT is highly related to shoot development [41 , 42] , we therefore speculated that the MKK7-MPK6 cascade may regulate shoot development through affecting PAT . To verify this speculation , we first examined the hypocotyl elongation of the wild-type , bud1 , mpk3 , mpk6 , mpk3bud1 , and mpk6bud1 plants under a high temperature condition . Abnormal hypocotyl elongation upon a high temperature treatment indicates the deficiency in auxin-related pathways [43] . As previously reported , when grown in the light at high temperature ( 29°C ) , the wild-type seedlings exhibited dramatic hypocotyl elongation compared with the seedlings grown at 20°C , whereas the hypocotyl of bud1 could not elongate under 29°C [34] . Our present study showed that this temperature-dependent growth response of the mpk6bud1 hypocotyl was comparable to that of the wild-type , whereas mpk3bud1 hypocotyl was still similar to that of the bud1 mutant ( Fig 4A and 4B ) , indicating that the PAT deficiency of bud1 is due to the constitutive activation of MKK7-MPK6 cascade . We then measured the PAT in the hypocotyl segments of light-grown seedlings . To measure basipetal movement of 3H-indole-3-acetic acid ( 3H-IAA ) , a single microdroplet was applied to the apex of 4 . 5-d-old , light-grown seedlings . The auxin transport in the bud1 hypocotyl segments was reduced to 46 . 7% of that of the wild-type , and mpk3bud1 double mutants still showed the similar level as that in bud1 mutants , although it was restored to 82 . 5% of that of the wild type in mpk6bud1 double mutants ( Fig 4C ) . We extended our assay to measure the inflorescence stem basipetal PAT of wild-type , bud1 , mpk3 , mpk3bud1 , mpk6 , and mpk6bud1 plants . As shown in Fig 4D , the auxin basipetal transport in bud1 and mpk3bud1 was significantly reduced to 28% of that of the wild type , whereas in the mpk6bud1 inflorescence stem , auxin basipetal transport was restored to a comparable level to that of the wild type . Taken together , these data demonstrated that it is the MKK7-MPK6 but not MKK7-MPK3 cascade that is responsible for PAT in shoots . To test whether the change of the PAT is correlated to auxin distribution in the main stem , we visualized the auxin distribution by DR5-green fluorescent protein ( DR5-GFP ) . Our results showed that , unlike in the wild-type plants , DR5 activity can be barely detected in bud1 inflorescence stems , whereas the mpk6bud1 double mutant showed similar DR5 activity to that in wild-type plants; however , significantly decreased DR5 activity was observed in mpk3bud1 , which was comparable to the bud1 mutant ( S7 Fig ) . These results demonstrate that the MKK7-MPK6 cascade is involved in PAT and has a direct impact on the auxin distribution in inflorescence stems . The Arabidopsis PIN1 protein is basally localized in stem xylem parenchyma cells , where it is required for auxin transport [44] . To determine whether the MKK7-MPK6 cascade regulates PAT through PIN1 , PIN1pro::PIN1-GFP plants were crossed into bud1 , mpk3 , mpk3bud1 , mpk6 , and mpk6bud1 mutants , respectively . The inflorescence stems of 30-d-old homozygous plants were used for the analysis of PIN1-GFP localization . Unlike the wild-type , mpk3 , and mpk6 plants , in which the PIN1-GFP is basally localized in xylem parenchyma cells , the bud1 and mpk3bud1 mutants did not show typical basal localization of PIN1-GFP ( Fig 5A–5E ) , whereas PIN1-GFP in the mpk6bud1 double mutants exhibited the typical basal localization in xylem parenchyma cells ( Fig 5F ) , demonstrating that the MKK7-MPK6 cascade is responsible for regulating the PIN1-GFP basal localization in the inflorescence stem . Evolutionary conserved phosphorylation sites within the central HL of PIN proteins were found to be essential for the apical and basal polar PIN localizations [32] . D6 PROTEIN KINASE ( D6PK ) and PID kinases belong to the Arabidopsis AGCVIII kinase family and regulate PIN1 localization through phosphorylating different sites [29 , 31 , 32 , 45] . Although several phosphorylation sites within the central HL of PIN proteins have been identified , only three phosphorylation sites ( S231 , S252 , and S290 ) in the HL of PIN1 were verified to be the targets for PID and one phosphorylation site ( S271 ) for D6PK phosphorylation [29 , 31 , 32 , 45] , implying that protein kinases other than PID and D6PK may target PIN1 for phosphorylation . To verify whether the MKK7-MPK6 cascade may phosphorylate PIN1 , we performed an in vitro protein kinase assay by incubating Glutathione S-transferase ( GST ) -tagged HL of PIN1 ( PIN1HL ) , GST-tagged MPK6 , and Histidine ( HIS ) -NusA-tagged cMKK7 in an in vitro phosphorylation reaction . As shown in Fig 6A , cMKK7-MPK6-dependent phosphorylation of GST-PIN1HL was detected . To further elucidate molecular mechanisms of PIN1 polar localization regulated by MKK7-MPK6 cascade phosphorylation , we identified the MKK7-MPK6 phosphorylation sites in the PIN1HL . First , we performed Liquid Chromatograph-Mass Spectrometer/Mass Spectrometer ( LC-MS/MS ) analysis to identify the phosphorylation sites . A total of 12 phosphorylation sites were detected in the PIN1HL ( Fig 6B; S8 and S9 Figs ) , and five main phosphorylation sites ( S317 , S337 , T340 , T439 , and S446 ) were selected for further analysis ( Fig 6B and S8 Fig ) . These five sites showed more than 75% probabilities for the phosphorylation ( S9 Fig ) and are not conserved within the central HL of PIN proteins ( S10 Fig ) . Next , we tested the effect of Ser/Thr-to-Ala substitution of these five sites ( S317A , S337A , T340A , T439A , and S446A ) on MKK7-MPK6 phosphorylation using GST-tagged PIN1HL . As shown in Fig 6A , a single S337A substitution led to a dramatic reduction of phosphorylation by MKK7-MPK6 , whereas other single substitution could not alter the phosphorylation status of GST-tagged PIN1HL , indicating that S337 of PIN1HL is the dominant phosphorylation site by the MKK7-MPK6 cascade . To investigate the biological significance of the S337 site of PIN1 in planta , various mutant constructs were generated from 35S::PIN1-GFP , in which S337 in the encoded PIN1-GFP proteins was replaced by Ala ( A ) , an nonphosphorylatable residue , or by Asp ( D ) to mimic phosphorylation . The resulting constructs 35S::PIN1S337A-GFP and 35S::PIN1S337D-GFP were respectively transformed into Arabidopsis Columbia ( Col ) wild-type plants . Western blot result showed that PIN1-GFP in 35S::PIN1S337A-GFP and 35S::PIN1S337D-GFP transgenic plants expressed as well as in 35S::PIN1WT transgenic plants ( S11 Fig ) . First , we examined whether the PIN1-GFP subcellular localization in Arabidopsis inflorescence stems is influenced by the phosphorylation status of S337 . As shown in Fig 7A and S12A Fig , the subcellular localization of PIN1-GFP in 35S::PIN1S337D-GFP transgenic plants failed to properly establish polarity in the xylem parenchyma cells , showing similar apolar localization of PIN1-GFP in bud1 ( Figs 5B and 7A and S12A Fig ) . By contrast , the polarity of PIN1-GFP in 35S::PIN1S337A-GFP transgenic plants was not changed ( Fig 7A ) , suggesting that S337 phosphorylation status is essential for PIN1 polarity in Arabidopsis inflorescence stem . In addition , we performed a detailed analysis of PIN1-GFP localization in both roots and hypocotyls . As shown in S13 Fig , PIN1-GFP localization has no obvious difference in Col-0 , bud1 , mpk3 , mpk3bud1 , mpk6 , and mpk6bud1 roots . In hypocotyls , PIN1-GFP localizations in Col-0 , bud1 , mpk3 , mpk3bud1 , mpk6 , and mpk6bud1 are similar to those observed in inflorescence stems ( S14A Fig ) . However , PIN1S337D-GFP exhibited typical basal localization similar to PIN1-GFP and PIN1S337A-GFP ( S14B Fig ) . Consistent with this , the physiological analysis showed that the high temperature-dependent growth response in 35S::PIN1S337D hypocotyls was comparable to those in 35S::PIN1WT and 35S::PIN1S337A hypocotyls ( S14C and S14D Fig ) . These results demonstrated that PIN1 phosphorylation by MKK7-MPK6 cascade is likely organ-specific . To verify whether the altered PIN1 localization mediated by phosphorylation of S337 affects the shoot branching , we compared the branching phenotypes of the transgenic plants . The results showed that the branch number of 35S::PIN1-GFP and 35S::PIN1S337A-GFP transgenic plants were comparable to those of the wild type ( Fig 7B–7F ) . However , the 35S::PIN1S337D-GFP transgenic plant displayed more branches than the wild type ( Fig 7G and S12B–S12F Fig ) , demonstrating that constitutive phosphorylation of the PIN1 S337 may contribute significantly to the branching phenotype of the bud1 mutant . To further understand the role of PIN1 phosphorylation on S337 by the MKK7-MPK6 cascade , we transformed the phospho-deficient version of PIN1 ( PIN1S337A ) in the bud1 background . The homozygous T3 line in which MKK7 expression level was as high as that in the bud1 mutant was identified for further analysis ( S15 Fig ) . The result showed that overexpression of PIN1S337A in the bud1 mutant could rescue the branching phenotype of bud1 ( Fig 7H and 7I ) , suggesting that the regulation of PIN1 polar localization through phosphorylation of S337 by the MKK7-MPK6 cascade is specific to the regulation of shoot branching . Taken together , our results demonstrated that MPK6 and MPK3 make different contributions downstream of MKK7 . The MKK7-MPK6 cascade plays predominant roles in diverse developmental processes , including leaf venation architecture , gravitropism , filament elongation , lateral root formation , and shoot branching , whereas the MKK7-MPK3 cascade mainly regulates leaf morphology in a coordinative manner with the MKK7-MPK6 cascade ( Fig 8 ) . Furthermore , we propose a new mechanism that the MKK7-MPK6 signaling pathway regulates PAT through phosphorylating PIN1 to determine shoot branching ( Fig 8 ) . In the wild type , PIN1 basal localization is controlled by reversible phosphorylation of the S337 site by the MKK7-MPK6 cascade , which in turn determines PIN1 polarity and auxin flow . In the bud1 plant , constitutively activated MKK7-MPK6 signaling leads to sustained phosphorylation of the PIN1 S337 site , which disturbs the PIN1 polarity and auxin gradients and results in branching phenotype . Previously , we identified a semidominant Arabidopsis bud1 mutant , which results from the increased expression of MKK7 , and demonstrated that MKK7 affects plant architecture by negatively controlling PAT , and the kinase activity of MKK7 is essential for its biological functions [34] . In addition , the bud1 mutant also showed elevated levels of salicylic acid ( SA ) , constitutive pathogenesis-related ( PR ) gene expression , and enhanced resistance to both Pseudomonas syringae pv . maculicola ( Psm ) ES4326 and Hyaloperonospora parasitica Noco2 , indicating that MKK7 positively regulates plant basal and systemic acquired resistance [47] . These studies shown above suggested that MKK7 may regulate plant development and resistance through activating different downstream MPKs . In this study , we found that MKK7 can phosphorylate MPK3 and MPK6 in vivo , and the MKK7-MPK6 and MKK7-MPK3 cascades perform distinct functions in planta . The MKK7-MPK6 module mainly contributes to plant growth and development , which includes shoot branching ( Fig 3 ) , venation pattern of both cotyledon and true leaves ( Fig 2A ) , filament elongation ( Fig 2B ) , hypocotyl gravitropism ( Fig 2C and 2D ) , and lateral root development ( Fig 2E and S5 Fig ) . However , the MKK7-MPK3 module contributes little to plant development . Given that MKK7 is also involved in plant basal and systemic acquired resistance , we suggest that the MKK7-MPK3 may contribute mainly to defense response . PAT is important for the establishment of auxin gradients , which are essential for plant polar growth and morphological patterning [48 , 49] . Although different PIN proteins contributing to intercellular and intracellular auxin transports have been reported previously , the roles of PINs in PAT-mediated shoot branching remain unclear . Previous studies revealed that PIN1 phosphorylation serves a key role in regulating PAT and auxin-related plant development [50 , 51] . For example , PID and PP2A antagonistically regulate PIN1 phosphorylation and mediate PIN1 apical-basal polar targeting in roots and shoots apex in Arabidopsis [31] . Further study showed that S231 , S252 , and S290 of PIN1 were the PID-dependent phosphorylation sites , and phospho-mimicking substitutions at these three sites induce apical localization of PIN1 [29] . PID belongs to a large family of AGCVIII kinases in Arabidopsis [52 , 53] . D6 PROTEIN KINASES ( D6PK ) , another subfamily of AGCVIII kinases , mainly targets S271 instead of S231 , S252 , and S290 of PIN1 to control its activation and polar distribution [45] . It is suggested that S271 is a novel PIN1 protein phosphosite with a role in promoting auxin efflux [45] . In addition , PIN1 phosphorylation at S337 and T340 mediates its polarity and auxin distribution as well [32] . Recently , a study showed that a peptidyl-prolyl cis/trans isomerase Pin1At effect on PIN1 subcellular localization is mediated by PIN1 phosphorylation at S337/T340 [54] . However , both of these two sites are not directly phosphorylated by AGCVIII kinases in vitro , indicating that S337 and T340 phosphorylation sites could be a target for other potential kinases [32] . In this study , we demonstrated that the MKK7-MPK6 cascade could phosphorylate PIN1 at S337 ( Fig 6 ) , which affects PIN1 polar localization in xylem parenchyma cells ( Fig 7A and S12A Fig ) . Overexpression of phospho-mimicking PIN1S337D resulted in more branching phenotype ( Fig7G and S12B–S12F Fig ) . However , the other phenotypes , such as plant height , filament elongation , hypocotyl gravitropism , and lateral root development , have not emerged in overexpression of phospho-mimicking PIN1S337D transgenic plants . In addition , overexpression of PIN1S337A in bud1 mutant resulted in significantly reduced branch number; however , the other phenotypes of bud1 have not been rescued ( Fig 7H and 7I ) . Based on these results , we proposed that the regulation of PIN1 polar localization through reversible phosphorylation of S337 residue of PIN1 by the MKK7-MPK6 cascade is an essential mechanism that might be specific to shoot branching regulation . Moreover , the previous report showed that MPK6 localized to the cytosol , nucleus , and the plasma membrane [55] . We further examined the subcellular localization of MKK7-GFP and MPK6-GFP fusion proteins in tobacco epidermal cells and verified that neither MKK7-GFP nor MPK6-GFP showed polar localization ( S16 Fig ) . Considering that PIN1 S337 within HL has a presumably cytoplasmic orientation , we speculated that phosphorylation of PIN1 S337 by the MKK7-MPK6 cascade most likely occurs in the cytosol . However , much more work needs to be done to elucidate where MPK6 regulates the PIN1 in the cell . Multiple phosphorylation sites in PIN proteins , of which some are targets of ACGVIII kinases , mediate PIN polarity [32] . Therefore , identification of the upstream components in the phosphorylation cascade is a big challenge [32] . Among all PIN1 phosphorylation sites , S337 and T340 are in the MFSPNTG sequence . As MAPKs preferably phosphorylate Ser or Thr residues followed by a Pro [56] , the speculation that S337 might be a target of MAPKs was proposed nearly ten years ago [57] . However , until now , there has been no direct evidence to support this hypothesis . Our results provided a solid genetic and cytological evidence to reveal an important role of the MKK7-MPK6 cascade-dependent PIN1 phosphorylation at S337 in controlling shoot branching . Here , we identified another new upstream component of PIN1 phosphorylation in addition to the well-studied ACGVIII kinases , establishing a novel relationship between a specific MAPK pathway and PIN1 polarity and its function in regulating shoot branching . Based on these studies , PIN proteins can be phosphorylated by at least three different protein kinases: D6PKs , PID , and MAPKs . PID-dependent PIN1 phosphorylation functions in early plant development stages , such as embryo development and organogenesis [29 , 31] , while our result showed that PIN1 plays an important role downstream of the MKK7-MPK6 cascade in regulating PAT-mediated shoot branching . Moreover , it has been shown that D6PKs and PID have different functions in the control of PIN3-mediated phototropic bending [58] , suggesting that these three different kinases regulate PIN-mediated auxin transport at the temporal and spatial level , which implies that spatial and temporal regulation of PIN polarity might be partly attributed to the specific phosphorylation sites of ACGVIII kinases and MAPKs . Another possibility that MAPKs or ACGVIII kinases maintain spatio-temporal feature is to phosphorylate specific PIN ( s ) in a given biological pathway . In our study , the MKK7-MPK6 cascade mainly regulates PIN1-mediated auxin transport in shoot . However , bud1 has other phenotypic defects such as plant height , the hypocotyl gravitropism , filament elongation , and lateral root development ( Fig 2 ) . This prompts us to speculate that these phenotypes of bud1 are the consequence of defective auxin transport activity caused by other PINs , such as PIN2 , PIN3 , PIN4 , or PIN7 . It was reported that phosphorylation status of PIN3 plays a decisive role in root gravitropism [59] . Our alignment analysis also showed that S337 of PIN1 is conserved in PIN3 , which is corresponding to S317 site of PIN3 ( S10 Fig ) , implying that the MKK7-MPK6 cascade might regulate hypocotyl gravitropism by phosphorylating the S317 site of PIN3 . However , five main predicted phosphorylation sites ( S317 , S337 , T340 , T439 , and S446 ) are not conserved within the central HL of PIN proteins ( S10 Fig ) . Thus , finding the major phosphorylation site by MAPK on other PINs will help to further establish the relationship between a specific MAPK pathway and PIN polarity during plant development . Arabidopsis thaliana plants were grown on the mixture of vermiculite and soil ( 2:1 ) saturated with 0 . 3 × B5 medium under long day condition ( 80–120 μE m-2 s-1 ) at 22°C . For plants grown in Petri dishes , seeds were surface sterilized with 70% ( v/v ) ethanol for 3 min and 12% ( v/v ) commercial bleach solution for 15 min , rinsed 5 times with sterile water , and suspended in 0 . 2% agar . The sterilized seeds were plated on 0 . 5 × Murashige & Skoog ( MS ) medium containing 0 . 8% agar and pre-incubated at 4°C in the dark for 3 d before being cultured under the conditions as indicated . For temperature treatment , plants were germinated and grown on 0 . 5 × MS medium in versatile environmental test chambers ( Sanyo ) under continuous illumination at 20 and 29°C , respectively . The mpk3 mutant results from a deletion of 6 . 3 kb fragment by fast neutron mutagenesis [40] , and the mpk6 mutant ( SALK_073907 ) is a T-DNA insertion mutant obtained from ABRC . The double mutants were generated from the cross of homozygous bud1 with mpk3 and mpk6 , respectively , and identified by PCR-based method ( S4A–S4C Fig ) [34] . All the genotyping primers can be found in S1 Table . The mkk7 mutant ( CS110477 ) obtained from ABRC results from transposon insertion . Homozygous mutant plants were identified by PCR with MKK7-specific primers ( S2A Fig ) . RT-PCR data showed that mkk7 did not produce detectable MKK7 RNA ( S2A Fig ) . The primers for genotyping were listed in S1 Table . Auxin transport from the shoot apex into roots in seedlings was conducted using intact light-grown seedlings as described previously [34] with the following modifications: seedlings used in this assay were grown on 0 . 6% phytagel ( SIGMA , P-8169 ) plates containing 0 . 25 × MS ( pH 5 . 8 ) and 0 . 5% sucrose . Seedlings were grown 4 . 5 d after germination . Before assay , 10 seedlings were transferred to vertically discontinuous filter paper strips saturated in one-quarter MS medium and allowed to equilibrate for at least 2 h . Auxin solution used to measure transport was made up in 0 . 25% agarose containing 25 mM MES ( 2-[N-Morpholino] ethanesulfonic acid ) , pH 5 . 2 . A 0 . 2 μl microdroplet containing 500 nM unlabeled IAA or 500 nM 3H-IAA ( specific activity 26 Ci/mmol ) was placed on the shoot apical tip of seedlings using a 0 . 5 μl glass syringe . Seedlings were incubated in the dark for 6 h . After incubation , the hypocotyls and cotyledons were removed . A 2 mm section of filter paper , upon which the S-R TZ was centered , was harvested along with the 2 mm segment of tissue containing the S-R TZ . The samples were allowed to immerge in 2 ml of universal scintillation fluid for at least 18 h before being counted in a liquid scintillation counter . Auxin transport in inflorescence stems was detected using 5-wk-old plants as described [60] . Stem segments , 2 . 5 cm in length cut under the first silique , were placed in 0 . 5 ml microcentrifuge tubes in inverted orientation and submerged in 20 μl of radioactive IAA solution ( 100 nM 3H-IAA in 0 . 05% MES , pH 5 . 5 ) at 22°C in darkness for 18 h . The base of the inflorescence submerged was used to measure background IAA movements . After incubation , a 5 mm section was excised from the nonsubmerged end of the segment and was transferred into 1 ml scintillation fluid . The samples were counted in a liquid scintillation counter after 18 h . The ORFs of MPKs were cloned into pET-28a vector ( Novagen ) , and cMKK7 was cloned into pET-44a vector ( Novagen ) for recombinant protein expression . Fifteen MPKs were successfully expressed , and the other five MPKs ( MPK11 , 13 , 15 , 16 , 18 ) failed to be expressed . The primers used for construction of recombinant proteins are listed in S2 Table . PIN1HL was cloned into the pET-60-DEST vector ( Merck ) , and Quickchange XL site-directed mutagenesis kit ( Stratagene ) was employed to generate mutant constructs . The primers used for mutated PIN1HL are listed in S3 Table . Recombinant proteins were expressed and purified according to the manufacturer’s protocols , and the protein concentration was determined by Bio-Rad protein assay reagent . The recombinant proteins were confirmed by western blot using anti-GST and anti-HIS antibodies . The in vitro kinase assay was carried out as previously reported with minor modifications [61] . The recombinant MPKs ( 2 μg ) and cMKK7 ( 0 . 5 μg ) were incubated in 20 μl kinase reaction buffer ( 50 mM Tris-HCl pH 7 . 5 , 10 mM MgCl2 , 10 mM NaCl , 0 . 1 mM ATP , and 1 mM DTT ) at room temperature for 30 min . Then MBP ( 2 μg ) and 3 μCi [γ-32P] ATP ( 3000 Ci/mmol , GE Health ) were added for another 30 min incubation at room temperature . The phosphorylated MBP and MPKs were visualized by Typhoon Trio after being separated by 15% ( w/v ) SDS-PAGE . The in vitro phosphorylation assay was conducted as previously described [61] . Recombinant GST-tagged MPK6 was firstly activated by recombinant cMKK7 in the kinase reaction buffer at the presence of 50 μM ATP at 22°C for 30 min . Then , activated MPK6 ( 20:1 substrate enzyme ratio ) was incubated with GST-PIN1HL or GST-PIN1HL with substitution of various phosphorylation sites in the kinase reaction buffer with 25 μM ATP and 3 μCi [γ-32P] ATP ( 3 , 000 Ci/mmol ) at 22°C . The reactions were stopped by adding SDS loading buffer after 60 min . Phosphorylated PIN1HL was visualized by autoradiography after being resolved in a 10% ( w/v ) SDS-PAGE gel . PIN1pro::PIN1-GFP plants were crossed into bud1 , mpk3 , mpk3bud1 , mpk6 , and mpk6bud1 mutants , respectively , and homozygous lines were used for further analysis . Longitudinal hand sections were made from basal internodes of inflorescence stems ( approximately 1 cm above the rosette ) of 30-d-old plants . Sections were mounted in water , and GFP fluorescence was immediately observed on an OLYMPUS FV 1000 confocal laser scanning microscope . For each genotype , at least 20 samples were examined . Agarose sectioning was employed to visualize PIN1-GFP in 35S::PIN1-GFP transgenic lines . Briefly , inflorescence stems , approximately 1 cm above the rosette , were embedded in agarose ( 7% low melting agarose ) . After cooling in 4°C for 15 min , the segments coated by agarose were sliced with the Leica VT1200S in longitudinal direction at the thickness of 110 μm . The following microscopic observation procedure is the same as PIN1pro::PIN1-GFP transgentic plants . DR5-GFP plants were crossed into bud1 , mpk3 , mpk3bud1 , mpk6 , and mpk6bud1 mutants , respectively , and homozygous lines were used for further analysis . Transverse cross-sections of basal internodes of inflorescence stems were employed . Material preparation and GFP fluorescence detection were as described above . Agrobacterium tumefaciens strain EHA105 harboring binary vectors in 5 ml of Luria-Bertani ( LB ) medium with appropriate antibiotics was cultured overnight . Bacteria solution was prepared and infiltrated into the 4-wk-old tobacco leaves as described [16] . Two to three d after the infiltration , the leaf disks were excised and mounted onto slides for confocal imaging . GFP fluorescence detection were as described above . The reaction mixture was lyophilized and reconstituted with 8 M urea in 25 mM NH4HCO3 ( pH 7 . 4 ) . The proteins were then digested with trypsin as previously described with slight modifications [62] . Briefly , the proteins were reduced with 10 mM DTT at 37°C for 4 h and alkylated with 25 mM iodoacetamide at room temperature for 1 h in the dark; in-solution trypsin digestion was performed at 37°C for 18 h using a trypsin:substrate ratio 1:50 . The phosphopeptides were enriched by immobilized metal affinity chromatography ( IMAC ) using a well-established protocol [63] and then analyzed by LC-MS/MS using LTQ-Orbitrap elite mass spectrometer with enabled multistage activation . Phosphopeptides were identified by searching the International Protein Index database ( IPI , Arabidopsis , version 3 . 85 ) using the software Proteome Discoverer ( version 1 . 3 ) . The phosphorylation probability was analyzed according to method as described in previous report [64] . PIN1-GFP was amplified from PIN1pro::PIN1-GFP marker line [50] genomic DNA and then was cloned into the pB2GW7 , 0 vector ( Ordered from the Department of Plant Systems Biology at Ghent University , Belgium ) . PIN1-GFP S337A and S337D substitutions were made as PIN1HL site mutations described above on entry vector . The 35S::MPK6-GFP and 35S::MKK7-GFP constructs were made by inserting MPK6-GFP and MPK7-GFP between the BamHI and EcoRI sites of binary vector PBI121 , respectively . All the primers used for above cloning are shown in S4 Table . Total RNA were isolated from aerial parts of 3-wk-old plants using a TRIzol kit ( Invitrogen ) according to the user manual . Real-time PCR was performed as described [65] and using primers listed in S5 Table .
MAPK cascades play important roles in transducing environmental and developmental signals into adaptive and programmed responses . Because of the complexity of MAPK cascades , revealing the specificity of the MAPK modules is key to forming a functional and fully connected signal transduction system in higher plants . In the MAPK signaling module , MAPK kinases ( MKKs ) are of particular importance because they serve as the convergence and divergence points in the MAPK signal transduction . Our previous study had characterized an Arabidopsis bushy and dwarf1 ( bud1 ) mutant , in which the MAP Kinase Kinase 7 ( MKK7 ) was constitutively activated , leading to multiple auxin-related developmental defects . Here , we used the bud1 mutant to discover the signaling events of MKK7 downstream modules . Our results demonstrated that MPK6 and MPK3 are two major downstream targets of MKK7 . Furthermore , we found that MKK7-MPK6 cascade phosphorylates the Ser 337 ( S337 ) site of PIN1 , affecting PIN1’s polar localization and thus modifying shoot branching . Our findings specify the functions of the MKK7-MPK6 cascade and explain how the MKK7-MPK6 signaling pathway regulates polar auxin transport to determine shoot branching in Arabidopsis .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "phosphorylation", "plant", "anatomy", "plant", "growth", "and", "development", "plant", "embryo", "anatomy", "brassica", "hormones", "developmental", "biology", "plant", "science", "model", "organisms", "signaling", "cascades", "plant", "hormones", "seedlings", "inflorescences", "plants", "mapk", "signaling", "cascades", "arabidopsis", "thaliana", "research", "and", "analysis", "methods", "protein", "kinase", "signaling", "cascade", "plant", "embryogenesis", "plant", "development", "proteins", "biochemistry", "signal", "transduction", "plant", "biochemistry", "plant", "and", "algal", "models", "hypocotyl", "cell", "biology", "post-translational", "modification", "embryogenesis", "biology", "and", "life", "sciences", "auxins", "cell", "signaling", "organisms", "fruit", "and", "seed", "anatomy" ]
2016
Mitogen-Activated Protein Kinase Cascade MKK7-MPK6 Plays Important Roles in Plant Development and Regulates Shoot Branching by Phosphorylating PIN1 in Arabidopsis
Models of early protein evolution posit the existence of short peptides that bound metals and ions and served as transporters , membranes or catalysts . The Cys-X-X-Cys-X-X-Cys heptapeptide located within bacterial ferredoxins , enclosing an Fe4S4 metal center , is an attractive candidate for such an early peptide . Ferredoxins are ancient proteins and the simple α+β fold is found alone or as a domain in larger proteins throughout all three kingdoms of life . Previous analyses of the heptapeptide conformation in experimentally determined ferredoxin structures revealed a pervasive right-handed topology , despite the fact that the Fe4S4 cluster is achiral . Conformational enumeration of a model CGGCGGC heptapeptide bound to a cubane iron-sulfur cluster indicates both left-handed and right-handed folds could exist and have comparable stabilities . However , only the natural ferredoxin topology provides a significant network of backbone-to-cluster hydrogen bonds that would stabilize the metal-peptide complex . The optimal peptide configuration ( alternating αL , αR ) is that of an α-sheet , providing an additional mechanism where oligomerization could stabilize the peptide and facilitate iron-sulfur cluster binding . Metals in proteins play important roles in stabilizing structure , promoting electron transfer and performing catalysis . Whole-genome analyses of phylogenetically diverse microorganisms suggest the earliest proteins incorporated metals and that metal usage over biological history evolved to match the availability of inorganic components in the environment [1] , [2] , [3] . The mechanisms by which the ligand environment modulates metal affinity and specificity are of significant interest in the study of metalloprotein evolution , function and design . Geometric requirements of metal coordination are predicted to impose specific constraints on the structure and topology of a bound polypeptide chain . In this study , we computationally model the accessible conformations of a ferredoxin-like peptide bound to an Fe4S4 cubane cluster in order to better understand how a putative early metalloprotein may have evolved . It has been proposed that a set of core genes encode proteins that carry out key redox reactions essential for promoting life and driving biogeochemical cycles [4] . These proteins would be among the earliest to emerge in the ancient oceans . Identifying members of this set of core genes is an important step in understanding the evolution of microbial metabolism and emergent biogeochemical cycles . A number of features of ferredoxins make them an attractive as key players in the evolution of redox active proteins . Sequence analysis suggests that ferredoxins evolved very early in the origins of biological catalysis of redox reactions [5] , [6] . All ferredoxins have a simple , conserved fold that binds two Fe4S4 clusters and is composed of fifty to sixty amino acids . Sequence and structural symmetry suggest it may have evolved from a gene duplication event of a thirty amino acid sequence , each capable of binding one iron-sulfur cluster [7] , [8] , [9] , [10] . An early study of the ferredoxin sequence by Eck and Dayhoff in 1961 revealed even shorter repeats of four amino-acids [5] , suggesting a prebiotic “protoferredoxin” was potentially composed of a primeval subset of the twenty amino acids [11] , [12] . Midpoint potentials ( −700 to −300 mV ) of ferredoxins are lower than most other proteins , consistent with the mildly reducing early oceans [13] , [14] . It has been speculated that the iron-sulfur cluster utilized in many redox proteins [15] may be an evolutionary relic of prebiotic chemistry catalyzed by mineral surfaces . Mineral surfaces can effectively adsorb and concentrate organic molecules and catalyze various chemical reactions implicated in the origin of non-equilibrium redox reactions . Chiral mineral surfaces can selectively interact with chiral amino acids , and thus have been extensively studied as a potential origin of life on Earth [16] . Iron-sulfur mineral surfaces especially have gained much attention in the context of deep-sea iron-sulfur rich hydrothermal vents where the earliest biologically relevant redox reactions are postulated to have occurred [17] , [18] . Assuming ferredoxin is one of the select core genes that originated from a mineral surface catalyst - what might intermediates in this progression from mineral to protein look like ? ( Figure 1 ) : ( A ) Iron-sulfur minerals such as pyrite and mackinawite can spontaneously catalyze carbon fixation to generate essential organic molecules for life [19] , [20] , [21] , [22] , ( B ) The regular mineral concentrates amino acids [23] , permitting new chemistry or enhancing existing reactions . ( C ) Condensation of small polypeptides occurs at the water-mineral interface [24] . These polypeptides could have sequences similar to Dayhoff's proposed tetrapeptides [25] and would be capable of stabilizing specific oxidation states of bound iron-sulfur fragments . ( D ) Small polypeptides are used as components of ferredoxin-like proteins . This is the transition from prebiotic chemistry to life and could occur within the context of models for such a transition such as an RNA-world where peptides are co-opted by small RNA hairpins [26] . ( E ) Ferredoxin is retained in all kingdoms and becomes a domain of larger proteins that include many of the core redox genes of life . Although each of these stages is poorly understood and arguably controversial , this conceptual framework allows the design of specific simulations and experiments to explore the feasibility of ferredoxin evolution from a mineral precursor . The structural properties of a putative proto-ferredoxin peptide in Stage C have implications beyond origins of life models to metalloprotein design . Although several iron-sulfur binding sites have been designed into existing proteins [27] , [28] and de novo folds [29] , [30] , [31] , very few have shown any significant stability to cycles of oxidation-reduction , diminishing their utility in catalysis or bioenergy applications [32] , [33] . By elucidating the geometric and energetic constraints on a polypeptide bound to an iron-sulfur cluster , one can potentially understand the physical rules governing biological redox reactions and the designing novel protein structures . In the ferredoxin fold , iron-sulfur cluster has a quasi-tetrahedral structure with four coordination sites , which are most commonly occupied by four cysteine thiolates . The iron-sulfur cluster itself is achiral and the protein topology is mainly dependent on how the cysteine groups from a peptide chain are linked with four iron atoms in the cluster [34] . Topologically , two different modes of protein-cluster interactions , right-handed or left-handed , are possible ( Figure 2 ) . These two topological states cannot be superimposed onto each other by bending or stretching the representative molecular graphs [34] . Previous studies analyzing iron-sulfur proteins in the Protein Data Bank ( PDB ) reported that all redox active proteins had a right-handed fold; although left-handed configurations existed for redox inactive proteins [35] . Herein , we present the work that elucidates why a right-handed heptapeptide topology may have evolved in the context of metal-protein energetics . The achiral iron-sulfur ( Fe4S4 ) cluster has a D2d point group symmetry and is generally bonded to four cysteine thiolate groups [36] , [37] . Three of the coordination sites are occupied by cysteine thiolates from a conserved heptapeptide sequence motif ( CXXCXXC ) and the remaining fourth coordination site is occupied by an outlier cysteine , which is most frequently followed by a proline ( CP ) [38] . This particular binding motif accounts for approximately 25% ( 36 out of 137 ) of iron-sulfur binding motifs from 104 crystal structures available from PDB ( Table S1 ) . Among the CXXCXXC motifs , about 85% ( 31 out of 36 ) have a ferredoxin fold and approximately 15% have globin-like folds and others as defined by Structural Classification of Proteins ( SCOP ) [39] . Topologically , the CXXCXXC heptapeptide motif can interact with an iron-sulfur cluster in two different ways , right-handed or left-handed ( Figure 2 ) . For the discussion of these topological states , we quantitatively describe the handedness of the folding using a “topology angle” , θ aligning the outlier cysteine on a z-axis of an internal coordinate frame ( Figure 3 ) . Once the outlier cysteine is specified , handedness in this study is defined relative to the N- to C-terminus chain direction , either proceeding clockwise ( right-handed: 0°<θ<90° ) or counterclockwise ( left-handed: 90°<θ<180° ) around the cluster ( Figure 4 ) . The outlier cysteine residue can be located before or after the CXXCXXC motif ( CP…CXXCXXC or CXXCXXC…CP ) . Since the initial analysis on protein structure database [35] , the number of solved protein structures has increased at an exponential rate . A non-redundant subset ( 30% sequence similarity filter ) of the PDB was searched for structures with an iron-sulfur ( Fe4S4 ) cluster coordinated by a CXXCXXC sequence . The topology angle , θ , was calculated from the PDB coordinates ( Figure 4 ) . A histogram of the topology angles reveals that only right-handed folds are involved in an iron-sulfur cluster binding ( Figure 5 ) . The CXXCXXC motif always has a topology angle around 75° . Left-handed configurations of CXXCXXC were not observed , leading us to examine whether such configurations were energetically plausible . An ensemble of CGGCGGC polypeptide configurations was generated . Glycine was chosen for non-Cys positions due to its high backbone flexibility , ensuring the primary conformational constraints came from metal-peptide interactions . The protCAD software platform ( protein Computer Assisted Design ) [29] , [40] was used to exhaustively enumerate all combinations of backbone and sidechain torsions in 60° intervals for Φ , ψ and 120° intervals for the cysteine χ1 rotamer ( Figure 6 and Figure 7 ) . Out of 5 . 8×1010 ( 33×612 ) configurations , 232 exhibited net-favorable van der Waals interactions ( less than 0 kcal/mol ) , Fecluster··· Sγ distances ( <3 A ) and Cβ-Sγ···Fecluster angles ( 120° to 180° ) that would permit binding to an iron-sulfur cluster . The protein structures were then minimized in AMBER to reduce strain from distortions caused by discrete conformation sampling [41] . Topology angles of the computationally generated dataset clustered into two distinct populations - right and left-handed folds - suggesting the CGGCGGC heptapeptide could bind to the iron-sulfur cluster with either topology ( Figure 8 ) . In fact , the simulation identified more left-handed structures ( 67% ) than right-handed structures ( 32% ) , indicating left-handed topologies were entropically favorable . Conducting the same simulation on CAACAAC resulted in 54% left-handed and 46% right-handed structures , suggesting that the steric hindrance of amino acid side chains itself is not sufficient to discriminate the handedness of the topological state . A histogram of the energy distributions for left and right-handed topologies show no significant difference ( Figure 9 ) , indicating intrinsic stability of the fold alone is unlikely to account for evolution of a unique topology . The reduced state of the iron-sulfur cluster can be stabilized by hydrogen bonds contributed by nearby backbone amides [42] . The number of hydrogen bonds around the iron-sulfur cluster is also related to the solvent accessibility to the cluster , thereby tuning the midpoint potential [43] , [44] . A typical ferredoxin fold exhibits six such interactions with backbone amides directing the proton toward the cluster . Hydrogen bond formation is at the expense of unfavorable backbone dihedral angles , particularly the positive Φ values at X2 and X3 positions ( Table S2 ) . For the analysis of the hydrogen bonding environment of computationally generated structures , interactions were counted based on discrete distance and angular cutoffs: a hydrogen-sulfur distance less than 3 . 5 Å and N-H···S angles between 120 to 180° [45] . The number of hydrogen bonds between nitrogen and sulfur were counted based on cutoffs: 3 . 8 Å and 110 to 180° . Right-handed folds could accommodate six hydrogen bonds , but a maximum of three hydrogen bonds were found in structures with left-handed folds ( Figure 10 ) . The electrostatic stabilization of a bound cluster by proximal backbone amides was estimated by comparing the total energies of charged versus uncharged clusters in the context of a coordinating peptide . The net contribution of hydrogen bonds can represented several ways: the average of pairwise distances between hydrogen and sulfur atoms ( Figure 11A ) and discrete number of hydrogen bonds plotted against the peptide-cluster interaction energies ( Figure 11B ) . The interaction energy improves as the distances between sulfur atoms to hydrogen atoms are reduced . The result also indicates that the right-handed peptide-cluster interaction can have a stabilization effect up to −80 kcal/mol , whereas a left-handed fold can only achieve −50 kcal/mol . For comparison , we generated a CGGCGGC peptide using coordinates from experimental ferredoxin structures , including proteins with non-ferredoxin fold ( Figure 11A inset , Supplementary data ) . The right-handed topology in natural ferredoxin and non-Fd proteins presents a network of stabilizing backbone amides that interact strongly with the Fe4S4 cluster . The result shows the best right-handed structure contributes more stabilizing hydrogen bonds than the best left-handed structure . Additionally , the inset to figure 11A reveals tightly clustered experimental results , all which cluster around the same right-handed configuration and present six hydrogen bonds , suggesting the right-handed heptapeptide topology is a unique entactic state . A microscopic analysis of the Fe4S4 binding region of ferredoxin provides some insights into the predicted features of an ancient , short proto-ferredoxin . The right-handed topology observed in redox-active iron-sulfur proteins is not dictated by the peptide chain . In fact , left-handed chain topologies are entropically favored and have slightly improved stabilities in the absence of the cluster . Only when considering electrostatic interactions with the cofactor is the natural right-handed topology the optimal solution . Thus short CxxCxxC peptides alone are unlikely to serve as early redox active species without additional external stabilizing interactions . These may have taken the form of longer sequences with super-secondary structure such as those in designed peptide maquettes [38] , [46] . It is interesting to note that the model conformation with the best peptide-cluster interaction energy and the ferredoxin-like conformations are both an α-sheet , characterized by residues in alternating αL and αR conformations . This motif was first described by Pauling and Corey as the ‘pleated sheet’ [47] . α-sheets are thought to be intermediates in a number of protein aggregation disorders [48] , [49] . The conformation is also implicated in early peptides due to their anion binding properties [50] . It is possible that stabilization of α-sheets provides the entactic state required for favorable cluster binding . The identification of a specific iron-sulfur binding topology may point the way to a mechanism by which the first core metalloproteins evolved . To have a quantitative measure for the fold-handedness , an arbitrary plane was defined with two vectors , which were defined by Cα coordinates from three cysteine residues . The topology angle , a quantitative measure of fold-handedness , was then defined as the angle between a normal vector of the arbitrary plane and a vector from the middle cysteine Cα to the cluster . By definition , the quantitative measurement of fold-handedness ( topology angle ) can take any numeric value from 0° to 180° . Iron-sulfur cluster coordinates were extracted from the PDB file , 2FDN . We created a hybrid artificial amino acid residue ( Clu ) by linking an iron-sulfur cluster to a cysteine residue . The artificial amino acid was added to the amino acid library of protCAD . Initially a peptide ensemble ( Cys-Gly-Gly-Cys-Gly-Gly-Cys ) was created and subsequently the central Cys was substituted to Clu . For a given ensemble , there are six Φ ( C′-N-Cα-C′ ) , six ψ ( N-Cα-C′-N ) . For each cysteine residue , there three χ1 ( N-Cα-Cβ-Sγ ) dihedral angles . For the central iron-sulfur cluster fused cysteine residue , there are additional dihedral angles , which are χ2 ( Cα-Cβ-Sγ-FeClu ) and χ3 ( Cβ-Sγ-FeClu-SClu ) . All phi and psi dihedral angles were increased by a step size of 60° and all chi dihedral angles were set at −180° , −60° , or 60° . The entire protein structural space was searched by the permutations of seventeen dihedral angles . Plausible protein structures were then determined by geometric parameters , such as a distance from Sγ to FeClu with a cutoff ( <3 . 0A ) . Energy parameters calculated based on a Lennard-Jones equation [45] was also used to detect feasible structures ( total energy<0 kcal/mol ) . The structures obtained from the ProtCAD simulations were subjected to energy minimization calculations using Amber 11 [51] , with a generalized Born solvent model [52] , [53] . Protein atoms were described with the parm99SB [54] , [55] , [56] force field parameterization . The atomic charges were modified so that an oxidized Fe4S4+2 cluster bound to 3 Cys had a net charge of −1 , yielding the following charges: qFe = 0 . 6518 e , qS ( cluster ) = −0 . 5552 e , qSG ( cysteine ) = −0 . 6042 e . The maximum number of minimization cycles was set to 105 , and the structures were considered minimized when the root-mean-square of the Cartesian elements of the gradient was less than 10−4 kcal/mol-Å . To compare the degree of electrostatic stabilization of the cluster in the different peptide models , the charge of the S atoms of the Fe4S4 cluster was set to zero , and a single point energy calculation was performed . A number of structures converged to an identical structure after the energy minimization process . The redundant structures were then removed by MMTSB ( Multiscale Modeling Tools in Structural Biology ) k-clustering algorithm [57] .
The ferredoxin fold is one of the oldest structures capable of catalyzing electron transfer reactions . In nature , only a right-handed topology exists in the ferredoxin fold . To understand how a specific fold-handedness was selected , we analyzed the structural motif using the tools of de novo protein design , searching in an unbiased fashion for backbone geometries that can favorably interact with the tetrahedral iron-sulfur cluster . In silico , we found both left-handed and right-handed folds can be formed , however the right-handed folds provide up to six hydrogen bonds that can stabilize the reduced iron-sulfur cluster , whereas left-handed folds at most form three hydrogen bonds . The difference in electrostatic conformational energy may have influenced selection of topology early in the evolution of iron-sulfur cluster containing proteins . This observation led us to establish a fundamental protein design principle that only right-handed peptide folds can properly interact while maintain redox function . Our results provide guidance in the creation of artificial proteins capable of carrying out redox reactions .
[ "Abstract", "Introduction", "Results/Discussion", "Materials", "and", "Methods" ]
[ "biochemistry", "computational", "chemistry", "earth", "sciences", "chemistry", "biology", "computational", "biology", "biophysics", "geochemistry" ]
2012
Energetic Selection of Topology in Ferredoxins
Face expressions are a rich source of social signals . Here we estimated the proportion of phenotypic variance in the brain response to facial expressions explained by common genetic variance captured by ∼500 , 000 single nucleotide polymorphisms . Using genomic-relationship-matrix restricted maximum likelihood ( GREML ) , we related this global genetic variance to that in the brain response to facial expressions , as assessed with functional magnetic resonance imaging ( fMRI ) in a community-based sample of adolescents ( n = 1 , 620 ) . Brain response to facial expressions was measured in 25 regions constituting a face network , as defined previously . In 9 out of these 25 regions , common genetic variance explained a significant proportion of phenotypic variance ( 40–50% ) in their response to ambiguous facial expressions; this was not the case for angry facial expressions . Across the network , the strength of the genotype-phenotype relationship varied as a function of the inter-individual variability in the number of functional connections possessed by a given region ( R2 = 0 . 38 , p<0 . 001 ) . Furthermore , this variability showed an inverted U relationship with both the number of observed connections ( R2 = 0 . 48 , p<0 . 001 ) and the magnitude of brain response ( R2 = 0 . 32 , p<0 . 001 ) . Thus , a significant proportion of the brain response to facial expressions is predicted by common genetic variance in a subset of regions constituting the face network . These regions show the highest inter-individual variability in the number of connections with other network nodes , suggesting that the genetic model captures variations across the adolescent brains in co-opting these regions into the face network . Interactions with peers are of high relevance to our mental health . Patients with various psychological disorders show impairments in face perception and emotion recognition [1]–[4] . Similarly , differential neural responses to faces have been reported in various psychological disorders including depression [5] , psychopathy and/or aggressive tendencies [6] , autism [7] , and schizophrenia [8] . Our ability to process faces is modulated by both environment and genes . Using a twin design , Zhu and colleagues observed that inter-individual variations in face perception are heritable , with the genetic component as high as 50% in adolescents performing various face tasks [9] . Although the key elements of the neural network underlying face processing are well known [10] , whether or not brain response to facial expressions show comparable levels of heritability is unknown . Here we address this question using genomic-relationship-matrix restricted maximum likelihood ( GREML ) , applied using Genome-wide Complex Trait Analysis ( GCTA ) software [11] to a dataset of functional magnetic resonance images ( fMRI ) obtained in over 1 , 600 typically developing adolescents while they were observing videoclips of ambiguous or angry facial expressions [The IMAGEN Study; 12] . The GREML approach allows one to estimate how much phenotypic variance is attributable to the genetic variance captured by all common genetic variations ( single nucleotide polymorphisms; SNPs ) assayed in a typical Genome Wide Association Study ( GWAS ) . We will ask here whether “heritability” of the response to facial expressions – as estimated using the GREML approach – varies across these regions . We will then examine possible reasons for such regional variations . The GREML-based metrics were calculated from a total of 1 , 620 unrelated adolescents ( age M ( SD ) = 14 . 4 ( 0 . 39 ) range 12 . 7–16 . 3 years , n = 879 male , n = 945 female ) with complete , quality controlled fMRI and genomic data ( 511 , 089 SNPs ) . In fMRI , brain response to a stimulus is inferred from the variations in hemodynamics detected as the blood oxygenation-level dependent ( BOLD ) signal on T2*-weighted MR images . This signal relies on the fact that brain activity is associated with an oversupply of oxygenated blood to the brain region engaged by the stimulus; consequently , small veins that drain this region contain some of the unused oxygenated blood . Thus , the BOLD signal reflects the proportion of oxygenated and de-oxygenated blood in a given brain region at a given moment; most likely , this hemodynamic signal is proportional to the local field potentials generated by ( excitatory ) inputs . Here , summary measures for the BOLD response ( %BSC ) were calculated for two face viewing conditions ( Ambiguous movements and Angry Expressions ) , each compared with a non-biological motion control condition; this was done for 25 brain regions of interest ( ROIs ) , as defined previously using a probabilistic map of the brain response to facial expressions [13] . GREML-based estimates of “heritability” were calculated using the GCTA package [13][11] . We observed significant estimates of “heritability” for the Ambiguous vs . Control contrast %BSC in 9 out of these 25 ROIs ( Fig . 1 , Supplementary Table S1 ) . No significant estimates were observed for any ROI in the Angry vs . Control contrast ( Supplementary Table S2 ) . By chance we would expect 25*0 . 05 = 1 . 25 false positives when examining 25 ROIs ( in each contrast ) , or 2 . 5 when examining 50 ROIs ( both contrasts combined ) at the α = 0 . 05 level . As we found 9 ROIs with P<0 . 05 this appears to be evidence against the null hypothesis . To provide a P-value for this count-rate approach , we conducted a Monte Carlo simulation for each contrast using the observed correlation matrix ( see Supplementary Figure S1 for the phenotypic correlation matrix between all 25 ROIs ) [14] . Using 50 , 000 realizations , we simulated the null-hypothesis test statistics for each ROI ( using correlated outcomes ) and tabulated the distribution of the number of P-values significant at 0 . 05; this empirical distribution can be used to compute P-values for this count statistic . For the Ambiguous Facial Expressions contrast , this simulation confirmed that we could reject the null hypothesis of observing 9 significant tests by chance at P value of 0 . 03 . For Angry Facial Expressions contrast , the P-value must be 1 . 0 for a count of 0 significant tests . To illustrate the relationship between the GREML-based estimates of heritability ( VG/Vp ) and the number of SNPs with p values lower than a certain threshold , we have calculated Pearson's correlation coefficients between these two measures across 25 ROIs , as done by Yang and colleagues in their GREML-based study of 47 different traits [15] . We obtained the following results: p<0 . 001: R2 = 0 . 08; p<0 . 01: R2 = 0 . 47; p<0 . 05: R2 = 0 . 54; p<0 . 1: R2 = 0 . 74; p<0 . 15: R2 = 0 . 82; p<0 . 2: R2 = 0 . 76; p<0 . 25: R2 = 0 . 74; and p<0 . 3: R2 = 0 . 64 . In Figure 2 , we plot the number of SNPs with p<0 . 15 and the VG/Vp values across the 25 ROIs . Given the high heritability of general intelligence [16] , we have examined the possibility that intelligence correlates with the inter-individual variations in the brain response to facial expressions in the Ambiguous condition . In a subset of 1 , 772 individuals with available scores on four subtests of the Wechsler Intelligence Scale for Children – IV ( similarities , vocabulary , block design and matrix reasoning ) , we found no correlation between these scores and the mean %BSC across all ROIs ( p>0 . 3 ) or between the scores and the mean %BCS in the Optional ( p>0 . 2 ) and Obligatory ( p>0 . 4 ) networks . The lack of the relationship between general intelligence and %BSC suggests that the former does not contribute to the above heritability estimates of the brain response to the ambiguous facial expressions . Next , we asked whether the above GREML-based estimates of heritability reflect any properties of the brain response across the examined ROIs . For example , are the ROIs with stronger response to facial expressions more heritable ? Table 1 provides the population means and standard deviations for %BSC for all ROIs in the ambiguous contrast . Neither the population means ( r = −0 . 28 , p = 0 . 18 ) nor the population variance ( r = 0 . 07 , p = 0 . 73 ) of %BSC values across the 25 ROIs predicts the GREML-based ( Genetic Variance [VG]/Phenotypic Variance [Vp] ) estimates of heritability . The brain regions considered here may be viewed as nodes of a “face” network . The strength of each region's contribution to this network may differ across regions ( ROIs ) and across individuals . To quantify this phenomenon , we extracted mean time-courses in the BOLD signal from all ROIs in each participant and used these to calculate matrices of functional connectivity for all participants . From these matrices , we estimated the number of connections of a given region with the other members of the face network using the graph-theory metric of nodal “degree” [17] . Table 1 provides the population means and standard deviations for the nodal degree for all ROIs ( Ambiguous Facial expressions ) . We then examined whether differences in this measure of functional connectivity across the 25 ROIs constituting the face network predict their GREML-based estimates of heritability ( VG/Vp estimates ) . This is the case: the population ( inter-individual ) variance in the nodal degree predicts strongly the “heritability” ( R2 = 0 . 38 , p<0 . 001 , Fig . 3a ) . Furthermore , the population variance in the nodal degree shows an inverse U-shaped relationship with the mean nodal degree ( 2nd order polynomial fit: R2 = 0 . 48 , F ( 2 , 22 ) = 10 . 32 , p<0 . 001 , Fig . 3b ) and the mean %BSC ( 2nd order polynomial fit: R2 = 0 . 32 , F ( 2 , 22 ) = 5 . 07 p = 0 . 02 , Fig . 3c ) . Similarity of this inverse U-shaped relationship for the mean nodal degree and the mean %BSC is not surprising given a strong correlation between these two measures ( r = 0 . 84 , p<0 . 001 ) . Note , however , that the population means of nodal degree do not predict their GREML-based estimates of heritability across the 25 brain regions ( R2 = 0 . 06 , p = 0 . 23 ) . This is likely related to the fact that the mean nodal degree shows an inverted-U relationship with the population variance of this measure across these regions ( Fig . 3b ) . Finally , we have repeated these analyses for the Angry Facial expressions; the only significant relationship observed in this condition was that between the population variance and the population mean in nodal degree ( Supplementary Figure S2 ) . Population means and standard deviations for %BSC and nodal degree for all ROIs of the Angry condition are given in Supplemental Table S3 . To illustrate the relationship between population variance and mean in the number of connections ( nodal degree ) in the Ambiguous contrast , we selected two groups of ROIs that differ in the combination of these two measures of degree: ( 1 ) ROIs with the highest variance and an intermediate mean; and ( 2 ) ROIs with the highest mean and the lowest variance . As shown in Figure 4 ( and Table 2 ) , proportion of individuals with connections between a given pair of ROIs ( i . e . , pair-wise correlations with r >0 . 3 ) is intermediate within the first subnetwork ( 30 to 60% of participants ) and very high within the second subnetwork ( 70 to 96% of participants ) . Importantly , the posterior STS ( region #5 in Fig . 4 ) appears to act as a “bridge” between the two subnetworks: it is the only member of the second subnetwork with connections to all four nodes of the first subnetwork in 50% ( or more ) of participants . We then examined genetic covariance within and between the two subnetworks , which we term “Optional” and “Obligatory” ( see Discussion for comparison with the “Extended” and “Core” systems of Haxby and colleagues [10] ) . Using a bivariate GCTA approach [18] , we observed significant ( p<0 . 05 ) genetic covariances in three pairs of ROIs: [R MVLFC - R Ant STS] , [R MVLFC – R Post STS] and [R AntSTS – R Post STS] . Furthermore , we found marginal ( p<0 . 1 ) covariances in four additional pairs of ROIs , three within the “Optional” network and one between the “Optional” and “Obligatory” network ( L PMC - R Post STS ) . The full genetic-covariance matrix for the eight ROIs constituting the two subnetworks is provided in Supplemental Table S4 . The key limitation of the present report is sample size; with 1 , 620 unrelated individuals , we are at the lower limit of the GREML-based approach for estimating contributions of common SNPs to phenotypic variations . Therefore , we were able to detect only relatively high values of heritability; note that these estimates have fairly large confidence intervals ( standard error [SE]; e . g . , R-MDLFC: VG/Vp = 0 . 52±0 . 22 ) and must be therefore interpreted cautiously . Simulations conditional on empirical GWAS data are consistent with these observations; sample size of 1 , 999 individuals is adequate for estimating correctly high ( h2 = 0 . 5 ) narrow-sense heritability [25] . The limited sample size also affected the significance values . Nonetheless , the uneven distribution of the nominally significant results between the Ambiguous ( 9/25 ) and Angry ( 0/25 ) speaks against a chance nature of these findings , as confirmed by the Monte-Carlo simulations . The above sample-size limitation must be viewed in the context of the phenotype under study , however . The previous twin-based studies of fMRI-based phenotypes employed between 20 and 141 twin pairs , with heritability ( a2 ) estimates varying widely between 0 and 65 [27] . A total of 333 related individuals were included in a pedigree-based study of heritability of resting-state fMRI; h2 for functional connectivity of the different components of so-called default-mode network varied between 10 ( SE = 13 ) and 42 ( SE = 17 ) [28] . Working with unrelated individuals , only a few other studies are acquiring functional brain phenotypes with a sample size comparable to the present report . For example , the Human Connectome Project plans to collect paradigm-based and resting-state fMRI datasets in 1 , 200 individuals [29] . In the Generation R cohort , scanning is under way to collected resting-state fMRI in up to 5 , 000 children ( White , personal communication ) . Given the challenges related to test-retest reliability of fMRI data in general , and resting-state fMRI in particular , we suggest that the GREML approach provides an excellent test-bed for evaluating various approaches aimed at improving the fidelity of functional brain phenotypes . Such a GREML-based approach would be particularly powerful for fine-tuning functional phenotypes for meta-analyses of genome-wide association studies ( similar to those carried out with structural brain phenotypes [30] ) , which require pooling of fMRI datasets ( paradigm-based or resting ) collected under varied conditions and on different scanners; GREML-based estimates of “heritability” would provide a useful metric for selecting appropriate post-processing steps and/or modifying inclusion criteria before launching the GWAS . Overall , this report indicates that GREML-based estimates of heritability of the brain response to facial expressions vary across regions and paradigms , possibly as a function of inter-regional differences in the population variance of functional connectivity . As such , it demonstrates the usefulness of this approach in identifying functional phenotypes with properties suitable for genetic studies . As part of the IMAGEN project [12] , 2 , 000 adolescents ( ∼14 years of age ) were recruited through local high schools in eight European cities across four countries: France ( Paris ) , Germany ( Mannheim , Hamburg , Dresden and Berlin ) , Ireland ( Dublin ) and United Kingdom ( London and Nottingham ) . Local ethics boards approved the study protocol: Comité de protection des personnes Ile de France ( CPP IDF VII ) ; Ethics Committee of the German Psychological Society ( DPG ) ; Hamburg Chamber of Physicians Ethics Board ( Hamburg Medical Association ) ; Medical Ethics Commission of the Faculty of Clinical Medicine Mannheim; Medical Faculty Carl Gustav Carus Ethics Commission , Technical University Dresden; Nottingham University Medical School Research Ethics Committee; Psychiatry , Nursing & Midwifery Research Ethics Committee , King's College London; Ruprecht-Karls-University of Heidelberg; and School of Psychology Ethics Committee , Trinity College Dublin . The parents and adolescents provided written informed consent and assent , respectively . Scanning was performed on 3 Tesla scanners from four different manufacturers ( Siemens: 4 sites , Philips: 2 sites , General Electric: 1 site , and Bruker: 1 site ) . High-resolution T1-weighted anatomical images were acquired using 3D Magnetization Prepared Rapid Acquisition Gradient Echo ( MPRAGE ) sequence ( TR = 2 , 300 ms; TE = 2 . 8 ms; flip angle = 9°; voxel size: 1 . 1×1 . 1×1 . 1 mm3 ) . Functional T2*-weighted images were acquired using Gradient-Echo Echo-Planar-Imaging ( GE-EPI ) sequences ( field of view: 22 cm; pixel size: 3 . 4×3 . 4 mm2; slice thickness of 2 . 4 mm; slice gap 1 . 0 mm; effective final voxel size 3 . 4×3 . 4×3 . 4 mm3; TE = 30 ms and TR = 2 , 200 ms; flip angle = 75° ) . During the fMRI session participants viewed short videoclips displaying ambiguous facial expressions ( gestures such as nose twitching ) , angry facial expression or control stimuli ( non-biological motion ) . The control stimuli were adapted from a study of Beauchamp and colleagues [31] . The face stimuli were created as follows . Eight actors ( four females ) were filmed for the face movements . They were instructed to express different emotions starting from a neutral point . We also extracted short video-clips from the periods when the actors were not expressing the emotions but were nonetheless moving their face ( e . g . twitching their nose , opening their mouth , blinking their eyes ) . Twenty video-clips were selected for the angry and ambiguous face movements respectively . Four raters judged the intensity of each of emotion from those clips . The average rating for the angry face movements , on a scale of 1 ( not angry at all ) to 9 ( very angry ) was 7 . 94 ( Standard Deviation [SD] = 0 . 77 ) . The average rating for the ambiguous facial expressions was 2 . 18 ( SD = 0 . 84 ) for anger , 2 . 97 ( SD = 1 . 07 ) for sadness and 3 . 49 ( S = 1 . 03 ) for happiness; combined across the three scales , the rating of ambiguous facial expressions was 2 . 92 ( SD = 1 . 18 ) . The control stimuli consisted of black-and-white concentric circles of various contrasts , expanding and contracting at various speeds , roughly matching the contrast and motion characteristics of the faces and hands clips [32] . We presented dynamic video clips of faces because , compared with static faces , they elicit more robust responses in brain regions critical for face processing , such as the fusiform gyrus and amygdala , and engage a more elaborate network for face processing , including regions in the frontal cortex and along the superior temporal sulcus [33] . The three viewing conditions were organized into 18-second blocks ( 5 Ambiguous , 5 Angry , 9 control ) for a total of 160 EPI volumes in a single six-min fMRI run . From 2 , 000 participants , a total of 1 , 926 completed both the Face paradigm and T1-weighted scan . Data from 79 participants were excluded due to excessive head movement during functional MR scanning ( more than 2 mm in translation or 2 degrees in rotation errors in either direction ) , 8 participants were excluded due to unknown age , 5 participants had poor quality of fMRI data , and 1 participant was excluded because of abnormal ventricles . Scans from 1 , 831 participants were preprocessed using SPM8 toolbox ( Statistical Parameter Mapping: Wellcome Department of Cognitive Neurology , London , UK ) in MATLAB 7 . 0 ( www . mathworks . com ) . Functional ( EPI ) images were motion-corrected with respect to the first volume . Subsequently , the EPI images were aligned to the corresponding high-resolution T1-weighted images ( co-registration ) . The co-registered EPI images were transformed to the ICBM152 template space using the deformation parameters from the nonlinear registration of the corresponding structural image to the ICBM152 template . The nonlinear registration was achieved using the Unified Segmentation tool in SPM package . Further details of the pre-processing pipeline is provided in Tahmasebi et al . [13] . Regions of interest ( ROIs ) relevant for face processing were defined from a probabilistic map computed in a subsample ( n = 1 , 110 ) of the IMAGEN dataset , as reported in Tahmasebi et al [13] . From this map , 25 ROIs were defined that are consistently ( population probability >0 . 5 ) engaged during the ambiguous and angry face processing , relative to control ( non-biological motion ) condition . For each ROI , mean percent BOLD signal change ( %BSC ) for each ROI was extracted for all participants , as in Tahmasebi et al . [13] , and analyzed as phenotypes of interest in GREML analyses . Values of %BSC were standardized ( Z-Scored ) for each acquisition site to account for scanner effects . Sex was added as a covariate . The connectivity matrix for each face condition was calculated as follows . Nuisance covariates including white matter ( WM ) signals , and cerebrospinal fluid ( CSF ) signals were regressed out from the BOLD signals; WM and CSF voxels were identified by thresholding ( at 90% ) the WM and CSF tissue probabilistic maps from the ICBM152 standard template . For each ROI , the mean BOLD signal time-series was calculated by averaging the BOLD signal from all voxels constituting the ROI at every time point ( 160 time points in total ) . The BOLD time-series for each face condition were then realized by concatenating the mean-centered signal from the corresponding blocks ( 5 blocks per face condition and 9 blocks for control; each block consists of 8 time points ) , shifted by 2 TRs ( 4 . 4 s ) to accommodate for the rise in the hemodynamic response . The correlation matrix was calculated between the time-series from every pair of the 25 ROIs . This yielded a 25-by-25 symmetric functional-connectivity matrix for each participant and face condition . We reduce these matrices into undirected graphs by thresholding each pair-wise correlation at r>0 . 3 . This creates a graph ( network ) with ROI's as nodes and edges between them representing functional connections . Within each participant , we calculate node degree for each ROI ( node ) to summarize the graph . Node degree is simply the count of other ROI's in which the BOLD time series correlates ( r>0 . 3 ) with the given ROI . This analysis was performed with the Brain Connectivity Toolbox [17] . Given the importance of measurement error in estimating heritability , we have evaluated reproducibility of the brain response to facial expressions by correlating – across the 25 ROIs - the %BSC values obtained in two randomly selected subsamples: Group A ( 434 males , 483 females ) and Group B ( 448 males , 459 females ) . In the absence of test-retest reliability measurements , such a cross-group comparison provides an indirect index of measurement error . As shown in Supplemental Figure S3 , variations of the %BSC across the 25 ROIs were highly predictable in Group B from measures obtained in Group A ( R2 = 0 . 96 ) . Whole genome data were acquired from 2 , 089 participants using Illumina Human610-Quad Beadchip and Illumina Human660-Quad Beadchip . Quality control of the genotypes was accomplished using Plink software [34] . Of the 588 , 875 SNPs overlapping present on both chips , a total of 42 , 506 single nucleotide polymorphisms ( SNPs ) were excluded for missingness of more than 5% , 15 individuals excluded for low genotyping rate ( less than 97% ) , 16 , 385 SNPs were excluded for failing to reach Hardy-Weinberg equilibrium ( p< = 0 . 0001 ) , and 20 , 131 SNPs were excluded for low minor allele frequency ( MAF<0 . 01 ) . In total , 511 , 089 SNPs were used to calculate genetic relationship matrices using GCTA ( http://www . complextraitgenomics . com/software/gcta ) . We excluded adolescents with a genetic relationship >0 . 05 ( i . e . , more related than 2nd degree cousins ) to remove the influence of potential shared environment effects or familial causal variants not captured by SNPs . We included the top 10 principal components of the identity-by-state matrix as a covariate in all analyses to control for population stratification in our cohort . For each ROI , we have calculated GREML-based estimates of “heritability” , defines as Genetic Variance [VG]/Phenotypic Variance [Vp] , for the brain response to facial expressions ( %BSC ) . In order to examine the relationship between the VG/Vp estimates and the number of SNPs reaching a nominal level of significance [15] across the 25 ROIs , we have carried out Genome-wide Association Studies ( GWAS ) of %BSC using the same set of 1 , 620 unrelated adolescents . To do so , we used PLINK software [34] . Mean %BSC values where standardized ( Z-Scores ) in order to control for effects of Sex and Scanning Site . The top 10 principal components of the identity-by-state matrix as a covariate in all analyses to control for population stratification in our cohort .
We measured brain response to facial expressions in a large sample of typically developing adolescents ( n = 1 , 620 ) and assessed “heritability” of the response using common genetic variations across the genome . In a subset of brain regions , we explained 40–50% of phenotypic variance by genetic variance . These brain regions appear to differ from the rest of the face network in the degree of inter-individual variations in their functional connectivity . We propose that these regions , including the prefrontal and premotor cortex , represent “Optional” part of the network co-opted by its “Obligatory” members , including the posterior part of the superior temporal sulcus , fusiform face area and the lateral occipital cortex , concerned with processing complex visual stimuli .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "genetics", "neuroscience", "biology", "and", "life", "sciences", "genomics" ]
2014
Global Genetic Variations Predict Brain Response to Faces
Many large genome-wide association studies ( GWAS ) have identified common blood pressure ( BP ) variants . However , most of the identified BP variants do not overlap with the linkage evidence observed from family studies . We thus hypothesize that multiple rare variants contribute to the observed linkage evidence . We performed linkage analysis using 517 individuals in 130 European families from the Cleveland Family Study ( CFS ) who have been genotyped on the Illumina OmniExpress Exome array . The largest linkage peak was observed on chromosome 16p13 ( MLOD = 2 . 81 ) for systolic blood pressure ( SBP ) . Follow-up conditional linkage and association analyses in the linkage region identified multiple rare , coding variants in RBFOX1 associated with reduced SBP . In a 17-member CFS family , carriers of the missense variant rs149974858 are normotensive despite being obese ( average BMI = 60 kg/m2 ) . Gene-based association test of rare variants using SKAT-O showed significant association with SBP ( p-value = 0 . 00403 ) and DBP ( p-value = 0 . 0258 ) in the CFS participants and the association was replicated in large independent replication studies ( N = 57 , 234 , p-value = 0 . 013 for SBP , 0 . 0023 for PP ) . RBFOX1 is expressed in brain tissues , the atrial appendage and left ventricle in the heart , and in skeletal muscle tissues , organs/tissues which are potentially related to blood pressure . Our study showed that associations of rare variants could be efficiently detected using family information . High blood pressure ( BP ) is a common condition associated with multiple health outcomes , including heart , brain , and kidney diseases [1 , 2] . Previous studies have shown that BP is a genetically determined trait with estimated heritability of 30% to 60% [3 , 4] . Multiple large genome-wide association studies ( GWAS ) meta-analysis and admixture mapping studies have identified over 190 genetic variants that explained only a small variation in BP [5–21] . For complex traits such as BP , rare variants are suggested to play a greater role in heritability than anticipated in the common disease-common variant hypothesis [22] . A Framingham Heart Study reported rare mutations in three renal salt handling genes causing large reductions in blood pressure and estimated that the overall prevalence of hypertension is reduced by about 1% because of the effects [23] . Linkage studies of family data can be used to uncover missing heritability and identify genetic markers linked to BP [24 , 25] . However , the identified linkage regions from well-designed linkage studies such as the US Family Blood Pressure Program ( FBPP ) and the UK Medical Research Council British Genetics of Hypertension ( BRIGHT ) study [26–29] do not overlap with many BP loci identified by large BP GWAS of mostly unrelated individuals . In general , GWAS have good power to detect common variants of modest effect with attainable sample sizes , but less power for detecting rare variants with intermediate effect . In contrast , linkage analysis can have good power to detect multiple rare or lower frequency BP variants in a gene or region with relatively larger effect sizes [25] . Thus , we hypothesize that a linkage region observed in a family study , if not overlapping with the BP loci in reported GWAS , may harbor multiple rare or lower frequency BP variants . Recently , many statistical approaches for rare variant association analyses have been developed for unrelated samples [30–34] and family data [35–38] . It has been suggested that rare or lower frequency variants can be enriched in families [35 , 37] , and therefore improving the statistical power for their identification . However , the existing rare variant association methods have not incorporated linkage evidence . In this study , we performed variance-component linkage analysis with BP traits , including systolic blood pressure ( SBP ) , diastolic blood pressure ( DBP ) , and pulse pressure ( PP ) in the Cleveland Family Study ( CFS ) . We searched the published GWAS to examine whether there are reported BP variants in the linkage regions . Using the combined linkage and association analyses , we searched for potential functional variants that can explain linkage evidence and replicated the variants in independent cohorts . Table 1 presents the characteristics of white participants in the CFS data . S1 Fig presents the distributions of the residuals of SBP , DBP , and PP , which are all approximately normally distributed . Linkage analysis identified a peak ( LOD = 2 . 81 ) on chromosome 16p13 linked to SBP ( S2 Fig , Materials and Methods ) . Linkage analysis of further pruned markers using a R2 threshold of 0 . 1 or modeling marker-marker linkage disequilibrium resulted in a slight decrease of LOD score in the same region ( LOD = 2 . 30–2 . 42 , S3 Fig ) . We selected a candidate region of 20cM with 2-LOD score drop for association analysis ( Fig 1 ) . This region did not overlap with published GWAS of BP variants . Therefore , we tested the hypothesis that the observed linkage evidence for SBP is due to the presence of multiple rare , coding variants in a gene ( s ) within the region . The CFS was genotyped by an exome array , with most of the variants being coding variants . Within the linkage region , there are 454 exonic variants defined by the Cohorts for Heart and Aging Research in Genomic Epidemiology ( CHARGE ) consortium that are genotyped on the exome array [39] . We identified 13 exonic variants ( S1 Table ) that satisfy the following filtering criteria: 1 ) either have a SBP association p-value ≤ 0 . 1 or absolute regression coefficient beta ≥ 5; and 2 ) present at least twice in at least one family with a family-specific LOD score ≥ 0 . 1 . A risk score based on the 13 identified SNPs has an effect size of 0 . 948 ± 0 . 135 for association with SBP in CFS . After adding this risk score as a covariate in linkage analysis , the MLOD score dropped from 2 . 809 to 1 . 055 , suggesting that these 13 variants were able to account for most of the observed linkage evidence . To further assess the significance of this LOD drop , we sampled 1 , 000 independent SNPs from chromosomes other than chromosome 16 . Hence , these SNPs should not contribute to the LOD score observed on chromosome 16 . We performed linkage analysis with each of these 1 , 000 SNPs as a covariate in the linkage analysis . We calculated the differences between the original MLOD score and the MLOD scores of the 1 , 000 linkage analyses with a SNP as a covariate . The largest LOD score drop in these 1 , 000 linkage analyses was 0 . 347 , suggesting the observed LOD score drop on the risk scores of 13 selected variants is statistically significant ( p-value<0 . 001 ) . Among these 13 exonic variants , two variants ( rs149974858 and rs145873257 ) are present in RBFOX1 and the remaining 11 variants are each in separate genes . When adjusting for the risk scores of rs149974858 and rs145873257 , we also observed a drop in LOD score ( LOD = 1 . 97 ) , which suggests that rs149974858 and rs145873257 account for a portion of the observed linkage evidence . The variant rs149974858 shows association evidence with SBP ( p-value = 0 . 0016 ) in CFS . The minor allele frequency of rs149974858 is 0 . 0036 and only segregates within a 17-member family with family-specific LOD of 0 . 697 ( Fig 2 ) . This missense variant ( c . 112C>G ) results in a proline to alanine substitution ( p . Pro38Ala ) . Five members from this family carrying this rare missense mutation had on average lower SBP ( carrier average = 117 mmHg , noncarrier average = 125 mmHg ) but higher BMI than other family members ( carrier average = 60 kg/m2 , noncarrier average = 31 kg/m2 ) . A single SNP association test revealed that rs149974858 is also significantly associated with BMI in CFS ( beta = -26 . 8±4 . 35 , asymptotic p-value = 7 . 28E-10 ) . The exonic variant rs145873257 segregates within a different family with family-specific LOD score of 0 . 215 . A c . 1072G>A base change resulted in a glycine to serine substitution ( p . Gly358Ser ) . Six members of this family are heterozygous for the AT genotype . The estimated effect of this variant is protective in CFS , although it is not statistically significant ( S1 Table ) . Since both variants rs149974858 and rs148751394 consistently show a protective effect in two large families , we examined the other coding and rare variants in RBFOX1 genotyped on the exome array . Two exonic variants , rs151214012 and rs145873257 , and one rare , intronic variant rs2345080 are available in the exome array . These three variants show protective effect despite not satisfying the filtering criteria ( S2 Table ) . Single SNP associations and annotations for all exome array variants of RBFOX1 are provided in the S3 Table . Applying either the family-based burden or SKAT analysis , these five variants are significantly associated with SBP and DBP ( Table 2 ) . We next sought the replication of the rare variants in RBFOX1 in four large independent cohorts ( ARIC , WHI , BioUV , and HRS ) of whites for the traits SBP , DBP and PP . We specifically looked at the five rare variants of RBFOX1 found in CFS and their associations with BP traits . Within the ARIC data , 4 of the 5 variants were present and all the 4 variants showed a consistent protective effect . For WHI , all 5 variants were present but only 1 variant ( rs145873257 ) had a protective effect size . BioUV contained 4 out of 5 variants found in CFS; 2 variants were protective for SBP and 3 variants were protective for DBP . HRS contained all 5 variants found in CFS; 4 variants were protective for SBP and 3 variants were protective for DBP . Among the total 23 tests ( CFS: 5 , ARIC: 4 , BioUV: 4 , WHI: 5 , HRS: 5 ) conducted across all cohorts , 16 of them were protective ( p-value = 0 . 0173 based on binomial distribution ) , suggesting a consistent protective effect . We next conducted a gene-based association analyses for RBFOX1 and BP traits using exome array data from the CFS with weights Beta ( 1 , 25 ) . Burden , SKAT , and SKAT-O tests were performed using the 5 rare variants of RBFOX1 found in CFS ( Table 2 ) . In CFS , the association between RBFOX1 and SBP was found to be significant by the burden test ( p-value = 0 . 00214 ) and SKAT-O ( p-value = 0 . 00403 ) , but not by SKAT ( p-value = 0 . 0702 ) . All three gene-based tests for DBP were statistically significant ( burden test p-value = 0 . 0148 , SKAT p-value = 0 . 0494 , SKAT-O p-value = 0 . 0258 ) . When we conducted gene-based analyses using only the 4 coding variants ( rs149974858 , rs148751394 , rs151214012 , rs145873257 ) identified in CFS , the associations are also significant for SBP and DBP ( p-value<0 . 037 ) . The same gene-based analysis for rare variants was done for all four replication cohorts separately and the results were meta-analyzed ( Table 2 ) . Individually , the ARIC cohort had significant gene-based associations for SBP ( burden test p-value = 0 . 00572 , SKAT p-value = 0 . 00259 , SKAT-O p-value = 0 . 00356 ) and PP ( burden test p-value = 0 . 00140 , SKAT p-value = 0 . 000273 , SKAT-O p-value = 0 . 000392 ) . After meta-analyzing the results for ARIC , WHI , BioVU , and HRS , we found significant associations for SBP ( burden test p-value = 0 . 0172 , SKAT p-value = 0 . 00635 , SKAT-O p-value = 0 . 0126 ) and PP ( burden test p-value = 0 . 00377 , SKAT p-value = 0 . 00266 , SKAT-O p-value = 0 . 00234 ) . We observed that the variant rs149974858 co-segregated with BMI in the 17-member CFS family . Subsequently , we performed linkage analysis for BMI in CFS on chromosome 16 , after adjusting for gender , age , age2 , and PC1 . We did not observe linkage evidence in this region ( LOD = 0 . 721 ) . The gene-based analysis of BMI using the same set of variants was only significant in CFS but not in any of the replication cohorts ( Table 2 ) . We performed a linkage analysis of BP traits using the families collected in CFS . The largest linkage peak identified is on 16p13 linked to SBP . The 16p13 region has been reported of linkage evidence with BP in two studies: the Victorian Family Heart Study [40] , the extreme-sib-pair study in Chinese adults [41] . In addition , the longitudinal change of BP in Mexican Americans [42] , and the Hypertension Genetic Epidemiology Network Study in whites [43] reported linkage regions partially overlapped with the current study . The reported linkage evidence from multiple ethnic populations strongly suggests the linkage evidence on 16p13 is real . Linkage analysis using microsatellite markers was performed with 363 sib-pairs of CFS whites for a hypertensive status , adjusted for age , age2 , sex , BMI , and BMI2 . This analysis did not find linkage evidence in the 16p13 region . This is unsurprising because the power of using a binary hypertensive status is lower than that of quantitative phenotypes , such as SBP , DBP , or PP . In addition , hypertensive status was defined as either SBP ≥ 140 , DBP ≥ 90 , or taking antihypertensive medications and the sample size in the sib-pair analysis was smaller than the current study , all of which contribute to the lack of linkage evidence observed . Our study demonstrates that high-density SNP genotyping arrays are informative for detecting linkage signals . We searched among published large GWAS studies of BP traits [5 , 7–10] and did not identify any BP variants previously reported on 16p13 , suggesting that multiple low frequency or rare variants with relatively large effect sizes are possibly contributing to the observed linkage evidence . We further assumed that variants with relatively large effect sizes are more likely to be coding and rare variants . Thus , we limited our search to only the coding and rare variants under the linkage peak genotyped on the exome array . By examining the associated variants that are able to account for the observed linkage evidence on 16p13 , we were able to identify 13 exonic variants . Among these 13 variants , 2 of them fall in RBFOX1 , which encodes for the ataxin-2 binding protein 1 ( also known as A2BP1 ) , and show a consistent protective effect for SBP in CFS . Gene-based analysis of the four available exonic variants and one rare intronic variant in RBFOX1 are significantly associated with SBP and DBP ( p-value = 0 . 00403 , 0 . 0258 , respectively ) using SKAT-O . Replication analysis of the rare variants at the gene level ( but not at the variant level ) is also significant for SBP and PP in the meta-analysis of four large cohorts of whites with a total replication sample size N = 57 , 234 . This study also provides evidence that rare variants within RBFOX1 are protective for BP traits among obese individuals . Among individuals of European ancestry within the CFS , 5 individuals within the same family carried the minor allele for rs149974858 , the variant showing significantly protective effect with SBP by single SNP association test . All of the 5 individuals are morbidly obese ( with average BMI of 60 ) . However , their SBP ( mean = 117 ) and DBP ( mean = 78 ) are within the normotensive range . Ma et al . conducted a GWAS of BMI in Pima Indians using Affymetrix 100K array and identified two common variants in RBFOX1 associated with BMI [44] . The same two variants could be replicated in non-overlapped Pima Indians but not in French Caucasians , Amish Caucasians , German Caucasians , or Native Americans [44] . In our analysis , we identified four exonic and one intronic rare variants in RBFOX1 that are significantly associated with BMI but the association evidence could not be replicated ( Table 2 ) . Therefore , it is inconclusive whether RBFOX1 is an obesity gene . In all our analysis , either linkage or association analysis , BMI is included as a covariate . Furthermore , no linkage evidence was found for BMI on chromosome 16 , after adjusting for gender , age , age2 , and PC1 . Our result indicates the RBFOX1 contributes to BP variation independent of obesity , although we are unclear whether RBFOX1 has a pleiotropic effect on both BP and obesity . We also observed that the effect direction of single variant replication analysis in the four cohorts is not always consistent with that of CFS . However , 16 of the 23 tests were protective ( p-value = 0 . 017 ) , suggesting a consistent protective effect . Assuming a causal rare variant with an effect size equal to one quarter of the BP standard deviation , we estimate the probability of observing an opposite direction in a study to be 40 . 1% , which is consistent with 7 opposite directions among 18 single SNP replication tests in 4 replication cohorts . It is interesting that all the four exonic variants in RBFOX1 are either monomorphic or extremely rare in African ancestry populations ( S4 Table ) . Furthermore , the BP admixture mapping analysis by Zhu et al . also suggest local ancestry in this region is associated with SBP and DBP; however , the evidence is not genome-wide significant [11] . Thus , our result suggests that the rare exonic variants in RBFOX1 may contribute to a protective effect for hypertension and further work will be needed to establish whether the lack of these protective variants contribute to the disparity in hypertension occurrence and early age of onset between African Americans and whites . To our knowledge , only one GWAS study so far has reported on the association of RBFOX1 variants with blood pressure using human genotyping data . Wang et al . reported an association between rs1507023 , a candidate SNP in RBFOX1 involved in vitamin D metabolism and signaling , and SBP , DBP , and PP . Its association with blood pressure was significant before , but not after correction for multiple testing [45] . Under the linkage region of 16p13 , there are 11 additional variants that either have an association test p-value less than 0 . 1 or an effect size larger than 5 mmHg in CFS . When we used the risk scores of these 11 variants as a covariate in linkage analysis , the MLOD dropped to 1 . 932 , suggesting that there may be additional variants that contribute to linkage evidence in this region . However , the current exome array data is limited for further dissection of genes or variants contributing the linkage evidence . Whole genome sequencing data , including the sequencing data from the Trans-Omics for Precision Medicine ( TOPMed ) Program ( https://www . nhlbi . nih . gov/research/resources/nhlbi-precision-medicine-initiative/topmed ) , will be necessary to identify the genes contributing blood pressure variation in this region . Gene expression data and previous studies have demonstrated that RNA splicing factor RBFOX1 is important for heart and skeletal muscle development and function [46–48] . RBFOX1 expression has been associated with cardiac hypertrophy and heart failure in mice models [49] . Gao et al . found that RBFOX1 expression was significantly diminished in both mouse and human failing hearts [49] . We searched the GTEx database and RBFOX1 is highly expressed in multiple human brain tissues , atrial appendage and left ventricle of the heart , as well as muscle skeletal tissues ( Fig 3; http://www . gtexportal . org/home/gene/RBFOX1 ) . Further biological studies are needed to establish the direct role of RBFOX1 in regulating blood pressure . Our study suggests that family-based linkage evidence can be extremely successful in searching for rare variants contributing to complex traits . In summary , we identified rare , exonic variants in RBFOX1 that have a protective effect on BP traits , which can be important in searching new drugs for cardiovascular disease . However , it should be pointed out that association analysis was performed using variants available in the exome array of this study . The variants identified in RBFOX1 may still be reflecting in LD with the causal variants to BP . While RBFOX1 is expressed in multiple tissues that may relate to blood pressure , the mechanism underlying how this gene contributes to BP variation needs to be further studied . The identification of these rare coding variants will facilitate precision medicine in treating cardiovascular disease . We calculated residuals of SBP , DBP , and PP after adjusting for gender , age , age2 , body mass index ( BMI; kg/m2 ) and the first principal component of genotype values in CFS separately . The residuals of these regressions were used for linkage analysis using the software MERLIN [59] . The principal components ( PCs ) were calculated using the software FamCC , which can be applied to family data [60] . Since the results were essentially the same for including the first PC or the first 10 PCs , we reported the linkage results including the first PC . We used the pairwise linkage disequilibrium ( LD ) pruning approach with a window size of 50 kb , step size of 5 variants , and R2 threshold of 0 . 2 . We also required a minor allele frequency ( MAF ) ≥ 0 . 2 . This resulted in 56 , 992 autosomal SNPs using PLINK [51] . Because marker-marker LD may result in biased linkage calculations , we performed linkage analysis by further reducing the R2 threshold to 0 . 1 and by modeling the marker-marker LD using MERLIN [59] . Linkage analysis using MERLIN decomposes phenotypic variance into three parts: the variance contributes to the quantitative trait locus ( σQTL2 ) , the variance contributes to the polygenetic effect ( σG2 ) , and the variance contributes to the random effect ( σE2 ) . It also tests the null hypothesis of no linkage H0:σQTL2=0 vs . HA:σQTL2>0 . We examined exonic variants genotyped in the exome array in the region of 2-LOD score drop from the linkage peak . We performed the family-based association analysis for the exonic variants only in the 2-LOD drop region using the ASSOC package in S . A . G . E [61] . The family-based association analysis was conducted using a linear mixed model y = β0 + β1g + δ + ε , where g is a genotype value vector , β0 is the intercept , β1 is the regression coefficient , δ ~ N ( 0 , 2ΦσG2 ) where Φ is the kinship coefficient matrix and σG2 is the polygenic variance , and ε ~ N ( 0 , Iσε2 ) where σε2 is the random error . ASSOC applies the likelihood ratio test to test the null hypothesis of H0: β1 = 0 . For each of the variants , we first performed an association analysis with a BP trait using ASSOC and identified variants with either p-value ≤ 0 . 1 ( marginal effect ) or absolute regression coefficient beta ≥ 5 ( large effect ) . We next estimated family-specific LOD scores and identified families with LOD score ≥ 0 . 1 . We kept the variants with association p-value ≤ 0 . 1 or absolute regression coefficient ≥ 5 , and that were present at least twice in at least one family with family-specific LOD score ≥ 0 . 1 . We defined the risk score as ri=xiTβ , where β is the regression coefficients of the SNPs , and xi is a vector of the number of risk alleles carried by individual i for these SNPs . Linkage analysis was further performed conditional on the risk scores . We performed family-based burden and SKAT tests for CFS using the software famSKAT and for the replication cohorts using the R package SKAT [30 , 32 , 62] . The weight was set to wj=Beta ( MAFj , 1 , 25 ) as suggested to increase the weight of rare variants .
Hypertension is a risk factor for cardiovascular disease and the most important risk factor for stroke . Family studies suggest that hypertension related traits are heritable . Previous genome-wide association studies ( GWAS ) have identified multiple common blood pressure ( BP ) variants but these variants do not overlap with the linkage evidence observed from family studies . Rare variants have been suggested to play a substantial role and contribute to missing heritability of BP . In this study , linkage analysis identified 16p13 linked to SBP in a cohort of 517 white individuals in 130 families from the Cleveland Family Study ( CFS ) . By combining linkage and association analyses , we searched for rare , coding variants that can explain the linkage evidence . Rare , coding variants within RBFOX1 were associated with lower systolic ( p-value = 0 . 00403 ) and diastolic ( p-value = 0 . 0258 ) blood pressures , and explained significant amount of observed linkage evidence . We replicated the identified variants in four independent cohorts ( with total sample size N = 57 , 234 ) and further observed consistent evidence that rare RBFOX1 variants are protectively associated with blood pressure traits . Our study clearly shows that family-based designs are powerful for identifying rare , coding variants underlying complex diseases .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]
[ "genome-wide", "association", "studies", "body", "weight", "medicine", "and", "health", "sciences", "cardiovascular", "anatomy", "variant", "genotypes", "chromosome", "16", "genetic", "mapping", "physiological", "parameters", "heart", "genome", "analysis", "chromosome", "biology", "hypertension", "blood", "pressure", "chromosome", "pairs", "cell", "biology", "anatomy", "heredity", "physiology", "genetics", "body", "mass", "index", "biology", "and", "life", "sciences", "genomics", "vascular", "medicine", "computational", "biology", "linkage", "analysis", "chromosomes", "human", "genetics" ]
2017
Rare variants in fox-1 homolog A (RBFOX1) are associated with lower blood pressure
In inflammation , pain is regulated by a balance of pro- and analgesic mediators . Analgesic mediators include opioid peptides which are secreted by neutrophils at the site of inflammation , leading to activation of opioid receptors on peripheral sensory neurons . In humans , local opioids and opioid peptides significantly downregulate postoperative as well as arthritic pain . In rats , inflammatory pain is induced by intraplantar injection of heat inactivated Mycobacterium butyricum , a component of complete Freund's adjuvant . We hypothesized that mycobacterially derived formyl peptide receptor ( FPR ) and/or toll like receptor ( TLR ) agonists could activate neutrophils , leading to opioid peptide release and inhibition of inflammatory pain . In complete Freund's adjuvant-induced inflammation , thermal and mechanical nociceptive thresholds of the paw were quantified ( Hargreaves and Randall-Selitto methods , respectively ) . Withdrawal time to heat was decreased following systemic neutrophil depletion as well as local injection of opioid receptor antagonists or anti-opioid peptide ( i . e . Met-enkephalin , β-endorphin ) antibodies indicating an increase in pain . In vitro , opioid peptide release from human and rat neutrophils was measured by radioimmunoassay . Met-enkephalin release was triggered by Mycobacterium butyricum and formyl peptides but not by TLR-2 or TLR-4 agonists . Mycobacterium butyricum induced a rise in intracellular calcium as determined by FURA loading and calcium imaging . Opioid peptide release was blocked by intracellular calcium chelation as well as phosphoinositol-3-kinase inhibition . The FPR antagonists Boc-FLFLF and cyclosporine H reduced opioid peptide release in vitro and increased inflammatory pain in vivo while TLR 2/4 did not appear to be involved . In summary , mycobacteria activate FPR on neutrophils , resulting in tonic secretion of opioid peptides from neutrophils and in a decrease in inflammatory pain . Future therapeutic strategies may aim at selective FPR agonists to boost endogenous analgesia . The four cardinal signs of inflammation are rubor ( redness ) , calor ( hyperthermia ) , dolor ( pain/hyperalgesia ) and functio laesa ( impaired function ) . Bacteria and their components play a critical role in eliciting pain since inflammatory pain is significantly decreased in animals raised under germ free conditions [1] . Experimentally , inflammation can be elicited by local injection of heat inactivated Mycobacterium butyricum ( “complete Freund's adjuvant” ) resulting in spontaneous activity of nociceptive Aδ and C nerve fibers [2] , [3] . Pain is elicited by proalgesic mediators including proinflammatory cytokines ( tumor necrosis factor-α , interleukin-1β ) , bradykinin , and protons [2] , [4] . Bacteria and their components are recognized by pattern recognition receptors including toll like receptors ( TLR ) as well as formyl peptide receptors ( FPR ) . Peptidoglycan ( a TLR-2 agonist ) , lipopolysaccharide ( a TLR-4 agonist ) and R-848 ( a TLR-7 agonist ) can elicit pain [5]–[7] . Furthermore , pain is decreased in TLR-4 deficient mice with bacterial cystitis [8] as well as in TLR-2 or -4 deficient mice with neuropathic lesions [9] , [10] . In contrast to these pronociceptive effects of TLR agonists , FPR agonists were shown to decrease pain induced by formalin , but the underlying mechanism remained unclear [11] . The intensity of inflammatory pain is not only dependent on proalgesic mediators , but is counteracted by endogenous analgesic mediators including opioid peptides [12] . Both neutrophils and monocytes contain opioid peptides ( Met-enkephalin and β-endorphin ) and they are the predominant leukocyte subpopulations during the first 4 days of complete Freund's adjuvant-induced inflammation [13]–[15] . Opioid peptides are released , bind to opioid receptors on peripheral sensory neurons and induce analgesia ( i . e . decrease of inflammatory pain ) . Releasing agents such as hormones ( e . g . corticotrophin releasing hormone [16] ) or chemokines ( CXCL2/3 ) [17] , [18] trigger opioid release from leukocytes in vitro and induce opioid-mediated analgesia in vivo . In the experimental model , peripheral endogenous opioid analgesia requires injection of these releasing agents at the site of inflammation . This effect is short lasting ( max . 10 min ) making this approach not attractive for the clinical setting . Interestingly , however , both clinical [19] and experimental studies [20] indicate that opioid peptides might be continuously released at the site of surgery or experimental inflammation and decrease inflammatory pain . At present , it is unclear how continuous release is regulated . It is tempting to speculate that Mycobacterium butyricum , as a component of complete Freund's adjuvant , triggers opioid peptide release from leukocytes and , thereby , induces analgesia . Mycobacteria activate both TLR [21] , [22] and FPR [23] that are expressed on neutrophils and monocytes/macrophages [24]–[26] . Of the ten known TLR , mycobacteria predominantly interact with TLR-2 and TLR-4 through lipoproteins and lipomannans [21] , [27] . TLR and/or FPR stimulation of neutrophils induce L-selectin shedding , enhanced CD11b expression and/or release of reactive oxygen species [28]–[31] . FPRs are coupled to Gi proteins [24] and receptor activation triggers intracellular signaling through phospholipase C , diacylglycerol , inositol phosphates and Ca2+ mobilization from intracellular stores , as well as activation of phosphoinositol-3 kinase ( PI3K ) [32] . In contrast , TLR activation induces coupling to an adapter protein , MyD88 , and stimulation of several intrinsic kinases including interleukin-1 receptor accessory protein kinase leading to NF-κB activation . In this study we examined whether heat inactivated Mycobacterium butyricum triggers opioid peptide release from rat and human neutrophils and monocytes and whether this requires FPR and/or TLR activation . We further studied the downstream signaling mechanisms of receptor activation . Finally , we tested the in vivo functional relevance of FPR agonist- and of Mycobacterium butyricum-induced opioid peptide release as an endogenous pathway of pain control in complete Freund's adjuvant-induced inflammation . We found that Mycobacterium butyricum induced opioid peptide release from neutrophils through FPR but not TLR stimulation . Mycobacterium-triggered opioid peptide release required intracellular calcium mobilization and PI3K activation . In vivo this mechanism decreased inflammatory pain mainly in early inflammation . Intraplantar complete Freund's adjuvant injection containing Mycobacterium butyricum resulted in a significant decrease in thermal nociceptive thresholds ( paw withdrawal latency ) in comparison to noninflamed contralateral paws indicating inflammatory pain ( paw withdrawal latency in inflamed paws 8 . 9±2 . 4 s vs . paw withdrawal latency in noninflamed contralateral paws 19 . 3±2 . 0 s ) . To assess whether pain after intraplantar complete Freund's adjuvant injection was affected by infiltrating neutrophils at the site of inflammation , systemic neutrophil depletion was performed . Consistent with previous findings , neutrophils in the circulation and at the site of complete Freund's adjuvant-induced paw inflammation were reduced by >90% while monocytes/macrophages were unaffected [14] , [17] . Neutropenia was associated with significantly lower thermal nociceptive thresholds ( paw withdrawal latency; Fig . 1A ) . Since neutrophils were previously shown to contain and release Met-enkephalin and β-endorphin upon stimulation ( e . g . by CXCR2 ligands ) [17] , we examined whether tonic opioid release attenuates inflammatory pain . Intraplantar injection of naloxone , an opioid receptor antagonist ( Fig . 1B ) , anti-Met-enkephalin or anti-β-endorphin antibodies ( Fig . 1C , D ) significantly reduced thermal nociceptive thresholds for up to 30 min ( data not shown ) . No changes were seen after subcutaneous application of the same dose of naloxone , anti-Met-enkephalin or anti-β-endorphin antibody into a skin fold of the back , indicating the absence of systemic effects ( data not shown ) . Taken together , these data suggest that neutrophils tonically secrete opioid peptides and , thereby , significantly reduce inflammatory pain . We hypothesized that Mycobacterium butyricum might directly trigger opioid peptide release . Incubation of human and rat neutrophils with Mycobacterium butyricum resulted in dose-dependent release of Met-enkephalin ( Fig . 2A , B ) . In contrast , no release of Met-enkephalin after Mycobacterium butyricum stimulation was observed in human blood monocytes following short term ( 7 min; Fig . 2C ) or long term stimulation ( up to 2 h; data not shown ) although monocytes express FPR [33] , [34] and TLR [35] . However , human monocytes were able to secrete Met-enkephalin after stimulation with ionomycin , a calcium ionophore , as a positive control . Similarly , human and rat neutrophils released β-endorphin upon stimulation with Mycobacterium butyricum but human monocytes only secreted β-endorphin after ionomycin stimulation ( Fig . S1 ) . Since mycobacteria activate TLR-2 and TLR-4 on neutrophils [22] , [27] , [28] , we hypothesized that agonist stimulation of these receptors might induce opioid peptide release . In line with previous studies [26] , both TLR-2 and TLR-4 were expressed on human neutrophils as measured by flow cytometry ( Fig . 2D ) . However , no Met-enkephalin release was seen after stimulation of TLR-2 with peptidoglycan [36] or stimulation of TLR-4 with lipopolysaccharide [36] in human or rat neutrophils ( Fig . 2F , G , I , J ) . Mycobacteria also contain formylated peptides activating FPR [23] , [37] . In accordance with previous studies [38] FPR were expressed on human blood neutrophils ( Fig . 2E ) . Incubation of human and rat neutrophils with formyl-Met-Leu-Phe ( fMLP ) , an FPR agonist , resulted in dose-dependent release of Met-enkephalin ( Fig . 2H , K ) . No release of Met-enkephalin was observed after fMLP stimulation of human monocytes ( data not shown ) . Similar results were obtained for release of β-endorphin from human and rat neutrophils ( Fig . S1 ) . We next evaluated whether fMLP can induce analgesia in rats with complete Freund's adjuvant -induced hindpaw inflammation . Complete Freund's adjuvant injection resulted in inflammatory pain measured by a significant decrease in both mechanical ( paw pressure threshold ) and thermal nociceptive thresholds ( paw withdrawal latency ) in comparison to noninflamed contralateral paws ( Fig . 3A , C ) . fMLP injected intraplantarly into inflamed hindpaws elicited significant and dose-dependent analgesia as indicated by a rise in mechanical and thermal nociceptive thresholds ( Fig . 3A , C ) . fMLP-induced analgesia peaked at 5 min , was still elevated after 10 min and returned to baseline after 20 min ( data not shown ) . Higher doses of fMLP were needed to reverse thermal nociceptive threshold in inflamed hind paws ( Fig . 3A ) . FPR is expressed on neutrophils as well as monocytes/macrophages [34] . We detected FPR expression on CD45+RP-1+ neutrophils as well as CD45+ED1+ macrophages isolated from the inflamed paw ( Fig . 3D ) . fMLP-induced analgesia was abolished by selective systemic neutrophil depletion ( Fig . 3E ) , by peripherally selective blockade of mu-opioid receptors ( naloxone , Fig . 3B , F , CTOP , Fig 3G ) or by neutralization of opioid peptides ( i . e . anti Met-enkephalin antibodies , Fig . 3B , H ) . Blockade of delta-opioid receptors partially but significantly reduced nociceptive thresholds after fMLP injection ( naltrindole , Fig . 3G ) . To verify the involvement of FPR we employed two FPR inhibitors , N-t-Boc-Phe-D-Leu-Phe-D-Leu-Phe ( Boc-FLFLF ) and cyclosporine H [31] , [39] , [40] . Boc-FLFLF dose-dependently reduced fMLP-FITC binding to human neutrophils ( Fig . 4A ) . In parallel , fMLP-triggered elevation of intracellular calcium in human neutrophils was inhibited by preincubation with Boc-FLFLF ( Fig . 4B ) or cyclosporine H ( data not shown ) . Met-enkephalin release from human neutrophils was completely blocked by preincubation with 10 µM Boc-FLFLF ( Fig . 4C ) and was reduced by 62 and 72% after preincubation with 10 or 100 µM cyclosporine H , respectively ( Table 1 ) . In rat neutrophils , higher doses of FPR inhibitors were necessary . The fMLP-induced Met-enkephalin release was inhibited by 69% using 100 µM Boc-FLFLF ( Fig . 4D ) and by 44% using 100 µM cyclosporine H ( Table 1 ) . To test these FPR antagonists in vivo we intraplantarly injected rats with complete Freund's adjuvant and either antagonist together with fMLP . Both FPR antagonists dose-dependently blocked fMLP-induced analgesia ( Fig . 4E , Table 1 ) . Mycobacterium butyricum triggered intracellular Ca2+ mobilization in FPR - , but not in mock-transfected human embryonic kidney ( HEK ) 293 cells ( Fig . 5A , B ) . This was blocked by preincubation with the FPR antagonist Boc-FLFLF ( Fig . 5C ) or cyclosporine H ( data not shown ) . Similar changes were observed in human neutrophils ( Fig . 5D–F ) . Acute receptor desensitization was observed because stimulation of human neutrophils with fMLP almost completely abolished subsequent stimulation with Mycobacterium butyricum ( Fig . 5F ) . We further examined the role of TLR and FPR in mycobacterial stimulation of opioid peptide release . No change in Mycobacterium butyricum-triggered opioid peptide release was seen after blockade with single or combined anti-TLR-2 and anti-TLR-4 ( Fig . 6A ) while the addition of two FPR antagonists , Boc-FLFLF and cyclosporine H , resulted in an 80% and 74% reduction of Met-enkephalin release , respectively ( Fig . 6B and Table 2 ) . Activation of neutrophils leads to the translocation of primary granules to the plasma membrane to allow for release [18] , [41] . Unstimulated neutrophils exhibited a homogeneous cytoplasmic granular staining for Met-enkephalin ( Fig . 6C ) . Following stimulation with Mycobacterium butyricum , large aggregates of Met-enkephalin-containing granules formed in submembranous regions and overall staining was significantly reduced as a sign of degranulation ( Fig . 6D ) . Preincubation with Boc-FLFLF inhibited Mycobacterium butyricum-induced translocation of Met-enkephalin-containing granules to the cell membrane ( Fig . 6E ) . Elevation of intracellular Ca2+ is required for opioid peptide release [17] . FPR is known to signal through Gi proteins stimulating phospholipase C leading to mobilization of Ca2+ from intracellular stores and to activation of PI3K [24] . Both chelation of intracellular calcium by BAPTA/AM ( Fig . 6F ) as well as pretreatment with the PI3K inhibitors ( LY294002 and wortmannin , Fig . 6H ) prevented Mycobacterium butyricum-induced opioid peptide release . In contrast , Mycobacterium butyricum-induced Met-enkephalin release was independent of extracellular calcium ( Fig . 6G ) . Tonic opioid peptide release from neutrophils in vivo would require that stimulation with Mycobacterium butyricum does not completely empty all stores of opioid peptides after a single stimulation . To test this we repeatedly stimulated neutrophils with Mycobacterium butyricum in vitro . After the second stimulation with Mycobacterium butyricum we detected the same amount of Met-enkephalin in the supernatant ( Fig . 7A ) . In addition , we compared Met-enkephalin release after Mycobacterium butyricum with maximal stimulation elicited by the calcium ionophore ionomycin . Mycobacterium butyricum only mobilized 19% of the ionomycin-induced Met-enkephalin release ( data not shown ) . Similar to human neutrophils , mycobacterium-triggered Met-enkephalin release from rat neutrophils was unaltered by TLR-2/4 blockade ( Fig . 7B ) but was inhibited by preincubation with Boc-FLFLF by 49% ( Fig . 7C ) and with cyclosporine H by 41% ( Table 2 ) . To test whether formyl peptides might be involved in tonic analgesia through the release of opioid peptides in vivo , we treated rats with complete Freund's adjuvant-induced inflammation with intraplantar injection of Boc-FLFLF ( Fig . 7D ) or cyclosporine H ( Table 2 ) using optimal doses determined in prior dose response experiments ( Fig . 4E and Table 1 ) . Both treatments significantly lowered thermal nociceptive thresholds for up to 30 min indicating increased inflammatory pain . Boc-FLFLF treatment significantly reduced thermal nociceptive thresholds also after 12 and 24 h complete Freund's adjuvant inflammation ( Fig . 7E ) . However , the effect was less prominent after 24 h ( 1 . 6 s difference vs . 3 . 3 s difference at 2 h of complete Freund's adjuvant inflammation ) . Basal nociceptive threshold progressively fell during inflammation indicating that hyperalgesia increased over time ( Fig . 7E ) . Bacteria have long been believed to trigger inflammatory pain by activating immune cells of the innate immune system . In this study , we demonstrate that bacteria simultaneously decrease pain by stimulating tonic release of endogenous opioid peptides like Met-enkephalin and β-endorphin at the site of inflammation . In vitro , heat inactivated Mycobacterium butyricum triggers opioid peptide release from neutrophils , but not from monocytes . This requires activation of FPR as well as intracellular Ca2+ mobilization and PI3K activation , while TLR-2 and -4 do not seem to be involved . These pathways are relevant in vivo since pain increases if FPR are blocked at the site of complete Freund's adjuvant-induced inflammation . Local injection of Mycobacterium butyricum ( i . e . complete Freund's adjuvant ) induces inflammatory pain as demonstrated by a decrease in thermal and mechanical nociceptive thresholds . The thermal pain threshold is further decreased by prior systemic neutrophil depletion ( Fig . 1A ) . Intuitively , one would expect that the removal of neutrophils reduces the inflammatory reaction . However neutrophil depletion in CFA inflammation does not significantly change paw volume or local prostaglandin E2 production but leads to a reduction in total IL-1ß content . Despite the neutrophil depletion nociceptive thresholds were not decreased [42] . Similarly , selective neutrophil recruitment by intraplantar CXCL2/3 injection does not elicit signs of inflammation or lowered nociceptive thresholds [42] . This suggests that neutrophils contribute modestly to the inflammatory reaction and are more important for the inhibition than the generation of pain . In previous studies , neutrophils were shown to be the major opioid peptide containing leukocyte population in early inflammation ( within 24 h of injection ) while monocytes/macrophages are predominant in later stages [13] , [14] , [17] , [18] . Opioid peptide release requires a stimulus such as cold water swim [43] or intraplantar injection of corticotrophin releasing hormone , cytokines ( e . g . interleukin-1 ) [44] or chemokines ( CXCL2/3 ) [17] , [18] . Although the resultant analgesia is potent , it only lasts for 5–10 min . Thus , it has been an open question whether there is a biological role of this system under basal inflammatory conditions . In line with previous studies in postoperative pain in humans [19] , we now demonstrate that peripherally mediated opioid analgesia is active under basal conditions in complete Freund's adjuvant-induced inflammation . Local injection of the opioid receptor antagonist naloxone , anti-Met-enkephalin or anti-β-endorphin antibodies resulted in a further decrease in thermal nociceptive thresholds and , thus , enhanced inflammatory pain ( Fig . 1B–D ) . These reductions were detectable when measuring thermal ( i . e . Hargreaves method ) but not mechanical nociceptive thresholds ( i . e . Randall Selitto test ) presumably because of the limited sensitivity of the latter test ( data not shown ) . We next identified the molecular mechanisms responsible for tonic opioid peptide release . Complete Freund's adjuvant contains heat inactivated Mycobacterium butyricum . In vitro , neutrophil but not monocyte stimulation with Mycobacterium butyricum resulted in a significant and dose-dependent release of Met-enkephalin ( Fig . 2A–C ) . To further delineate the molecular pathways , we first explored TLR-2 and TLR-4 , the major receptors transmitting the signals of mycobacteria [22] , [28] . Expression of TLR-2 and TLR-4 on neutrophils has been shown on mRNA and protein levels [29] , [45] as well as functionally through stimulation with lipopolysaccharide or peptidoglycan [29] , [30] , [45] . We could not detect Met-enkephalin or β-endorphin release after selective TLR activation ( Fig . 2 and Fig . S1 ) despite receptor expression . In line with the lack of opioid peptide release after TLR-2 or TLR-4 stimulation , the Mycobacterium butyricum-induced release of Met-enkephalin could not be blocked by single or combined TLR-2/4 blockade ( Fig . 6A , 7B ) . Antibodies against TLR-2 and -4 , although widely used , have mostly shown partial inhibitory effects [46]–[52] . Therefore , the additional involvement of TLR cannot completely be excluded and would have to be studied in TLR knockout mice . Other studies demonstrated that TLR can induce production of reactive oxygen species [29] , [30] and PI3K-dependent tumour necrosis factor-α and CXCL2/3 secretion [53] . Costimulation with fungi and TLR-4 agonists enhances secretion of primary granules , whereas TLR-2 agonists increase tertiary granule secretion . In contrast to our study , neutrophils were stimulated for 4 h , TLR agonists were not tested in the absence of fungal products and the TLR-2/4-induced increase in release was modest in this study [54] . In line with these data , short term TLR stimulation ( i . e . minutes ) in the absence of costimulators does not induce substantial granule release from neutrophils [55] . Since opioid peptides are stored in primary granules in neutrophils [18] and can be released within minutes , our results are in accordance with the current literature . Mycobacteria contain formyl peptides [23] , which are released during bacterial lysis [25] . Activation of human neutrophils with mycobacteria or fMLP induced a more than fivefold increase in Met-enkephalin secretion ( Fig . 2B and 2H , respectively ) . Both fMLP- and Mycobacterium butyricum-induced opioid peptide release was blocked by the specific FPR antagonists Boc-FLFLF ( Fig . 4C , 6B ) and cyclosporine H ( Tables 1 and 2 ) . Previous studies delineated signaling requirements for fMLP-triggered release [24] . We now demonstrate that the same signaling pathways are activated following mycobacterial stimulation . Specifically , we found that mycobacteria and fMLP triggered intracellular Ca2+ mobilisation in neutrophils ( Fig . 4D ) and in HEK293 cells transfected with human FPR but not in Mock-transfected cells ( Fig . 4B ) . Mycobacterium butyricum did not induce intracellular Ca2+ mobilisation if cells were pretreated with fMLP demonstrating acute FPR desensitisation . Furthermore , opioid peptide release was dependent on intracellular calcium mobilisation as well as PI3K activation ( Fig . 6F–H ) . Both are known to be downstream signals of FPR but not TLR activation . To underline the in vivo relevance of our findings we demonstrate that formyl peptides ( i . e . , fMLP ) can induce analgesia in complete Freund's adjuvant-induced inflammation , mediated through mu- and delta opioid receptors ( Fig . 3B , G ) and that local injection of FPR antagonists significantly impairs local endogenous pain control ( Fig . 7 and Table 2 ) . FPR mediated endogenous pain control was seen for up to 24 h , but became less prominent because baseline thermal nociceptive threshold decreased during the time course of inflammation ( Fig . 7E ) . This is consistent with the number of infiltrating neutrophils in complete Freund's adjuvant inflammation [15] . Constant recruitment of neutrophils from the circulation as well as submaximal stimulation could account for the tonic release of opioid peptides without FPR desensitisation . Indeed , only around 20% of the total opioid peptide content from neutrophils was secreted during the first simulation , and repetitive stimulation of neutrophils allowed for repeated release of Met-enkephalin if the stimulus was washed away in between ( Fig . 7A ) . In conclusion , intracellular stores of opioid peptides seem to contain enough opioid peptides to permit tonic release after repetitive stimulation with Mycobacterium butyricum . In our studies fMLP- as well as Mycobacterium butyricum-induced Met-enkephalin release could be completely blocked by Boc-FLFLF and partially blocked by cyclosporine H in human neutrophils ( Fig . 4C , 6B and Tables 1 and 2 ) . In rats , both Boc-FLFLF and cyclosporine H were only partially effective and higher doses were required ( Fig . 4D , 7C and Tables 1 and 2 ) . While species differences cannot be fully excluded , they appear unlikely . In contrast to mice [56] , human and rat FPR show a comparable and high affinity for fMLP [57] . The two FPR antagonists Boc-FLFLF and cyclosporine H are functional in rodents since they significantly reduce monocyte and neutrophil recruitment in murine pneumococcal pneumonia [58] , [59] and impair the protective effect of fMLP on infarct size in a rat model of ischemia reperfusion injury [60] . Alternatively , differences in activation state of neutrophils might be important . We performed our experiments in purified human peripheral blood neutrophils from healthy volunteers , while rat neutrophils were obtained by sterile peritonitis , which induces significant preactivation [61] . Therefore it is conceivable that other receptors ( e . g . chemokine receptors [62] ) need to be blocked in addition to completely abolish opioid peptide secretion . This view is supported by our previous study [17] in which chemokine ( i . e . CXCR1/2 agonist ) -triggered opioid peptide release was less effectively blocked in rat compared to human neutrophils . Opioid peptides can be readily detected in the inflamed synovial tissue of patients with arthritis [63] as well as in surgical wound [64] . Local opioid-mediated analgesia significantly reduces postoperative pain in humans since intraarticular naloxone administration enhances pain and consumption of pain medication , indicating a continuous release of opioid peptides [19] , [65] . In the present study in rats , we delineated a molecular pathway of tonic opioid release from neutrophils in complete Freund's adjuvant-induced inflammation involving mycobacterially triggered FPR activation . Mycobacteria or bacterial products may trigger opioid peptide release in arthritic joints or at the site of surgery with accompanying infection . In addition , formyl peptides can also be released from mitochondria of eukaryotes [66]–[68] . Alternatively , other releasing agents such as chemokines ( CXCR1/2 ligands ) can trigger opioid peptide release from rat and human neutrophils [17] , [18] and these are produced in complete Freund's adjuvant-induced inflammation [14] as well as in surgical wounds [69] . Rabbit anti-Met-enkephalin or anti-rat-β-endorphin Abs as well as purified Met-enkephalin and Boc-FLFLF were purchased from Bachem , Weil am Rhein , Germany . Naloxone , D-Phe-Cys-Tyr-D-Trp-Orn-Thr-Pen-Thr-NH2 ( CTOP ) , naltrindole hydrochloride ( NTI ) and fMLP were obtained from Sigma-Aldrich Chemie , Deisenhofen , Germany , and desiccated Mycobacterium butyricum was from BD Bioscience , Heidelberg , Germany . Complete Freund's adjuvant , LY294002 , wortmannin and 1 , 2-bis ( o-aminophenoxy ) ethane-N , N , N′ , N′-tetraacetic acid acetoxymethyl ester ( BAPTA/AM ) were purchased from Calbiochem , San Diego , CA , USA . BAPTA/AM , LY294002 , wortmannin , fMLP , and Boc-FLFLF were dissolved in dimethyl sulfoxide ( maximal final concentration 1% ) . Anti-neutrophil serum was obtained from Accurate Chemical&Scientific Corporation , Westbury , NY , USA . Cyclosporine H was purchased from Eton Bioscience , San Diego , CA , USA . Anti-TLR-2-phycoerythrin ( PE , clone TL2 . 1 ) and anti-TLR-4-PE ( clone HTA125 ) as well as mouse IgG2a were obtained from eBioscience , San Diego , CA , USA . Anti-TLR-2 ( clone TL2 . 1 ) and anti-TLR-4 ( clone HTA125 ) were from Alexis , Lörrach , Germany . fMLP-fluorescein isothiocyanate ( FITC ) was obtained from Invitrogen-Molecular Probes , Karlsruhe , Germany . CD45-CyC , RP-1-PE and ED1-PE were obtained by BD Biosciences , Heidelberg , Germany and Serotec , London , Great Britain , respectively Male Wistar rats weighing 180–220 g were injected intraplantarly with 150 µl complete Freund's adjuvant in the right hind paw as described [70] . Experiments were conducted at 2–24 h after inoculation . All injections were performed under brief isoflurane anesthesia . Animal protocols were approved by the animal care committee of local authorities and were in accordance with the guidelines of the International Association for the Study of Pain [71] . Mechanical nociceptive thresholds were assessed using the paw pressure algesiometer ( modified Randall-Selitto test; Ugo Basile ) as described before [14] , [17] . The pressure required to elicit paw withdrawal using a blunt piston onto the dorsal surface of the hind paw , the paw pressure threshold , was determined . The treatments were randomized and the experimenter was blinded to the treatments . A decrease in the paw pressure threshold was interpreted as hyperalgesia ( pain ) whereas a rise in the paw pressure threshold was interpreted as analgesia ( antinociception ) . Thermal nociceptive thresholds were measured by the Hargreaves test [42] . The latency ( time; s ) required to elicit paw withdrawal was measured with an electronic timer ( IITC Inc/Life Science ) after application of radiant heat to the plantar surface of a hind paw from underneath the glass floor with a high-intensity light bulb . The stimulus intensity was adjusted to give 20 s paw withdrawal latency in noninflamed paws , and the cutoff was 25 s to avoid tissue damage . The average of two measurements taken with 20 s intervals was calculated . A decrease in paw withdrawal latency was interpreted as pain ( hyperalgesia ) whereas a rise in paw withdrawal latency was interpreted as analgesia ( antinociception ) . fMLP-induced analgesia was evaluated in rats with complete Freund's adjuvant inflammation after intraplantar ( i . pl . ) injection of 0 . 1–3 ng fMLP dissolved in 100 µl of NaCl 0 . 9% or of solvent only . Paw pressure threshold or paw withdrawal latency were measured 5 min after fMLP injection . In some experiments the opioid receptor antagonist naloxone ( 0 . 56 ng i . pl . ) , CTOP ( 2 µg i . pl . ) , NTI ( 50 µg i . pl . ) , or an antibody against Met-enkephalin ( 1 . 25 µg i . pl . ) were injected concomitantly . Optimal doses were determined in pilot experiments and in previous studies [17] , [18] . To deplete rats of neutrophils , animals were injected with 80 µl anti-neutrophil serum intravenously 18 h before complete Freund's adjuvant injection as described previously [14] , [17] . Modulation of baseline inflammatory thermal hyperalgesia was analyzed in rats with complete Freund's adjuvant ( 2–24 h ) inflammation after i . pl . treatment with naloxone ( 0 . 56 ng ) , anti-Met-enkephalin ( 1 . 25 µg ) , anti-β-endorphin ( 2 µg ) , FPR antagonists ( Boc-FLFLF: 0 . 3 and 3 µg or cyclosporine H: 0 . 9 and 9 µg ) or after systemic neutrophil depletion . To obtain human monocytes from healthy blood donors , red blood cells were lyzed using buffer EL ( Qiagen , Hilden , Germany ) . The remaining white blood cells were incubated with anti-CD14-coupled magnetic beads ( Miltenyi Biotec , Bergisch-Gladbach , Germany ) in phosphate-buffered saline ( PBS ) containing 0 . 5% bovine serum albumin and 2 mM ethylene diamine tetraacetic acid . CD14+ monocytes were isolated using a LS column ( Miltenyi Biotec ) . Purity was confirmed by staining with anti-CD14 FITC antibody ( BD Biosciences ) and FACS analysis ( see below ) . Human neutrophils from healthy blood donors were purified using dextran sedimentation , Ficoll separation and hypotonic lysis ( all Amersham Biosciences ) . Rat peritoneal neutrophils were obtained 4 h after intraperitoneal injection of 1% oyster glycogen ( Sigma-Aldrich Chemie ) [17] , [72] . For determination of opioid peptide release , 5×107 neutrophils or 1×107 CD14+ monocytes were stimulated with fMLP ( 1–1000 nM ) or Mycobacterium butyricum ( 0 . 06–0 . 66 mg/ml ) after preincubation with cytochalasin B ( 5 µg/ml ) for 5 min in Hank's balanced salt solution containing the proteinase inhibitors bestatin ( 5 µg/ml ) , aprotinin ( 40 µg/ml ) and thiorphan ( 100 µM , all Sigma-Aldrich Chemie ) [17] , [73] , [74] . Doses of fMLP and Mycobacterium butyricum were based on pilot experiments and the literature [74] , [75] . In some experiments , cells were concomitantly incubated with inhibitors as described in the results section . Doses were established in pilot experiments and according to the literature [17] , [31] , [35] . Control samples with the solvent dimethyl sulfoxide did not induce significant release . Doses for anti-TLR-2 ( clone TL2 . 1 ) and anti-TLR-4 ( clone HTA125 ) antibodies were chosen based on their blocking effects according to the literature [46] , [49]–[52] . Supernatants were obtained after 7 min stimulation and stored at −20°C until further analysis by radioimmunoassay using commercially available kits for rat or human Met-enkephalin and β-endorphin ( Bachem ) [17] , [76] , [77] . Construction of plasmids coding for the human fMLP receptor has been described elsewhere [78] . HEK293 cells were grown at 37°C and 5% CO2 in Dulbecco's modified Eagle's medium or minimal essential medium with Earle's salts , supplemented with 10% fetal calf serum , 2 mM glutamine , 100 µg/ml streptomycin , and 100 units/ml penicillin . HEK293 cells were transfected using Fugene 6 transfection reagent ( Roche Applied Science , Mannheim , Germany ) according to the manufacturer's recommendations . The amount of transfected human FPR plasmid cDNA was 250 ng per 35 mm dish and was kept constant by addition of empty expression vector ( pcDNA3 up to 2 µg ) where necessary . Fluorescence imaging was performed with a monochromator-equipped xenon lamp and a cooled CCD camera ( TILL-Photonics ) connected to an inverted epifluorescence microscope ( Axiovert 100; Carl Zeiss ) . All imaging experiments were performed in a Hepes-buffered solution containing 128 mM NaCl , 6 mM KCl , 1 mM MgCl2 , 1 mM CaCl2 , 5 . 5 mM glucose , 10 mM Hepes ( pH 7 . 4 ) , and 0 . 2% ( wt/vol ) bovine serum albumin . For determination of [Ca2+]i , neutrophils or FPR transfected HEK293 cells were placed on dishes coated with poly-L-lysine and then loaded with 1 or 2 µM Fura 2/AM ( Molecular Probes-Invitrogen ) for 30 min at 37°C as previously described [79] . After basal recordings , cells were stimulated by subsequent addition of 1 µM fMLP or dimethyl sulfoxide extracted Mycobacterium butyricum . Fura-2 loaded cells were alternately excited at 340 and 380 nm , and fluorescence was detected through a 505 nm filter . Calibration of [Ca2+]i was performed as described [17] , [79] . TLRs were labeled in human neutrophils after preincubation with 10% mouse serum for 10 min using PE conjugated anti-human-TLR-2 , anti-human-TLR-4 or isotype control antibodies according to manufacturer's instructions . The FPR was stained using FITC-conjugated fMLP ( 1 µM ) . In selected experiments samples were pretreated for 10 min with different concentrations of Boc-FLFLF before addition of FITC-fMLP [13] , [14] , [17] , [76] . FPR staining in subcutaneous paw tissue was performed as described before [17] . Neutrophils were identified by CD45+ and RP1+ staining while macrophages were CD45+ ED1+ . Immunofluorescence staining was performed using human neutrophils ( 5 min preincubation with cytochalasin B , then addition of 0 . 66 mg/ml Mycobacterium butyricum for 15 min ) [41] as well as neutrophils preincubated with Boc-FLFLF . After centrifugation for 10 min at 300 g , cell pellets of neutrophils were reconstituted in 5 ml PBS , and 50 , 000 neutrophils in suspension were then centrifuged by a Shandon Cytospin 3 ( Thermo Shandon , Pittsburgh , PA ) at 20 g for 3 min on glass slides . Neutrophils were fixed for 30 min and confocal analysis was carried out as previously described [80] . Briefly , neutrophil cytospins were incubated with rabbit polyclonal antibodies against Met-enkephalin ( 1∶1000 , Peninsula Laboratories , Belmont , CA , USA ) and subsequently with a Texas red-conjugated goat anti-rabbit antibody . Thereafter , cytospins were washed with PBS and mounted in vectashield . Images were acquired on a Zeiss LSM510META confocal laser scanning system ( Zeiss AIM; Jena ) using a 63×/1 . 4 Plan-Apochromat or 40×/1 . 3Plan-Neofluar oil immersion objective in a series of optical sections of about 1 µm thickness . Each experiment was repeated three times . To demonstrate specificity of staining , the following controls were included as mentioned in detail elsewhere [14] , [81]: ( 1 ) preabsorption of diluted antibody against Met-enkephalin with purified Met-enkephalin ( Peninsula laboratories-Bachem ) and ( 2 ) omission of either the primary or the secondary antibodies . Data are presented as raw values ( mean±SEM ) . Normally distributed data were analyzed by student's t-test or Mann-Whitney test . Not normally distributed data were analyzed by Wilcoxon Signed Rank Test . Multiple comparisons were analyzed by one-way ANOVA or by one-way ANOVA on ranks in case of not normally distributed data . If necessary repeated measures ( RM ) one way ANOVA was used . Posthoc comparisons were performed by Student-Newman-Keuls' , Dunnett's or Duncan's method , respectively . Differences were considered significant if p<0 . 05 . Dose dependency was evaluated by linear regression analysis . Sigma Stat was used to analyze the data .
Inflammation of peripheral tissue can be caused by bacteria and is frequently accompanied by pain . Pain severity depends on the balance of enhancing ( proalgesic ) and decreasing ( analgesic ) mediators . Local endogenous pain control involves the release of opioid peptides from immune cells at the site of inflammation . These opioid peptides bind to opioid receptors on peripheral nerves and inhibit transmission of nociceptive impulses . We hypothesized that bacteria can directly stimulate immune cells to release opioid peptides and thereby decrease pain . In a rat model , inoculation of the paw with heat-inactivated Mycobacterium butyricum led to local inflammation and pain responses . Nociceptive thresholds were further decreased ( i . e . pain was enhanced ) following immune cell ( i . e . neutrophil ) depletion , local injection of anti-opioid peptide antibodies or opioid receptor antagonists . Immune cells recognize bacteria by toll-like and/or formyl peptide receptors . Previous research indicated that mycobacteria enhance nociceptive responses via toll like receptors-2 and -4 . We now demonstrate that mycobacteria also activate formyl peptide receptors on neutrophils leading to opioid peptide release and the inhibition of such responses . Since bacteria can simultaneously induce the generation of pro- and analgesic mediators , our results might be a further explanation for differences in pain between individual patients following bacterial infections .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "neuroscience/behavioral", "neuroscience", "neuroscience/sensory", "systems", "neurological", "disorders/pain", "management", "immunology/immune", "response", "microbiology/innate", "immunity", "immunology/innate", "immunity", "microbiology/applied", "microbiology", "immunology/leukocyte", "signaling", "and", "gene", "expression", "immunology/leukocyte", "activation" ]
2009
Mycobacteria Attenuate Nociceptive Responses by Formyl Peptide Receptor Triggered Opioid Peptide Release from Neutrophils
Enterohemorrhagic E . coli ( EHEC ) is a human intestinal pathogen that causes hemorrhagic colitis and hemolytic uremic syndrome . No vaccines or specific therapies are currently available to prevent or treat these infections . EHEC tightly attaches to the intestinal epithelium by injecting the intimin receptor Tir into the host cell via a type III secretion system ( T3SS ) . In this project , we identified a camelid single domain antibody ( nanobody ) , named TD4 , that recognizes a conserved Tir epitope overlapping the binding site of its natural ligand intimin with high affinity and stability . We show that TD4 inhibits attachment of EHEC to cultured human HeLa cells by preventing Tir clustering by intimin , activation of downstream actin polymerization and pedestal formation . Furthermore , we demonstrate that TD4 significantly reduces EHEC adherence to human colonic mucosa in in vitro organ cultures . Altogether , these results suggest that nanobody-based therapies hold potential in the development of much needed treatment and prevention strategies against EHEC infection . Enterohemorrhagic E . coli ( EHEC ) is a major public health concern in industrial countries with most severe infections linked to serotype O157:H7 . In addition to diarrhoea , EHEC can cause hemorrhagic colitis as well as life-threatening hemolytic uremic syndrome ( HUS ) damaging the kidneys and central nervous system [1–4] . EHEC naturally resides in the intestinal tract of cattle , and most infections are acquired by consumption of undercooked beef products or cross-contaminated vegetables or sprouts [5] . Upon infection , EHEC adheres to the epithelium of the distal ileum and colon by forming attaching and effacing ( A/E ) lesions , which are characterized by intimate bacterial attachment and effacement of the brush border microvilli [6 , 7] . This is mediated by the Locus of Enterocyte Effacement ( LEE ) [8] , a pathogenicity island encoding a filamentous type III secretion system ( T3SS ) [9 , 10] , the outer membrane adhesin intimin and the translocated intimin receptor ( Tir ) , and other effector proteins involved in pathogenesis [11 , 12] . After formation of the translocation filament consisting of EspA proteins , Tir is injected into intestinal epithelial cells ( IECs ) , where it integrates into the plasma membrane in a hairpin loop topology , presenting an extracellular domain of about 100 residues ( TirM ) [13 , 14] that serves as a binding site for the C-terminal lectin-like domain of intimin [15–17] . Binding of intimin to Tir leads to intimate bacterial attachment , Tir clustering , activation of actin polymerization pathways and subsequent formation of actin pedestals and A/E lesions [7 , 18–22] . Other key virulence factors of EHEC are the phage-encoded Shiga toxins ( Stx ) which are released into the bloodstream and cause the systemic effects associated with HUS [23 , 24] . So far , there is no specific treatment for HUS , and application of antibiotics is discouraged as it induces Stx expression and thereby increases the risk of developing HUS [25 , 26] . Therefore , there is a need to develop alternative therapies , and the use of antibodies ( Abs ) has been proposed for treatment of infectious diseases [27] . In particular , members of the family Camelidae ( e . g . dromedaries , llamas ) produce a class of Abs devoid of light chains [28 , 29] . In these heavy-chain-only Abs , the antigen-binding site is formed by a single variable domain termed VHH [30] . The recombinant expression of camelid VHHs yields single domain Ab fragments , which are also referred to as nanobodies ( Nbs ) [31] . The VHHs have extended complementarity determining regions ( CDRs ) capable of adopting novel conformations and recognizing epitopes located in otherwise non-accessible clefts or protein cavities , such as active sites of enzymes [32 , 33] and inner regions of surface proteins from pathogens [34] . They also show strict monomeric behavior , reversible folding properties , higher resistance to proteolysis and thermal degradation , when compared with the variable domains of conventional antibodies [31 , 35 , 36] . In addition , the high similarity between VHHs and human VH3 sequences opens their potential use in therapeutic applications [31] . These beneficial properties offer opportunities to use Nbs for the development of therapeutic inhibitors against extracellular pathogens [37 , 38] . We have previously isolated a set of Nbs binding to EspA , the C-terminal receptor-binding domain of intimin ( Int280 ) and the TirM domain from a library of VHHs obtained after immunization of a dromedary ( Camelus dromedarius ) . Nanobodies were secreted to the extracellular medium using the hemolysin ( Hly ) transport system of E . coli and purified from the culture supernatants [39] . Here , we have investigated the ability of the selected Nbs to inhibit EHEC adhesion to HeLa cells and human colonic mucosa . We have identified a Nb clone that binds TirM , named TD4 , which reduces the interaction of TirM with Int280 and interferes with actin pedestal formation and the intimate attachment of EHEC to human cells . Importantly , using infection of human in vitro organ cultures ( IVOC ) , we demonstrate that Nb TD4 can also inhibit the attachment of EHEC to human colonic tissue . To determine if purified Nbs against EspA , Int280 and TirM affected EHEC A/E lesion formation , HeLa cells were infected with EHEC for 3 h in the presence or absence of Nbs . Actin pedestal formation was visualized and quantified by immunofluorescence staining . While EHEC attachment and pedestal formation was not affected by Nb clones recognizing EspA ( EC7 ) or Int280 ( IB10 ) at concentrations 200 nM ( Fig 1A ) , a Nb clone binding TirM ( TD4 ) significantly reduced the accumulation of actin beneath the attached bacteria . As clustering of Tir is necessary for A/E lesion formation , we also evaluated the Tir localization in the presence of Nb TD4 or an unrelated Nb . As shown in Fig 1B , localization of Tir beneath adherent EHEC was evident in samples incubated with the control Nb or non-treated controls , while no Tir accumulation was observed in the presence of TD4 . No Tir staining was detected in HeLa cells infected with EHECΔtir . We quantified the effect of TD4 by determining the mean of pedestals formed on infected cells under various concentrations of Nb TD4 . This revealed a significant decrease in the number of actin pedestals per infected cell when TD4 Nb was added at concentrations ≥ 100 nM ( Fig 1C ) . We wanted to rule out the possibility that the lack of Tir clustering beneath the bound bacteria could be due to a block of Tir translocation through the T3SS and not to the direct interaction of the Nb TD4 to the exposed region of Tir upon its translocation and insertion in the plasma membrane of the host cell . We tested this possibility and simultaneously evaluated whether TD4 can interfere with EHEC actin-pedestal formation when added at different times during infection . To this end , we increased the infection rate by halving the volume of the medium and added 200 nM of Nbs TD4 or control ( Vamy ) simultaneously with the infection , or at 1 or 2 h post-infection . After a total of 3 h of infection , all samples were stained for Tir and the HA-tagged Nbs to test for their co-localization ( Fig 2 ) . Due to the higher infection rate , some Tir signal could be observed beneath the bound bacteria with TD4 , but the arrangement of Tir staining in the host cells was altered , being distributed in the cytoplasm and showing only weak staining marks at the site of the EHEC adhesion ( Fig 2A ) . Furthermore , we could detect colocalization of the HA-tagged TD4 with Tir , showing that the interaction between TD4 and Tir occurs at the surface of infected host cells , once Tir has been translocated , and suggesting that this interaction is responsible for the observed phenotype . In contrast , infections incubated with the control Nb ( Vamy ) showed strong Tir signals accumulated beneath EHEC bacteria and no staining of the cytoplasm ( Fig 2A ) . Importantly , this inhibitory effect of TD4 was observed at similar levels independently of the time of addition of the Nb ( 0 , 1 or 2 h post-infection ) as determined by quantification of Tir clusters in the infected cells ( Fig 2B ) . Lastly , we assessed the inhibitory effect of TD4 at longer times of infection . HeLa cells were infected with EHEC for 6 h in the presence or absence of TD4 , and stained for bacteria , Tir and F-actin . In this experiment fresh medium and Nbs were added after 3 h of infection . Inspection of these samples revealed the presence of a high number of intimately attached EHEC bacteria and dense clusters of Tir in the absence of TD4 ( Fig 3 ) . The high density of EHEC bacteria did not allow us to visualize individual bacteria with Tir clustering for quantification purposes . Nonetheless , we clearly observed that the presence of TD4 dramatically reduced the number of EHEC bound to HeLa cells , as well as the intensity of actin and Tir signals in those bacteria that were bound to the cells ( Fig 3 ) . Taken together , the above data showed that Nb TD4 reduces EHEC attachment , Tir clustering and actin polymerization by binding to the extracellular TirM domain exposed after Tir translocation . The Nb TD4 shows this inhibitory activity even when added once infection has begun . One mechanism by which Nb TD4 could interfere with the attachment of EHEC to human cells is by directly competing with intimin for binding TirM . To investigate this , ELISA plates coated with purified TirM were incubated with biotinylated Int280 in the presence of different Nbs ( Fig 4 ) . While Int280:TirM interaction was not affected by the presence of camel pre-immune serum or Nb EC7 binding EspA ( control ) , incubation with camel immune serum or Nb TD4 inhibited the interaction . In addition , Nb IB10 ( anti-Int280 ) also reduced Int280:TirM interaction , but to a lesser extent than TD4 . These results show that Nb TD4 is a potent inhibitor of Int280-TirM interaction . To further characterize the binding of Nb TD4 to TirM and its inhibitory activity , we compared the affinities of Int280 and TD4 for TirM using surface plasmon resonance ( SPR ) . Biotinylated TirM was immobilized onto a chip for SPR , and purified Int280 and TD4 Nb-HlyA fusion were passed over the chip at different concentrations in successive rounds of binding and regeneration . The change in resonance units ( RU ) with time was recorded as a direct indication of the binding of these proteins to TirM . The sensograms obtained are represented in Fig 5 . These experiments revealed a distinct pattern of binding of Int280 and TD4 to TirM . While Int280 quickly bound to and dissociated from TirM after stopping Int280 injection ( Fig 5A ) , TD4 bound to TirM more slowly and the interaction remained stable without any detectable dissociation even >300 sec after the injection stopped ( Fig 5B ) . The kinetic constants of association ( kon ) and dissociation ( koff ) of Int280-TirM binding could be calculated directly from the obtained sensograms ( Fig 5A ) . A model 1:1 Langmuir interaction fitted the binding curves , suggesting the formation of a 1:1 complex , as observed by protein crystallography of the EPEC Int280:TirM complex [15] . Using this binding model , we determined a koff of 3 . 75·10−2 s-1 and a kon of 7 . 85·105 M-1s-1 for EHEC Int280:TirM interaction . The equilibrium dissociation constant ( KD ) for EHEC Int280-TirM interaction was calculated from the ratio of these kinetic constants ( koff/kon ) and determined to be 48 . 1 nM . In contrast to Int280 , the fact that TD4 had no detectable dissociation of TirM during SPR analysis impeded the determination of its kinetic constants kon and koff from the obtained sensograms . In addition , the KD could not be determined from RU values at equilibrium since the steady state was only reached at the highest concentration of TD4 ( Fig 5B ) . Using the RU values closer to an apparent binding plateau at the different concentrations tested , we could estimate an apparent KD ~4 . 8 nM for the TD4:TirM interaction ( Fig 5B ) . The actual KD for this interaction is likely to be below this estimated value ( KD < 4 . 8 nM ) as the actual steady state would be reached with higher RU values . Hence , this quantitative binding analysis indicated an at least 10-fold higher affinity of TD4 for TirM than the one of its natural ligand , Int280 . Using SPR we also investigated whether TD4 recognised an epitope of TirM overlapping the binding site of Int280 , taking advantage of the extremely slow dissociation of TD4 . We injected 40 nM of TD4 into the TirM-chip until reaching RU values close to steady state followed by 80 nM of Int280 ( Fig 5C ) . We compared the increment of RU values obtained by Int280 injection in this condition ( with bound TD4 ) with those obtained by injecting the same concentration of Int280 to the TirM-chip in the absence of TD4 . This experiment showed that the RU values of Int280 binding to TirM were reduced in the presence of TD4 , but binding of Int280 occurred simultaneously to TD4 , indicating that the binding sites of Int280 and TD4 are not identical , although they could partially overlap . Interestingly , when Int280 injection was stopped , Int280 quickly dissociated whereas TD4 remained bound to TirM and the RU in the assay came back to those of TD4 binding alone . Thus , while Int280 quickly dissociates from TirM , TD4 remains stably bound to it . To identify the specific binding site of TD4 to TirM , we synthesized 12-mer peptides of EHEC TirM covering its sequence , with a 10 amino acid ( aa ) overlap between consecutive peptides on a PVDF membrane . After incubation with TD4 , bound Nb was subsequently detected with a secondary antibody . This identified two peptides recognized by TD4: VNIDELGNAIPS ( aa 296–307 ) and GVLKDDVVANIE ( aa 308–319 ) ( Fig 6A ) . These consecutive peptides are localized within the interaction interface between Int280 and Tir ( Fig 6B ) [15–17 , 40] . A BLASTP search [41] with non-redundant DNA sequences in databases ( S1 Data ) allowed us to determine that the 24-mer sequence of these peptides is 100% identical ( BLASTP score 77 . 4 in S1 Data ) in Tir proteins from all EHEC strains , including O157 , O55 , O145 and other relevant non-O157 serotypes [42] . The sequence of TirM is highly conserved among the related A/E pathogens EHEC , enteropathogenic E . coli ( EPEC ) and Citrobacter rodentium ( CR ) but the sequence of these Tir peptides is not identical in EPEC and CR strains , so we were therefore interested to determine the affinity of TD4 to purified TirMEPEC and TirMCR . We found that TD4 bound TirMCR with lower affinity ( ca . 10-fold ) than TirMEHEC . Surprisingly , no binding of TD4 to TirMEPEC was detected ( Fig 6C ) . Comparing the aa sequences of the TD4 binding site in TirMEHEC with corresponding regions in TirMEPEC and TirMCR ( Fig 6D ) revealed that TirMEHEC differs from both TirMEPEC and TirMCR in residues V309 , N317 and E319 , suggesting that these changes may affect the affinity of TD4 towards TirM . Moreover , TirMEPEC specifically differs from TirMEHEC in residues E300 , L301 , V314 and A316 , suggesting that these residues may be essential for TirM recognition by TD4 . We tested the effect of TD4 on EHEC binding to human colonic mucosa by employing IVOC . Human colonic biopsy samples were infected with EHEC in the presence or absence of TD4 or control Nb ( Vamy ) . In addition , infection with EHECΔtir was included as a negative control . Immunostaining of biopsy samples showed a significant reduction in the number of adherent EHEC in the presence of TD4 but not of the control Nb ( Fig 7 ) . As expected , very few adherent bacteria were observed in biopsy samples infected with EHECΔtir ( Fig 7 ) . These results demonstrate that Nb TD4 reduces EHEC binding to human colonic mucosa ex vivo . EHEC infections are associated with severe diseases such as bloody diarrhoea and HUS [1 , 2] . Efficient therapies against EHEC infections are lacking , and current treatment is based on fluid replacement and supportive care [43] . However , increasing knowledge on EHEC virulence factors and infection mechanisms is contributing to the development of new treatment strategies [44] , such as inhibition of quorum sensing [45] , use of EHEC LPS-specific bacteriocins [46] and inhibition of Stx binding to its host receptor globotriaosylceramide ( Gb3 ) with antibodies [47 , 48] or other ligands [49] . In this work , we tested the possibility of using specific Nbs against the EHEC proteins EspA , intimin and Tir as an alternative approach to interfere with EHEC infection . Nb clones binding Int280 ( IB10 ) or EspA ( EC7 ) did not interfere with EHEC infection . Since intimin covers the entire surface of EHEC , binding of Nb IB10 in the concentration used might not be sufficient to mask all the Tir-binding sites , despite some inhibitory activity of this Nb in the in vitro Int280:TirM binding assay . Similarly , binding of Nb EC7 to EspA , which forms the translocation filament , did not affect EHEC infection nor inhibit Tir translocation . In contrast , a Nb binding TirM ( TD4 ) reduced the attachment of EHEC and actin pedestal formation in HeLa cells . As the TirM domain is exposed on the host cell surface after Tir translocation [13 , 14] , binding of TD4 appears to block intimin binding . The fact that TirM is only presented on infected cells , suggests that a relatively low Nb concentration is needed for inhibition . Staining of Tir after EHEC infection showed that TD4 hindered the formation of actin pedestals by preventing the characteristic Tir clustering produced at the bacterial:host interface [19 , 50] , which is achieved even after its addition 2 h post infection and is maintained for 6 h . In vitro protein interaction assays confirmed a strong inhibition of Int280:TirM interaction by the presence of TD4 , which prevents Tir clustering by binding to TirM . SPR analysis of this interaction demonstrated an extremely slow dissociation rate of TD4 . This analysis also revealed that the affinity for TD4 to TirM ( KD ≤ 4 nM ) is at least 10 times higher than the affinity of Int280 to TirM ( KD ~40 nM ) . Strikingly , Int280 showed a fast dissociation of TirM , suggesting a dynamic interaction . SPR experiments also determined that the TirM epitope recognized by TD4 could partially overlap with the binding region of Int280 as the addition of TD4 reduces the binding of Int280 to TirM . We mapped two TirM consecutive non-overlapping peptides bound by TD4: VNIDELGNAIPS ( 296–307 ) and GVLKDDVVANIE ( 308–319 ) . It may be possible that each of these peptides is recognized by different CDRs of the Nb TD4 , but this experiment does not exclude that TD4 may recognize a conformational structure of TirM . Its CDRs could still bind to the primary structure of these peptides , albeit with reduced affinity . Importantly , these recognized peptides are fully conserved in all Tir sequences from EHEC strains . We also determined that TD4 does not bind to TirMEPEC and has a weak interaction with TirMCR , which are highly similar but not identical to TirMEHEC . This information helped us to narrow the interaction site of TD4 and TirMEHEC by comparing the TirM sequences of the three pathogens . Differences between TirM of EHEC and CR—i . e . V309 , N317 and E319- reduce but do not abolish the interaction with TD4 . On the other hand , differences with TirM of EPEC—i . e . E300 , L301 , V314 and A316—could be critical for the binding of TD4 , likely representing energetic hotspots of protein-protein interaction [51 , 52] . We could further localize the residues that may participate in the interaction of TD4 with TirM based on the crystal structure of Int280 and Tir of EPEC [15] . Within the TirM sequence , it has been identified the so-called Int280-binding domain ( IBD ) [13] , composed of two long alpha-helices ( HA , residues 271–288 , and HB , residues 312–331 ) separated by a ß-hairpin ( residues 294–308 ) . The described complex of EPEC reveals that the Int:Tir interaction is primarily mediated by the lectin-like D3 domain of Int280 and the ß-hairpin and the N-terminal part of the HB of Tir IBD , corresponding to residues 294–313 of TirMEHEC . The peptides identified to which TD4 binds ( residues 296–319 of TirMEHEC ) , are enclosed within the IBD of Tir , indicating that TD4 is directly interfering with the Int:Tir interaction . Importantly , we have shown that TD4 can also block the interaction of EHEC to intestinal human colonic tissue ex vivo [7] , as the number of bacteria bound to the epithelium was significantly reduced in the presence of this Nb . This result opens the possibility of testing TD4 protection in humans , which could be administered using a passive immunization strategy . The fact that TD4 shows inhibitory activity once EHEC infection has already begun opens also the possibility of using this Nb as a therapeutic Ab to treat infections . Nbs can be overproduced in bacteria , yeast , plants and mammalian cells to obtain highly concentrated purified proteins [53–56] . A purified Nb recognising EHEC toxins Stx1 and Stx2 has been administered , in combination with IgG , for the treatment of HUS [57] . However , the use of purified antibodies is a costly strategy for therapy development . To circumvent this problem , some studies describe the production of Abs and Nbs in edible plants and seeds . The production of Abs in edible tissues allows oral passive immunization at the gastric mucosal surface . For instance , a Nb against rotavirus infection has been expressed in rice and shown to protect infant mice from severe diarrhoea [58] . Abs contained in seeds enable long-term storage and the direct use for passive immunization with oral administration , which is particularly advantageous . Interestingly , a Nb against enterotoxigenic E . coli ( ETEC ) has been fused to the constant region ( Fc ) of immunoglobulins and produced in seeds . Piglets fed with these seeds were protected against ETEC infection [59 , 60] . Alternatively , probiotic strains such as E . coli Nissle 1917 ( EcN ) [61] could be used for delivery of TD4 to the gastrointestinal tract . EcN is known to compete with EHEC for colonisation of the mouse intestine [62] through specific mechanisms including the secretion of microcins [61 , 63] . Hence , secretion of TD4 by EcN could enhance its natural anti-microbial activity and leads to the development of a superior therapeutic strain against EHEC infection . Other probiotic bacteria can be considered for local delivery of Nb TD4 . For instance , Gram-positive Lactobacillus strains producing surface-bound or secreted Nbs against rotaviruses have been shown to reduce the severity and duration of rotavirus-induced diarrhoea in mice [64–66] . Overall , this study demonstrates that a Nb recognising Tir reduces intimate attachment of EHEC to human cells and colonic tissue by competing with its natural partner , intimin , thereby preventing colonization of the epithelium . These results open the possibility for passive immunization and therapeutic strategies that could prevent EHEC adhesion to intestinal tissues during infection . This could also be applied to reduce the prevalence of EHEC in its natural bovine host and minimize the risk of EHEC contamination into the food chain . This study was performed with approval from the University of East Anglia Faculty of Medicine and Health Ethics Committee ( ref 2010/11-030 ) . All samples were registered with the Norwich Biorepository which has approval from the National Research Ethics Service ( ref 08/h0304/85+5 ) . Biopsy samples from the transverse colon were obtained with informed written consent during colonoscopy of adult patients . All samples were anonymized . All E . coli strains used in this work are listed in Table 1 . Bacteria were grown at 37°C on Lysogeny broth ( LB ) agar plates ( 1 . 5% w/v ) , in liquid LB or Dulbecco’s Modified Eagle’s Medium ( DMEM ) . Ampicillin ( Ap , 150 μg/ml ) , Chloramphenicol ( Cm , 30 μg/ml ) and Kanamycin ( Km , 50 μg/ml ) were added for plasmid selection as required . For infection of HeLa cells , EHEC strains were grown for 8 h at 37°C ( 200 rpm ) in a flask with 10 mL of liquid LB , inoculated in capped Falcon tubes ( BD Biosciences ) with 5 mL DMEM , and incubated o/n at 37°C in a CO2 incubator ( static ) for the induction of the T3SS . For infection of biopsy samples , 2 ml of LB media were inoculated with an EHEC colony from an LB-agar plate and grown standing at 37 oC overnight ( o/n ) . Plasmids used in this study are listed in Table 1 . Strain E . coli DH10B-T1R was used as a host for the cloning and propagation of plasmids . TD4-HlyA and Vamy-HlyA DNA fragments were excised with BglII from pEHLYA5-TD4 and pEHLYA5-Vamy , respectively , and cloned into the same site of pVDL9 . 3 [71] . TirM sequences of EPEC ( aa 255–363 ) and CR ( aa 253–360 ) were amplified by PCR using primers listed in Table 2 , cloned after EcoRI-HindIII digestion into the same sites of pET28a plasmid backbone . The TirM constructs in this plasmid are under the T7 promoter and fused to an N-terminal His-tag for purification . PCR reactions were performed with Taq DNA polymerase ( Roche , NZyTech ) for standard amplifications in screenings . All DNA constructs were fully sequenced ( Secugen SL , Madrid , Spain ) . Cultures of E . coli BL21 ( DE3 ) carrying the corresponding pET28a-derivative were grown at 30°C in 500 ml of LB with Km to an optical density at 600 nm ( OD600 ) ~0 . 5 and subsequently induced with 1 mM isopropyl-1-thio-β-D-galactoside ( IPTG ) for 2 h . Bacteria were harvested by centrifugation ( 10 min , 10 , 000 x g , 4°C ) , resuspended in 20 ml of 50 mM NaPO4 pH 7 , 300 mM NaCl , DNase ( 0 . 1 mg/ml; Roche ) and protease inhibitor cocktail ( Roche ) , and lysed by passing through a French-Press at 1200 psi three times . The resultant lysate was ultracentrifuged ( 60 min , 40000 x g , 4°C ) to obtain a cleared lysate supernatant . For purification of the His-tagged Int280EHEC , TirMEHEC , TirMEPEC and TirMCR , lysates were passed through 2 ml of pre-equilibrated Cobalt-containing resin ( TALON , Takara ) in a chromatography column and washed with 20 mM HEPES pH 7 . 4 , 200 mM NaCl . The bound His-tagged proteins were eluted by adding the same buffer complemented with 150 mM imidazole . The eluted fractions were dialyzed against HEPES-buffer ( sterile filtered and degassed ) and concentrated 10-fold in a 3-kDa centrifugal filter unit ( Amicon Ultra-15 ) . Proteins were loaded onto a gel filtration column ( HiLoad 16/600 Superdex 75 preparative grade , GE Healthcare ) , pre-equilibrated with HEPES-buffer and calibrated with protein markers ( Gel Filtration Standards , Bio-Rad ) and Blue dextran ( for exclusion volume Vo; Sigma ) . Fractions of 1 ml containing the purified proteins were collected and checked for purity by SDS-PAGE . Protein concentration was estimated using the Bicinchoninic acid protein assay kit ( Thermo Scientific ) . Cultures of E . coli strain HB2151 carrying pVDL9 . 3 ( hlyB hlyD ) and the indicated pEHLYA5-derivative , or pVDL9 . 3-derivatives with Nb-HlyA fusions ( Table 1 ) , were grown o/n at 30°C ( 170 rpm ) in liquid LB with the appropriate antibiotics . Next , bacteria were inoculated in fresh medium ( 200 ml of liquid media in 1L flask ) and grown at 37°C ( 170 rpm ) until OD600 reached 0 . 4 . At this point , bacteria were induced with 1 mM ( IPTG and further incubated for 6 h with shaking ( 100 rpm ) . The cultures were centrifuged twice ( 10 min , 10000 g , 4°C ) to retrieve the supernatants , which were mildly sonicated ( 3 pulses of 5 seconds ) and filtered ( 0 . 2 μm syringe filters ) . Then , they were loaded in columns for metal affinity chromatography ( IMAC ) purification . The supernatants were loaded at ca 4 ml/min onto chromatography columns with pre-equilibrated Cobalt-containing resin ( TALON , Takara ) . Columns were washed with Tris pH 7 . 5 ( 50 mM ) NaCl ( 150 mM ) or HEPES buffer and eluted with a gradient of imidazole reaching 500 mM . A second purification step by gel filtration was performed for His-tagged antigens and Nb-HlyA fusions used in Surface Plasmon Resonance ( SPR ) . Fractions eluted from metal-affinity chromatography were dialysed against HEPES-buffer ( sterile filtered and degassed ) and concentrated to 2 ml in a 3 kDa centrifugal filter unit ( Amicon Ultra-15 , Millipore ) . Next , protein samples were loaded onto a calibrated gel filtration column ( HiLoad 16/600 Superdex 75 , GE Healthcare ) , pre-equilibrated with HEPES-buffer . The elution of Nb-HlyA proteins was performed using HEPES buffer and collecting 1 ml fractions . Protein concentration was estimated using the BCA protein assay kit ( Thermo Scientific ) . For the generation of 12-mer TirMEHEC peptides on a PVDF membrane , a MultiPep RSi synthesizer ( Intavis ) with SPOT module ( Proteomics Service , CNB-CSIC ) was used . The resulting membrane was blocked in PBS containing 0 . 1% Tween 20 ( PBST ) and 3% ( w/v ) skimmed milk for 1 h at room temperature ( RT ) and subsequently incubated in purified TD4-HlyA dissolved in PBST , 3% skimmed milk for 2 h . After washing in PBST , the membrane was sequentially incubated with anti-E tag mAb ( Phadia , 1:5000 ) and secondary rabbit anti-mouse IgG-POD ( 1:5000 , Sigma ) . Signal detection was performed using the Clarity Western ECL Substrate kit ( Bio-Rad ) and exposure to X-ray films ( Agfa ) . ELISA was performed as described previously [39] . Briefly , 96-well immunoplates ( Maxisorp , Nunc ) were coated for 2 h at RT with 5 μg/ml of purified TirM ( from EHEC , EPEC or CR , as indicated ) diluted in PBS . Bovine serum albumin ( BSA , Roche ) was used as a negative control antigen . Nb-HlyA fusions were added at the indicated concentrations for 1 h and plates were subsequently washed three times with PBS . For detection of bound Nb-HlyA fusions , anti-E-tag mAb ( 1:2000; Phadia ) and anti-mouse IgG-POD ( 1:2000; Sigma ) , as secondary antibody , were added . The reaction was developed with o-phenylenediamine ( Sigma ) and H2O2 ( Sigma ) , as previously reported [72] , and the OD490 was determined using a microplate reader ( iMark ELISA plate reader , Bio-Rad ) . For the neutralization assay , 1 mg/ml Int280 was biotinylated using a 20-fold molar excess of Biotinamidocaproate N-hydroxysuccinimide ester ( Sigma ) . After incubation on a gyratory wheel for 1 h at RT , the reaction was stopped by addition of 50 mM Tris-HCl pH 7 . 5 , and placement on ice for 1 h . The reaction mix was subsequently loaded onto a pre-packed column for gel filtration chromatography ( Sephadex G25 PD-10; GE Healthcare ) and the biotinylated protein was eluted in 500-μl fractions with PBS . Protein concentrations were estimated using the BCA protein assay kit ( Thermo Scientific ) . For the assay , 5 μg/ml non-biotinylated TirM was bound to plastic 96 wells plates for 2 h . The wells were blocked with 3% ( w/v ) skimmed milk in PBS for 1 h . At the same time , biotinylated Int280 ( 50 μg/ml ) was incubated with a 1:50 dilution of the camel immune or preimmune serums or 1 μM ( 50 μg/ml ) of the corresponding purified Nb-HlyA . These solutions were added to the microtiter wells for 1 h incubation after removing the blocking solution . Then , the wells were developed as a standard ELISA using Streptavidin-POD ( Roche , Sigma ) . SPR experiments were performed using BiaCore3000 ( GE Healthcare ) . All proteins solutions were dialyzed against HEPES-buffer ( sterile filtered and degassed ) at 4°C for 2 h . TirMEHEC was biotinylated ( as described above ) at 0 . 1 μg/ml and immobilised on a Streptavidin SA chip ( GE Healthcare ) at 150 response units ( RU ) at a flow rate of 10 μl/min in HEPES-buffer containing 0 . 005% ( v/v ) of the surfactant Polysorbate 20 ( P20 , GE Healthcare ) . To determine binding kinetics , dilutions of purified TD4-HlyA or Int280 ( as indicated ) were run at 30 μl/min in HEPES-buffer and sensograms were generated . Regeneration of TD4-HlyA was performed by sequential injections of 10 μl 10 mM glycine-HCl pH 1 . 7 , 5 μl 5 mM NaOH and 10 μl 10 mM glycine-HCl pH 1 . 7 . No regeneration was needed for Int280 . Sensograms with different concentrations of analyte were overlaid , aligned and analysed with BIAevaluation 4 . 1 software ( GE Healthcare ) under assumption of the 1:1 Langmuir model and using both the simultaneous kinetics model and the steady-state equilibrium analysis [73] . The human cervix carcinoma cell line HeLa ( ATCC , CCL-2 ) was grown in DMEM supplemented with 10% fetal bovine serum and 2 mM glutamine at 37°C in a 5% CO2 atmosphere . For infection , cells were seeded out on glass coverslips in 24-well plates at a concentration of 105 cells/well . Cells were inoculated with EHEC at a multiplicity of infection ( MOI ) of 1000 for 3 h at 37°C in a 5% CO2 atmosphere . The purified Nb-HlyA fusions , at the indicated concentrations , were added to the cells simultaneously with EHEC bacteria , or 1 h or 2 h post-infection , as indicated , in a final volume of 0 . 5 or 1 ml . The infection was stopped by three washes with sterile PBS . In the case of EHEC infections for 6 h , cells were washed with PBS after 3 h of infection , fresh medium and Nbs were then added , and incubation was continued for another 3 h . Cells were fixed with 4% ( w/v ) paraformaldehyde in PBS for 20 min at RT and permeabilized in 0 . 1% ( v/v ) of saponin ( Sigma ) in PBS for 10 min . All antibodies were diluted in PBS with 10% goat serum ( Sigma ) , and mouse monoclonal anti-O157 ( Abcam , 1:500 ) , mouse monoclonal anti-HA ( Cambridge bioscience , 1:200 ) and rabbit polyclonal anti-TirEHEC ( 1:200 ) were used to detect EHEC bacteria , HA-tag and TirEHEC , respectively . After incubation for 1 h at RT , coverslips were washed three times with PBS , and incubated for 45 min with secondary antibodies , Alexa477-conjugated goat anti-mouse IgG or Alexa647-conjugated goat anti-rabbit-IgG ( 1:500 , ThermoFisher Scientific ) , Tetramethylrhodamine ( TRITC ) -conjugated phalloidin ( 1:500 , Sigma ) and 4' , 6-Diamidino-2-phenylindole ( DAPI ) ( 1:500 , Sigma ) to label F-actin and DNA , respectively . Coverslips were washed 3 times with PBS after incubation , mounted in of ProLong Gold anti-fade reagent ( ThermoFisher Scientific ) , and analysed with an SP5 confocal microscope ( Leica ) . Biopsy samples from the transverse colon were taken from macroscopically normal areas , transported to the laboratory in IVOC medium and processed within the next hour . IVOC was performed as described previously [74] . Briefly , biopsies were mounted on foam supports in 12 well plates and incubated with 30 μl EHEC standing overnight culture ( approximately 107 bacteria ) and 200 nM of TD4 or Nb control ( Vamy ) . Samples were incubated for 8 h on a rocking platform at 37°C in a 5% CO2 atmosphere with medium changes after 4 and 6 h of incubation . At the end of the experiment , tissues were washed in PBS to remove the mucus layer and fixed in 3 . 7% formaldehyde/PBS for 20 min at RT . Samples were permeabilised with 0 . 1% Triton X-100/PBS , and blocked with 0 . 5% BSA/PBS for 20 min . Tissues were incubated with goat polyclonal anti-E . coli ( 1:400 , Abcam ) for one hour , followed by incubation in Alexa Fluor 568-conjugated donkey anti-goat IgG ( 1:400 , ThermoFisher Scientific ) and DAPI for 30 min to counterstain cell nuclei . Biopsy samples were mounted with Vectashield mounting medium ( Vector Labs ) and analysed using an Axio Imager M2 motorized fluorescence microscope ( Zeiss ) . EHEC colonisation of colonic biopsies was quantified by counting adherent bacteria in a surface area of 1 mm2 . Means and standard errors of experimental values were calculated using Prism 5 . 0 ( GraphPad software Inc ) . Statistical analyses comparing multiple groups were performed using one-way ANOVA and Dunnett’s post- test . Statistics for Fig 2 were done using One Way ANOVA analysis doing logarithms for normal distribution . Data was corrected with the Bonferroni test . A value of p<0 . 05 was considered significant .
Currently , there is no effective treatment or vaccine against enterohemorrhagic E . coli ( EHEC ) , a bacterial pathogen that infects human colon after the ingestion of contaminated food . It thrives in the colon thanks to its ability to attach intimately to the intestinal epithelium . Here , we have identified and characterised a small antibody fragment ( nanobody ) that recognises Tir , a receptor injected by the bacterium into the host cell to mediate intimate attachment . This nanobody shows higher affinity against Tir than its natural bacterial ligand ( intimin ) and , most importantly , blocks the intimate attachment of the pathogen to the human colonic tissue . Our results show the potential of this nanobody to prevent and treat EHEC infection .
[ "Abstract", "Introduction", "Results", "Discussion", "Methods" ]
[ "medicine", "and", "health", "sciences", "enzyme-linked", "immunoassays", "biopsy", "hela", "cells", "biological", "cultures", "surgical", "and", "invasive", "medical", "procedures", "bacterial", "diseases", "cell", "cultures", "immunologic", "techniques", "enterohaemorrhagic", "escherichia", "coli", "digestive", "system", "research", "and", "analysis", "methods", "contractile", "proteins", "infectious", "diseases", "actins", "specimen", "preparation", "and", "treatment", "staining", "escherichia", "coli", "infections", "proteins", "immunoassays", "cell", "lines", "gastrointestinal", "tract", "biochemistry", "cytoskeletal", "proteins", "cell", "staining", "anatomy", "biology", "and", "life", "sciences", "cultured", "tumor", "cells", "colon" ]
2019
A nanobody targeting the translocated intimin receptor inhibits the attachment of enterohemorrhagic E. coli to human colonic mucosa
Entomopathogenic fungi represent a promising class of bio-insecticides for mosquito control . Thus , detailed knowledge of the molecular mechanisms governing anti-fungal immune response in mosquitoes is essential . In this study , we show that CLSP2 is a modulator of immune responses during anti-fungal infection in the mosquito Aedes aegypti . With a fungal infection , the expression of the CLSP2 gene is elevated . CLSP2 is cleaved upon challenge with Beauveria bassiana conidia , and the liberated CLSP2 CTL-type domain binds to fungal cell components and B . bassiana conidia . Furthermore , CLPS2 RNA interference silencing significantly increases the resistance to the fungal challenge . RNA-sequencing transcriptome analysis showed that the majority of immune genes were highly upregulated in the CLSP2-depleted mosquitoes infected with the fungus . The up-regulated immune gene cohorts belong to melanization and Toll pathways , but not to the IMD or JAK-STAT . A thioester-containing protein ( TEP22 ) , a member of α2-macroglobulin family , has been implicated in the CLSP2-modulated mosquito antifungal defense . Our study has contributed to a greater understanding of immune-modulating mechanisms in mosquitoes . Female mosquitoes require repeated blood feedings during their life cycle to satisfy their reproductive nutritional needs and , as a consequence , they serve as vectors of numerous human diseases [1] . Malaria , transmitted by the Anopheles genus , is the most devastating vector-borne human disease and causes about one million deaths per year . The annual number of cases of Dengue fever , a viral disease transmitted by Aedes aegypti , has reached over a hundred million . Major reasons for this serious situation include the lack of effective vaccines against major mosquito-borne diseases , rapidly developing drug and insecticide resistance , and socio-economic problems in endemic countries . It is imperative to design novel specific biological pesticides , since mosquitoes have developed resistance to most currently used chemical insecticides [2] . While entomopathogenic fungi Beauveria bassiana and Metarhizium anisophliae infect insects by direct penetration of the cuticle , bacteria and viruses often need to be ingested , which makes fungi more promising as pesticides . However , fungal pathogens still require improvements due to the relatively low virulence when compared with chemical pesticides [3] . Detailed studies of antifungal immunity in mosquitoes are essential for future improvements of fungal biocontrol agents . Multicellular organisms have evolved complex and powerful systems of immune responses to counteract continuous attacks of various pathogens . An essential feature of the immune system in any organism is its capacity to sustain equilibrium between reactivity and quiescence [4] . A loss of such a balance leads to severe consequences , such as autoimmune and inflammatory diseases in humans . Inhibitory receptor systems modulating immune responses have been identified in vertebrates [4 , 5] . However , the detail mechanism of the analogous system in insect is still not very clear . Our studies have revealed that CLSP2 functions as a key modulator of the mosquito immune system and contributed to a better understanding of immune modulating mechanisms in insects . In insects , Toll is the principal innate immune pathway responsible for the anti-fungal response [6 , 7 , 8] . The Toll pathway is induced by fungal β1 , 3-glucan and also by Gram-positive bacteria harboring Lys-type peptidoglycan [7] . This pathway is crucial in activating immune responses especially in production of antimicrobial and anti-fungal peptides ( AMPs ) [6 , 9 , 10] . Gram-negative binding protein 3 ( GNBP3 ) , a member of the β-1 , 3-glucan recognition protein ( βGRP ) family , binds to fungal cell components and initiates the Toll pathway [11] . Two Clip domain serine proteases ( CLIPs ) —Persephone and Späetzle-processing enzyme ( SPE ) —are components of an extracellular serine protease ( SP ) cascade and cause the cleavage of a cysteine knot cytokine , Späetzle ( Spz ) [12 , 13] . The cleaved Spz then functions as a ligand of the Toll receptor , which in turn passes the signals into the intracellular signal cascade consisting of MyD88 , Tube , Pelle and TRAF6 . Mosquitoes have a single orthologue of Dorsal , Rel1 [6] . Activation of the intracellular signal cascade by Toll results in the phosphorylation and degradation of Cactus , which is an inhibitor of the NF-кB transcription factors Dorsal and Dif [14] . Removal of Cactus releases and causes nuclear translocation of Dorsal and Dif , which eventually leads to the expression of AMPs , including Drosomycin , an antifungal peptide . Previously , Ae . aegypti orthologues of Drosophila genes of the Toll pathway—Spz1C , Toll5A , CLIPB5 , and CLIPB29—have been identified and shown to mediate the Toll pathway in response to fungal infection [6 , 15] . In mosquitoes , Cecropins and Defensins are the major AMPs involved in the systemic antifungal immune responses [16] . Melanization represents a second immune pathway that is essential in the systemic antifungal immune responses [17] . It is the arthropod-specific defense mechanism that plays an essential role in wound healing and innate immunity [17] . The key enzymes for this reaction are prophenoloxidases ( PPOs ) , which , once activated , catalyze the formation of toxic melanin . Melanin is then deposited around the wound or invading pathogens , including fungi . CLIPs constitute a cascade for amplification of a signal triggered by pathogen infection that results in PPO cleavage into an active PO by a melanization protease ( MP ) . The melanization cascade is tightly regulated by serine protease inhibitors ( SRPNs ) , which prevent spontaneous initiation of the reaction . The analysis of the mosquito genomes has shown that genes encoding immune signaling and effector molecules , and the number of melanization pathway genes have undergone major expansion [16] . For example , there are 10 PPO genes in the Ae . aegypti genome [13] . However , the precise roles of each PPO in melanization process are poorly understood . Our previous study revealed a novel level of complexity in the melanization cascade of the mosquito Ae . aegypti . Namely , we identified that there are several independent pathways leading to melanization , each requiring a different protease/SRPN regulatory module [15] . Of particular interest is a clear separation of tissue melanization , represented by melanin tumors often associated with the damage of host tissues , and immune melanization involved in the recognition and killing of pathogens , including fungi [13] . The melanization response has also been shown to significantly retard the growth and dissemination of B . bassiana in the An . gambiae mosquito [18] . Previously , we have identified an immune factor in Ae . aegypti , CLSP2 ( AAEL011616 ) , that is composed of an elastase-like serine protease ( ESP ) and CTL-type domains [19] . In this study , we show that CLSP2 is the key negative modulator of immune responses during anti-fungal infection . The expression of the CLSP2 gene is elevated upon B . bassiana infection . CLSP2 is cleaved upon challenge with B . bassiana and the liberated CLSP2 CTL-type domain binds to fungal cell components . Moreover , RNAi depletion of CLPS2 ( iCLSP2 ) significantly increases the resistance to the fungal challenge . RNA-sequencing ( RNA-seq ) -based transcriptome analysis indicated that the Toll pathway and melanization genes are highly up-regulated in CLSP2 RNAi-silenced mosquitoes infected with B . bassiana ( iCLSP2Bb ) . TEP22 , a member of α2-macroglobulin family , was identified to be regulated by CLSP2 and to participate in the antifungal immune response in the Ae . aegypti mosquitoes . We investigated the CLSP2 responses to fungal infection at the gene and protein levels . Real-time RT-PCR ( qPCR ) analysis showed that the CLSP2 mRNA level in mosquitoes was significantly up-regulated after septic injections of conidia of the fungus B . bassiana ( S1A Fig ) . This result was consistent with our previously reported Northern results [19] , indicating a CLSP2 response to infections at the gene level . Aedes CLSP2 consists of two domains: the N-terminal elastase-like serine protein ( ESP ) and the C-terminal galactose-type C-type lectin ( CTL ) , which includes a signature QPD sequence . CLSP2 includes a signal peptide and no transmembrane domain , suggesting that it is a secreted peptide . To investigate the infection effect on the CLSP2 protein composition , hemolymph samples were resolved on SDS-PAGE and subjected to immunoblot analysis utilizing anti-CLSP2 polyclonal antibodies . In the hemolymph of control mosquitoes injected with sterile phosphate buffered saline ( PBS ) , CLSP2 remained as a single band of about 47 kDa , which disappeared in RNA-interference ( RNAi ) CLSP2 silenced mosquitoes ( iCLSP2 ) ( Fig 1A ) . However , two bands , corresponding to the 33-kDa ESP and 14-kDa lectin domains , were detected in the mosquito hemolymph after infection with B . bassiana conidia ( Fig 1A ) . This suggested that CLSP2 was cleaved upon immune challenge . When we used the hemolymph from mosquitoes with silenced CLSP2 , no bands were evident in the immunoblot , indicating that the 33-kDa and 14-kDa bands observed in the mosquito hemolymph after infection with B . bassiana conidia belong to CLSP2 . Additional controls for the specificity of anti-CLSP2 antibodies are presented in S1B and S1C Fig . In an attempt to understand the biochemical properties of CLSP2 , we cloned and produced its lectin domain , designated as rLectin , using an Escherichia coli expression system . Myc tag rLectin fused with hexahistidine-tagged SUMO was purified using an affinity Ni-NTA agarose column . The isolated product was cleaved by SUMO protease , and then reloaded onto the Ni-NTA column , so that rLectin was in the flow-through fraction and the His-tagged protease was retained on the column . The purified rLectin migrated as a single band with the expected molecular weight ( MW ) of 14 kDa on SDS-PAGE ( Fig 1B ) that was not recognized by the anti-Histidine monoclonal antibody ( Fig 1B ) . To address the CLSP2 role in immune responses , we investigated the susceptibility of iCLSP2 mosquitoes to fungal infections . Three days after CLSP2 dsRNA injection , mosquitoes were infected with B . bassiana and their survival rate was evaluated . The survival rate of iCLSP2 mosquitoes challenged with B . bassiana ( iCLSP2Bb ) was significantly higher than that of mosquitoes infected with this fungus alone in the iLuc background ( iLucBb ) . Mosquitoes with Luciferase ( Luc ) gene silencing served as a control ( iLuc ) ( Fig 1C ) . qPCR and immunoblotting tests confirmed efficiency of CLSP2 RNAi ( S2A–S2C ) Fig . Thus , silencing of CLSP2 in mosquitoes led to an increased resistance to fungal infection , suggesting a role of CLSP2 in modulating immune activation . In order to decipher the interaction between CLSP2 and fungi , we examined binding properties of rLectin by means of the agglutination assay . As fungal representatives , we tested zymosan , which is a component of the Saccharomyces cerevisiae cell wall composed of β-glucans and mannan , and GFP-conjugated B . bassiana in the rLectin agglutination assay ( Fig 1D ) . Neither zymosan nor GFP-conjugated B . bassiana aggregates were observed in the presence of bovine serum albumin ( BSA ) used as a control . Only minor aggregates were found in the presence of EDTA . However , large aggregates were observed in the presence of Ca2+ , indicating that it was required for the agglutination reaction by rLectin of either zymosan or GFP-conjugated B . bassiana ( Fig 1D ) . Next , we performed enzyme-linked immunosorbent assay ( ELISA ) to test whether rLectin directly bound to the fungal cell component curdlan . Different amounts of rLectin ( 20 , 30 , 50 , 70 and 80 μg/ml; 50 μl each ) were added to microtiter plate wells coated with curdlan , and the bound rLectin was detected using Myc antibodies . ELISA has demonstrated that rLectin effectively binds to curdlan ( Fig 1E ) . Taken together , these results suggest that the CLSP2 lectin domain is capable of recognizing and binding to fungal carbohydrate surface molecules in a saturable and Ca2+ dependent manner . Next , CLSP2 effect on expression of immune genes was elucidated by means of the RNA-sequence-based transcriptome analysis ( RNA-seq ) linked with RNAi screens . For this analysis , we used mosquitoes after three different treatments: control iLuc infected with B . bassiana ( iLucBb ) , silenced with CLSP2 RNAi ( iCLSP2 ) , silenced with CLSP2 RNAi and infected B . bassiana ( iCLSP2Bb ) . iLuc mosquitoes served as a control . Regulated immune gene repertoire ( fold change ≥ 1 . 5 ) in different groups is shown in S1–S3 Tables . The hierarchical clustering analysis has revealed a stunning up-regulation of major immune gene transcripts in iCLSP2Bb mosquitoes ( Fig 2A and S2D Fig ) . Serine proteases ( SPs ) play important roles in a wide range of biological processes , including innate immunity . They constitute an integral part of immune reactions , such as the Toll and melanization cascades in arthropods [7 , 14] . There were 40 CLIPs ( almost half of Ae . aegypti genome CLIPs ) in the iCLSP2Bb upregulated transcriptome ( S3 Table ) . A high elevation of expression levels of several immune genes in iCLSP2Bb mosquitoes was confirmed by means of the qPCR analysis ( Fig 2B and S2E Fig ) . Our previous study has shown that CLIPB5 and CLIPB29 are involved in the activation of Toll pathway by fungal infection or by infection-independent manner , respectively [15] . Indeed , we found that both CLIPB5 and CLIPB29 were moderately up-regulated in iCLSP2Bb ( S3 Table ) . Two Toll pathway regulators—Spz2 and Spz3A—were also dramatically up-regulated in the iCLSP2Bb mosquitoes ( Fig 2A and 2B and S2E Fig ) . Moreover , the gene encoding the pattern recognition receptor GNBP1 was also significantly activated in iCLSP2Bb mosquitoes . Thus , our results have shown that CLSP2 modulates the transcriptional expression of the Toll pathway upstream genes ( Fig 2C ) . However , the expression of genes encoding intracellular components of the intracellular Toll pathway signaling , including Rel1 , was not significantly affected in these mosquitoes ( Fig 2B and 2C ) . Interestingly , Cactus was elevated as a result of B . bassiana infection , however its transcript was reduced in iCLSP2Bb ( Fig 2B ) . Genes encoding pattern recognition receptors from the fibrinogen-related protein family ( FREP ) represented another highly elevated group of genes in the iCLSP2Bb mosquitoes ( Fig 2B and S2E Fig , S3 and S4 Tables ) . FREP3 , FREP5 and FREP10 were particularly up-regulated . The FREPs are an evolutionarily conserved immune gene family found in mammals and invertebrates [20] . It is the largest pattern recognition receptor gene family in mosquitoes , with 59 putative members in An . gambiae [20] and 35 in Ae . aegypti [16] . Genes encoding thioester-containing proteins , TEP2 , TEP3 and TEP22 were also up-regulated . These data suggest that CLSP2 is an immune factor working upstream of the pattern-recognition receptor system , modulating their responses . We then studied the effect of CLSP2 on mRNA levels of anti-microbial effector peptides ( AMPs ) . Defensin A ( DefA ) and Cecropin A ( CecA ) represent the major mosquito AMPs [16] , which also convey anti-Plasmodium activity [21] . As shown using qPCR , the mRNA levels of AMP genes , DefA , CecA , CecE , and CecF were induced in B . bassiana infected mosquitoes at 20- to 40-fold levels . Impressively , much higher induction levels of DefA , CecA , CecE , and CecF were observed in the iCLSP2Bb mosquitoes ( Fig 3A ) . Thus , the CLSP2 played essential role in systemic immunity in mosquitoes by preventing the spontaneous transcription activation of downstream AMP immune genes . Next , we investigated interrelationship of CLSP2 and Rel1 , which is a factor directly controlling the expression of AMP genes . As expected , the high expression of DefA and CecA brought by iCLSP2 was significantly decreased in mosquitoes with double CLSP2 and Rel1 RNAi silencing ( Fig 3B ) . Moreover , the extremely high level of AMP expression in iCLSP2Bb mosquitoes was almost completely eliminated in mosquitoes with double knockdown of CLPS2Bb and Rel1 ( Fig 3C ) . These experiments indicate that the action of CLSP2 due to modulation of upstream regulatory factors of the Toll immune cascade . According to the result from our RNA-seq analysis and qPCR , the induced immune genes belong mainly to the Toll pathway ( Fig 2C ) , whereas the genes involved in the IMD and JAK/STAT pathways were not affected by the depletion of CLSP2 ( S3A Fig ) . The only exception is PGRP-LC , which was influenced by CLSP2 and surprisingly by fungal challenge ( S3B Fig ) . However , unlike GNBP1 , it was not up-regulated the in iCLSP2Bb mosquitoes , thus pointing to the lack of modulation of this gene encoding the IMD pattern-recognition receptor by CLSP2 . Moreover , CLSP2 had no effect on expression of the Rel2 gene , the principal regulator of the IMD pathway ( S3A and S3B Fig ) . Toll and IMD share regulation of AMPs [22] . However , our experiments strongly suggest that CLSP2 effect on expression of the AMP genes is likely solely due to its influence on the Toll pathway . We next selected up-regulated gene cohorts from mosquitoes after three different treatments—iCLSP2 ( 72 genes ) , iLucBb ( 93 genes ) and iCLSP2Bb ( 108 genes ) for further analysis . Forty immune genes were induced under all three experimental conditions ( Fig 4A and S4 Table ) . The ontology analysis demonstrated that , except for several effector genes , the majority of co-upregulated genes belonged to regulatory categories located upstream of immune cascades ( Fig 4B and S4 Table ) . The hierarchical clustering indicated that transcript levels of most of these genes are considerably higher in iCLSP2Bb than in the other two groups ( Fig 4B ) . These results further suggest that CLSP2 is the modulator of the immune response involved in the anti-fungal infection . Our analysis suggests that the modulating factor CLSP2 acts upstream of immune cascades , possibly interacting with other factors . To explore this possibility , we analyzed eight genes selected from those co-up-regulated in iLucBb , iCLSP2 and iCLSP2Bb mosquitoes ( S5 Table ) , and their functions were studied by means of RNAi depletions in a combination with B . bassiana infection . After treatment with TEP22 dsRNA , a member of α2-macroglobulin family ( S4 Fig ) , mosquitoes became extremely sensitive to the B . bassiana infection , and the survival rate dramatically decreased . However , the survival of affected mosquitoes could be partially rescued after the knockdown of CLSP2 was performed simultaneously with that of TEP22 ( Fig 4C ) . Additionally , TEP22 was significantly regulated in iCLSP2Bb mosquitoes ( Fig 4D ) . The results indicate that TEP22 is required for the anti-fungal response in a mosquito . Moreover , these results suggested that CLSP2 is likely mediated the response to fungal infection via interaction with TEP22 as a recognition molecule . Seven other tested genes from the iCLSP2Bb did not yield a similar phenotype indicating that they were not involved in the CLSP2 immune modulation directly ( S5 Fig ) . CLSP2 has been shown to be a negative modulator of hemolymph melanization [19] . To examine whether CLSP2 was involved in regulation of PPO gene expression , we utilized CLSP2 RNAi silencing in combination with B . bassiana infection ( Fig 5A ) . The RNA abundance of 10 Aedes PPOs was investigated by means of qPCR analysis . Whereas transcript abundance of PPO genes did not change significantly after infection with B . bassiana alone , a highly pronounced activation of several PPO genes was observed in the iCLSP2Bb mosquitoes . PPO1 transcript increased dramatically by 9-fold , while levels of PPO2 , PPO3 , PPO4 , PPO5 and PPO8 were elevated to about 4- to 6- fold . These results suggest that CLSP2 is an essential modulator of PPO gene expression . Moreover , this modulation is highly specific to just a few PPO genes that are most likely involved in immune responses during fungal infection . In addition to PPO genes , according to the results of RNA-seq , we also found that transcripts of several genes involved in melanization were up-regulated in iCLSP2Bb mosquitoes , including CLIPB9 and SRPN2 ( S3 Table ) . The elevation of these gene transcripts in iCLSP2Bb was confirmed by qPCR ( S2E Fig ) . We selected PPO3 for further protein analysis , because its gene transcript was elevated in response to the fungal infection in iLucBb and was also highly upregulated in the iCLSP2Bb mosquitoes . Proteolytic cleavage of hemolymph PPO3 was detected by immunoblotting using polyclonal antibodies against Aedes PPO3 . There was only a precursor PPO band in the iLuc control mosquitoes ( Fig 5B ) . However , it was cleaved in the hemolymph of B . bassiana-infected and CLSP2-silenced mosquitoes as marked by the appearance of around 20-kDa-protein band ( Fig 5B ) . The PPO3-derived cleavage proteins were also observed in iCLSP2Bb mosquitoes . At present we cannot assume that CLSP2 is responsible for cleavage of only PPO3 as we lack specific antibodies to other PPOs to investigate this question . The cleavage of PPO is likely not a direct effect of CPLS2 and occurs as a consequence of activation of melanization pathway factors by iCPLS2 and/or infection with B . bassiana as shown above . However , this experiment demonstrates importance of CLSP2 as a modulating factor working upstream in the PPO cascade . Innate immune responses are initiated by the interaction between pathogen surface molecules and pathogen-related receptors ( PRRs ) . C-type lectin recognition receptors ( CTL or CTR ) comprise a large family of PRRs that are engaged in the recognition of a broad spectrum of pathogens . They are also defined as Ca2+—dependent carbohydrate ( lectin ) binding proteins identified in a wide range of animal groups [23] . CTLs interact with glycans on cell surfaces , in the extracellular matrix , or on soluble secreted glycoproteins , mediating processes such as cell adhesion , cell-cell interactions and pathogen recognition [23] . CTLs have been implicated in pathogen evasion of a host immune system . In mammals , a large family of C-type lectin receptors modulating immune responses has been characterized[24] . Two CTLs ( CTL4 and CTLMA2 ) have been shown to act as protective agonists during the development of Plasmodium ookinetes to oocysts in the mosquito An . gambiae [25] . Similarly , another C-type lectin , mosGCTL-1 , an equivalent to CTLMA15 , was found to facilitate the infection of West Nile virus in the mosquito Ae . aegypti [26] . Although CTLs have been identified as negative regulators of the immune response against malaria parasites and virus [25 , 26] , details of mosquito CTL-based pathways are still unknown . Mosquitoes with the CLSP2 RNAi depletion displayed an elevated resistance to B . bassiana infection as compared to those with pathogens alone . Previously , we have also demonstrated that CLSP2 also functions during Plasmodium infection [17] . Thus , our study has uncovered an important role of CLSP2 as a factor modulating immune responses in the mosquito Ae . aegypti . Ae . aegypti CLSP2 is a composite protein consisting of an elastase-like SP ( ESP ) domain located at the N-terminal and a CTL-type domain at the C-terminal . Composite immune proteases , such as Manduca sexta HP14 and the factor C of the horseshoe crab Tachypleus tridentatus , undergo cleavage after immune challenge [27 , 28] . Our experiments have shown that CLSP2 is also cleaved upon challenge with B . bassiana . Two bands were detected corresponding to the 14-kDa lectin and 33-kDa ESP domains in the hemolymph samples of mosquitoes with fungal infection by means of immunoblot analysis utilizing anti-CLSP2 antibodies . We have provided two lines of evidence clearly showing that the CLSP2 CTL-type domain binds to fungal sugar cell components . The purified recombinant CTL-type domain ( rLectin ) agglutinates zymosan and B . bassiana conidia in a calcium-dependent manner . Moreover , ELISA has shown that rLectin directly binds to the polysaccharide fungal cell component , curdlan , and this binding is saturable . However , whether the CTL domain binding to fungal surface molecules occurred before or after the CLSP2 cleavage in the hemolymph could not be determined . It also remains to be clarified whether upon infection-induced cleavage the CLSP2 domains undergo conformational changes still remaining as a single molecule in its native state or yielding completely separate molecules corresponding to the C-Type Lectin and SP domains . Our study of the CLSP2-mediated immune activation using RNAseq-based transcriptome analysis further supports a hypothesis that CLSP2 is a modulator of the transcription responses involved in innate immunity and suggests that CLSP2 acts upstream of extracellular pathogen-recognition factors . CLSP2 depletion affected genes encoding FREP pattern recognition receptors and TEPs indicating that CLSP2 is an immune factor working upstream of the pattern-recognition receptor system , modulating their responses . We identified TEP22 as an important player of the anti-fungal response in Aedes mosquitoes . TEPs are immune effectors genes that are conserved from insects to mammals . TEP molecules contain a motif harboring an intra-chain β-cysteinyl-γ-glutamyl thioester bond , which binds to target surfaces and prompts a series of complement cascades against microbes and parasites [29] . Knockdown of AgTEP1 in the resistant strain of An . gambiae led to a massive increase in the number of Plasmodium oocysts [30] . AgTEP1 is essential for blocking oocyst development in the midgut of An . gambiae by forming complex with two proteins from leucine-rich repeat family , LRIM1 and APL1C [31 , 32 , 33] . Nine TEP genes have been identified in the Ae . aegypti genome [16] . Our phylogenetic analysis has shown that Aedes TEP20 , 22 , 23 , and 25 form an independent clade supported by high bootstrap value ( S4 Fig ) . Transcript levels of TEP20 , 22 , and 23 were elevated in the fat body Rel1 and Rel2 gain-of-function transgenic mosquitoes and also in response to the Plasmodium infection [22] . We have shown in the present study that the TEP22 expression is dramatically elevated in iCLSP2Bb mosquitoes . Furthermore , TEP22-depeleted mosquitoes are extremely sensitive to B . bassiana infection , while CLSP2 knockdown in these mosquitoes rescues their survival . Thus , our findings suggest that TEP22 is involved in the antifungal immune pathway and it could interact with CLSP2 in this immune response . This interaction would be reminiscent of the mannose-binding lectin ( MBL ) triggered complement activation in mammals [34] or TEP1/LRIM1/APL1C complex in An . gambiae [30–33] . However , the detailed mechanism of complement-like factor action in the anti-fungal immunity and TEP22 association with the immune modulating factor CLSP2 requires further mechanistic study . Our study has demonstrated that the intracellular signal transduction components of the Toll pathway are not regulated by CLSP2 at the transcriptional level . In vertebrates , inhibitory receptor systems modulating immune responses depend on the intracellular phosphorylation pathway and not regulation at the transcription level [4 , 5] . Similar mechanism is also identified in the negative regulation of Toll-like receptor mediated pathways [35 , 36] . Interestingly , we observed that the activation of Cactus , the Rel1 inhibitor in Toll-mediated infection [37] , brought by fungal infection was abolished by the RNAi depletion of CLSP2 . This iCLSP2 effect on Cactus is completely opposite from those on other immune genes . Although Cactus target Rel1 is not affected by CLSP2 , the downstream gene cohorts highly activated in the iCLSP2Bb mosquitoes include those encoded effector molecules such as AMPs . The unique interaction of CLSP2 with Cactus suggests that it contributes in the control of AMP gene activation . Moreover , the abolishment of activation of AMPs , brought by iCLSP2 by the double knockdown of CLSP2 and Rel1 , indicates that Rel1 mediates the action of CLSP2 on these immune genes . We also have uncovered the CLSP2 role in modulating the melanization pathway in Ae . aegypti . represents a second immune pathway that is essential in the systemic antifungal immune responses [17] . CLSP2 not only modulates the hemolymph activation of PPO , but also negatively regulates the expression of PPO genes . The melanization cascade is tightly regulated by serine protease inhibitors ( SRPNs ) , which prevent spontaneous initiation of the reaction . The analysis of the mosquito genomes has shown that genes encoding immune signaling and effector molecules , and the number of melanization pathway genes have undergone major expansion [16] . For example , there are 10 PPO genes in the Ae . aegypti genome [13] . However , the precise roles of each PPO in melanization process are poorly understood . Our previous study revealed a novel level of complexity in the melanization cascade of the mosquito Ae . aegypti . Namely , we identified that there are several independent pathways leading to melanization , each requiring a different protease/SRPN regulatory module [15] . Of particular interest is a clear separation of tissue melanization , represented by melanin tumors often associated with the damage of host tissues , and immune melanization involved in the recognition and killing of pathogens , including fungi [13] . The melanization response has also been shown to significantly retard the growth and dissemination of B . bassiana in the An . gambiae mosquito [18] . Multicellular organisms have evolved complex and powerful systems of immune responses to counteract continuous attacks of various pathogens . An essential feature of the immune system in any organism is its capacity to sustain equilibrium between reactivity and quiescence [4] . A loss of such a balance leads to severe consequences , such as autoimmune and inflammatory diseases in humans . Inhibitory receptor systems balancing immune responses have been identified in vertebrates [4 , 5] . Our study has revealed that CLSP2 functions as a key modulator of the mosquito immune system and contributes to a better understanding of immune mechanisms in insects . The UGAL strain of Ae . aegypti mosquitoes was maintained in the laboratory as described previously [38] . Adults were fed continuously on water and 10% sucrose solution . To initiate egg development , mosquitoes were blood fed on chickens . All procedures for using vertebrate animals were approved by the Institute of Zoology Animal Care and Use Committee . B . bassiana strain ARSEF 2680 and B . bassiana strain expressed GFP were cultured on potato dextrose agar plates at 25°C and 80% humidity [39] . B . bassiana strain ARSEF 2680 was used in immune challenge and the strain 252-GFP was used in the agglutination assay . Conidia ( fungal spores ) , used for mosquito challenge were harvested from 3- to 4-week-old cultures and diluted to 5×107 conidia/ml in PBS . Septic injures were carried out by pricking the rear part of the mosquito abdomen with an acupuncture needle dipped into fungal conidia suspension [6] . For the immune response of CLSP2 to fungal infection , 3 days old adult mosquitoes were divided into two groups ( 30 adults / group ) : the control group ( control ) was challenged with PBS; the experiment group ( Bb 24h ) with B . bassiana spores . Tissue samples were collected 24 h later . For the RNA-seq , immune genes expression and survival rate analysis , new emergence mosquitoes were divided into four groups ( 30 adults / group ) : two groups ( luciferase groups ) were injected with luciferase dsRNA; another two groups ( CLSP2 groups ) were injected with CLSP2 dsRNA . 3 days later , one of the luciferase groups were challenged with PBS ( iLuc ) , and the other one were challenged with B . bassiana spores ( iLucBb ) . One of the CLSP2 groups was challenged with PBS ( iCLSP2 ) , and the other one with B . bassiana spores ( iCLSP2Bb ) . The same treatments were also used in the survival rate analysis of TEP22 and other immune genes . cDNA templates of target genes were generated by means of RT-PCR using both sense and antisense primers fused with T7-phage promoter sequences . RT-PCR was performed using the cDNA samples as templates to generate 400-bp to 1-kb gene-specific cDNA fragments . Synthesis of dsRNA was accomplished by simultaneous transcription of both strands of template DNA using T7 RNA polymerase from the T7 RiboMAX Express RNAi kit ( Promega ) . The luciferase gene was used to generate control iLuc dsRNA . A Nanoliter 2000 injector ( World Precision Instrument ) was used to introduce corresponding dsRNA into the thorax of CO2-anesthetized mosquito females within 1 day post-eclosion . Primers used for generating dsRNA are listed in S6 Table . The transcripts of specific genes decreased to 50–70% 1 week after dsRNA injection , confirmed by real-time RT-PCR . At 3 days after eclosion , 30 female mosquitoes were challenged with B . bassiana conidia [6] . The mosquitoes were maintained in individual containers and fed continuously on water and 10% sucrose solution . The survival curves were compared using Kaplan-Meier , and the threshold of p value was calculated with a Log-rank or Mantel Cox test , and p < 0 . 01 were considered to be statistically significant . Graphpad 6 . 0 software was used in all statistical analyses . Hemolymph from 20 decapitated mosquitoes was collected into 20 μl of 1×protease inhibitor cocktail ( Roche ) by centrifugation at 5 , 000 rpm for 5 min with Qiashredder column ( QIAGEN ) [15] . Aliquots of hemolymph samples were resolved on 4–15% gradients SDS-polyacrylamide gels ( Bio-Rad ) and electrotransferred to PVDF membranes ( Invitrogen ) . After blocking , the membranes were incubated with the primary antibody against CLSP2 or PPO3 overnight at 4°C . We used polyclonal antibodies against Ae . aegypti Lipophorin II [15 , 40] and β-actin ( Sigma ) as the loading controls . Immune complexes were visualized by means of SuperSignal West Pico Substrate ( Pierce ) . rLectin ( Lectin domain of CLSP2 ) was amplified by RT-PCR from cDNA with specific primers ( S6 Table ) . The PCR product was subcloned into PSFM ( a kind gift from Dr . Haobo Jiang , Oklahoma State University ) , a vector with a Sumo at the N-terminal , which increases the solubility of the fusion protein and can be removed by SUMO protease afterwards . The N-terminal FLAG and the C-terminal Myc are short sequences for detection of the expression of fusion protein and its cleavage products using commercially available monoclonal antibodies against these two tags , respectively . SUMO-rLectin was first purified on a Ni-NTA ( nickel-nitrilotriacetic acid , Qiagen ) agarose column . Then , SUMO-rLectin was cleaved using SUMO protease , as per the manufacturer’s protocol ( GeneCopoeia ) , and re-purified on the Ni-NTA agarose column . Monoclonal antibodies were prepared against KLH-peptide from CLSP2 ( Beijing Protein Innovation ) . Polyclonal antibodies were prepared against recombinant CLSP2 and recombinant PPO3 ( Beijing Protein Innovation ) . Specificity tests of these antibodies are presented in S1 Fig . FITC-conjugated zymosan ( Molecular Probes ) or GFP-conjugated B . bassiana conidia suspended in Tris-buffered saline ( TBS ) ( 25 mM Tris-HCl , 137 mM NaCl and 3 mM KCl , pH 7 . 0 ) were incubated with purified rLectin ( 80 μg/ml ) for the agglutination assay , as described by Yu et al . [41] . After incubation for 45 min at RT , samples were examined using fluorescence confocal microscopy ( Zeiss 710 ) . For binding assay , wells of a flat-bottom , 96-well plate ( Nunc , Fisher Scientific ) were coated with 2 mg ( 50 μl of 40 mg/ml per well ) of curdlan ( Sigma ) as described [41] . The plate was then blocked with BSA ( 100 μl/well of 1 mg/ml ) for 2 h at 37°C and rinsed with binding buffer ( 50 mM Tris-HCl , 50 mM NaCl , pH 8 . 0 ) ( 200 μl/well ) . rLectin diluted with binding buffer containing 5 mM CaCl2 and 0 . 1 mg/ml BSA was adjusted to 50 μl/well binding at RT for 4 h . The plates were rinsed as before , and bound rLectin was measured using mouse anti-C-myc antibody ( 1:1000 ) , and horseradish peroxidase ( HRP ) conjugated antibodies against mouse . The pre-immune antiserum was used as control . Soluble TMB Substrate Solution ( 100 μl/well , Tiangen ) was added to react for 20 min , and then stopped with 8 . 5 M acetic acid . Absorbance at 450 nm of the samples in each well was determined using a microplate reader ( Molecular Devices ) . To investigate the immune response to B . bassiana infection in the mosquito Ae . aegypti , we used a high-throughput sequencing ( HTS ) platform ( HiSeq 2000 ) to analyze gene expression in carcasses of fungal infected mosquitoes . Four fat body libraries were built from iLucBb , iCLSP2 , iCLSP2Bb , and iLuc mosquitoes . Three replicates of each sample ( 25 mosquitoes/sample ) were pooled for analysis and 100 ng of total mRNA from each sample was used to construct libraries with an Illumina kit v2 . Raw reads generated from the sequencing were preprocessed using in-house perl scripts , including adaptor removing and low quality reads filtering . Those with average quality lower than 20 and read length shorter than 35 bp were discarded automatically . To minimize the sequencing noise from other species , we mapped the filtered reads against both the bacteria and virus databases in NCBI , and made sure the remainder was highly reliable . The genome of Ae . aegypti was downloaded from VectorBase ( https://classic . vectorbase . org/genomes ) , as was the annotation file . The clean reads were mapped to the genome using GSNAP to estimate the expression level of all of the transcripts [42]: three mismatches were tolerated during processing , and the parameter of new transcript finding was shut down to guarantee the precise matching . We used flux-capacitor to calculate the FPKM of the transcripts , and DEGseq package in R Scripts to determine the DEGs [43] . P values less than 0 . 05 indicated genes were differentially expressed . All immune genes were then assigned according to immunodb [16] . Hierarchical clustering of gene expression intensity was performed using Pearson distance as the distance measure between genes and libraries [44] . Cross comparison performing within each treated sample was normalized by their reads count ( FPKM ) , and iLuc sample was considered as background value while the ratio of fold change was calculated . Phylogenetic trees were constructed using MEGA6 by the neighbor-joining method [45] . Total RNA samples were prepared from dissected abdominal carcasses of 10–15 individual mosquitoes . Malpighian tubules , midguts , and ovaries were removed , then abdominal carcasses with adhered fat body tissue and sessile hemocytes were rinsed in PBS , transferred into TRI reagent ( Sigma ) , and homogenized using a motor-driven pellet pestle mixer ( Kontes , Vineland , NJ ) . A 2-μg sample of total RNA was treated with DNase I ( Invitrogen ) to remove contaminating genomic DNA , and then used for cDNA synthesis ( M-MLV reverse transcriptase kit , Promega ) . Actin was used as an internal standard to normalize the templates in a preliminary PCR experiment . After template adjustment , PCRs were performed to detect relative levels using specific primers . Primers were designed by software Primer5 . Real-time RT-PCR ( qPCR ) reaction was performed on the MX3000P system ( Stratagene , CA ) , and we used a SYBR green PCR Master Mix ( Tiangen , Beijing ) for these reactions . Thermal cycling conditions were: 94°C , 20 s; 59°C , 20 s; 68°C 20 s . Quantitative measurements were performed in triplicate and normalized to the internal control of S6 ribosomal protein mRNA for each sample . Primers and gene accession numbers are listed in S6 and S7 Tables , online . Real-time RT-PCR data were collected and exported to EXCEL for analysis . Values were represented as the mean ± SEM , and the statistically significant difference between samples was calculated using the Student-t test ( Graphpad 6 . 0 ) .
Entomopathogenic fungi represent a promising class of bio-insecticides for mosquito control . Detailed knowledge of molecular mechanisms governing anti-fungal immune response in mosquitoes is essential . CLSP2 composed of serine protease and lectin domains functions as a modulator of the mosquito immune system during the anti-fungal response . Transcriptome analysis indicated that the Toll pathway and melanization genes are highly up-regulated in CLSP2 RNA interference depleted mosquitoes infected with the fungus Beauveria bassiana . A thioester-containing protein TEP22 , a member of α2-macroglobulin family , is involved in the CLSP2-modulated mosquito antifungal defense . Our study has contributed to the understanding of immune-modulating mechanisms in mosquitoes .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[]
2015
A Critical Role for CLSP2 in the Modulation of Antifungal Immune Response in Mosquitoes
Lactoferrin binding protein B ( LbpB ) is a bi-lobed outer membrane-bound lipoprotein that comprises part of the lactoferrin ( Lf ) receptor complex in Neisseria meningitidis and other Gram-negative pathogens . Recent studies have demonstrated that LbpB plays a role in protecting the bacteria from cationic antimicrobial peptides due to large regions rich in anionic residues in the C-terminal lobe . Relative to its homolog , transferrin-binding protein B ( TbpB ) , there currently is little evidence for its role in iron acquisition and relatively little structural and biophysical information on its interaction with Lf . In this study , a combination of crosslinking and deuterium exchange coupled to mass spectrometry , information-driven computational docking , bio-layer interferometry , and site-directed mutagenesis was used to probe LbpB:hLf complexes . The formation of a 1:1 complex of iron-loaded Lf and LbpB involves an interaction between the Lf C-lobe and LbpB N-lobe , comparable to TbpB , consistent with a potential role in iron acquisition . The Lf N-lobe is also capable of binding to negatively charged regions of the LbpB C-lobe and possibly other sites such that a variety of higher order complexes are formed . Our results are consistent with LbpB serving dual roles focused primarily on iron acquisition when exposed to limited levels of iron-loaded Lf on the mucosal surface and effectively binding apo Lf when exposed to high levels at sites of inflammation . Neisseria meningitidis is a diplococcal , Gram-negative bacteria that lives commensally in the nasopharyngeal tract of approximately 10–20% of humans [1] . N . meningitidis is an opportunistic pathogen that can cause serious invasive infections including meningitis and sepsis . This pathogen acquires iron–an essential cofactor required for redox reactions in biological processes–from the iron-loaded host glycoproteins , human transferrin ( hTf ) and human lactoferrin ( hLf ) using a set of specialized receptors with specific affinity for these host glycoproteins[2] . The transferrin and lactoferrin receptors from N . meningitidis are both comprised of an integral outer-membrane ‘A’ protein ( TbpA , LbpA ) and a bi-lobed , lipidated ‘B’ protein associated with the outer membrane ( TbpB , LbpB ) . hTf is present in serum , within interstitial fluids and on mucosal surfaces , whereas hLf is localized to the mucosal surface , secretions , and sites of inflammation–possibly providing different niches for the functionality of these receptors [3] . The molecular mechanism by which the transferrin receptor hijacks iron from hTf has been well studied from a structural and biophysical perspective . Crystal structures of TbpB , TbpB:hTf , and TbpA:hTf from N . meningitidis have all been determined [4 , 5] , providing an in-depth picture of the iron acquisition pathway ( Fig 1 ) . The N-lobe of TbpB captures the iron-loaded C-lobe of hTf and brings it to TbpA where iron is removed , transported across the outer membrane , captured by FbpA and transported into the cytoplasm through the FbpBC complex . LbpA is required for acquiring iron from hLf [6] and binds to both domains of the C-lobe of hLf [7] , suggesting that its removal of iron from hLf is analogous to the TbpA:Tf complex [5] . However , there is uncertainty in the interaction of LbpB with Lf and whether or not it plays a role in the iron acquisition process , particularly since it has been shown to be released from the meningococcal outer membrane by NalP [8] ( Fig 1 ) . Although the structures of the N-terminal lobe of LbpB from N . meningitidis and Moraxella bovis have been determined [9 , 10] , attempts to crystallize the intact LbpB protein from N . meningitidis have failed [10] , likely due to large flexible clusters of anionic amino acids present in the C-terminal lobe . Computational docking with the structure of the N . meningitidis LbpB N-lobe predicted a binding interaction with the hLf N-lobe that contrasts the binding interface seen in the TbpB:hTf interaction . In contrast , Noinaj and Cornelisson et al . [11] proposed a model in which LbpB binds to the C-lobe of hLf . Neither of these models are based on experimental data and ignore the presence of LbpB’s defining characteristic–the presence of negatively charged loops in its C-lobe . Recent work [12–14] has also implicated LbpB in defending the bacteria against neutrophil exudates and anti-microbial peptides ( including human lactoferricin; a peptide proteolytically derived from human lactoferrin ) , indicating this protein may perform additional functions outside of iron-acquisition in vivo–or this may now be its primary or sole function . This function would not be compromised by the release of LbpB by NalP-mediated cleavage of the anchor peptide [8] but NalP-mediated release would impact iron acquisition if LbpB played a similar role as TbpB in the process . These observations , coupled with the conflicting models and incomplete molecular picture of LbpB call for an in-depth characterization of this protein if we are to understand N . meningitidis pathogenicity . In this study , we employ the use of several biophysical and biochemical strategies in order to characterize the interaction between LbpB and lactoferrin . We provide a more comprehensive picture of the binding mechanisms of LbpB to hLf and novel insights as to how the interactions may serve different roles under iron-limited and inflammatory conditions N . meningitidis would experience within the host . In TbpB , the N-lobe and C-lobe are in a tight association with the two lobes in a perpendicular orientation [4 , 15] that may serve an important functional role in the iron acquisition process . The published structures of the LbpB N-lobes [9 , 10] confirm that the core structural features of the individual lobes , the antiparallel beta-strand handle and barrel domains , are conserved between TbpBs and LbpBs . However , the lack of success in obtaining structural information for intact LbpB and the uncertainty regarding its main physiological role raise questions as to whether LbpB shares the specific inter-lobe interactions with TbpB . To address this issue we initiated crosslinking coupled to mass spectrometry ( XL-MS ) experiments [16] to compare TbpB and LbpB . Although the identified intra-protein crosslinks will validate the overall structure of the individual lobes , our focus was on identifying inter-lobe crosslinks . A crosslinking reagent was used that adds an 11 . 4Å spacer group between accessible primary amine groups situated on lysine residues which are located up to 30Å from one another . We treated full-length LbpB and TbpB from N . meningitidis strains MC58 and B16B6 , respectively , with a homobifunctional N-hydroxysuccinimide ester crosslinker ( disuccinimidyl suberate; DSS ) , and analyzed the trypsin-digested products via LC-MS/MS . LbpB crosslinks were mapped onto an in silico homology based model of LbpB using Swiss-Model that was modelled against N . meningitidis TbpB [4] ( PDB entry 3V8U ) as a template . The LbpB model should be quite reliable for the core beta-barrel and handle structures but will be least reliable in the loop regions that harbour the clusters rich in aspartic acid and glutamic acid residues . TbpB crosslinks were mapped onto the crystal structure of TbpB from N . meningitidis strain B16B6 [17] as a control . Distances between alpha carbons of crosslinked lysines were measured in PyMOL . The structural models with mapped crosslinks are illustrated in S1 Fig and the details are provided in S1 Table and S2 Table . There is a single crosslink supporting the perpendicular association of the N-lobe and C-lobe in both the LbpB model ( Panel A , red and blue residues ) and TbpB model ( Panel B , yellow residues ) , suggesting that LbpB shares the perpendicular association of the lobes with TbpB . Although the inter-lobe crosslink distance for LbpB is greater than the 30Å limit , it is influenced by the positioning of the large negatively charged C-lobe loops , the least reliable portion of the model . Thus it is not unreasonable to propose that the loops extend upwards near the N-lobe β-handle to fit within the crosslink limits . The only other crosslink which deviated from the 30Å limit was a crosslink between the LbpB-N barrel and anchor peptide . The variable positioning of the anchor peptide in structures of TbpB [15 , 18] are consistent with flexibility that would result in the K26 to K287 crosslink . TbpB has been shown to bind to both domains of the iron-loaded form of human Tf C-lobe , effectively trapping it in the closed conformation [4] . Thus , the preference of LbpB for holo or apo lactoferrin was examined , reasoning that it would implicate LbpB in binding to Lf in a similar fashion that TbpB binds to Tf . A competitive solid-phase binding assay was carried out in which increasing concentrations of the apo and holo forms of hTf or hLf were incubated with labeled hTf or hLf and immobilized TbpB or LbpB ( Fig 2A ) . Both receptor proteins preferentially bound the holo form of their respective glycoprotein over the apo form in a competitive environment . However , apo-hLf was able to block binding of the labeled holo-hLf at high concentrations . Affinity capture experiments were performed in which LbpB and TbpB were incubated with hLf- and hTf-conjugated resins , respectively , in both iron-loaded ( holo ) and iron-free ( apo ) forms . Fig 2B indicates that both LbpB and TbpB preferentially bound the iron-loaded form of their cognate glycoprotein , though LbpB appears to have the capacity to bind apo-lactoferrin . The demonstrated preference of LbpB for the iron-loaded form of Lf , implicates a similarity to TbpB that binds the iron-loaded C-lobe of Tf with its N-terminal lobe [15] . Similarly , the presence of regions rich in acidic amino acids in the C-terminal lobe capable of binding lactoferricin , suggests that the C-lobe would be capable of binding to the N-terminal lobe of Lf [2] . In order to address the binding properties of the individual LbpB lobes , we generated several recombinant domains of LbpB to tease apart binding affinities between each domain of LbpB with hLf using biolayer interferometry ( BLI ) . Each recombinant protein harbored an N-terminal biotin acceptor peptide [19] that would mediate binding onto streptavidin-coated BLI sensors even from crude extracts [20] . These proteins included intact-LbpB , the N-terminal lobe , the C-terminal lobe , LbpB with the anionic loops removed ( LbpB-lgsm; removal of amino acids 469–533 and 691–721 ) , and the C-terminal lobe with the anionic loops removed ( LbpB-C-lgsm ) . Cartoon representations of these proteins can be seen in Fig 3 alongside each of their respective steady-state binding curves with holo-hLf . Binding activity was observed for intact LbpB and both of its constituent lobes , with the binding characteristics of the N-lobe more closely resembling that of intact LbpB ( Fig 3 ) . The LbpB-C-lobe yielded a sigmoidal steady state binding curve ( Fig 3C ) . LbpB-C-lgsm showed no binding ( Fig 3E ) , suggesting that the two anionic loops are responsible for the sigmoidal binding observed with the LbpB C-lobe . The observation that the C-lobe bound Lf with a relatively high KD that was eliminated by removal of the anionic loops is consistent with the expectation that these loops mediated binding to the cationic region of the hLf N-lobe . Absence or differences in binding hLf cannot be attributed to problems with binding the LbpB derivatives as they were all observed to bind the streptavidin BLI sensors during the loading step ( S2 Fig ) . To ensure the lack of binding seen from LbpB-C-lgsm to hLf was due to removal of the anionic loops and not simply misfolding , we performed intra-protein crosslinking on the recombinant protein and observed localized regions of crosslinks , that , when mapped onto a hypothetical model of LbpB-C-lgsm were all within the 30Å limit ( S2A Fig , S3 Fig , S3 Table ) . Similarly , the observation that the LbpB N-lobe was capable of binding hLf with a KD similar to intact LbpB with the anionic loops removed , is consistent with the proposal that the binding interaction is comparable to that observed with TbpB . Intact LbpB , which has binding contributions from both lobes , had a lower calculated KD value than LbpB-lgsm , as would be expected . The ability of LbpB to bind Lf at two different sites complicates the analysis of specific interactions at the individual sites . Our recent success at using XL-MS to probe the known Tf-TbpB interaction [21] prompted us to use XL-MS to capture individual LbpB:Lf complexes and analyze the composition and interactions involved . Similarly , our prior experience at probing the TbpB:Tf interaction with hydrogen/deuterium exchange coupled to mass spectrometry ( HX-MS ) [18 , 22 , 23] prompted us to use this complementary approach to characterize the LbpB:hLf interaction . Since our BLI experiments were performed with LbpB fused to MBP , our initial attempts utilized MBP-LbpB in crosslinking reactions with hLf . Mixtures of MBP-LbpB and hLf were treated with DSS to covalently ligate lysines within the protein complexes and the individual complexes were isolated by SDS-PAGE for analysis ( S4 Fig ) by LC-MS/MS . Since we were not interested in characterizing the structure of MBP or hLf , and had already evaluated intra-protein crosslinks ( S1 Table ) , only crosslinked peptides that included peptides derived from LbpB and hLf were selected for analysis . The peptides derived from 1:1 complexes were shown to contain linkages between the LbpB N-lobe and the hLf C-lobe ( Table 1 ) indicating that this interaction predominated when holo-Lf was incubated with LbpB . For the HX-MS analysis we isolated LbpB after TEV cleavage of the MBP-LbpB preparation and also used this material for additional XL-MS experiments ( S4 Fig ) . Preparations of hLf , LbpB and an hLf:LbpB complex were subjected to HX-MS analysis to evaluate the protein:protein interactions and conformational changes associated with complex formation . The data in S5 Fig illustrates that the main protection from deuterium was observed in peptides derived from the LbpB N-terminal lobe and the hLf C-terminal lobe ( red boxes ) , consistent with an interaction predominantly involving these regions . It is important to note that there are substantial gaps in peptide coverage in both of these regions such that the full impact of binding might not be detected . In order to gain a better sense of how the experimental data could be reconciled and the interaction visualized , we performed data-directed docking experiments with the LbpB-N lobe crystal structure from N . meningitidis ( PDB entry 4U9C ) and holo-hLf ( PDB entry 2BJJ ) . To dock these two proteins , we used the HADDOCK 2 . 2 web interface and included the inter-protein constraints relevant to the LbpB-N:hLf-C interaction noted in Table 1 . The crosslinked residues were converted to ambiguous restraints for docking with a distance constraint of 5-25Å between the alpha carbons . A model from the top scoring cluster was used to illustrate the XL-MS and HX-MS data ( Fig 4 ) and select site-directed mutants to provide experimental support for the proposed interaction . We generated site-directed mutants of LbpB-N after manually and computationally assessing the residues likely to be actively involved in the LbpB-N:hLf-C interaction . Residues which were manually chosen were in agreement with residues predicted by the online hotspot predictor KFC2 [24 , 25] . Six mutants were created ( two double mutants , four single mutants ) with mutations predicted to have different effects on binding affinity ( Table 2 ) . The mutated residues that resulted in loss in binding activity are indicated as magenta sticks in Fig 4 . A selected set of lysine residues involved in crosslinking and their respective crosslinked partner are denoted with dashed yellow linker lines ( Fig 4 ) . Notably several lysine residues at the edge of the LbpB:hLf interaction form crosslinks with several different regions of hLf ( Table 1 ) which provided useful information for the data-driven computational docking . The regions of LbpB with reduced exchange of deuterium in complex with hLf are colored in green and the regions of hLf with reduced exchange of deuterium in complex with LbpB are colored in red . Notably there are substantial gaps in the coverage of peptides in the HX-MS analyses ( S5 Fig ) so the limited presence of green or red colored loops in the LbpB:hLf interface is not surprising . The substantial amount of red labeling in the hLf regions at the base of the iron binding cleft away from the binding interface is reminiscent of what was observed in HX-MS analyses of the TbpB-hTf interaction [18 , 23] and likely reflects conformational constraints imposed by stabilizing the hLf C-lobe in the closed , iron-bound conformation . The data-directed model for the LbpB:hLf complex is strikingly similar to the TbpB:hTf complex ( S6A and S6B Fig ) and is perhaps best illustrated in an overlay of the two structures ( S6C Fig ) . S6D Fig shows the solid-phase binding assay results of each mutant beside the wild type LbpB control summarized in Table 2 . Binding assays were performed in two different conditions ( pH = 5 . 9 , pH = 7 . 5 ) as we had noticed previously [26] that the affinity of LbpB for hLf varies with pH and notably the R224 mutant resulted in reduced binding at high pH . In the experiments with LbpB and hLf , incubation with DSS followed by SDS-PAGE also resulted in the identification of a higher molecular weight species that was most consistent with a 2:1 hLf:LbpB complex ( S4 Fig ) . The 260kDa band from experiments with the MBP-LbpB fusion protein was excised from the gel and trypsin-digested for analysis by LC-MS/MS . High scoring , redundant crosslinked peptides were obtained for internal LbpB peptides , internal Lf peptides , internal MBP peptides , peptides identified in Table 1 for the LbpB N-lobe:hLf C-lobe interaction and a peptide between LbpB residue K379 and hLf residue K39 . This peptide represents a linkage between a residue at the base of the negatively charged loops in the LbpB C-lobe and the lactoferricin region of the hLf N-lobe , consistent with the binding activity observed with BLI ( Fig 3 ) . In the various experiments we performed evaluating the formation of complexes between LbpB and hLf we commonly observed the formation of precipitates that were not observed in the control preparations with hLf or LbpB alone , particularly over longer incubation periods and with higher concentrations of the proteins . Although XL-MS and HX-MS were useful in characterizing the 1:1 complex between iron-loaded hLf and LbpB that could reflect the situation on the mucosal surface , their utility decreased substantially when dealing with higher order complexes that might occur at sites of inflammation where higher concentrations of apo hLf are present . The ability to utilize host iron-binding glycoproteins as a source of iron for growth has been observed in Gram-negative bacteria that reside in the upper respiratory or genitourinary tracts of a variety of vertebrate hosts ranging from birds to humans [27 , 28] . The relatively conserved composition and structural features of the bipartite Tf receptor amongst these diverse hosts argue for the presence of these receptors in bacteria that resided in ancestral hosts prior to the divergence of birds and mammals . The demonstration that positive selection in regions of Tf involved in binding to TbpA drove the evolution of transferrin responsible for the host specificity amongst these pathogens in primates [29] , suggests that these same forces are responsible for the host specificity observed in pathogens from other vertebrate hosts [2 , 30] . The ability to proliferate independently from other members of the upper respiratory or genitourinary microbial communities due to direct utilization of host Tf provides a selective advantage , to the extent that some bacteria have become dependent upon this mechanism for survival [31 , 32] . The ability to directly use host Tf also enables these bacteria to proliferate in serum and interstitial tissue fluids inside the body , one reason that these bacteria are also important pathogens in their respective hosts . The bipartite Lf receptors were likely derived from ancestral Tf receptors after establishment of the mammalian lineage and have also been shown to be important for growth and survival on the mucosal surface [3] . However , the Neisseria LbpB has been shown to protect against cationic peptides derived from the Lf N-terminal region [12 , 13] and to be selectively released from the bacterial surface by NalP [8] , raising questions about its role in the iron acquisition process . The fact that positive selection analysis of lactoferrin evolution in mammals primarily identified residues in the N-terminal lactoferricin and lactoferrampin regions [33] , highlights the importance of the interaction of Lf with bacterial factors such as the Lf binding protein PspA [34] and LbpB . Computational docking was used to develop two recent models for the LbpB:Lf interaction that proposed that the LbpB N-lobe bound to the Lf C-lobe [11] or to the Lf N-lobe [10] . Since both models were developed without any supporting experimental data , it was important to implement experimental studies to probe the LbpB-Lf interaction . In this study the combined results from the biolayer interferometry binding studies ( Fig 3 ) , crosslinking coupled to mass spectrometry ( XL-MS ) and hydrogen/deuterium exchange coupled to mass spectrometry ( HX-MS ) ( Fig 5 ) clearly indicate that the LbpB N-lobe binds to the hLf C-lobe analogous to what has been demonstrated for the Tf-TbpB interaction [4] , thus supporting its potential role in iron acquisition . Furthermore , our crosslinking studies support the orthogonal orientation of the N-lobe and C-lobe ( S1 Fig , S1 Table , S2 Table ) and preferential association of the anchor peptide with the C-lobe ( S1 Fig , S1 Table ) , features that have been proposed to be important for the transfer of iron-loaded Tf to TbpA [35] . Although hLf is secreted from mucosal glandular cells in the apo form , due to its ability to sequester iron , there may be a substantial proportion of holo ( iron-loaded ) hLf at the relatively low levels present under normal conditions on the mucosal surface [3] . Similarly , during invasive infection within the bloodstream hLf may predominantly be in the iron-loaded form . The ability to discriminate between the holo and apo forms of hLf ( Fig 2 ) suggests that LbpB could serve an analogous function as TbpB by preferentially capturing holo-hLf under these conditions . Since the prevalence of the NalP gene is estimated to only be 88% in carriage and invasive isolates and the gene is subjected to phase variation by the presence of poly C tracts [36] , there would be opportunities to select for isolates with efficient acquisition from hLf under these conditions . LbpB has been shown to be important for protecting N . meningitidis against the killing activity of cationic antimicrobial peptides derived from hLf [12] , and that this protection is mediated by the regions enriched in negatively charged residues in the C-lobe [13] . The results of our binding studies ( Fig 3 ) and crosslinking studies confirm that the N-lobe of hLf is bound by the negatively charged regions in the C-lobe of LbpB . We only observed a single redundant crosslink between the LbpB-C lobe and hLf-N lobe , though this low level of coverage was to be expected considering the chemical nature of the crosslinker ( reacts with primary amines ) and the anionic nature of the LbpB-C lobe loops . Further studies may utilize additional crosslinker chemistries ( i . e . carboxyl-to-amine crosslinkers ) to address this issue . Since activated neutrophils secrete large concentrations of apo-Lf , this mode of binding may be particularly important at sites of inflammation , potentially inhibiting the release of cationic antimicrobial peptides by proteolytic cleavage of apo Lf or directly complexing the released cationic peptides . The release of LbpB by NalP would not necessarily compromise this process , and could potentially facilitate the formation of large Lf-LbpB complexes that might delay the release of cationic peptides . Since NalP has been shown to mediate the release of LbpB and the cleavage of human complement C3 [37] , its expression upon crossing the mucosal barrier or at sites of inflammation would be advantageous . This study has demonstrated the duality and complexity of the LbpB:Lf interaction that reflects the dual roles that LbpB plays in iron acquisition and protection from cationic peptides . A schematic for the proposed pathways can be seen in Fig 5 . Considering the potential impact that LbpB release from the cell surface by selective NalP cleavage has on these two functions , there could be strong selective forces on NalP-on or NalP-off phase variants in different environments within the host . It would also be interesting to determine whether there may be other factors affecting the expression of NalP at sites of inflammation . Regions of the tbpB and lbpB genes from Neisseria meningitidis strain MC58 or B16B6 were PCR amplified and cloned into a custom expression vector encoding a N-terminal polyhistidine tagged maltose binding protein followed by a TEV cleavage site [15] . The amplified gene regions not only excluded the signal peptide , but different segments of the N-terminus anchor peptide region that starts with the cysteine that is normally lipidated for anchoring the protein in the outer membrane . N . meningitidis ( N . m . ) strain M982 TbpB protein used in affinity-capture or BLI experiments excluded amino acids 1–40 . N . m . MC58 LbpB protein excluded amino acids 1–15 , and LbpB N-lobe protein excluded amino acids 1–35 . The recombinant plasmids were used to transform Escherichia coli strain ER2556 and after 1 hour incubation in LB broth containing 100 μM ampicillin , 1 mL was directly inoculated into a 20 mL starter culture of LB with ampicillin . After growth at 37°C for 18 hours the cells were re-inoculated into a 1 L culture of ZY auto-induction media containing ampicillin and allowed to grow for 24 hours . Cells were pelleted by centrifugation at 11 , 200 × RCF , the broth supernatant was decanted and cell pellets were re-suspended in 50 mM sodium phosphate , 300 mM NaCl , 5 mM imidazole , pH 7 . 4 buffer ( resuspension buffer ) and lysed using an Avestin Emulsiflex-C3 homogenizer . Lysates were centrifuged at 48 , 200 × RCF for 1 hour and the supernatant was filtered through a 0 . 45μm syringe filter . The filtered sample was loaded onto a 5 mL HisTrap FF column ( GE Healthcare ) using an Amersham peristaltic pump at a flow rate of 2 mL/min . The column was then loaded onto an AKTA purifier , washed in resuspension buffer followed by wash buffer ( 50 mM sodium phosphate , 300 mM NaCl , 30 mM imidazole , pH 7 . 4 ) until UV signal baselined . The target protein was eluted with elution buffer ( 50 mM sodium phosphate , 300 mM NaCl , 250 mM imidazole , pH 7 . 4 ) and selected fractions were pooled and dialyzed into interaction buffer ( 50 mM Tris-HCl , 50mM NaCl , pH 7 . 4 ) and stored at 4°C . LbpB fused with MBP was released by TEV protease cleavage ( Qiagen , in-house purified ) overnight . MBP and LbpB were separated by ion exchange on a Q HP column ( GE Healthcare ) and the samples were eluted on a salt gradient ( 50 mM sodium phosphate , 2 M NaCl , pH 7 . 4 ) . The LbpB containing fractions were dialyzed into interaction buffer and stored at 4°C . 3 μl of receptor protein preparations ( recombinant MBP fusions ) at concentrations of 1 mg/mL were applied to a nitrocellulose membrane ( Pall , Hessen , Germany ) and allowed to dry . The membrane was then blocked with a 1% skim milk ( W/V ) in SPB buffer ( 20 mM sodium phosphate , 150 mM NaCl , pH 5 . 9 or 20 mM Tris-HCl , 150 mM NaCl , pH 7 . 5 ) for 1 hour . A 1:1000 dilution of HRP-conjugated ligand at an approximate concentration of 1 mg/mL was added to the blocking solution , and incubated overnight , shaking at 4°C . The blocking solution was removed , and the membrane was washed three times for 5 min with SPB Buffer . A development stock solution was created from 300 mg of HRP Color Development Reagent ( BioRad ) in 100mL methanol , then stored at -20°C . A diluted form of the development reagent stock was used on the membrane ( 20 mL SPB buffer , 4 mL color development reagent stock , 200 μL H2O2 ) . hTf and hLf-conjugated resins were generated using cyanogen bromide activated Sepharose 4B resin ( GE Healthcare ) . Iron-free ( apo ) resins were generated by washing iron-loaded resin in a low pH buffer ( 1% sodium citrate , 100 mM EDTA , pH 3 . 0 ) . 100 μL of 50% slurry was washed three times with binding buffer ( 10 mM sodium phosphate , 100 mM NaCl , pH 6 . 0 ) by centrifuging the resin at 6 , 000 × RCF for 1 min , decanting the supernatant , and resuspending the resin in binding buffer . Approximately 100 μg of free LbpB or TbpB protein was incubated with the resin for 2 hours at RT . Samples were centrifuged at 6 , 000 × RCF for 1 min , and supernatant was decanted . Resins were then washed three times in binding buffer with salt adjusted to 120 mM ( hLf resin ) or 1 M ( hTf resin ) for 20 min . 85 μL of 2 x SDS-loading buffer ( 100 mM Tris-HCl , 4% SDS , 0 . 2% bromophenol blue , 20% glycerol ) was added to the resin , and boiled for 5 min to release bound protein . BLI experiments were carried as outlined in [20] on a ForteBio Octet RED 96 machine ( Pall Biosciences ) . E . coli ER2566 cells were transformed with plasmids encoding the various recombinant protein constructs including a BirA biotin acceptor peptide ( BAP ) [19] , and plated on LB-Agar plates . A set of 5 colonies were selected from each plate and inoculated into 5mL of auto-induction media . Cells were grown for 18 hours at 37°C . Bacterial pellets were resuspended in lysis buffer ( 1 X PBS , 1% Triton X-100 ) . Lysozyme , DNase and PMSF were added to aid in lysis , decrease viscosity and prevent proteolysis . Resuspensions were incubated at room temperature for 15 min , and spun down in a table-top centrifuge at 16 , 100 × RCF for 25 min . The concentrations of hLf for the dilution series were chosen as to flank the KD value for the TbpB:hTf interactions ( ~50 nM ) by approximately tenfold in each direction . Concentration values were calculated on a logarithmic scale incrementing by 0 . 33 to obtain the values 10 nM , 21 nM , 46 nM , 100 nM , 213 nM , 467 nM , 600 nM and 800 nM . After an initial baseline in 1 X kinetics buffer ( 1 X PBS pH 7 . 4 , 0 . 002% Tween-20 , 0 . 1 mg/mL BSA ) and sensor loading , assay steps followed a repeat pattern of: baseline , association , dissociation , and regeneration . Regeneration was carried out in a 100 mM sodium citrate , 50 mM EDTA , pH 4 . 5 buffer . Association and dissociation steps were carried out in kinetics buffer . Steady state values were obtained by averaging the response values obtained in the last 5 seconds of the association step were plotted against concentration to generate saturation binding curves , and the data was fitted using Prism ( Graphpad ) . The “One site–Total binding” saturation curve was used for Intact , N-lobe and Intact-lgsm , whereas the “One site–specific binding with Hill slope” was used for C-lobe binding . Mutants were generated using splicing by overlap extension ( SOE ) PCR [38] . Primers harboring the desired mutations were incubated with vector-specific primers on the 5’ and 3’ end of the insertion site , and vector containing wild-type LbpB was used as template . Amplicons were then spliced together to create the recombinant mutant . N . m . MC58 LbpB and hLf ( Agennix ) were incubated together in interaction buffer at equimolar concentrations in a total volume of 50 μL , shaking gently for 4 hours at RT . A stock solution of disuccinimidyl suberate ( DSS , Thermo Fisher Scientific ) at 25 mM in DMSO was prepared and added to the complexed LbpB:hLf mixture to a final concentration of 1 mM . This mixture was gently mixed and incubated a further 30 min at room temperature . The DSS was then quenched using a 1 M NH4HCO3 solution added to a final concentration of 50 mM . Samples were loaded on a handcast 6% SDS-polyacrylamide gel until the 190kDa and 260kDa bands were completely separated from all other protein bands . The gel was stained with Coomassie blue , and the band of interest was excised with a clean scalpel blade for processing by conventional tryptic in-gel digestion methods [39] , followed by analysis using an Orbitrap Velos mass spectrometer , equipped with an EasyLC1000 nanochromatography system ( Thermo Scientific ) . Briefly , digests were reconstituted in 0 . 1% formic acid and loaded on an 8 cm x 75 μm self-packed picotip column ( Aeris Peptide XB-C18 , 3 . 6 μm particle size , Phenomenex ) . Separation was achieved using a 30 min 5–60% gradient of mobile phase B ( 97% acetonitrile with 0 . 1% formic acid ) at 300 nl/minute . Mobile phase A consisted of 3% acetonitrile and 0 . 1% formic acid . The mass spectrometer was operated in positive ion mode , with a high/high configuration , where MS resolution set at 60 , 000 ( 400–2000 m/z ) and MS2 resolution at 7500 . Up to ten of the most abundant ions were selected for fragmentation using higher energy collisional dissociation ( HCD ) , rejecting charge states 1 and 2 , and using a normalized collision energy of 40% . Data analysis was performed using the crosslinking plugin within Mass Spec Studio 2 . 0 , using default settings [21] with high-scoring ( score of 12 or higher ) , and redundancy ( observed in duplicate or greater ) used as criteria for selection of crosslinks . Each crosslinked peptide pair was manually inspected for data quality and correct assignment of linked residues , using the manual validation user interface in the Studio . The final crosslinked peptides as well as each unique pair of crosslinked residues were exported in . CSV format for use in molecular docking . HADDOCK ( High Ambiguity Driven protein-protein DOCKing ) [40 , 41] was used to generate data-driven models of the Lf:LbpB interaction . The docking process consists of three successive steps ( rigid body energy minimization , semi-flexible refinement and solvent refinement ) where data can be invoked , in the form of distance restraints , at each step to inform the development of docked models . Data can be defined as ambiguous or unambiguous , reflecting a degree of uncertainty in the correspondence of residues across an interface . Although this correspondence across the interface is determined by the crosslinking experiment , we chose to establish docking runs by treating crosslinked residues as ambiguous restraints , overlaid upon conventional center-of-mass restraints typically used in ab initio docking . This is approach is justified , as crosslinking data derived from DSS-based experiments typically generate a modest number of restraints , with poorly defined distances between linked residues . For docking we allowed crosslinks to vary between 5-25Å [42] , and generated docking runs on the Haddock 2 . 2 webserver , using default settings ( 10 , 000 samplings in it0 , 200 in it1 and 200 during refinement in explicit solvent ) . Results were clustered according to fraction of common contacts ( FCC ) , and the top-ranked cluster according to HADDOCK score was selected . Within the best cluster , the best-scoring model was chosen to represent the hLf:LbpB interaction . Stock solutions of hLf ( 20 μM ) and LbpB ( 10 μM ) were diluted to 5 μM in 10 mM Tris-HCl ( pH 7 . 4 ) buffer prior to the HX-MS experiment . Similarly , the hLf:LbpB complex solution was prepared from the stock solutions in a 10 mM Tris-HCl ( pH 7 . 4 ) buffer at a 1:1 ratio of hLf and LbpB with final concentration of 5 μM for each component protein . The complex solution was incubated for 1 hour prior to performing HX-MS analysis . HX was initiated by adding 2 μL diluted protein solutions to the labelling solution ( 90% D2O , in 10 mM Tris-HCl ) at 4°C to a final D2O level of 45% . Labelling was performed for 1 , 10 , and 100 min for individual protein and the complex solutions . At the end of the labelling period , the samples were incubated in 100 mM TCEP under quenching conditions ( 100 mM glycine , pH 2 . 5 ) for 1 minute , followed by digestion at 10°C with 6 μL of recombinant NepII ( 0 . 1μg/mL , in 100mM Gly-HCl , pH 2 . 5 ) for 2 min [43] . The digested samples were loaded onto a self-packed preconcentration cartridge ( 25 mm x 250 μm i . d . capillary , 200 Å , 5 μm Magic C18 beads , Michrom BioResources ) for 3 min ( 10 μL/min ) at 4°C and separated on a self-packed analytical column ( 70 mm x 150 μm i . d . capillary , 200 Å , 5 μm Magic C18 beads using a 10–40% gradient over 10 min ) . HX data for all labelling time points were collected in triplicates with a TripleTOF 5600 ( SCIEX ) coupled to an Eksigent nanoLC-ultra-2D pump . Mascot v2 . 4 was used to identify peptides for analysis . Briefly , a search was performed against a database containing all proteins present in this study with a mass tolerance for precursor ions of 20 ppm , 0 . 05 Da for fragment ions , and a probability cutoff of p = 0 . 05 . A peptide list was next imported into our in-house software package MS Studio for deuteration analysis [44] . Peptide quality was assessed based on intensity , signal to noise ratio and spectral overlap , and only those with reliable isotopic profiles were selected for analysis . The percent relative fitted deuteration for each high-quality peptide was exported . Woods plots for each protein state were created using a statistical module in MS Studio as previously described [45] .
Bacteria responsible for important infections in humans and food production animals survive and proliferate within their host by ‘hijacking’ iron from the host iron-binding proteins , transferrin and lactoferrin . The iron-hijacking process is mediated by a set of surface receptors that are specific for transferrin and lactoferrin from the host . In this study we focused on the receptors from important human pathogens responsible for meningitis and gonorrhea that are being targeted for development of vaccines , thus a detailed understanding of the structure and function of these proteins is needed to aid in vaccine design . Although there is detailed information available for the transferrin receptor proteins , currently information is lacking for the lactoferrin receptor proteins . This study focuses on a specific constituent of the lactoferrin receptor , lactoferrin binding protein B , that also serves to protect the bacteria against host defense mechanisms mediated by small peptides that kill microbes , including ones derived from host lactoferrin . We demonstrated that lactoferrin binding protein B has two different sites for binding lactoferrin , one associated with obtaining iron , the other related to protection against the antimicrobial peptides . This information enabled us to understand how this protein can effectively serve both roles and adapt to the local conditions .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]
[ "chemical", "bonding", "medicine", "and", "health", "sciences", "chemical", "characterization", "crystal", "structure", "pathology", "and", "laboratory", "medicine", "chemical", "compounds", "pathogens", "condensed", "matter", "physics", "microbiology", "organic", "compounds", "membrane", "proteins", "outer", "membrane", "proteins", "basic", "amino", "acids", "amino", "acids", "crystallography", "cellular", "structures", "and", "organelles", "bacteria", "bacterial", "pathogens", "physical", "chemistry", "research", "and", "analysis", "methods", "neisseria", "solid", "state", "physics", "neisseria", "meningitidis", "proteins", "medical", "microbiology", "microbial", "pathogens", "chemistry", "cross-linking", "recombinant", "proteins", "binding", "analysis", "cell", "membranes", "physics", "biochemistry", "peptides", "cell", "biology", "organic", "chemistry", "biology", "and", "life", "sciences", "lysine", "physical", "sciences", "organisms" ]
2017
Lactoferrin binding protein B – a bi-functional bacterial receptor protein
Destruction of the pulmonary epithelium is a major feature of lung diseases caused by the mould pathogen Aspergillus fumigatus . Although it is widely postulated that tissue invasion is governed by fungal proteases , A . fumigatus mutants lacking individual or multiple enzymes remain fully invasive , suggesting a concomitant requirement for other pathogenic activities during host invasion . In this study we discovered , and exploited , a novel , tissue non-invasive , phenotype in A . fumigatus mutants lacking the pH-responsive transcription factor PacC . Our study revealed a novel mode of epithelial entry , occurring in a cell wall-dependent manner prior to protease production , and via the Dectin-1 β-glucan receptor . ΔpacC mutants are defective in both contact-mediated epithelial entry and protease expression , and significantly attenuated for pathogenicity in leukopenic mice . We combined murine infection modelling , in vivo transcriptomics , and in vitro infections of human alveolar epithelia , to delineate two major , and sequentially acting , PacC-dependent processes impacting epithelial integrity in vitro and tissue invasion in the whole animal . We demonstrate that A . fumigatus spores and germlings are internalised by epithelial cells in a contact- , actin- , cell wall- and Dectin-1 dependent manner and ΔpacC mutants , which aberrantly remodel the cell wall during germinative growth , are unable to gain entry into epithelial cells , both in vitro and in vivo . We further show that PacC acts as a global transcriptional regulator of secreted molecules during growth in the leukopenic mammalian lung , and profile the full cohort of secreted gene products expressed during invasive infection . Our study reveals a combinatorial mode of tissue entry dependent upon sequential , and mechanistically distinct , perturbations of the pulmonary epithelium and demonstrates , for the first time a protective role for Dectin-1 blockade in epithelial defences . Infecting ΔpacC mutants are hypersensitive to cell wall-active antifungal agents highlighting the value of PacC signalling as a target for antifungal therapy . Spores of the mould pathogen Aspergillus fumigatus are agents of multiple human diseases , most of which initiate with inhalation of fungal spores and , dependent upon host immune status , compromise pulmonary integrity . Amongst the resultant diseases , invasive aspergillosis ( IA ) exerts the highest fatal toll , resulting globally in an estimated 200 , 000 deaths per annum [1] . Recipients of allogenic hematopoietic stem cell- or solid organ transplants , are particularly susceptible to IA which accounts for 43% and 19% of all invasive fungal infections in these cohorts and causes 58% and 34% mortality , respectively , at 12 weeks post-transplant [2]–[4] . Structural or immunological lung defects also lead to chronic , semi-invasive , pulmonary aspergillosis ( CPA ) having a 5 year mortality of 50% [5] . Amongst more than 200 Aspergillus species , Aspergillus fumigatus accounts for the majority of these diseases [6] . In diseases caused by A . fumigatus the initiating host-pathogen interaction occurs at pulmonary epithelia where inhaled spores can exit from dormancy , swell and generate invasive cells called hyphae , which traverse the lung epithelium . A key pathological feature of invasive- and semi-invasive aspergilloses is the destruction of the lung parenchyma , hypothesised to be governed by proteolytic enzymes secreted by the invading pathogen . Exposure of in vitro-cultured bronchial , or alveolar , epithelial cells ( ECs ) to fungal culture supernatants has revealed a role for fungal proteases in destruction of the mammalian F-actin cytoskeleton and loss of focal adhesion [7]–[9] . However , in whole animal studies of disease , it has not been possible to attribute lung damage solely to the activity of fungal proteases since A . fumigatus mutants lacking individual , or multiple , enzymes retain the ability to cause fatal invasive infections in immunocompromised hosts [9]–[15] . The interaction of A . fumigatus spores with alveolar epithelia can result in the internalisation of spores [16]–[18] but the role of this process in disease outcome remains unknown . Cells of the A549 pneumocyte cell line [18] and 16HBE14o- transformed human bronchial epithelial cells [19] internalise 30–50% of encountered spores , via an actin-dependent mechanism . Whilst the vast majority of internalised spores are killed , a small proportion ( ∼3% ) survives and germinates inside acidic organelles [20] . This has prompted hypotheses of latent occupation of host epithelia by A . fumigatus spores , which might thereby evade host immunity and initiate disseminated infections ( as reviewed by Osherov , 2011 [21] ) . Conversely , a curative role for epithelial activities is strongly supported by the observations of Chaudhary et al . , 2012 who reported that bronchial ECs harbouring a CFTR mutation ( ΔF508 ) demonstrate impaired uptake and killing of conidia [22] . The impact of EC-mediated activities upon disease outcomes in whole animal models of infection is presently unclear . In fungi a conserved regulatory pathway governs the pH-dependent expression of secreted proteins and adaptation to alkaline stress [23]–[27] . Acting via PacC/Rim101 transcription factors , this environmental adaptation mechanism promotes the energy-efficient production of exported enzymes and metabolites , and has a demonstrated role in the pathogenicity of Candida albicans [25] , [28] and Aspergillus nidulans [29] . Analysis of the transcriptomic response of invasive A . fumigatus hyphae to the mammalian pulmonary niche , identified 102 alkaline-responsive gene functions as being upregulated in leukopenic mice [30] . We therefore hypothesised that in A . fumigatus , PacC would be important for colonisation of the mildly alkaline murine lung . In this study we describe a functional genomics analysis of PacC-mediated activities which govern pathogenicity in mice . Unexpectedly , we discovered that PacC null mutants exhibit an unprecedented non-invasive phenotype , which is not an artefact of defective fungal growth , and can be recapitulated in vitro using cultured epithelia . The capacity to invade host tissues is therefore a genetically regulated trait , requiring PacC regulatory control . In this study we exploited the differentially invasive properties of wild-type and ΔpacC isolates , to address the cellular and molecular basis of pathogen-mediated epithelial damage during Aspergillus infections . This revealed a novel mode of epithelial entry , occurring in a cell wall-dependent manner prior to protease production , and via the Dectin-1 β-glucan receptor . Our findings reveal novel mechanistic insights , having direct relevance to infection of whole animals , which will focus the onward study of A . fumigatus-mediated lung diseases upon dissecting the synergistic and/or additive impacts of temporally distinct aspects of the host-pathogen interaction at pulmonary epithelia . The multiple deficits in pathogenic activities and heightened sensitivity to echinocandin drugs , observed in ΔpacC isolates , highlight the potential of this receptor-mediated fungal signalling mechanism as a target for antifungal therapies . To characterise the role of the A . fumigatus PacC transcription factor ( AFUA_3G11970 ) in pathogenicity we constructed null and complemented alleles in two distinct A . fumigatus clinical isolates CEA10 and ATCC46645 ( Figure S1 ) . Relative to non-mutated isolates , PacC null mutants assumed a compact colonial phenotype on supplemented solid DMEM medium pH 7 . 4 ( Figure 1A ) which , in contrast to colonies of wild type isolates , lacked peripheral invasive hyphae , and were composed of a denser hyphal network indicative of a hyperbranching morphology ( Figure 1B ) . This compact morphology was pH-independent , being also observed in colonies grown on Aspergillus complete medium pH 6 . 5 ( Figure S2A ) , where sporulation and pigmentation of ΔpacC mutants was equivalent to that of the wild type . To assess pH sensitivity of the ΔpacC mutants we examined , on pH-buffered minimal media , the extent of radial growth at pHs 8 . 0 and 7 . 2 , relative to growth at pH 6 . 5 ( Figures S2B and S2C respectively ) . Consistent with a role for PacC in alkaline adaptation , ΔpacC isolates suffered growth impairment at pH 8 . 0 , achieving approximately 10–20% of the radial growth attained at pH 6 . 5 ( Figure S2B ) , compared with 40% achieved by wild-type and reconstituted strains . However , sensitivity of ΔpacC isolates to growth at pH 7 . 2 , which approximates the pH of the mammalian lung , did not differ from that of the wild type isolates ( Figure S2C ) . To orchestrate alkaline adaptation , cytoplasmically localised PacC/Rim101 transcription factors must undergo pH-dependent proteolytic cleavage and nuclear entry [31] . Concordant with this model of transcription factor activation , we detected , by electrophoretic mobility shift assay ( EMSA ) , several PacC retardation complexes , the relative quantities of which were altered under acidic and alkaline growth conditions ( Figures S3A and S3B ) . Relative to growth at acidic pH , processed , activated forms of PacC increased in abundance upon shifting to alkaline conditions ( Figures S3A and S3B ) . These findings are consistent with a pH-responsive mode of PacC activation , and with a conserved role for the A . fumigatus transcription factor in alkaline adaptation . The mammalian pulmonary niche exerts multiple physiological stresses upon invading pathogens , including mildly alkaline pH , hypoxia , and iron- , zinc- and nutrient limitation [30] , [32] , [33] . To compare the growth rates of wild type and ΔpacC hyphae in a physiologically relevant setting , we devised an in vitro epithelial infection assay ( Figure 1C ) , comprising monolayers of A549 alveolar epithelial cells cultured in supplemented DMEM medium ( pH 7 . 4 ) . We used this assay to assess the growth rates of wild type and ΔpacC isolates , under 5% CO2 ( Figure 1C ) . To promote the visualisation of , and distinction between , fungal and host cells we stained host cells with FITC-labelled concavalin A and fungal cells with calcofluor white . We then performed a quantitative analysis of fungal growth rate ( Figure 1D ) and hyphal branching ( Figure 1E ) via immunofluorescence microscopy . Inspection of the infected monolayers revealed significant hyphal growth of both wild type and ΔpacC isolates ( Figure 1C ) , but quantitation of hyphal branching frequency revealed an approximately doubled frequency of hyphal branching amongst ΔpacC isolates relative to wild type . To normalise for heightened branching during quantitation of hyphal growth rates we performed a comparative assessment of individual cell sizes by enumerating the number of pixels per fungal particle . This analysis , revealed that the cross-sectional area of wild type and ΔpacC cells approximates 540 versus 387 µm2 respectively ( Figure 1D ) which , assuming a uniform hyphal radius for wild type and mutant cells of approximately 3 µm , equates to a maximum deviance of 20 µm in length after 16 hours of in vitro co-culture with mammalian epithelia . On this mathematical basis , the median length of an unbranched ΔpacC hypha would be ∼ 30 µm . Given that ∼ 50% of ΔpacC hyphae remain unbranched , and the thickness of the alveolar epithelium is comparatively tiny , the observed differences in hyphal length between wild type and mutant hyphae are insufficient to explain the non-invasive phenotype of the ΔpacC mutants . Leukopenia is an important risk factor for IA in humans , and cyclophosphamide-induced leukocyte depletion renders mice highly susceptible to pulmonary infection [34] . To assess the role of A . fumigatus PacC in pathogenicity we assessed the survival of leukopenic mice following infection , via the intranasal route , with spores of wild-type , ΔpacC or reconstituted isolates ( Figure 2A ) . Relative to wild-type strains , ΔpacCATCC and ΔpacCCEA10 mutants were significantly attenuated for virulence ( Figure 2A ) . At day 6 post-infection 100% of mice infected with the ΔpacC mutants remained alive while 78% and 92% of mice infected with wild type ( ATCC46645 and CEA10 respectively ) isolates were dead . Histological analysis of infected lung tissues revealed significant differences between mutant and wild-type isolates by 20 hours post-infection ( Figures 2B and C ) . While ΔpacC spores appeared to swell and form primary germ tubes by 12 hours post-infection ( Figure 2B ) , penetration of the lung epithelium was not evident and , despite being competent in hyphal production in the murine airway , ΔpacC germlings remain contained within the epithelial boundary of the airspace at 20 hours post-infection ( Figure 2C ) . Thus , ΔpacC germlings in leukopenic hosts exhibited a marked tissue non-invasive phenotype ( Figures 2B and 2C ) . To further characterise the non-invasive phenotype of ΔpacC isolates we used our in vitro co-culture assay ( Figure 1C ) to examine integrity of A549 alveolar epithelial monolayers following 16 hours co-culture with wild-type or ΔpacC isolates by enumeration of detaching epithelial cells . In monolayers infected with wild-type strains , extensive rounding and detachment of up to 40% of host cells was demonstrable , resulting in observable destruction of the epithelial monolayer ( Figures 1C and 3A ) . However , similar to PBS-challenged monolayers , infection with ΔpacC mutants led to detachment of less than 5% of monolayer cells ( Figures 1C and 3A ) . Although ΔpacC hyphae achieved similar hyphal growth rates ( Figure 1D ) and epithelial coverage ( Figure 1C ) to isogenic wild type isolates , ΔpacC hyphae were found to navigate the surface of the epithelial monolayer without effecting cellular detachment ( Figure 1C ) . To further characterise this deficit in epithelial damage , a modified 51Cr release assay [35] , adopting a similar time course of infection , was utilised to quantitatively assess epithelial cell lysis upon infection ( Figure 3B ) . Concordant with cell detachment assays , ΔpacC mutants reproducibly failed to fully elicit epithelial damage . Taken together these findings demonstrate that ΔpacC hyphae fail to elicit disintegration of alveolar epithelia during in vitro culture at pH 7 . 4 despite achieving similar growth rates as wild-type isolates ( Figures 1C and D and 3A and B ) . Several observations argue strongly against a trivial pH-dependent growth defect as the basis for the ΔpacC virulence defect . First , in A . fumigatus , a compact colonial morphology is not a robust correlate of reduced virulence . For example , despite compact colonial morphology and a highly branching mode of growth , null mutants lacking the ChsG chitin synthase remain fully virulent in an inhalational model of infection [36] . Second , in A . fumigatus , in vitro alkaline sensitivity is not a robust correlate of reduced virulence . For example , mutants lacking the mitogen-activated kinase MpkA , demonstrate more severe radial growth defects than ΔpacC mutants in Aspergillus minimal medium [37] and exhibit highly alkaline sensitive phenotypes [38] , but retain full virulence in a low dose inhalational model of aspergillosis . Third , during in vitro cell culture with A549 cells ( pH 7 . 4 ) , hyphae of ΔpacC mutants achieve similar growth rates to that of isogenic wild type isolates ( Figures 1C and D ) . Fourth , despite achieving similar growth rates to wild type hyphae during in vitro cell culture with A549 cells ( pH 7 . 4 ) , hyphae of ΔpacC mutants fail to elicit epithelial decay ( Figures 1C , 3A and B ) . Finally , hyphae of ΔpacC mutants fail to traverse the murine epithelium and are strictly confined to the airway ( Figure 2C ) . ΔpacC mutants are the first-reported non-invasive A . fumigatus mutants , revealing that tissue invasion is a genetically regulated trait under PacC regulatory control . We therefore exploited the differentially invasive properties of wild type and ΔpacC isolates to seek a more detailed mechanistic understanding of tissue invasive growth in this pathogen . Previously , we devised a strategy for analysing A . fumigatus gene expression during initiation of murine aspergillosis [30] . Here we applied a similar approach , this time performing time-series analyses ( 4 , 8 , 12 and 16 hours ) of A . fumigatus gene expression . This permitted the capture of stage-specific gene expression during invasive colonisation of the leukopenic murine lung , the first reported longitudinal study of gene expression during mammalian pulmonary infection . We adopted a comprehensive experimental design ( Figure S4A ) , incorporating 12 competitive hybridisations and 12 flip-dye experiments . This permitted the analysis of stage-specific gene expression in both infecting wild-type ATCC46645 ( Figure S4B ) and ΔpacCATCC isolates ( Figure S4C ) , as well as the directly comparative analysis of gene expression , by time-point , for the wild type and ΔpacCATCC mutants ( Figure S4D ) . Relative to ungerminated spores , transcript profiling of wild-type gene expression revealed a total of 3733 genes , ( log2≥ +/−1 . 5 ) , which were differentially expressed at a minimum of one time-point during invasive infection . The differentially regulated genes were assigned to three cohorts , corresponding to ( i ) genes consistently up- or down-regulated across the time series; or differentially regulated during ( ii ) early ( 4 , 8 and 12 hours ) or ( iii ) late ( 12 and 16 hours ) phases ( Dataset S1 ) . This revealed respiration , metabolism and amino acid biosynthesis as being prioritised during early infection of the leukopenic host , while cation transport , secondary metabolism and iron metabolism were subsequently emphasised during commencement of invasive growth ( Dataset S1 ) . Throughout the time series of growth in the host , upregulated expression of secreted gene products remained highly significant . A comprehensive functional , and statistical , analysis of differentially regulated gene products is provided in Dataset S1 . Directly comparative analysis of ΔpacCATCC and ATCC46645 activities ( Dataset S2 ) revealed 1116 genes to be differentially expressed . Of these , 577 were up-regulated and 539 were down-regulated in the ΔpacCATCC isolate , relative to the wild type isolate , in at least one time point of the analysis . Scrutiny of the datasets revealed dysregulated expression of secreted protein gene products , defined as having predicted signal peptide motifs ( Figure S5 ) , cell wall biosynthetic enzymes ( Figure S6 ) , and gliotoxin biosynthetic genes ( Figure S7 ) during infections caused by the ΔpacCATCC isolate . A comprehensive functional , and statistical , analysis of differentially regulated gene products is provided in Dataset S2 . The differential regulation of 5 genes was independently validated by quantitative PCR ( Figure S8 ) . A . fumigatus spores adhere rapidly ( within 30 minutes ) to lung pneumocytes and become quickly internalised and killed [16]–[18] , [20] . In response to challenge with A . fumigatus conidia , host injury can be observed as cell rounding and detachment from monolayers [16]–[18] , [20] , and cytoskeletal fibres of lung pneumocytes suffer major reorganisation , an effect which can be blocked by antipain-mediated protease inhibition [8] . We found secreted factors to be the major functional cohort amongst those aberrantly regulated during ΔpacC infections ( Figure S5 , and Dataset S2 ) . To assess the role of secreted factors in epithelial disintegration we exposed A549 monolayers to A . fumigatus culture filtrates derived from young ( 16 hours ) , or mature ( 48 hours ) , wild-type or ΔpacC cultures grown in supplemented DMEM culture medium . Filtrates obtained from mature cultures of wild-type or reconstituted A . fumigatus isolates , prompted significant reductions ( ∼30–40% ) in the numbers of adherent cells after 20 hours of co-incubation with alveolar epithelia ( Figure 4A ) . In contrast , filtrates derived from mature ΔpacC cultures led to detachment of less than 10% of monolayer cells ( Figure 4A ) . Concordant with a protease-mediated basis for epithelial destruction , pre-treatment of wild-type A . fumigatus culture filtrates with the protease inhibitor antipain [8] reduced cellular detachment by up to 50% ( Figure 4A ) . To further probe protease production by wild type and mutant isolates , we used a qualitative assay based upon the clearance of gelatin from the surface of unprocessed X-ray film [39] . In agreement with our assays of epithelial degradation ( Figure 4A ) we detected gelatin-degrading activity in filtrates of mature wild type and reconstituted A . fumigatus cultures , which was absent in cultures from ΔpacC isolates ( Figure S9 ) . Together , these findings are consistent with the release of a damaging proteolytic entity by mature A . fumigatus hyphae , the production of which is dependent upon PacC-mediated signalling . Notably , epithelia challenged with ΔpacC culture supernatants were somewhat protected by pretreatment of fungal extracts with antipain ( Figure 4A ) . This might indicate the production of a protective , host-derived enzyme which is degraded or inactivated by culture filtrates of wild type A . fumigatus isolates , or the existence of a protective , host-derived enzyme whose action , in this assay , is masked by the high degree of epithelial detachment imposed by wild type isolates . Critically , our analysis of filtrates obtained from younger fungal cultures ( 16 hours ) revealed a novel finding . Regardless of the fungal strain tested , exposure to filtrates from young cultures did not impact monolayer integrity ( Figure 4B ) . This finding suggested that epithelial disintegration occurring at 16 hours of spore and EC co-incubation ( Figure 4B ) requires direct interaction between host and pathogen cells and represents a genetically regulated fungal assault upon epithelial integrity which is temporally , and mechanistically distinct from protease-mediated damage . Further , that this damaging interaction between host and pathogen cells occurs during immediate proximity between host and pathogen cells , most likely in a contact-dependent manner . To substantiate this view we reiterated the detachment analysis , this time omitting monolayer washing to analyse only host cells directly contacting the pathogen ( Figure 4C ) . A549 cells in contact with ΔpacC hyphae underwent significantly less rounding and detachment than those contacting hyphae of wild-type or reconstituted isolates ( Figure 4C ) . Taken together , these data reveal that A . fumigatus elicits host damage in a biphasic manner and , that the pH-responsive transcription factor PacC governs functions required for epithelial disintegration during both early- and late-phases of the host-pathogen interaction . Amongst the functional cohorts aberrantly regulated during ΔpacCATCC infections ( Figures S5–S7 and Dataset S2 ) cell wall biosynthesis offered a plausible mechanism for contact-dependent host damage . To analyse cell wall compositions of mutant and wild-type isolates , strains were stained with the chitin-binding agent calcofluor white ( CFW ) . Microscopic examination revealed intensified CFW-staining of ΔpacC germ tube tips relative to those of the parental isolates ( Figure 5A ) and quantitative analysis of fluorescence intensities revealed significantly higher CFW in ΔpacC germ tubes ( Figure 5B ) . In addition , electron microscopy showed a thickened cell wall in the ΔpacCATCC mutant , which was highly evident after 16 hours of growth ( Figure S10 ) . Hyphal cell wall composition ( Figures 5C , D and E ) was assessed by high-performance anion exchange chromatography with pulsed amperometric detection ( HPAEC-PAD ) . After 16 hours of growth , cell wall chitin content was found to be 20% higher in extracts from ΔpacC isolates , relative to wild-type cells ( Figure 5C ) . The quantity of cell wall glucan and mannan was measured as equivalent between mutant and wild type cells ( Figures 5D and 5E ) . To probe the contribution of cell wall components to epithelial cell detachment , we exposed A549 monolayers to cell wall extracts . This revealed that , relative to the vehicle control , the percentage of adherent cells was drastically reduced upon exposure to cell wall extracts from wild-type isolates , an effect which was not elicited by ΔpacC cell wall extracts ( Figure 5F ) . If detachment of alveolar epithelial cells occurs via a cell wall-mediated mechanism , infection with dead hyphae would be predicted also to cause cellular detachment . To test this , A549 monolayers were incubated with thimerosal-killed hyphae and cell detachment , per unit of hyphal length , was measured from unwashed monolayers . Killed hyphae from parental isolates caused damage to the epithelial monolayer independently of fungal viability , an effect which was significantly impaired in monolayers incubated with killed ΔpacC hyphae ( Figure 5G ) . In conclusion , epithelial detachment elicited early in the interaction between A . fumigatus and host cells requires contact between host and pathogen and occurs independently of fungal viability . Crucially , the inability of killed ΔpacC hyphae to injure epithelial monolayers eliminated the trivial possibility that mere collision between host and fungal cells can account for loss of monolayer integrity . The mammalian C-type lectin receptor Dectin-1 , is predominantly expressed by myeloid cells and recognizes a variety of fungal β-1 , 3-linked and β-1 , 6-linked glucans [40]–[42] . Recent transcriptional and immunohistochemical analyses have revealed Dectin-1 gene expression in bronchiolar epithelia , and alveolar type II cells ( ATIIs ) of murine lungs [43] and Han et al . , showed that A549 cells internalise germinated A . fumigatus spores in a phospholipase D-dependent manner , a process inhibited by an anti-Dectin-1 antibody [44] . Given these observations and aberrant cell wall remodelling in ( Figure 5 ) , and epithelial damage by ( Figure 4 ) , ΔpacC mutants we hypothesised non- or reduced involvement of Dectin-1 in promoting A . fumigatus ΔpacC spore recognition and internalisation . Epithelial cells have been shown to internalise and kill up to 50% of the A . fumigatus spores they come into contact with [16]–[19] , [22] . We therefore hypothesised that contact-dependent perturbations of epithelial integrity might result from internalisation of fungal spores . To assess this we first assessed the numbers of internalised wild type and ΔpacC spores using a nystatin protection assay ( Figure 6A ) . This revealed that the proportion of wild-type spores internalised by A549 cells ranges from 16 to 23% ( Figure 6A ) . However , epithelial cells were found to internalise ΔpacC mutants significantly less avidly than the respective parental isolates ( Figure 6A ) , whereby only ∼10–12% of the initial inoculum had become internalised after 4 hours of co-incubation . At the concentrations used in this assay nystatin exposure was 100% efficient in killing A . fumigatus spores ( Figure S11 ) . Given the altered cell wall morphology of ΔpacC isolates a plausible explanation for their defective internalisation might include an adhesion defect . We therefore tested the ability of the isolates to adhere to plastic surfaces and epithelial monolayers after 30 minutes of incubation . However , for neither substrate was a difference in adherence observed , either for the ΔpacC mutants or wild type isolates ( Figure S12 ) . Taken together , these data suggest that internalisation of A . fumigatus spores , which is hampered by pacC deletion , contributes to contact-dependent monolayer decay . To assess the role of Dectin-1 in promoting epithelial decay in vitro we pre-incubated epithelial cells with the monoclonal anti-Dectin-1 antibody ( Mab1859 ) prior to performing nystatin protection assays . Concordant with a role for Dectin-1 in spore internalisation , epithelial monolayers pre-treated with Mab1859 exhibited a ∼20–30% reduction in internalised spores , relative to untreated epithelia ( Figure 6B ) . As a control we used 0 . 2 µM cytochalasin D ( CD ) , an inhibitor of actin polymerization , which prevents spore internalisation into A549 cells [18] . The impact of CD treatment ( 50% reduction ) was consistently greater than that of Mab1859 , possibly indicating the contribution of additional spore-detecting PRRs driving spore internalisation and/or opsonic phagocytosis and/or incomplete Mab1859-mediated inhibition of Dectin-1 activity . To assess the impact of spore internalisation upon epithelial integrity , A549 monolayers were pre-treated for 1 hour with CD and detachment after 16 hours was evaluated using our in vitro assay . For wild type infections , cellular detachment from epithelial monolayers was significantly reduced from ∼45% to ∼25% in the presence of CD ( Figure 6C ) but pre-incubation with CD did not alter integrity of ΔpacC-challenged monolayers ( Figure 6C ) . These results suggested that actin-mediated internalisation of wild-type spores contributes to epithelial detachment during initial interactions with fungal spores . To further probe the molecular basis of epithelial decay , monolayers were preincubated with the monoclonal anti-Dectin-1 antibody ( Mab1859 ) prior to co-incubation with A . fumigatus spores . Concordant with a damaging role for Dectin-1 mediated internalisation of A . fumigatus spores and hyphae in A549 alveolar monolayers , Mab1859 pre-treatment conveyed an almost complete protection of monolayer integrity during co-incubation with wild type A . fumigatus isolates ( Figure 6C ) . To characterise the cell wall defect inhibiting Dectin-1-mediated uptake of ΔpacC we examined β-glucan content in the cell walls of wild type and ΔpacC spores and hyphae , by incubating fungal cells with a soluble chimeric Dectin-1 Fc protein [45] , [46] and quantifying immunofluorescence . This revealed highly anomolous organisation of β-glucan content in ΔpacC spores ( Figure 6D ) . Relative to wild type spores which deposit β-glucan at a highly localised , and singular focus of the spore cell wall prior to germination , β-glucan in ΔpacC spores adopts a highly diffuse distribution . This phenotype is not a consequence of slowed spore swelling as wild type and ΔpacC spores demonstrate equivalent sizes at 4 hours of culture in supplemented DMEM ( Figure 6E ) . In Histoplasma capsulatum α-1 , 3-glucan promotes virulence by blocking innate immune recognition of β-glucan by Dectin-1 . To examine α-1 , 3-glucan content in A . fumigatus spores and hyphae we used immunofluorescence microscopy and anti-α-1 , 3-glucan antibody , which has previously been used for analysis of the H . capsulatum cell wall [47] . Immunofluorescence-mediated detection of this antibody revealed similar distributions of α-glucan in wild type and ΔpacC hyphae ( Figure S13 ) . Taken together these data support an important role for internalisation of A . fumigatus spores during invasion of the pulmonary epithelium which , in a cell wall- , actin- and Dectin-1 dependent manner permits endocytosis of fungal particles contacting alveolar epithelia . The extent of epithelial protection afforded by in vitro delivery of Mab1859 ( Figures 6B and C ) was suggestive of a detrimental role for Dectin-1 engagement during A . fumigatus-epithelial interactions . To decipher between protective and exacerbatory roles for Dectin-1 in maintenance of epithelial integrity in whole animals , we assessed pulmonary damage after 24 hours of A . fumigatus infection in Dectin-1+/+ and Dectin-1−/− mice . To study epithelial activities in the absence of confounding leukocyte responses , mice were depleted of leukocytes using a cyclophosphamide and hydrocortisone protocol and lung injury was scored via histological , biochemical and immunoblot assays . In the lungs of Dectin-1+/+ and Dectin-1−/− mice , fungal lesions were equivalent in size and invasiveness ( Figure 7A ) although frequency of fungal lesions was increased in Dectin-1−/− animals ( not shown ) . Epithelial damage was surveyed via quantitation of lactate dehydrogenase ( LDH ) in BALs ( Figure 7B ) and analysis of expression of the Dectin-1-independent damage associated molecular pattern ( DAMP ) protein S100B ( Figure 7C ) , whose major source during A . fumigatus infection is epithelial cells [48] . Both assays revealed heightened epithelial damage in the lungs of Dectin-1−/− animals relative to wild type counterparts . Our results indicate that , despite a highly protective role for the anti Dectin-1 antibody Mab1859 during in vitro epithelial infections , integrity of Dectin-1 ( Figures 7A–C ) is essential for limitation of epithelial damage in vivo . As neutrophil-depletion and macrophage dysfunction were chemotherapeutically implemented in our murine model we conclude that Dectin-1 activity is essential for protecting the lung epithelium from the damage inflicted by germinating A . fumigatus spores . PacC homologues in the fungal pathogens C . albicans and Cryptococcus neoformans indirectly modulate interactions with the host interface via governance of fungal cell wall architecture . For both organisms , cell surface defects in Rim101 null mutants appear to be a critical component of altered pathogenicity . In C . albicans , oropharyngeal pathogenicity , estimated from in vitro assessment of damage in the FaDu cell line , can be partially restored to attenuated Rim101 null mutants via overexpression of the Rim101 target genes ALS3 , CHT2 , PGA7/RBT6 , SKN1 or ZRT1 [49] . In C . neoformans Rim101 null mutants , via cell wall defects , prompt aberrant inflammatory responses , resulting in mild hypervirulence [50] . Thus , in the case of both organisms , host immune responses to altered cell wall composition play a functional role in disease outcome . To test the immunostimulatory capacity of wild-type and ΔpacC spores , immunocompetent CD1 mice were infected with 106 spores and the recruitment of macrophages ( F4/80+ ) and neutrophils ( Ly-6G+ ) to the pulmonary niche was quantified . Relative to mice infected with a wild-type isolate , no significant difference in the number of recruited neutrophils was recorded for mice infected with the ΔpacC mutant ( Figure 7D ) . Further , and in stark contrast to findings in C . neoformans , we did not observe ( in immunocompetent hosts ) a hyperinflammatory response to infection with ΔpacC mutants . We therefore conclude that anomalous innate immune responses are unlikely to contribute to the altered pathogenicity of A . fumigatus ΔpacC mutants . Therefore , in stark contrast to oral and pulmonary infections , respectively with C . albicans [49] and C . neoformans [50]–[52] , modulation of host innate immunity is unlikely to contribute to A . fumigatus disease outcome . Taken together our results indicate that the predominant host-mediated mechanism promoting the non-invasive phenotype of A . fumigatus ΔpacC mutants is a failure to engage the Dectin-1 receptor . It is therefore highly likely that A . fumigatus exploits this innate immune mechanism to gain entry to the pulmonary epithelium . The important role for Dectin-1 in epithelial protection in vivo implies that the full extent of systemic Dectin-1 depletion upon epithelial defences likely extends well beyond defective internalisation of inhaled fungal spores . However , given the propensity of A . fumigatus to exploit this mode of tissue entry , it remains possible that the targeted depletion of epithelial Dectin-1 activity would afford protection against invasive , and other , A . fumigatus diseases of the lung . The fungal cell wall is a premier , pathogen-specific target for antifungal drugs . Given the significant cell wall defect observed in ΔpacC mutants we predicted altered echinocandin sensitivity relative to wild type isolates . A standard EUCAST assay [53] was used to calculate the susceptibility of isolates , revealing increased susceptibility ( Figure 8A ) of ΔpacC mutants ( minimum effective concentration , MEC , of 0 . 11 µg/ml ) compared to that of ATCC46645 ( ∼ 0 . 58 µg/ml ) and CEA10 ( ∼ 0 . 75 µg/ml ) . A . fumigatus strains grown in the presence of 16 µg/ml caspofungin displayed aberrant morphology , elevated branching and shortening of hyphae . Heightened severity of these phenotypes was observed for ΔpacC mutants which demonstrated extensive ballooning of hyphal tips ( Figure 8B ) . Given the tendency for chitin increase to promote echinocandin tolerance the heightened susceptibility of ΔpacC mutants is surprising; however , an obvious explanation for this effect would be increased porosity due to altered cell wall architecture . To test the potency of echinocandin agents against pH non-sensing mutants in vivo we examined , via viable counts , the effect of caspofungin ( 5 mg/kg ) treatment at 48 hr post-infection . Relative to animals infected with a wild type isolate , fungal burden was significantly decreased in caspofungin-treated mice infected with ΔpacCCEA10 ( Figure 8C ) . Histological analysis of lung tissues recovered from mice infected with CEA10 or ΔpacCCEA10 in the presence or absence of caspofungin confirmed this observation ( Figure 8D ) . Thus , ΔpacC mutants are hypersensitive to the cell wall-active drug caspofungin , a phenotype which extrapolates to mammalian infections ( Figures 8C and 8D ) . Given that the A . fumigatus cell wall is essential for viability , agents which selectively inhibit the pH-dependent activation of PacC signalling might provide useful adjuncts to existing antifungal therapies . Amongst an annual global caseload of 1 . 5 million fatal mycoses , more than 75% of infections are initiated by inhalation of fungal particles [1] . Despite this , our understanding of the interactions between inhaled fungal pathogens and the respiratory epithelium remains in its infancy . This study addresses disease caused by the major mould pathogen of humans , A . fumigatus , and assigns an essential role for the transcription factor PacC in epithelial invasion and pathogenicity . A critical discovery made during this study is the inability of ΔpacC mutants to invade the mammalian respiratory epithelium , a hallmark of invasive diseases caused by A . fumigatus . Our study confirms that epithelial invasion by this pathogen is a genetically-regulated trait , under PacC regulatory control , and identifies the cellular basis of the deficit to lie with at least two temporally and mechanistically distinct processes , namely protease-mediated monolayer decay and epithelial entry . Compared to our previous studies of A . nidulans pH regulation [29] , loss of A . fumigatus PacC has a milder impact on alkaline tolerance and in vivo germination . At least one mode of essential micronutrient acquisition is different between the two species . In A . nidulans , siderophore-mediated iron acquisition is PacC-dependent [54] , in A . fumigatus it is not ( Hubertus Haas personal communication ) . Certainly this could explain the differences between these two species in germination/growth rates in the mammalian lung . The A . fumigatus genome is predicted to encode more than 100 hydrolytic enzymes [55] some of which are assumed as crucial for liberation of proteinaceous nutrients from host tissues [56] , [57] . Early studies found a correlation between high elastinolytic potency of A . fumigatus isolates and pathogenicity in mice [58] but a subsequent survey of 73 isolates revealed discordant production of extracellular elastase , acid proteinase and phospholipase amongst strains causing human disease . The elastinolytic neutral metalloprotease Mep ( AFUA_8G07080 ) , secreted in A . fumigatus culture filtrates and leukopenic murine hosts [59] , is dispensable for pathogenicity . A . fumigatus culture filtrates cause epithelial desquamation and destroy F-actin cytoskeletal fibres of in vitro-cultured pneumocytes [8] , [60] . Kogan et al . , 2004 found deletion of the alp1 gene ( AFUA_4G11800 ) encoding a secreted alkaline protease , or antipain treatment of wild-type culture supernatants to be equally ablative of secreted protease activity in vitro . In addition , immunolabelling of the F-actin cytoskeletal fibres of A549 cells revealed that F-actin disruption requires Alp1 integrity . However , an A . fumigatus mutant lacking Alp1 retained full virulence in both cortisone-treated [61] and leukopenic mice [15] . Attempts to implement wholesale depletion of A . fumigatus protease expression have also failed to unearth avirulent mutants . A doubly protease-deficient mutant lacking Alp1 and Mep , which is completely deficient in collagenic proteolytic activity at neutral pH in vitro , is fully virulent in a cortisone-treated murine model [13] . Furthermore , an A . fumigatus ΔprtT mutant lacking a conserved positive regulator of secreted proteases suffers a 70% reduction in casein proteolytic activity , but is also fully virulent in leukopenic mice . These findings cast doubt upon the true relevance of protease production to A . fumigatus pathogenicity [10] . Comparison of the PacC and PrtT [9] regulons revealed 83 and 31 secreted gene products as being down-regulated by PacC and PrtT ( AFUA_4G10120 ) respectively , amongst which only 8 are commonly down-regulated ( Dataset S3 ) . Given the fully virulent phenotype of the ΔprtT , mutant we can confidently surmise that none of these gene products , acting alone or in combination with each other , can support tissue-invasive growth in the mammalian host . Thus their aberrant expression in the ΔpacC mutant is unlikely to explain its non-invasive phenotype . Concordant with this conclusion , and with the fully virulent phenotype of a Δmep;Δalp1 mutant [13] , the inclusion of genes encoding both Alp1 and Mep1 ( Dataset S3 ) amongst those down-regulated in both PacC and PrtT null mutants cannot , alone , explain the non-invasive phenotype of PacC null mutants . Loss of PacC negatively impacts a further 75 uncharacterised secreted gene products . If the tissue invasive growth of A . fumigatus is solely proteolytically mediated , we predict that important enzymatic functions will be amongst them . Investigation of this hypothesis lies beyond the scope of this study but is an ongoing component of our further study , as is the co-operative activity of fungal protease- and toxin-mediated assaults upon the mammalian pulmonary epithelium . The finding that epithelial monolayers are resistant to culture filtrates obtained from earlier ( 16 hour ) time points of fungal growth highlights the existence of at least one additional , and earlier acting , perturbation of host tissue . Soluble effectors of epithelial detachment are not immediately secreted by metabolically active A . fumigatus spores and a phased mode of A . fumigatus assault , commencing with contact-dependent perturbation , is likely responsible for monolayer perturbation . In agreement with this hypothesis , we found cytochalasin D-mediated inhibition of actin polymerisation to be partially protective of epithelial monolayer integrity , and ΔpacC spores to be far less avidly internalised than wild-type counterparts . Amongst the repertoire of invasion tactics employed by fungal pathogens at host epithelia , induced endocytosis , active penetration and participation of host factors have been implicated [52] . The results of our study implicate all three activities as having relevance to the host-A . fumigatus interaction , but with several critical differences relative to studies of other fungal pathogens . Firstly , the occurrence of induced endocytosis , as evidenced by sensitivity of the internalisation process to cytochalasin D-mediated inhibition ( Figure 6C ) , promotes epithelial disintegration by both live and dead fungal elements . This finding stands in stark contrast to epithelial invasion by C . albicans where internalisation of live germinated cells , but not killed cells , leads to host damage [62] . Second , the existence of A . fumigatus invasins remains thus far unproven . Certainly , from bioinformatics analyses , evidence for highly conserved homologues for the C . albicans invasin Ssa1 can be gleaned . However the expression of this homologue is not impacted by pacC gene deletion and A . fumigatus lacks any homologue of the Als3 invasin altogether [63] , [64] . Our finding that cell wall extracts can impact epithelial integrity , and the relevance of Dectin-1 to this process , implies the existence of an invasin-independent mode of epithelial entry for A . fumigatus . In our studies cytochalasin D imposed an incomplete block upon spore internalisation suggesting that at least a subset of fungal elements can access the internal environment of epithelial cells via an ‘active penetration’ mechanism , as recently documented for C . albicans [65] . These important differences in the way in which different fungal pathogens interact with physiologically distinct epithelia highlight the current paucity of information on fungal interactions with pulmonary epithelia and , given that the vast majority of invasive mycoses are initiated via inhalation of fungal particles [1] , should prompt renewed scrutiny of fungal interactions with mammalian lung tissues . The mechanism by which epithelial cells recognise and internalise A . fumigatus conidia remains poorly characterised , as does the relevance of such activity to disease outcome . Our study demonstrates that uptake of A . fumigatus spores , by type II pneumocytes , is dependent upon a ) actin polymerisation b ) fungal cell wall/surface composition c ) integrity of PacC and d ) the β-1 , 3-glucan receptor Dectin-1 , and moreover , that such interactions can negatively impact epithelial integrity . Dectin-1 expression in non-myeloid cells is increasingly frequently reported and has demonstrated relevance in β-1 , 3-glucan- ( curdlan ) exposed bronchiolar and alveolar type II cells [43] , poly IC challenge of human bronchial epithelial cells [66] , Mycobacterium ulcerans infection of epidermal keratinocytes and Mycobacterium tuberculosis infection of A549 epithelia [67] , [68] . The A549 cell surface constitutively expresses Dectin-1 , regardless of infection by A . fumigatus [44] . In humans , a Y238X Dectin-1 polymorphism is a risk factor for invasive aspergillosis in haematopoietic stem cell recipients . Cunha et al . ( 2010 ) found , in experimental HSCT , that transplant of stem cells from Dectin-1- donors to wild-type recipients resulted in lessened susceptibility to invasive aspergillosis . Thus by restricting the Dectin-1 deficiency to cells of myeloid origin , susceptibility to invasive aspergillosis was not altered . We found Dectin-1 deficiency , in the absence of leukocytes , to heighten epithelial damage in the whole animal host relative to leukopenic wild-type animals . This observation and several of our findings support a curative role for Dectin-1 mediated internalisation of A . fumigatus spores . According to Han et al . , 2011 , internalisation of A . fumigatus by A549 epithelial cells can be correlated with membrane phosphatidylcholine cleavage , a process which is closely linked to alteration of cytoskeletal actin dynamics , and prompted by exposure to β-1 , 3-glucan . This finding is consistent with our observation that cell wall extracts and killed A . fumigatus hyphae can perturb epithelial integrity . Our data , and those of Chaudhary et al . , 2012 , who found that bronchial ECs from cystic fibrosis sufferers demonstrate impaired uptake and killing of conidia , are highly suggestive of a curative role for EC activities during exposure to A . fumigatus spores . It does , however , remain feasible that the pulmonary epithelium provides a reservoir for A . fumigatus spores , and the attenuated phenotype of the ΔpacC mutant is consistent with such a theory . It also remains feasible that contact-dependent perturbation of epithelia facilitates subsequent protease-mediated damage by exposing subepithelial structures and facilitating fungal adhesion . Studies of C . albicans have revealed several means of fungal entry into epithelial cells , including self-induced endocytosis , and protease-mediated decay , the latter impacting , via Rim101-mediated E-cadherin degradation , the disintegration of oral epithelia [69] . It is most likely that multiple mechanisms contribute also to the pathogenicity of A . fumigatus . What is unique about PacC in A . fumigatus , is the critical role played by PacC-dependent factors in all of these processes and the profound requirement for PacC to orchestrate epithelial entry , protease-mediated epithelial decay , invasive growth and pathogenicity . These findings not only identify PacC as a critical master regulator of pathogenicity determinants in A . fumigatus , but also heighten its relevance as an antifungal target . A . fumigatus strains used in this study are listed in Table S1 . A . fumigatus strains were cultured at 37°C in Aspergillus Minimal Media ( AMM ) or Aspergillus Complete Media ( ACM ) [70] . For preparation of A . fumigatus culture supernatants , 106 spores/ml were grown in Dulbecco's Modified Eagle Medium ( DMEM , Sigma ) supplemented with 10% foetal bovine serum ( FBS , Sigma ) and 10% penicillin and streptomycin ( Sigma ) at 37°C , 5% CO2 for either 16 , 48 , 60 or 72 hr . Supernatants were doubly filtrated through Miracloth and centrifuged for 10 min at 4000 rpm to remove any hyphal fragments . To inhibit protease activity in culture supernatants , filtrates were treated with the serine and cysteine protease inhibitor antipain ( 10 µg/ml ) for 1 hr . For preparation of A . fumigatus cell wall extracts , see Text S1 . For analyses of cell wall composition , A . fumigatus strains were harvested from an ACM plate ( 0 hr ) as previously described or from 105 spores/ml cultures grown in AMM broth for 4 , 8 and 16 hr at 37°C with shaking at 200 rpm . For in vitro challenge of epithelial monolayers with killed hyphae 104 spores/ml were incubated in supplemented DMEM at 37°C , 5% CO2 for 18 hr ( parental isolates and reconstituted strains ) or 36 hr ( ΔpacC mutants ) . A . fumigatus hyphae were killed by incubating them in PBS supplemented with 0 . 02% thimerosal ( Sigma ) overnight at 4°C [71] , a treatment which preserves cellular integrity . Before incubation with monolayers , killed hyphae were washed twice in PBS . Killing was verified by plating a 1∶100 dilution of killed hyphae in ACM plates . ΔpacC mutants in the genetic backgrounds CEA10 ( ΔpacCCEA10 ) and ATCC46645 ( ΔpacCATCC ) were constructed by gene replacement ( Figure S1A ) using a split-marker strategy [72] . For details , see Text S1 . Initial phenotypic analyses , including survival analyses were performed using both ΔpacCATCC and ΔpacCCEA10 mutants , while subsequent analyses of gene expression were limited to the ΔpacCATCC mutant . For analyses of ΔpacC fungal burden in mice we opted to use the lesser attenuated ΔpacCCEA10 to prolong fungal occupancy of the murine lung . Dilutions of 103 conidia were inoculated onto ACM or supplemented AMM ( as shown on Table S3 ) and incubated for 48 hr and 72 hr respectively . Images were captured using a Nikon Coolpix 990 digital camera . Spores were inoculated at a density of 1×106 to 2×106 spores/ml , in 100 ml of liquid ACM , and grown for 16 hr at 37°C . Next , media was buffered to pH 5 . 0 with 100 mM glycolic acid pH 5 . 0 or to pH 8 . 0 with 100 mM Tris-HCl pH 8 . 0 , pH shifts were performed for 1 hour . 10 mg of protein was extracted from washed mycelia as described previously [73] . Protein concentrations were determined using the Bradford assay [74] . The ipnA2 probe was synthesised and labelled as described previously [27] , [73] . Densitometry data were obtained by measuring pixel intensity/mm2 for the relevant bands using a Phosphorimager FLA-3000 ( FujiFilm ) and Multi-Gauge V3 . 0 software . Murine infections were performed under UK Home Office Project Licence PPL/70/6487 in dedicated facilities at Imperial College London . For all experiments A . fumigatus spores were harvested and prepared as previously described [75] and viable counts from administered inocula were determined , following serial dilution , by growth for 24–48 hr on ACM . Mice were housed in individually vented cages and anaesthetized by halothane inhalation and infected by intranasal instillation of spore suspensions . Mice were rendered leukopenic by administration of cyclophosphamide ( 150 mg/kg , intraperitoneal ) on days −3 , −1 , +2 and every subsequent third day , and a single subcutaneous dose of hydrocortisone acetate ( 112 . 5 mg/kg ) administered on day −1 . Survival analysis . Leukopenic male CD1 mice ( 18–22 g ) were infected by intranasal instillation of 5 . 0 × 104 or 6 . 0 × 105 conidia in 40 µl of saline solution . Mice were weighed every 24 hr from day of infection and visual inspection made twice daily . In the majority of cases the end-point for survival experimentation was a 20% reduction in body weight measured from day of infection , at which point mice were sacrificed . Histological and transcriptomic analyses . Leukopenic male CD1 mice ( n = 8 ) were infected with 108 conidia in 40 µl of saline solution . At the relevant time-points post-infection , mice were sacrificed and lungs were partitioned , using surgical sutures , into lobes destined for transcriptomic or histological analysis . Bronchoalveolar lavages ( BALs ) were performed using three 0 . 5 ml aliquots of pre-warmed sterile saline . BALs were snap frozen immediately following harvesting using liquid nitrogen . Lobes for histological sectioning were removed and immediately fixed in 4% ( v/v ) formaldehyde ( Sigma ) . Lungs were embedded in paraffin prior to sectioning and stained with haematoxylin and eosin or light green and Grocott's Methenamine Silver . Images were taken using a Reichert-Jung ( Slough , UK ) Polyvar microscope using brightfield illumination at 40X magnification . Fungal burden analyses . Immunosuppressed male CD1 mice ( n = 5 ) were infected with 3 . 75 × 105 spores . Mice were culled and whole lungs were collected after 24 and 48 hr of infection . BAL samples were centrifuged at 14000 rpm for 5 min and the pellet was washed with 500 µl ice cold H2O to lyse host cells . Seven BALs were pooled , resuspended in 450 µl ME-RLC buffer ( QIAGEN ) and ground in liquid nitrogen with a pestle and mortar . RNA was then extracted using RNeasy Kit ( QIAGEN ) . A reference RNA sample was extracted from A . fumigatus ATCC46645 conidia harvested from an ACM plate . Conidia were washed thoroughly with sterile water , quickly frozen in liquid nitrogen , and disrupted by grinding . Total RNA was extracted using RNeasy Kit ( QIAGEN ) . The quality of RNA used for microarray analysis was checked using a Nanodrop ND-1000 Spectrophotometer ( Nanodrop , Wilmington , USA ) . Only RNA with an A260/280 and an A260/230 ratio> 1 . 9 was used for the experiments . Labelled cDNA samples were synthesised as described previously [30] . Protocols for direct labelling and hybridisation of cDNA probes can be found on the JCVI website ( http://pfgrc . jcvi . org/index . php/microarray/protocols . html ) . The A . fumigatus oligonucleotide slides version 3 was used for microarray hybridization ( http://pfgrc . jcvi . org/index . php/microarray/array_description/aspergillus_fumigatus/version3 . html ) . The phase- or strain-specific comparative analysis of gene expression datasets was conducted in Genespring GX 11 . 02 ( Agilent ) . Normalised log2 expression ratios were filtered on expression level and differentially regulated transcripts were defined as having log2 ( Cy5/Cy3 ) greater than the arbitrary thresholds of ± 1 . 5 . Raw data have been deposited in the Gene Expression Omnibus ( GEO ) ( http://www . ncbi . nlm . nih . gov/geo/ ) under accession number GSE54810 . Functional analysis of differentially-expressed gene cohorts was implemented by DAVID ( http://david . abcc . ncifcrf . gov/ ) [77] , [78] . Microarray data was validated by qPCR as described in Text S1 . Human pulmonary carcinoma epithelial cell line A549 ( American type culture collection , CCL-185 ) was used throughout this study . For all experiments , cells were maintained at 37°C , 5% CO2 in supplemented DMEM . Epithelial cells were used after the second or third passage . For all experiments , 105 A549 cells were seeded in 6-well tissue culture plates and incubated to ≥ 90% confluence . Monolayers were challenged with 105 spores/ml , 200 µl of supernatant or 200 µl of cell wall extract . Following co-incubation with A . fumigatus spores , cell wall extracts or supernatants , monolayers were washed 3 times with PBS and adherent A549 cells were counted in 3 fields of view at magnifications of 20 or 40 ( Nikon Eclipse TS100 ) . Washing was omitted for analyses of contact-dependent damage . Damage to A549 epithelial cells by the various strains of A . fumigatus was determined using a previously described method at 16 , 20 and 24 hr of co-incubation [35] . The 51Cr content of the medium and lysates was measured and the degree of epithelial cell damage was calculated and corrected for spontaneous chromium release by uninfected epithelial cells . To analyse the localisation of α-1 , 3-glucan or β-1 , 3-glucan on A . fumigatus cell walls , isolates ( 105 or 104 spores/ml ) were grown in 8-well slide culture chambers ( Nalge Nunc International , Rochester , NY ) in supplemented DMEM for 4 , 8 , 12 or 16 hr . α-1 , 3-glucan was visualized using 0 . 1 mg/ml mouse IgMγ MOPC-104E ( Sigma , in PBS buffer ) as primary antibody and 0 . 1 mg/ml Alexa Fluor 488 goat anti-mouse IgM ( μ chain ) antibody ( Life technologies , in PBS buffer ) [47] , [82] , [83] . β-1 , 3-glucan was visualized 5 µg/ml Fc-dectin-1 fusion ( kind gift from Dr G . D . Brown , University of Aberdeen ) coupled with 15 µg/ml goat anti-human IgG ( H+L ) Fluorescein conjugated antibody [45] , [46] . Briefly , samples were incubated with primary antibodies for 30 minutes , before incubation with the secondary antibodies for 30 minutes in the dark . To visualise epithelial monolayers co-incubated with A . fumigatus strains , A549 cells were seeded in 2-well slide culture chambers ( Nalge Nunc International , Rochester , NY ) in supplemented DMEM . At 90% confluence , epithelial monolayers were incubated with A . fumigatus isolates ( 105 spores/ml ) for 16 hr . After washing the monolayers three times with PBS , samples were incubated in supplemented DMEM with 10 µg/ml FITC-labelled concanavalin A ( Molecular Probes ) and 0 . 4 mg/ml calcofluor white ( Sigma ) , to visualize respectively epithelial cells and hyphae . Labelling was performed for 30 minutes at 37°C , 5% CO2 . After rinsing with PBS , samples were imaged using a Nikon Eclipse TE2000E microscope with DIC optics , a 20× plan fluor objective or 60× ( 1 . 3 NA ) plan fluor objective , and equipped with an ORCA-ER CCD camera ( Hamamatsu , Welwyn Garden City , UK ) driven by the MetaMorph NX1 . 1 software for image acquisition . For Alexa Fluor 488 , FITC and fluorescein , a Nikon B-2A filter cube ( excitation filter 470/20 nm BP , dichroic mirror 500 nm LP , emission filter 515 nm LP ) was used . For calcofluor white , a Nikon UV-2A filter cube ( excitation filter 355/15 nm BP , dichroic mirror 400 nm LP , emission filter 420 nm LP ) was used . Images were processed and analysed using the software Image J version 1 . 47 . Adhesion was tested using a modification of the protocol in Gravelat et al . , 2012 [84] as described in Text S1 . The release of lactate dehydrogenase ( LDH ) was assessed in BALs using the Cytox 96 Non-Radioactive Cytotoxicity Assay kit ( Promega ) according to manufacturer's instructions . BAL samples were assessed in triplicate and averaged values were normalised to the total amount of protein as measured in triplicate using a bicinchoninic acid assay ( BCA ) assay ( Sigma ) according to manufacturer's instructions . Lungs were homogenised in 1 ml of PBS ( pH 7 . 4 ) containing protease inhibitor cocktail ( Roche ) and protein concentration was measured by BCA , using a BSA as standard ( Sigma ) . 9 µg of protein was analysed by western blotting [85] . A 1∶1000 dilution of a α-S100B antibody ( Abcam ) was used , in parallel with an α-actin antibody ( Cell Signaling ) for normalisation of loading . BALs were collected using 3 ml of PBS and a further 5 ml of PBS were added at the time of preparation of the samples for FACS analysis . Cell pellets were resuspended in 1 ml red blood lysis buffer ( Sigma ) . Blocking of the Fc receptor to remove unspecific signal was achieved by incubating the samples with 0 . 5 µg of an anti-Mouse CD16/CD32 antibody ( E-bioscience ) in 100 µl of 0 . 1% BSA PBS . 14 µl of antibody mix was added for labelling of macrophages ( α-F4/80-APC-Cy7 , 5 µl , Biolegend ) , leukocytes ( α-CD45-PE , 2 µl , E-bioscience ) and neutrophils ( α-Ly-6G-BV421 , 2 µl , Biolegend ) . Samples was analysed using a BD Fortessa cell analyser . Data acquisition and analysis were performed using respectively the software Diva and FlowJo . For each sample ( n = 4 , plus 2 controls ) , cell population size for macrophages ( F4/80+ ) and neutrophils ( Ly-6G+ ) were expressed as cells/ml . In vitro susceptibility testing of A . fumigatus strains was performed according to the European Committee for Antimicrobial Susceptibility testing ( EUCAST ) standard method [53] . Caspofungin was tested on A . fumigatus strains in biological and technical triplicate . 1 . 25 × 105 strains were grown with RPMI1640 , 0 . 165 mol/L MOPS , pH 7 . 0 and incubated at 37°C for 48 hr . The final concentration of caspofungin tested ranged from 0 . 03 to 16 ug/ml . GraphPad Prism was used to interpret data and p values were calculated through Log Rank analysis ( for comparative survival ) , unpaired t tests or 1-way ANOVA tests as indicated . Error bars show the Standard Error of the Mean ( SEM ) . ***p<0 . 001 , 0 . 001 <**p<0 . 01 , and 0 . 01 <*p<0 . 05 Figure S1 shows the strategies for construction and validation of A . fumigatus ΔpacC mutants . Figure S2 shows growth of A . fumigatus isolates on laboratory culture media , as images and as radial growth rates normalised to pH 6 . 5 . Figure S3 shows pH-dependency of PacC processing , as measured using EMSA analyses . Figure S4 shows the experimental set-up and outputs of in-host transcriptomic analysis of A . fumigatus wild-type and ΔpacC activities . Figure S5 shows a heat map of differentially expressed A . fumigatus gene products having predicted signal peptides . Figure S6 shows a heat map of differentially expressed A . fumigatus gene products having putative or demonstrated roles in cell wall biosynthesis . Fig . S7 shows a heat map of differentially expressed A . fumigatus gene products involved in gliotoxin biosynthesis ( AFUA_6G09570-AFUA_6G09740 ) . Figure S8 shows qPCR validation of microarray data . Figure S9 shows analysis of A . fumigatus protease activity using a qualitative gelatine degradation assay . Figure S10 shows the electron microscopy of A . fumigatus ΔpacCATCC mutant and the respective parental isolate at 0 , 4 , 8 and 16 hr of growth . Figure S11 shows equivalent nystatin-mediated killing of wild type and mutant isolates used in this study . Figure S12 shows equivalent adhesion of mutant and wild types isolates to plastic , and to epithelia in vitro . Figure S13 shows immunofluorescence analysis of α-glucan distribution in A . fumigatus germlings . Table S1 and S2 list the A . fumigatus strains and oligonucleotides used in this study respectively . Table S3 lists the A . fumigatus phenotypic testing conditions tested . Dataset S1 shows the temporal analysis of A . fumigatus gene expression during intiation of murine pulmonary aspergillosis . Dataset S2 shows the genes differentially regulated , relative to wild type , in a host-infecting A . fumigatus ΔpacCATCC mutant . Dataset S3 shows the expression of genes encoding secreted gene products which are regulated by either PrtT [9] or PacC , or both transcription factors . Text S1 contains the supplementary material and methods . The genes and gene products ( with accession numbers at http://www . cadre-genomes . org . uk/ ) studied in this work is pacC ( AFUA_3G11970 ) . Also mentioned in the text are prtT ( AFUA_4G10120 ) , mep ( AFUA_8G07080 ) , alp1 ( AFUA_4G11800 ) , β-tubulin ( AFUA_7G00250 ) and the gliotoxin cluster ( AFUA_6G09570-AFUA_6G09740 ) .
Inhaled spores of the pathogenic mould Aspergillus fumigatus cause fungal lung infections in humans having immune defects . A . fumigatus spores germinate within the immunocompromised lung , producing invasively growing , elongated cells called hyphae . Hyphae degrade the surrounding pulmonary tissue , a process thought to be caused by secreted fungal enzymes; however , A . fumigatus mutants lacking one or more protease activities retain fully invasive phenotypes in mouse models of disease . Here we report the first discovery of a non-invasive A . fumigatus mutant , which lacks a pH-responsive transcription factor PacC . Using global transcriptional profiling of wild type and mutant isolates , and in vitro pulmonary invasion assays , we established that loss of PacC leads to a compound non-invasive phenotype characterised by deficits in both contact-mediated epithelial entry and protease expression . Consistent with an important role for epithelial entry in promoting invasive disease in mammalian tissues , PacC mutants remain surface-localised on mammalian epithelia , both in vitro and in vivo . Our study sets a new precedent for involvement of both host and pathogen activities in promoting epithelial invasion by A . fumigatus and supports a model wherein fungal protease activity acting subsequently to , or in parallel with , host-mediated epithelial entry provides the mechanistic basis for tissue invasion .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "animal", "models", "of", "infection", "mycology", "aspergillus", "fumigatus", "medical", "microbiology", "microbial", "pathogens", "biology", "and", "life", "sciences", "immunology", "microbiology", "fungal", "pathogens" ]
2014
The pH-Responsive PacC Transcription Factor of Aspergillus fumigatus Governs Epithelial Entry and Tissue Invasion during Pulmonary Aspergillosis
The role of the basement membrane is vital in maintaining the integrity and structure of an epithelial layer , acting as both a mechanical support and forming the physical interface between epithelial cells and the surrounding connective tissue . The function of this membrane is explored here in the context of the epithelial monolayer that lines the colonic crypt , test-tube shaped invaginations that punctuate the lining of the intestine and coordinate a regular turnover of cells to replenish the epithelial layer every few days . To investigate the consequence of genetic mutations that perturb the system dynamics and can lead to colorectal cancer , it must be possible to track the emerging tissue level changes that arise in the crypt . To that end , a theoretical crypt model with a realistic , deformable geometry is required . A new discrete crypt model is presented , which focuses on the interaction between cell- and tissue-level behaviour , while incorporating key subcellular components . The model contains a novel description of the role of the surrounding tissue and musculature , based upon experimental observations of the tissue structure of the crypt , which are also reported . A two-dimensional ( 2D ) cross-sectional geometry is considered , and the shape of the crypt is allowed to evolve and deform . Simulation results reveal how the shape of the crypt may contribute mechanically to the asymmetric division events typically associated with the stem cells at the base . The model predicts that epithelial cell migration may arise due to feedback between cell loss at the crypt collar and density-dependent cell division , an hypothesis which can be investigated in a wet lab . This work forms the basis for investigation of the deformation of the crypt structure that can occur due to proliferation of cells exhibiting mutant phenotypes , experiments that would not be possible in vivo or in vitro . Colorectal cancer ( CRC ) is one of the leading causes of cancer-related death worldwide , demanding a response from scientists and clinicians to understand its aetiology and develop effective treatment . CRC is thought to originate via genetic alterations that cause disruption to the cellular dynamics of the crypts of Lieberkühn , test-tube shaped glands located in the small and large intestine , which are lined with a monolayer of epithelial cells ( see Fig . 1 ) . A delicate balance of cell division , migration and death is coordinated in the crypts to renew the epithelial layer every few days [1] , [2] . The regular upward migration and removal of cells from the crypt provides a frontline defense mechanism against potential damage from mutated cells , which are prevented from remaining in the crypt long enough to do significant damage . However , if cells accumulate genetic mutations that alter migration velocity or provide resistance to apoptosis cues , then such cells acquire the ability to persist and multiply in the crypts . This alone can increase stress on the walls of the crypts , but the problem will be aggravated if such cells acquire additional mutations that increase proliferation , or alter cell-cell adhesion . In turn , the increased stress can cause the walls of the crypt to buckle . Dysplastic crypts allow the formation of a benign adenoma if mutated cells do not leave the crypt as they should , but rather persist and proliferate in a localised area . Over time and via accumulated mutations , these growths can progress to a malignant lesion that can break through to the underlying tissue stroma , and so aid metastasis . The dynamic cell properties that are required to initiate crypt buckling are poorly understood , as it is difficult for biologists to observe experimentally , either in vivo or in vitro , the initial changes in this sequence of events . For example , the organoids grown in culture by Sato et al . [3] , while recapitulating the crypt geometry , have not yet been compared in detail with crypts in situ . The organoids lack some of the forces that are present in vivo , and the cells themselves do not migrate . However , performing in silico experiments using a computational model of the crypt in situ could highlight the conditions required for buckling to occur , and so provide crucial insight into the tissue-level effects of genetic mutations that lead to CRC . To achieve reliable predictions of the breakdown of the crypt structure that occurs at the onset of carcinogenesis , such a theoretical model of the crypt must link processes occurring at the subcellular , cellular and tissue levels . The model must also take into account the tissue structure and geometry . While a fully comprehensive model is not yet realised , this work concerns a key step in the development of a predictive , computational model of the crypt which defines structural components in accordance with the tissue architecture that is observed experimentally , and reported here . These elements are incorporated into a crypt model which also addresses the coordination of cell division , polarity , differentiation and apoptosis . As depicted in Fig . 1 , individual crypts are closely packed , surrounded and separated by connective tissue . Each crypt is lined with an epithelial monolayer that consists of contiguous cells separated from the connective tissue and musculature by the basement membrane , the primary contact site for epithelial cells to the extracellular matrix . Below the basement membrane are myofibroblasts that provide chemical and mechanical factors for normal crypt structure . There is an established proliferative hierarchy of cells within the epithelial layer: stem cells reside at the base and divide to produce transit amplifying cells , which migrate up the crypt and perform several symmetric divisions before terminally differentiating . The polarised epithelial cells are oriented with the apical membrane facing the crypt lumen and , during symmetric division , mitotic spindles align parallel to the tissue layer [4] , [5] . Consequently a cell places its daughter cell next to it within the plane and the monolayer is maintained throughout growth . Asymmetric division occurs as a consequence of the perpendicular alignment of the mitotic spindle . Differentiated epithelial cells , upon having reached the crypt collar , undergo apoptosis and/or are shed into the lumen [6] , [7] . This process permits the renewal of the epithelial layer every few days . In addition to this , a form of programmed cell death , anoikis , is triggered when there is inadequate adhesion of the epithelial cells to the extracellular matrix [8] , with detachment inducing apoptosis [9] . Functioning correctly , this maintains tissue homeostasis by restricting proliferation to the monolayer , thereby averting dysplasia , and by preventing cells from reattaching in another location and resuming growth . The Wingless/Int ( Wnt ) signalling pathway is involved in the control of cell proliferation , migration , differentiation and adhesion in the crypts [10] , [11] . The Wnt signalling pathway is required to maintain the stem cell compartment in the crypt , and so is crucial to stem cell renewal and differentiation [12] . Moreover , it has been observed that there is a spatial gradient of extracellular Wnt signalling factors along the vertical crypt axis , which suggests a localised source of diffusible Wnt factors in the stroma that surrounds the crypt base , and leads to the hypothesis that a Wnt gradient may be responsible for the observed proliferative hierarchy [13] . As described in Van Leeuwen et al . [10] , cells in the presence of high concentrations of Wnt cycle for longer than those exposed to low Wnt and hence cells at the base of the crypt are expected to remain proliferative . A number of mathematical models exist that aim to describe specific aspects of crypt behaviour , from Wnt dependent ordinary differential equation ( ODE ) cell cycle models that govern mitosis of individual cells [14] , [15] , to cellular automata and lattice-free mechanical models of cell proliferation and migration [16]–[19] . These ideas have been combined in a multiscale model that has been used to investigate clonal expansion and the disruption of crypt homeostasis that forms the first step in colorectal carcinogenesis [15] . However , these models restrict the domain of investigation by prescribing a rigid , cylindrical geometry to the crypt , and are limited by simplifying the tissue structure without considering the basement membrane and surrounding stroma . This prevents such models from realistically examining the tissue-level effects of abnormal cell behaviour . There also exist models that seek to describe crypt buckling . Edwards and Chapman ( 2007 ) [20] present a continuum representation of the crypt , modelled as a growing beam , while Drasdo and Loeffler ( 2001 ) [21] apply an off-lattice overlapping spheres model to describe a two-dimensional ( 2D ) chain of deformable circles such as occurs during blastula formation , and then restrict this to a U-shape for modelling the crypt . These models commonly assign a bending stiffness to the layer , and predict that buckling will occur if growth by cell division is not adequately matched by this force . Edwards and Chapman generalise cell division events and so do not implement a specific cell cycle model to govern mitosis , though possible in this framework , and none of these examples take into account the deformation of the surrounding tissue stroma . More recently , Nelson et al . ( 2010 ) [22] extended the continuum model due to Edwards and Chapman [20] to investigate how growth of an epithelial monolayer constrained to a flexible substrate can recapitulate the geometry of the crypt , and Hannezo et al . ( 2011 ) [23] present a model of the intestinal crypt-villus architecture arising from a buckling instability in a proliferating epithelial monolayer lying on an elastic substrate . A three-dimensional ( 3D ) agent-based crypt model was proposed by Buske et al . ( 2011 ) [24] , which defines lineage specification and differentiation according to threshold-dependent rules that correspond to the effects of Wnt- and Notch- signalling . This model addresses the pedigree concept of cell stemness , and reproduces the spatio-temporal organisation experimentally observed in the crypt without assuming an explicit stem cell population . For this purpose , the authors model the basement membrane as a fiber network with a defined local radius for each cell position , which thereby defines a fixed crypt geometry . Consequently , it is not possible to follow any deformation of the crypt structure , and the authors do not include more sophisticated subcellular pathways that determine cell division or fate . Also relevant to the work presented here are those cell-based models which consider , for example , generic epithelial monolayers . In particular , Galle et al . ( 2005 ) [25] present a 3D overlapping spheres model to examine growth regulation in epithelial layers , where deformation of the cells is calculated using the Hertz force law . This model considers the role of anoikis and density-dependent inhibition of cell division , and how failure of the former can be prevented from corrupting the monolayer if contact-mediated growth inhibition is applied and there is sufficiently strong cell-substrate anchorage . Schaller and Meyer-Hermann ( 2005 ) [26] propose a 3D model to investigate the growth of tumour spheroids , and while cell shapes are again defined as deformable spheres , the neighbour interactions are instead determined by a weighted Delaunay triangulation between cell centres . The dual Voronoi tessellation is applied to provide a more realistic definition of the contact surface between neighbouring cells , which is subsequently used throughout the calculations instead of the sphere contact surface . Drasdo et al . ( 2007 ) [27] also consider the growth of monolayers on a substrate and multi-cellular spheroids , and revisit single-layered tissues such as the blastula during development ( considered in [21] ) to examine the mechanical influence of contact inhibition on the growth of the cell population . Such examples demonstrate the usefulness of individual-based models to investigate the growth dynamics of epithelial cell populations . Dunn et al . ( 2011 ) [28] define a discrete off-lattice cell centre multiscale model that focuses on the role of the basement membrane beneath a growing epithelial monolayer in a simplified 2D geometry: a single layer of proliferating epithelial cells constrained to lie on a rectangular bed of stromal cells , which approximate the connective tissue . Spatial connectivity is determined by a Delaunay triangulation of cell centres , and interactive forces are modelled as springs that act along the edges of this triangulation . An additional force is applied to model the role of the basement membrane , which acts in proportion to the local curvature of the epithelial layer , and to maintain a zero spontaneous curvature . Results from this simple geometry show that a large enough basement membrane force successfully maintains a stable , flat monolayer throughout successive division events , and that increasing the strength of this force favours horizontal migration along the layer , reducing the incidence of epithelial cell detachment from the layer ( whereupon cells are removed by anoikis ) . This work presents the foundation of a realistic representation of epithelial cell growth and migration in a deformable environment , and is extended here to model a specific case in a realistic 2D geometry – the cross section of the crypt . Given the coupling that exists between events at the genetic level and the tissue level , it is necessary to extend the scope of theoretical modelling to address both the role of the crypt geometry and subcellular events . In addition , such a multiscale model should include a mechanical description of migration , cell-cell and cell-matrix adhesion; in so doing , the model can more fully describe all of the processes inherent in crypt dynamics and homeostasis . The remainder of this paper is composed as follows . Firstly , experimental results are discussed that examine the tissue structure of the crypt . These results identify the composition of the connective tissue and surrounding musculature , and how the components relate to crypt shape and function . These findings are incorporated into a new crypt model which assumes the basement membrane force proposed by Dunn et al . ( 2011 ) [28] , and investigations are conducted firstly using a simple rectangular geometry , to determine appropriate parameter balances and investigate the migration of epithelial cells out from the crypt base region . Conclusions from this modelling step inform parameter choices for a complete 2D cross-sectional geometry which is subsequently defined , and the behaviour of the extended model is demonstrated . The results and future work are discussed , where the advantages as well as the restrictions of the model are highlighted , and experiments to investigate model hypotheses are suggested . The direction for future work is outlined , centred on an extension of the 2D cross-sectional model to a realistic 3D geometry . Immediately beneath crypts lies a thin layer of smooth muscle , known as the muscularis mucosae ( MM ) that forms the boundary between the mucosa and submucosa , as shown in Fig . 2 . In the small intestine of the mouse , the MM is one or two cells thick and forms a network that follows the contours of the crypt bases ( Figs . 1 and 2 ) . By examining intact mouse tissue in three dimensions , we found that , contrary to the reported structure of human gut tissue , the smooth muscle cells of the small intestinal MM are oriented mostly parallel to the longitudinal muscle layer of the muscularis externa ( ME ) ( Fig . 2 ( A ) , ( B ) , ( C ) ) . In the small intestine , the smooth muscle fibres of the MM extend up into the villi . It is thought that the role of the MM is to constantly agitate the epithelium gently to help expel secretions from crypts and enhance contact between epithelium and luminal contents [29] . When viewed in transverse section ( Fig . 2 ( A ) ) , the MM appears to follow closely the outline of the base of each crypt . When viewed in longitudinal section ( Fig . 2 ( B ) ) the MM appears to form individual baskets beneath each crypt , analogous to an eggbox that contains each crypt base as a single egg . The MM of the colon is composed of two distinct layers of smooth muscle fibres , the outer orientated parallel with the longitudinal ME , the inner layer more disorganized , but generally oriented parallel with the circular ME ( Fig . 2 ( D ) , ( E ) ) . Other components of the mucosa are a laminin-rich basement membrane that is directly attached to the basal surface of gut epithelial cells ( Fig . 2 ( A ) ) and , just below , surrounding each crypt , a pericryptal fibroblast sheath ( PCFS ) , comprising a highly organized system of fibroblasts , collagen and mucopolysaccharide ground substance [30] . There are 38 PCFS cells per mouse small intestinal crypt and 124 per colonic crypt [31] . PCFS cells produce signaling factors involved in the growth and maintenance of the crypt . Beneath the MM lies the submucosa ( SM ) , which consists of loose connective tissue rich in collagen and elastic fibres . Embedded in this material are larger blood vessels , lymphatics and nerves . The SM is enclosed by the muscular wall of the gut , called the muscularis . It consists of outer longitudinal and inner circular layers of smooth muscle . The muscularis is responsible for peristalsis , the contractile movements involved in advancing intestinal contents . A discrete off-lattice cell centre model is defined , in which spatial connectivity is determined by a Delaunay triangulation of cell centres , and cell shapes are prescribed by the Voronoi tessellation of these centres . Interactive forces are modelled as springs that act along the edges of this triangulation , as described in the Materials and Methods section . Individual model components are now summarised . All parameters are given in Table 1 . Firstly , in silico experiments were run to demonstrate the effect of increasing the spontaneous curvature in the central region , , and the strength of the basement membrane force as governed by the parameter , which characterises the strength of adhesion of the epithelial layer to the basement membrane and the stiffness of the membrane itself . Figs . 4 ( a ) and ( b ) illustrate the change in behaviour of the monolayer by plotting the -coordinates of all epithelial cells at the final timestep for typical simulations . In ( a ) , the arrows on these plots indicate the direction of increasing , while in ( b ) the arrows indicate increasing . Also marked are the boundaries between the non-zero and zero target curvature regions . As these plots are generated from typical simulations , the curves are not symmetric due to recent division events . Simulations reveal that as the spontaneous curvature increases , the epithelial monolayer is pushed further down into the tissue stroma as the central portion of the monolayer bends , behaviour that is demonstrated clearly in Fig . 4 ( a ) , where . It is also observed that increasing decreases the radius of the circle that can be extrapolated from the arc length of the layer – this is as expected . Simulation snapshots are shown in Fig . 5 , taken after 60 hours , to illustrate the difference in deformation of the layer for and . As the basement membrane force increases , a stronger force acts on the outer edges to maintain a zero curvature , preventing these regions from bending to compensate the deformation of the region of non-zero curvature . This is emphasised in Fig . 4 ( b ) , which fixes . This plot shows that as increases , the outer edges flatten and are pushed further down into the stroma . Accordingly , there is less distinction with the crypt base region , and the central portion of monolayer is not pushed down as much . Fig . 4 ( c ) plots the total number of epithelial cells in the layer at the final timestep for and increasing , averaged over fifty simulations . This reveals the trend that the number of epithelial cells in the layer decreases as increases . As seen in Fig . 4 ( b ) , as increases the deformation of the epithelial layer decreases . Correspondingly , the arc length of the layer decreases and fewer cells are held within the monolayer . To relate this to the biology of the layer , it is necessary to know more about variability in the rigidity of , and adhesion of epithelial cells to , the basement membrane . The simple geometric framework employed for the investigations thus far is a limiting factor preventing realistic modelling of the colonic epithelium , and so the next step is to incorporate the crypt geometry . As shown in Fig . 12 , it is possible to deform an initially flat epithelial monolayer to adopt a test-tube crypt shape by suitable application of the basement membrane force ( see supplementary video S2 ) . However , in order to do so it is necessary to define the initial rectangular geometry to be sufficiently wide , which in turn increases the width of the tissue stroma surrounding the crypt once the layer has fully deformed . This is unrealistic , as the stroma between neighbouring crypts is only 2–3 cells thick . The starting point for the following simulations is instead an initial geometry that approximates the shape of the crypt , described below . Distinct proliferative compartments can be defined as dependent on an imposed Wnt gradient , and this also has the advantage of eliminating the time required to fully deform the flat layer . From the approximate geometry , the basement membrane force acts to maintain the test-tube shape within the tissue through local calculation of the discrete curvature . This is a key feature of the model , as the test-tube geometry emerges due to the action of the forces , rather than being fixed and imposed as in most earlier models . The results found for the simple rectangular geometry are now translated to the cross-sectional geometry . The conditions required for homeostasis are sought , which present a balance between the basement membrane force and the adhesion and repulsion between neighbouring cells , to allow constant upward migration that is matched by cell removal at the collar . Thus , the number of epithelial cells in the crypt should fluctuate only slightly around a constant value , the cells should not be overly compressed , and the structure should not buckle . The work described in this paper thus far constitutes the foundation of a realistic , theoretical representation of growth in a deformable environment within the colonic crypt . The usefulness and the need for mathematical modelling as a tool to guide and inform biological experimentation is becoming increasingly recognised [40] . In particular , the field of oncology lacks a comprehensive model to which existing data can be applied , nonetheless such a model is required to identify key system parameters [41] . The collaboration that resulted in this paper is intended to produce qualitative results that identify parameter balances and mechanisms that describe the behaviour of the system and which cannot be obtained by alternative methods for ethical , financial or viability reasons . Such results will inform experimental work , and identify areas for future investigation . A simple approach is adopted to describe the evolution of the tissue structure while linking subcellular processes ( Wnt signalling , cell cycle control , cell adhesion , cell differentiation ) with cellular mechanics that control division , migration and apoptosis . The role of the basement membrane is defined by an additional force which takes into account the structural support provided by the surrounding connective tissue , including the PCFS . Evidence for this is based on experimental observation of the tissue structure of the crypt in situ , reported here , which identifies key components that contribute towards crypt shape and function . Although only the colonic crypt is considered here , extension to consider the small intestinal crypt would be possible based on this work , but would require the definition of the villus and the incorporation of paneth cells . This is a possible direction for future work . In addition , one could examine the effect of possible gradients of adhesion along the crypt axis , via a spatially-dependent basement membrane force parameter , and monitor any subsequent change in cell migration or cell death . Simulations of the cross-sectional crypt model demonstrate that dynamic homeostasis can be achieved , in which repeated mitotic events evolve to force consistent epithelial cell migration towards the crypt collar , without compromising the overall structure and architecture . This is characterised by a steady , constant turnover of cells , achieved in the presence of known constraints on the number of dividing cells , and applying the two known mechanisms of cell death . It is by the application of the basement membrane force that the shape of the crypt evolves , and will allow the structure to deform , rather than imposing a fixed geometry . This is a key aspect of this work , given that all previous crypt models , with the exception of Drasdo and Loeffler ( 2001 ) [21] , have assumed a fixed geometry [15] , [16] , [18] , [24] , and permits investigation of the destabilisation of the crypt structure that occurs at the onset of carcinogenesis , and indeed which can aggravate the growth of a pre-cancerous adenoma . Moreover , two insights are proposed regarding mechanisms of cell dynamics within the crypts . Firstly , in the absence of sufficient apoptosis at the crypt collar and intercrypt table , epithelial cells reach a state of confluence , do not divide and migration is inhibited . This suggests that cell migration in the crypt may not be due solely to proliferative pressure from below , but that a feedback mechanism exists between cell birth and cell death , such that the epithelial cells move into the space created by cell death at the collar . Subsequently , cells below are able to grow , divide and migrate upwards , which has the secondary effect of maintaining barrier function . This is a theory that is in line with the extrusion process that occurs for apoptotic cells in epithelial layers [42] , [43] and is known as the negative pressure hypothesis [37] . In the model , apoptosis has been defined to occur randomly , given that the cause of cell removal at the crypt collar is currently unknown . It is likely that programmed cell death acts in combination with anoikis events that may be induced by the crowding of cells at the crypt collar , shown in Fig . 16 ( a ) , where the layer has a negative curvature , to remove cells and ultimately enable cell migration . To test this hypothesis in a wet lab , it is suggested that apoptosis could be induced uniformly along the crypt-villus axis , and any alteration to the typical migratory pattern subsequently monitored . Secondly , the model demonstrates a high incidence of anoikis events at the crypt base . This is not commonly observed by experimentalists [34] . However , when these results are considered in light of the process of asymmetric division in stem cells , whereby one nuclei is positioned apically ( towards the lumen ) before reinserting basally by an as yet unknown mechanism , it is suggested that the crypt shape may play a role in forcing the alignment of the mitotic spindle for the compressed cells at the base . In the model , the compression of cells at the base forces one of the daughter cell centres to lose contact with the basement membrane , whereupon it is removed and this registers as an anoikis event . That a high incidence of anoikis events happen following division at the base of the crypt therefore indicates a mechanosensory cause for asymmetric division in the stem cells at the base of the crypt . This hypothesis is supported by experimental results which demonstrate that cells do respond to their mechanical environment , and moreover that cytokinesis is a mechanical process [44] . It has also been suggested that the shape of cells and tissues can influence cell division via cortical tension heterogeneity which guides spindle orientation [45] . The simulation results also show that anoikis events occur at the curve of the crypt collar . As cells in this region are now differentiated , it is hypothesised that such events arise due to the negative curvature of the layer at this point , rendering cells vulnerable to extrusion . To test this , one could grow epithelial cells on curved substrates to examine the incidence of extrusion on negatively curved regions . Alternatively , cells could initially be grown on a flat substrate , which is bent once the cells reach confluence . The experiments suggested above identify ways in which model development and wet lab experiments can enter a feedback loop to advance understanding of the system . Support or invalidation of the hypotheses proposed will guide future model iterations , generally advancing the understanding of the system . Ideally , the most useful experiments would be those imaging live tissue using appropriate markers , so as to measure individual cell behaviours over time . This would be another way to parametrise the model out of imaging data ( in addition to that used to generate the data shown in Fig . 14 ) . It has been demonstrated that crypt-like organoids can be grown in matrigel in the presence of growth factors distributed uniformly throughout the gel , which appear similar to crypts in tissue . That additional forces do play a significant role in vivo is illustrated by the fact that crypts which are mutant in Apc form apparently normal shapes in whole tissue , but do not in matrigel [3] . Therefore , at this stage , it is not yet possible to state decisively how crypt organoids could be used to test hypotheses generated by theoretical models . When constructing a mathematical model of a biological system , it is wise to keep the model as simple as possible , focussing accurately on the key components and understanding the outcome of crucial interactions without over-complicating the description and analysis required [46] . However , the computational framework within which the crypt model has been developed ( Chaste ) makes it extensible and amenable to additional complexity , should it be required . For example , more detailed cell cycle models can be applied , and many currently exist within the Chaste framework . At present , however , there are limiting factors that prevent full examination of the destabilisation of the crypt structure that occurs in the progression from a healthy system to the growth of a malignant tumour . As the cross-sectional model consists of a 1D chain of cells , it is only possible for epithelial cells to move vertically or to displace the surrounding stroma . Consequently , the introduction of a mutant cell that migrates aberrantly , e . g . more slowly , will always affect those cells directly beneath it in the chain . In reality , these cells would be able to move around a blockage by moving laterally across the inner surface of the crypt . To correct this , it would not be sufficient to simply apply radial symmetry to the model , a method that would produce unrealistic results due to the imposed symmetry , causing mutations to spread uniformly upwards as cells migrate . In contrast , a full deformable 3D model which permits lateral movement could eliminate false positives observed in the 2D model . For example , a detailed response of the system to the introduction of mutant cell populations could be obtained , and meaningful experiments to investigate the “top-down” [47] and “bottom-up” [48] , [49] theories of mutant cell invasion could be conducted . In addition , a full 3D model makes it possible to define a localised stem cell compartment within the crypt that is distinguished from the transit-amplifying cell compartment by specific proliferative properties . ( In 2D , the cell chain would force stem cells out of the base of the crypt . ) This would enable investigation of stem cell number in the crypt , an open question within the field , and how this affects cell dynamics . The additional degrees of freedom associated with cell movement introduced by a deformable 3D model will increase the scope for accurately modelling the response of the system to different cellular events . For example , the merging or rearrangement of cell columns , causing the lumen to narrow or widen should the number of cells decrease or increase . This will have bearing on the persistence of mutations in the crypt , as well as on the incidence of anoikis events , which are likely to decrease as cells can exploit movement and growth in more directions . However , it is necessary to re-evaluate the structure of forces in 3D , firstly in light of the additional complexity of the Delaunay triangulation , and also to consider the effect of shear forces that may contribute to the stability of the structure . This work is underway . Mouse gut tissue was prepared for imaging following the methods described in Appleton et al . ( 2009 ) [50] . Sectioned gut was imaged on a Leica DMIRB fluorescent microscope and the wholemounts imaged on a Zeiss 710 confocal microscope . 3D images of fixed whole-mount tissue stained with DAPI and rhodamine-phalloidin were acquired using multiphoton fluorescence microscopy [50] . The outer surface of the crypt is defined by the basement membrane on which the epithelial cells sit , and the outer crypt area and crypt lumen area were measured in a cross section half way along the crypt length using Volocity image analysis software ( Perkin Elmer ) . The cell area was estimated by subtracting the lumen area from the outer crypt area , followed by dividing by the number of DAPI-stained nuclei present in the center cross section . A total of 150 crypts were examined , taken from the colons of three wild type C57BL/6J mice . A discrete multiscale model is considered , where cell centres are defined as nodes which evolve spatially according to an off-lattice definition of cell-cell mechanics [15] , [16] . As such , spatial connectivity is determined by a Delaunay triangulation of cell centres , and the corresponding cell shapes are subsequently defined and visualised by the dual Voronoi tessellation , which has been shown to produce realistic polygonal cell shapes [51] . An example of this triangulation and tessellation is illustrated in Fig . 3 . Please note , the authors would like to refer the reader to the article by Eisenhoffer et al . ( doi:10 . 1038/nature10999 ) , which was published during the final proof stages of this publication . This paper examines cell extrusion in the crypt , and deduces that the overcrowding of cells at the crypt collar and intercrypt table leads to anoikis events , which is in agreement with the results found using the cross-sectional model .
At the onset of colorectal carcinogenesis , marked changes can be observed in the structure and dynamics of the crypts of Lieberkühn . These test tube shaped glands regularly punctuate the surface of the gut and are lined with a monolayer of epithelial cells which divide and migrate upwards to renew the intestinal surface every few days . The process by which the crypt structures breakdown , and the compliant environment that can be subsequently provided to mutated cells to allow the formation of adenomatous growths , is not yet well characterised . A limiting factor in the understanding of this process is the ability to observe easily the initial changes that occur , and which are necessary to disrupt the normal behaviour of the system . However , a predictive , theoretical model of the crypt that mimics the geometry and the tissue architecture can be used to perform in silico experiments and further such understanding . A model is introduced here that addresses the tissue structure of the crypt , and the stability it provides to the epithelial layer , while remaining deformable and without imposing a fixed geometry .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods", "Notes", "Added", "in", "Proof" ]
[ "histology", "biophysic", "al", "simulations", "biology", "computational", "biology" ]
2012
A Two-Dimensional Model of the Colonic Crypt Accounting for the Role of the Basement Membrane and Pericryptal Fibroblast Sheath
Plasma cholesterol lowering ( PCL ) slows and sometimes prevents progression of atherosclerosis and may even lead to regression . Little is known about how molecular processes in the atherosclerotic arterial wall respond to PCL and modify responses to atherosclerosis regression . We studied atherosclerosis regression and global gene expression responses to PCL ( ≥80% ) and to atherosclerosis regression itself in early , mature , and advanced lesions . In atherosclerotic aortic wall from Ldlr−/−Apob100/100Mttpflox/floxMx1-Cre mice , atherosclerosis regressed after PCL regardless of lesion stage . However , near-complete regression was observed only in mice with early lesions; mice with mature and advanced lesions were left with regression-resistant , relatively unstable plaque remnants . Atherosclerosis genes responding to PCL before regression , unlike those responding to the regression itself , were enriched in inherited risk for coronary artery disease and myocardial infarction , indicating causality . Inference of transcription factor ( TF ) regulatory networks of these PCL-responsive gene sets revealed largely different networks in early , mature , and advanced lesions . In early lesions , PPARG was identified as a specific master regulator of the PCL-responsive atherosclerosis TF-regulatory network , whereas in mature and advanced lesions , the specific master regulators were MLL5 and SRSF10/XRN2 , respectively . In a THP-1 foam cell model of atherosclerosis regression , siRNA targeting of these master regulators activated the time-point-specific TF-regulatory networks and altered the accumulation of cholesterol esters . We conclude that PCL leads to complete atherosclerosis regression only in mice with early lesions . Identified master regulators and related PCL-responsive TF-regulatory networks will be interesting targets to enhance PCL-mediated regression of mature and advanced atherosclerotic lesions . Atherosclerosis , primarily in coronary artery disease ( CAD ) or carotid stenosis , is the main cause of myocardial infarction ( MI ) and stroke , which together are responsible for more than 50% of deaths worldwide [1] . Although the extent of atherosclerosis in the arterial bed is an unreliable marker of risk for future events , advanced atherosclerotic plaques are present in nearly all cases of MI and in most cases of stroke . It is therefore important to prevent early harmless atherosclerotic lesions from progressing to rupture-prone plaques , and if possible , to induce regression of advanced atherosclerosis into more stable forms [2] . Drugs that lower LDL cholesterol , such as statins , slow atherosclerosis progression and reduce morbidity and mortality from MI and stroke by 30–45% [3]–[5] . More potent statin regimens can even cause atherosclerosis regression [6]–[8] but sometimes have severe side effects . Although statins and lifestyle changes reduce the risk for secondary cardiovascular events [9] , mortality from MI and stroke are still increasing [1] . About 10% of persons at increased risk for CAD/MI have elevated plasma cholesterol levels , making them eligible for primary statin treatment . The extent to which plasma cholesterol lowering ( PCL ) benefits healthy persons who are at increased risk for CAD/MI and have relatively normal plasma LDL-cholesterol levels is unclear [10] , [11] . Vulnerable atherosclerotic lesions may respond better to PCL ( i . e . , leading to regression and more stable plaques ) in some cases than in others , depending on inherited genetic and environmental co-factors within the plaque . In part , these factors are likely reflected in gene expression patterns within the plaque [12] . A better understanding of such changes in response to PCL at different stages of plaque development is necessary to define key genes that in themselves or in parallel with PCL help improve atherosclerosis regression . To effectively study atherosclerosis regression , the use of animal models is required . In earlier studies of atherosclerosis regression , mainly mouse models were used . Among these models were wildtype normolipidemic mice transplanted with atherosclerotic arterial segments from Apoe−/− mice [13]–[18] , Apoe−/− mice treated with apoE-encoding adenoviral vectors [19] , Ldlr−/− mice treated with an microsomal triglyceride transfer protein ( MTP ) inhibitor [20] , and mice that have a plasma lipid profile similar of that of hypercholesterolemia ( Ldlr−/−Apob100/100 ) and a genetic switch to block hepatic synthesis of lipoproteins and thereby lower plasma lipoproteins ( Mttpflox/floxMx1-Cre ) [21]–[23] . These studies established that PCL leads to atherosclerosis regression . mRNA profiling of atherosclerotic lesions before and after regression led to the identification of several candidate target genes that may mediate atherosclerosis regression after PCL . However , some important aspects of atherosclerosis regression were overlooked in these studies [13]–[23] . First , the main focus was to identify individual atherosclerosis genes . In contrast , we believe that mRNA profiles are best interpreted by inferring groups of functionally linked genes in disease networks [24] , [25] . Another concern relates to the interpretation of molecular changes ( reflected by gene expression ) in atherosclerotic lesions before , during , and after regression . Typically , atherosclerosis regression candidate genes were identified by comparing mRNA profiles of lesions isolated before and after regression . In our experience , most genes identified in this fashion reflect morphological changes in the atherosclerotic plaque , such as shrinking of the lesion and alterations in the relative cell type composition . Such changes are likely a response to , but not a cause of , atherosclerosis regression . Finally , in a study of gene expression patterns during atherosclerosis progression [12] , we observed that the extent of gene expression changes in the atherosclerotic lesions drastically expands and varies as atherosclerosis progresses . Thus , it is highly likely that gene targets to improve PCL-mediated atherosclerosis regression will vary with the stage and severity of the lesions . In this study , we identified PCL-responsive atherosclerosis genes and their interactions in networks before regression at three stages of atherosclerosis development . We then compared these genes with those responding to the atherosclerosis itself . Specifically , we analyzed the extent , composition , and mRNA profiles of early , mature , and advanced atherosclerotic aortic lesions from Ldlr−/−Apob100/100Mttpflox/floxMx1-Cre mice [22] immediately before and after Cre-induced PCL and at 10 and 20 weeks after PCL . To study regression of atherosclerosis at different stages , we lowered plasma lipoprotein levels in Ldlr−/−Apob100/100Mttpflox/floxMx1-Cre mice by recombining the floxed gene ( Mttpflox/flox ) [26] with polyinosinic-polycytidylic acid ( pI-pC ) injections after 30 , 40 , and 50 weeks of atherosclerosis progression ( i . e . , age of the mice ) . pI-pC injections in Mttpwt/wtMx1 mice do not affect plasma cholesterol levels or transcriptional activity in the arterial wall [12] . After recombination of microsomal triglyceride transfer protein ( Mttp ) , plasma total cholesterol levels were reduced by 80–95% , HDL-cholesterol 50–60% and , triglyceride levels by 40–60%; plasma glucose levels were generally unaffected ( Table 1 and Table S1 ) . Plasma cholesterol ( both total and HDL ) and triglyceride levels were reduced to similar levels in mice with early ( week 30 ) , mature ( week 40 ) , and advanced ( week 50 ) lesions , and remained at these levels throughout the regression study period ( 10 and 20 weeks after Mttp recombination; Table 1 and Table S1 ) . In littermate Ldlr−/−Apob100/100Mttpflox/floxMx1-Cre mice injected with PBS ( controls ) and sacrificed at 20 , 30 , 40 , 50 , and 60 weeks , plasma cholesterol and triglyceride levels were unaffected ( Table 1 ) . Since Mttp recombination primarily affected plasma cholesterol levels ( e . g . , LDL-cholesterol ) , we will refer to plasma lipid lowering as PCL . The extent of atherosclerosis progression and regression was assessed by en face analysis of the lesion surface area of pinned-out aortic trees stained with Sudan IV . Early lesions in the aortic arch were small and had distinct borders ( Figure 1A and 1B ) . Mature and advanced lesions were substantially larger but still had distinct borders , with small lesions appearing in the ascending aorta . Atherosclerosis regression occurred at all lesion stages after PCL . In mice with early lesions ( 30 weeks ) , PCL led to near-complete regression after 20 weeks ( Figure 1A and 1B ) , from 4 . 3% of surface area to 0 . 5% ( down by 88% ) ( P = 0 . 0003 ) . However , in mice with mature lesions ( PCL at 40 weeks ) and advanced lesions ( PCL at 50 weeks ) , regression was substantial but never complete . During the first 10 weeks of PCL , mature lesions shrank from 12 . 6% to 4 . 1% of surface area ( P = 5×10−5 ) and advanced lesions from 15 . 2% to 6 . 2% ( P = 7×10−7 ) . During the last 10 weeks of PCL , however , there was little further regression . Mature lesions shrank from 4 . 1% to 3 . 5% of surface area ( P = 0 . 6 ) and advanced lesions from 6 . 2% to 6 . 0% ( P = 0 . 8 ) ( Figure 1A and 1B ) . Thus , after 10 weeks of PCL , mature and advanced lesions became resistant to PCL , whereas early lesions continue to regress . Although regression of the extent of atherosclerosis ( atherosclerosis burden ) increases plaque stability as reflected by the cellular , collagen , and lipid composition of the plaque , compositional changes do not always parallel changes in atherosclerosis burden . Therefore , we compared the histological features of aortic root sections isolated before and 10 and 20 weeks after PCL ( Figure 2 ) . Over 20 weeks of regression , the most robust changes in plaque composition were in neutral lipids identified by Oil-Red-O staining ( Figure 2A ) and in the percentage of lesion macrophages identified by staining for CD68 ( Figure 2B ) . Oil-Red-O staining decreased from 5 . 6% to 2 . 7% of surface area in early lesions , from 12 . 6% to 5 . 1% in mature lesions , and from 22 . 6% to 5 . 1% in advanced lesions ( all P<0 . 001 ) . Similarly , the percentage of lesion macrophages decreased from 5 . 1% to 0 . 2% in early lesions , from 7 . 1% to 0 . 5% in mature lesions , and from 10 . 3% to 0 . 8% in advanced lesions ( all P<0 . 001 ) . In early lesions , the extensive reduction in the percentage of macrophages ( 5 . 1% to 0 . 2% ) was paralleled by near-complete regression after 20 weeks of PCL ( Figure 1A ) . Of note , between weeks 10 and 20 of PCL , the percentage of macrophages in advanced lesions decreased from 2 . 5% to 0 . 8% ( P<0 . 05 ) despite no further reduction in the extent of lesions ( Figure 1A ) . In early lesions , PCL increased the collagen content by 80–130% ( P<0 . 01 at 10 and 20 weeks ) . In mature lesions , the collagen content was unaffected by PCL . In advanced lesions , collagen content decreased by about 30% 10 weeks after PCL ( P<0 . 05 ) and remained at this level at 20 weeks . The pattern of changes in the lesion content of smooth muscle cells ( SM22α-positive ) was similar to that of collagen content; however , owing to higher variation , none of these changes were statistically significant ( data not shown ) . To assess how changes in lesion composition after atherosclerosis regression alter plaque stability , we calculated a stability score: ( SM22α+collagen areas ) / ( CD68+Oil-Red-O areas ) [27] . Since this score indicates stability for each plaque and does not consider the total risk for plaque rupture in a given mouse , the plaque score was divided by the total atherosclerosis burden/mouse ( i . e . , lesion surface area ) . The resulting stability scores decreased during atherosclerosis progression and increased during regression ( Figure 2D ) . Early lesions showed the greatest improvement in plaque stability score , which was 17-fold higher after 10 weeks of regression ( from 1 . 3 to 24 , P<0 . 001 ) and 54-fold higher after 20 weeks ( 1 . 3 to 72 , P<0 . 001 ) . In comparison , after 20 weeks of regression , plaque stability scores had increased only 13-fold in mature lesions ( 0 . 5 to 6 . 6 , P<0 . 001 ) and 11-fold in advanced lesions ( 0 . 3 to 3 . 4 , P<0 . 001 ) . Clearly , plaque stability is generally improved by PCL-induced regression; however , in mice with mature and advanced lesions , the baseline score was substantially lower and the improvement much less than in mice with early lesions . Thus , the greatest gain in plaque stability is achieved by PCL in mice with early lesions . For mRNA profiling studies , PCL was again induced in mice with early ( 30 weeks ) , mature ( 40 weeks ) , and advanced ( 50 weeks ) lesions . Atherosclerotic aortic arch was isolated for RNA isolation immediately before and after PCL and after 10 weeks of regression . Affymetrix arrays ( Mouse Gene 1 . 0 ST ) were used for mRNA profiling . First , to assess atherosclerotic arterial wall genes that respond to the PCL before atherosclerosis regression ( i . e . , the PCL-responsive gene set ) , we compared mRNA profiles immediately before and after PCL ( Figure 3A , Tables S2 , S3 , S4 ) . Since the time between “immediately before and after PCL” is about 1 week , we observed no morphological changes in the lesion composition , including the percentages of different cell types . As a consequence , the PCL-responsive gene sets represent genes with primary changes in their expression levels rather than changes due to alterations in the cellular composition of the plaque . Next , to identify atherosclerotic arterial wall genes whose expression changed during atherosclerosis regression ( i . e . , regression-reactive gene sets ) , we compared mRNA profiles immediately after PCL and after 10 weeks of regression ( Figure 3B , Tables S5 , S6 , S7 ) . In contrast to PCL-responsive gene sets , changes in the expression of many genes in the regression-reactive set likely reflect changes in the cellular composition of the plaque . As atherosclerotic lesions develop , their molecular complexity increases [12] . So it was not surprising that the number of PCL-responsive genes increased from 261 transcripts ( corresponding to 238 mouse genes ) in early lesions , to 1752 transcripts ( 1306 genes ) in mature lesions and to 2702 transcripts ( 2231 genes ) in advanced lesions ( Figure 3A , Figure 4A , Tables S2 , S3 , S4 ) . Similarly , the number of regression-reactive genes increased from 50 transcripts ( 42 genes ) in early lesions , to 1902 transcripts ( 1556 genes ) in mature lesions , and up to 8569 transcripts ( 6273 genes ) in advanced lesions ( Figure 3B , Figure 4B , Tables S5 , S6 , S7 ) . These observations suggest that the PCL responses of the atherosclerotic lesions become increasingly complex as atherosclerosis progresses and are mirrored by a greater complexity in the regression response . Interestingly , the PCL-responsive gene sets were largely unique at each time point/stage of atherosclerosis; the fraction of unique genes was 24% , 66% , and 84% in early lesions , mature , and advanced lesions , respectively ( Figure 4A ) . Of PCL-responsive genes in early lesions , 68% were uniquely shared with PCL-responsive genes in mature lesions but only 5% with advanced lesions ( Figure 4A ) . In contrast , regression-reactive gene sets were more shared between stages of atherosclerosis progression; all regression-reactive genes in early lesions were present in the reactive gene sets of mature and advanced lesions , and 50% of the reactive gene set in mature lesions was present in advanced lesions ( Figure 4B ) . Thus , PCL-responsive atherosclerosis genes largely vary with the stage of atherosclerosis , whereas regression-reactive genes sets expand as atherosclerosis progresses . Genes that are causally linked to ( i . e . , that drive or protect against ) a disease typically harbor DNA variants that affect the risk of developing the disease , whereas genes reacting to a disease typically do not [25] . To examine the causal relationships of the PCL-responsive and regression-reactive gene sets to atherosclerosis regression , we identified single nucleotide polymorphisms ( SNPs ) affecting the expression of the human orthologs of those genes ( Tables S8 , S9 , S10 , S11 , S12 , S13 ) and determined the extent to which these expression SNPs ( eSNPs ) carry more risk for CAD/MI than would be expected by chance . For this purpose , we used a well-established genome-wide association ( GWA ) study of CAD/MI , MIGen [28] . eSNPs affecting the expression of genes in the PCL-responsive gene sets in early , mature , and advanced atherosclerosis were all risk-enriched compared to 5000 randomly selected equally sized sets of SNPs ( early , 2 . 0-fold , P = 3 . 1×10−14; mature , 1 . 4-fold , P = 6 . 8×10−4; advanced , 1 . 5-fold , P = 1 . 3×10−6 ) . In contrast , eSNPs affecting the expression of genes in the regression-reactive gene sets were not ( early/mature/advanced , <1 . 1-fold , P>0 . 05 ) . These results support the notion that PCL-responsive genes are causally linked to regression of atherosclerosis and that regression-responsive genes are , as hypothesized , secondary . From the standpoint of understanding genes that drive atherosclerosis regression , genes that respond acutely to PCL and are risk-enriched for CAD/MI—that is , the PCL-responsive gene sets of early , mature , and advanced atherosclerosis—were considered the most interesting . According to GO analysis of the PCL-responsive gene set of early lesions ( n = 261 ) , the top molecular and cellular function was lipid metabolism and the top disease category was connective tissue disorder ( Table S14 ) . Next , to investigate the connectivity of the human orthologs of the PCL-responsive genes of early lesions ( n = 215 ) , we inferred the TF-regulatory gene network by using mRNA profiles from blood macrophages of CAD patients [29] . Fifty-three of 215 human orthologs belonged to the TF-regulatory network ( Figure 5A , P<0 . 0051 , Table S8 ) . Peroxisome proliferator-activated receptor alpha and gamma ( PPARA , PPARG ) were master regulators ( highly connected genes ) in this network , with 17 and 13 edges , respectively ( Table 2 ) . To validate the PCL-responsive TF-regulatory gene network in early lesions , including its key master regulators , we used a THP-1 regression model . In brief , THP-1 cells were differentiated into macrophages in vitro and incubated with acetylated-LDL ( Ac-LDL ) to form foam cells . The cells were then treated with siRNA ( to silence the key master regulators ) or mock treated ( controls ) and examined for effects on the expression levels of network genes and cholesterol-ester ( CE ) accumulation . CE levels were assessed from lipids in the THP-1 foam cells after siRNA silencing of the master regulator and compared CE levels in mock-treated cells . If CE accumulation increased , the master regulator was judged to promote atherosclerosis regression . If CE accumulation decreased , the master regulator was judged to prevent regression . To assess gene expression in THP-1 foam cells , RNA isolated after silencing was analyzed with an Agilent Human Custom Gene Expression Microarray; the degree of silencing of master regulators was assessed by RT-PCR . When PPARG was silenced , 28% ( 15/53 ) of the PCL-responsive network genes in early atherosclerosis were affected ( down- or up-regulated at a false-discovery rate ( FDR ) <0 . 1; Table 3 and Tables S8 and S17 ) . A hypergeometric test ( Methods ) showed that the effects of silencing PPARG were specific to the 53 genes in the TF-regulatory gene network of early lesions ( P = 0 . 020 , Table 4 ) . In the THP-1 foam cell model , CE accumulation increased by 12% ( P = 0 . 008 ) after silencing of PPARG ( Table 5 ) . According to GO analysis of the PCL-responsive gene set ( n = 1752 ) of mature lesions , the top molecular and cellular function was lipid metabolism ( Table S15 ) and the top disease categories were connective tissue disorder and metabolic disease ( Table S15 ) . Next , we investigated the connectivity of these genes by inferring the TF-regulatory gene network , again using the mRNA profiles from blood macrophages of CAD patients [29] . Of 1087 human orthologs ( corresponding to 1306 mouse genes , Table S9 ) , 185 were part of the inferred TF-regulatory gene network ( P<0 . 0013 , Figure 5B , Table S9 , Figure S1 ) . The master regulators of this network were high mobility group box 2 ( HMGB2 ) , adenosine A2a receptor ( ADORA2A ) , telomeric repeat binding factor 1 ( TERF1 ) , and mixed lineage leukemia 5 ( MLL5 ) , with 61 , 59 , 55 and 38 connections , respectively ( Table 2 ) . To validate the PCL-responsive TF-regulatory gene network and its key master regulators in mature lesions , we again used a THP-1 foam cell regression model . After silencing of ADORA2A , 31% ( 58/185 ) of the mature network genes were affected; after silencing of both ADORA2A and MLL5 , 36% ( 67/185 ) were affected ( FDR<0 . 1 , Table 3 , Table S9 and Table S17 ) . MLL5 was the only key master regulator that was specific for the mature atherosclerosis network according to the hypergeometric test ( P = 0 . 0059 , Table 4 ) . After MLL5 silencing , CE accumulation in the THP-1 foam cell model increased 21% ( P = 0 . 01 , Table 5 ) . According to GO analysis of the PCL-responsive gene set ( n = 2702 ) in advanced lesions , the top molecular and cellular functions were protein synthesis and degradation ( Table S16 ) and the top disease categories were immunological disease and cardiovascular disease ( Table S16 ) . Next , we investigated the connectivity of these genes by inferring the TF-regulatory gene network , using the same mRNA profiles from blood macrophages of CAD patients [29] ( Figure 5C ) . Of 1865 human orthologs ( corresponding to 2231 mouse genes , Table S10 ) , 379 were part of the inferred regulatory gene network ( P<0 . 00042 , Figure 5C , Table S10 , Figure S2 ) . The master regulators in this network were serine/arginine-rich splicing factor 10 ( SRSF10 ) , 5′-3′-exoribonuclease 2 ( XRN2 ) , and HMGB1 , with 71 , 67 , and 62 connections , respectively ( Table 2 ) . In the THP-1 foam cell regression model , silencing of both SRSF10 and XRN2 affected 22% ( 83/379 ) of the advanced network genes ( FDR<0 . 05 , Table 3 , Table S10 and Table S17 ) . Silencing of SRSF10 and XRN2 individually affected 19% and 15% , respectively , of the advanced lesion network genes . Both SRSF10 and XRN2 were specific master regulators for the advanced atherosclerosis network ( P = 0 . 035 , P = 0 . 040 , respectively , Table 4 ) . In the THP-1 foam cell model , CE accumulation decreased by 17% ( P = 0 . 0008 ) after silencing of SRSF10 and by 15% ( P = 0 . 003 ) after silencing of XRN2 ( Table 5 ) . PCL decreases the risk for clinical complications of atherosclerosis , but individual responses vary , from slowing or preventing further progression to inducing regression . This study of a mouse model with human-like plasma lipoprotein profile and advanced atherosclerotic lesions showed that atherosclerosis regression occurs regardless of the lesion stage at which PCL is induced . However , as lesions progress , they become increasingly resistant to PCL . In mice with early lesions , PCL led to a complete regression and nearly healthy arteries ( plaque stability score >70 after 20 weeks of regression ) . In mice with mature lesions , the regression was incomplete , leaving plaque remnants that were substantially smaller but relatively instable ( stability score <10 ) . And in mice with advanced lesions , the plaque remnants were even less stable ( stability score <5 ) . Thus , if early atherosclerosis in humans is equally sensitive to plasma cholesterol levels , patients at increased risk for CAD and MI would benefit greatly from PCL while their lesions are still in the early stage . The increasing resistance to PCL as atherosclerotic plaques progress suggests that specific molecular processes in atherosclerosis regulate PCL sensitivity and thus the response to atherosclerosis regression . We therefore performed mRNA-profiling immediately before and after PCL to identify PCL-responsive atherosclerosis genes and examined their interplay in TF-regulatory gene networks . Consistent with the differences in plaque sensitivity to PCL , plasma cholesterol-responsive genes in the atherosclerotic arterial wall were largely different in early , mature , and advanced lesions . In early lesions , we identified PPARG as a specific master regulator of other PCL-responsive genes that collectively led to near-complete regression . In mature and advanced plaques , we identified nonspecific master regulators ( affecting both mature and advanced PCL-responsive genes in THP-1 foam cells ) , such as ADORA2A , HMGB1 , HMGB2 , and TERF1 , as well as specific master regulators of partial regression in mature lesions ( MLL5 ) and advanced lesions ( SRSF10 and XRN2 ) . In validation studies in THP-1 foam cells , siRNA targeting individual master regulators either decreased ( SRSF10 , XRN2 ) or increased ( PPARG and MLL5 ) CE accumulation . These genes are plausible targets to improve PCL-mediated regression of mature and advanced atherosclerosis . In studies to validate the inherited risk-enrichment [25] of the PCL-responsive and regression-reactive gene sets , we found that only PCL-responsive genes were enriched with inherited risk for CAD/MI ( >1 . 4-fold , P<6 . 8×10−4 ) . The causal gene set of early atherosclerosis was especially risk enriched ( 2 . 0-fold , P = 3 . 1×10−14 ) , perhaps indicating that the causal gene set of early lesions precedes those of mature and advanced lesions and has a more important role in carrying inherited risk . We [12] , [25] and others [24] have shown that molecular processes with key roles in disease have at least some degree of risk enrichment . However , genes affected by DNA variants ( i . e . , eSNPs ) might be disease relevant despite not necessarily carrying inherited risk . Thus , some regression-reactive genes are likely important for atherosclerosis regression despite their lack of enrichment in inherited risk of CAD/MI . The lack of risk enrichment in the regression-reactive gene sets does not imply that every gene or pathway in these sets is irrelevant for atherosclerosis regression . For example , the regression-reactive gene sets included many genes in the transendothelial migration of leukocytes ( TEML ) pathway that are thought to be important in regression [19] . Interestingly , master regulatory genes did not harbor any disease-associated eSNPs according to the MIGen GWA dataset [28] , although many other PCL-responsive network genes did . What is responsible for this difference ? One possibility is that SNPs or mutations in genes that encode key transcription regulatory proteins ( i . e . , master regulators ) often are deleterious and therefore are effectively eliminated by natural selection from the gene pool [30] . In support of this notion , disease risk loci identified by GWA studies so far have not yet identified key master regulators of lipid metabolism in CAD , like SREBPs , PPARs and LXR [31] , [32] . Instead , genes in lipid metabolism that have been identified by GWA studies , such as PCSK9 , ABCG5 and ABCG8 [31] , [32] , are , to our understanding , important modifiers but not master regulators . Although some regression-reactive genes may contribute to atherosclerosis regression , they did not respond to PCL . Responsiveness to PCL , we believe , is key to the atherosclerosis regression response . Interestingly , PPARG was identified as a PCL-responsive master regulator of the TF-regulatory network of early lesions . Recently a study of the same mouse model we used showed that treatment with pioglitazone ( a PPARG agonist ) in addition to PCL improved the inflammatory profile of CD68 cells [21] . Thus , the PPARG agonist modified the response of the atherosclerotic arterial wall to PCL . Our validation of stage-specific master regulators in the foam cell model of regression suggests that MLL5 , SRSF10 , and XRN2 will be useful targets for improving atherosclerosis regression after PCL in individuals with mature or even advanced lesions . Studies targeting these genes either genetically or with drugs in parallel to PCL are warranted . Atherosclerosis regression after PCL has been investigated in several mouse models [13]–[23] , [33] and the results prompted debate about the mechanisms of regression . According to a leading theory , regression is caused by increased macrophage emigration from the plaque [13] , [16] , [18] , [33] . Another study suggested that the key mechanism is suppressed migration of leukocytes to the arterial wall [19] . The notion that monocyte migration is a key process in regression is supported by our transcriptional profiling data and immunohistological characteristics of atherosclerosis regression ( loss of CD68-positive cells and decrease in Oil-Red-O staining ) . In contrast , we found that expression of chemokine ( C-C motif ) receptor 7 ( Ccr7 ) and liver X receptor alpha ( Lxr ) was downregulated in response to atherosclerosis regression , not upregulated ( not shown ) . These findings suggest that deactivation of TEML pathway genes , rather than increased emigration of macrophages , is more essential for atherosclerosis regression . In relation to the PCL-responsive gene sets , the TEML pathway may be a key event but is activated further downstream , since a majority of these genes did not respond to PCL . In addition , our findings clearly indicate that atherosclerosis regression is too complex to be explained by changes in TEML activity alone . Besides migrating and emigrating , macrophages within the plaque also proliferate , affecting plaque size [34] . Specifically , at a high turnover rate , lesion macrophages can be replenished by local proliferation rather than de novo influx of monocytes [34] . How macrophage proliferation rate is affected ( if at all ) by PCL at different stages of atherosclerosis progression is unknown but is certainly of interest for future studies . Our findings show that enhancing atherosclerosis regression after PCL will require targeting regulatory genes at the top of the regulatory hierarchy ( e . g . , master regulators ) rather than individual effector genes ( e . g . , Ccr7 and Lxr ) or specific pathways , such as TEML or cell proliferation . Interestingly , even though mature and advanced plaque remnants became increasingly resistant to PCL-mediated regression , the number of CD68-positive cells decreased between weeks 10 and 20 of PCL in all plaques . This was most clear in early lesions , leading to near-complete regression . However , in mature plaques , and particularly in advanced plaques , despite a substantial decrease in CD68-positive cells between weeks 10 and 20 , lesion size was mainly unaffected . Over this period , the plaque stability scores improved , but it is difficult to interpret the underlying biology of these changes . One plausible explanation is that macrophage death ( necrosis ) is responsible for the reduction of CD68-positive cells in advanced plaques; the lack of change in plaque size would reflect a larger necrotic core despite the lower number of macrophages . Another plausible explanation is that there are fewer but larger macrophages; however , since there could be many CD68 proteins/macrophage , this explanation seems less likely . Regardless , the plaque stability scores clearly coincided with the degree of regression , indicating that when all regression-induced compositional changes to the plaques were jointly considered ( i . e . , CD68-positive cells , lipid accumulation , collagenous matrix , and vascular smooth muscle cells ) , the remnants of mature and advanced plaques remained relative unstable after both 10 and 20 weeks of regression . The mouse model used in this study is , in our opinion , the most relevant model for investigating atherosclerosis regression and associated changes in gene expression after PCL . First , these mice develop advanced atherosclerotic lesions on a normal chow diet and have a plasma lipid profile very similar to that of patients with familial hypercholesterolemia , who are highly susceptible to CAD [22] , [35] . Second , the high cholesterol levels in these mice can effectively be lowered after inducing expression of Mx1-Cre in the liver upon pI-pC treatments resulting in the recombination of Mttp . This can be achieved at any time during lesion progression without affecting vitality [22] . Of note , however , unlike human atherosclerotic plaques those in mice rarely rupture [36] . There are other ways to induce atherosclerosis regression than by lowering “bad” LDL-cholesterol levels ( the main form of “PCL” in the present study ) . It is also possible to overexpress ApoA1 , the major apolipoprotein of HDL-cholesterol ( “good” cholesterol ) , to enhance reverse cholesterol transport from the plaques to the liver . This strategy is motivated by the fact that plasma levels of HDL-cholesterol and ApoA1 in humans correlate inversely and independently with coronary heart disease [37] , [38] . Furthermore , in Apoe−/− mice , infusion of recombinant ApoA1 lowers lipid and macrophage levels in the plaque [39] . However , adenoviral transfer of ApoA1 into Ldlr−/− mice has had inconsistent results [40]–[42] . In one study , preexisting atherosclerotic lesions regressed after ApoA1 gene transfer [41]; however , in other studies , regression was not detected , but progression was slowed , and plaques in the aortic root had fewer macrophages and more collagen content [40] , [42] . Yet , the consensus is that increased levels HDL-cholesterol on top of LDL-cholesterol lowering could have additive effects on atherosclerosis regression and result in a more stable phenotype [39] , [42] , [43] . However , the plasma HDL-cholesterol levels in our study mice were reduced after PCL , which has also been shown by others [22] , indicating that reverse cholesterol transport might not play a major role in the regression of atherosclerosis in our model . Investigating transcriptional responses in the whole atherosclerotic arterial wall is not entirely trivial . As alluded to already , gene expression changes in the arterial wall may reflect gene activation ( i . e . , changes in the cellular mRNA concentrations ) or changes in the cellular composition of the lesion . However , in our experience , it is vital to have data from the entire lesion , as many molecular processes are intermixed and depend on gene activity across cell types . The ideal situation would be to have mRNA profiles of single cell types ( e . g . , macrophages ) together with the total mRNA profile of the same arterial wall . Unfortunately , this is not feasible in most instances . Another challenge of cell-type-specific mRNA profiles is to accurately distinguish different cell types before RNA isolation . This is especially difficult in atherosclerosis where , for example , smooth muscle cells sometimes change their phenotype and become macrophage-like cells [44] . In sum , as atherosclerotic lesions develop , different cell types become increasingly similar , sharing many phenotypes—a fact that favors the use of mRNA profiles from the entire lesion rather than from isolated cell types . However , since gene networks inferred from whole-lesion mRNA data will be incomplete and have missing nodes , it is essential to evaluate these networks in appropriate cell models of atherosclerosis , such as the THP-1 foam cell model we used in this study . We also reasoned that the transcriptional profiles of blood macrophages isolated from CAD patients [29] would be most relevant for establishing the wiring diagram of the identified PCL-responsive mouse genes in TF-regulatory gene networks . This choice allowed us to understand mouse genes from the perspective of pathophysiological processes in humans [25] , [29] , [45] and to use a human foam cell culture model to validate the TF-regulatory networks , including their hubs ( i . e . , master regulators ) . In summary , in this study we identified comprehensive compendiums of atherosclerosis regression genes and discovered that the sensitivity of atherosclerosis to PCL depends on the stage of lesion progression . Our findings provide insight into PCL-responsive genes upstream of atherosclerosis regression and challenge aspects of our understanding of this clinically important event [13] , [15]–[23] , [33] . In particular , our findings emphasize the need to determine how PCL-responsive genes collectively initiate downstream regression mechanisms , such as the TEML pathway and possibly macrophage emigration and proliferation . Master regulators such as PPARG , MLL5 , XRN2 , and SRSF10 merit further study to determine the extent to which combinations of these genes can be activated or deactivated to achieve complete regression of advanced atherosclerotic lesions , with or without parallel PCL regimens . The use of human samples [29] in this study was approved by the Ethics Committee of Karolinska University Hospital . All patients gave written , informed consent . The animal studies were approved by Stockholm's Norra Djurförsöksetiska nämnd , Sweden . Ldlr−/−Apob100/100Mttpflox/floxMx1-Cre mice have a plasma lipoprotein profile which resembles that of familial hypercholesterolemia and causes rapid atherosclerosis progression [22] . For Mttp deletion , mice were injected with 125 µl of pI-pC ( 1 µg/µl; Invivogen ) every other day for 6 days to induce Cre expression and thereby Mttp recombination ( MttpΔ/Δ ) . The mice were sacrificed 1 , 10 , or 20 weeks after Mttp depletion . Littermate controls received PBS ( Mttpflox/flox ) . The study mice were backcrossed 5 times to C57BL/6 mice ( <5% 129/SvJae and >95% C57BL/6 ) , housed in a pathogen-free barrier facility ( 12-hour light/12-hour dark cycle ) , and fed rodent chow containing 4% fat . Plasma cholesterol ( total and HDL ) and triglyceride concentrations in fasting blood samples were determined with colorimetric assays ( Infinity cholesterol/triglyceride kits; Thermo Scientific and HDL quantification colorimetric kit; BioVision ) , and plasma glucose levels with Precision Xtra ( MediScience ) . Aortas were pinned out flat on black wax surface as described [46] , stained with Sudan IV , photographed with a Nikon SMZ1000 microscope , and analyzed with Easy Image Analysis 2000 software ( Tekno Optik , Sweden ) . Lesion area was calculated as the percentage of the entire aortic surface between the aortic root and the iliac bifurcation . Aortic roots were isolated , immediately frozen in liquid nitrogen , embedded in OCT compound ( Histolab , Sweden ) , cut into 10-µm sections , and stained with hematoxylin and Oil-Red-O ( Sigma-Aldrich ) for neutral lipids [47] or Picrosirius Red ( Sigma-Aldrich ) for collagen as described [48] . Other sections were incubated first with rat anti-mouse CD68 antibody ( Serotec ) or rabbit anti-mouse SM22α ( Abcam ) overnight at 4°C and then with biotinylated secondary anti-rat IgG or anti-rabbit IgG antibodies ( Vector Laboratories ) and counterstained with hematoxylin ( Sigma-Aldrich ) . Biotin emission was developed with diaminobenzidine ( Vector Laboratories ) . Except for sections stained for collagen , which were photographed with a Leica DMRD microscope and a Leica DC480 color video camera , sections were photographed with an Apotome microscope ( Carl Zeiss ) and quantified with an AxioGraphic station ( Carl Zeiss ) at 50× magnification . For en face analysis , we examined aortas from 44 Ldlr−/−Apob100/100Mttpflox/flox mice ( n = 12 , 12 , 8 , 8 , and 4 for weeks 20 , 30 , 40 , 50 , and 60 , respectively ) and 51 Ldlr−/−Apob100/100MttpΔ/Δ mice ( early lesions: n = 10 after 10 weeks and n = 6 after 20 weeks of PCL; mature lesions: n = 8 after both 10 and 20 weeks of PCL; advanced lesions: n = 9 and 10 after 10 and 20 weeks of PCL ) . The plaque stability score for each mouse was calculated as ( SM22α+collagen ) / ( CD68+Oil-Red-O ) % areas [27] and normalized to plaque burden ( lesion surface area ) . Missing data points ( n = 19 , 16% ) were imputed with PROC MI in SAS version 9 . 3 . The statistical significance of differences between time points was determined with two-tailed t tests . Aortas were perfused with PBS and then with RNAlater ( Qiagen ) , and the aortic arch ( third rib to aortic root ) was removed ( to get RNA from the most atherosclerotic part of the aorta ) and homogenized with FastPrep ( Qbiogene ) . Total RNA was isolated with an RNeasy Mini-kit with a DNAse I treatment step ( Qiagen ) . RNA quality was assessed with a Bioanalyzer 2100 ( Agilent Technologies ) , and RNA quantity with NanoDrop ( Thermo Scientific ) . Global mRNA expression profiles were generated with Mouse Gene 1 . 0 ST arrays ( Affymetrix ) according to the manufacturer's protocol . In brief , amplified and biotinylated cRNA was generated from 100 ng of high-quality RNA with the GeneChip WT Sense Target Labeling and Control Reagents kit ( No . 900652 , Affymetrix ) . The arrays were hybridized in a GeneChip Hybridization Oven 640 , further processed with a Fluidics Station 450 , scanned with a GeneArray Scanner 3000 7G , and analyzed with GeneChip Operational Software 2 . 0 . For global gene expression profiling , 18 Ldlr−/−Apob100/100Mttpflox/flox control mice ( n = 6 , 6 , and 6 at week 30 , 40 , and 50 , respectively ) and 48 Ldlr−/−Apob100/100MttpΔ/Δ mice ( early lesions: n = 6 immediately after PCL and n = 10 after PCL for 10 weeks; mature lesions: n = 6 immediately after PCL and n = 10 after PCL for 10 weeks; advanced lesions: n = 6 immediately after PCL and n = 10 after PCL for 10 weeks ) were used . All samples were randomized and run simultaneously on the arrays at the Bioinformatics and Expression Analysis Core Facility at the Karolinska Institutet . Global mRNA expression data were pre-processed with the three-step Robust Multichip Average [49] procedure ( background correction , quantile normalization , and summarization ) . No batch effects were detected that needed to be adjusted for . However , when comparing within groups of samples a few samples were considered outliers and excluded from further analyses . Groups of samples were compared by using differential expression ( FDR<0 . 30 ) [50] . The FDR level was selected partly on the basis of sensitivity analysis [51] to capture an adequately large portion of the true-positive genes for the following downstream analysis . Mathematica 7 . 0 and 8 . 0 or R 2 . 9 . 2 “package: affy” was used for all calculations . Selected probe sets were annotated with NetAffx ( Affymetrix ) and DAVID [52] , [53] . Ingenuity Systems Pathway Analysis ( IPA , www . ingenuity . com ) was used to for functional analyses of sets of differentially expressed genes . The top bio-function categories—molecular and cellular functions and disease and disorders— were used . Primary human monocytes from carotid endarterectomy patients [29] were isolated from 80 ml of EDTA-treated blood by density-gradient centrifugation and Ficoll-Paque Plus ( Amersham Biosciences ) . The monocyte-enriched layer was collected , washed , and plated in RPMI 1640 ( Gibco-Invitrogen ) supplemented with penicillin ( 100 U/ml ) and streptomycin ( 100 µg/ml ) ( PEST ) and 10% human AB serum ( Sigma-Aldrich ) in six-well plates ( BD Bioscience ) . The next day , nonadherent cells were removed , and the remaining monocyte/macrophage-enriched cells were given fresh RPMI 1640 medium supplemented as described above . After 7 days , total RNA from adherent macrophages was isolated with the RNeasy Mini-kit ( Qiagen ) . Thirty-eight mRNA expression profiles were generated with custom microarrays ( Affymetrix GeneChip HuRSTA-2a520709 ) . The Robust Multichip Average algorithm in Affymetrix Power Tools ( version 1 . 14 . 2 ) was used for background subtraction , normalization , and summarizing of raw microarray data . If the gene activity of the identified mouse gene sets ( PCL-responsive and regression-reactive ) are important for atherosclerosis progression/regression ( rather than being reactive markers of disease development ) , eSNPs of the identified gene sets could be enriched for CAD/MI risk . An eSNP indicates a functional relationship between the SNP and the expression of the identified gene ( within 1 Mb upstream and downstream of transcription start site ) [29] , [54] . To investigate this , we first identified human orthologs of the differentially expressed mouse genes using HUGO Gene Nomenclature Committee's ( HGNC ) Human and Mouse Orthologous Gene Nomenclature and National Center for Biotechnology Information's ( NCBI ) HomoloGene ( Tables S8 , S9 , S10 , S11 , S12 , S13 ) . Enrichment of eSNPs with CAD/MI risk was determined with GWA data from MIGen [28] . eSNPs were identified from global genotype data ( n = 156 ) and mRNA expression profiles of in vitro differentiated blood macrophages [29] . eSNPs were expanded with SNPs in strong linkage disequilibrium ( r2>0 . 9 ) within 200 kb of the eSNPs using HapMap . The expanded SNP sets for the causal gene sets consisted of 168 , 511 , and 1170 SNPs and the reactive gene sets of 17 , 276 , and 1057 SNPs for 30 , 40 , and 50 weeks , respectively; overlapping SNPs between causal and reactive SNP sets were removed from the reactive SNP sets . A total of 5000 random samples of SNPs were used to determine whether the expanded SNP set was more likely to be associated with CAD/MI than randomly selected sets with the same characteristics ( i . e . , equal number of SNPs , chromosomal distribution , and minor allele frequency >5% ) . Finally , fold enrichment in risk was calculated as the ratio between the relative number of significant SNPs ( P<0 . 05 ) in the expanded SNP set and the relative number of significant SNPs ( P<0 . 05 ) in the random sets . MATLAB R2011a was used for all computations . TF-regulatory co-expression networks were reconstructed from global mRNA expression profiles from blood macrophages [29] . Regulatory gene networks were inferred from human homologs of causal mouse genes using the context likelihood of relatedness ( CLR ) method with Pearson correlation [55] , [56] . The CLR method computes the significance of a given regulator-target similarity score for a gene regulatory network . In brief , by using Pearson correlation , co-expression similarity between all gene pairs was computed and stored in a matrix , M [55] . Next , background corrections using positive z-scores were computed for each entry for M , considering both row and column values . Then , the joint likelihood of pairwise z-scores for each M was assessed [56] . TF-regulatory interactions used for the networks in early , mature , and advanced lesions had P values of <0 . 0051 , <0 . 0013 , and <0 . 00042 , respectively , corresponding to the 50% most probable interactions in each network . CLR with Pearson correlation was implemented in C++ . For each time point , the 10 most connected TFs are shown in the visualization of the networks with Cytoscape 2 . 8 . 2 [57] . Human THP-1 monocytes were plated at 5×105 cells/well in six-well culture dishes ( Becton Dickinson ) containing 10% fetal calf serum ( FCS ) -RPMI-1640 supplemented with PEST . The cells were incubated with PMA ( 50 ng/mL ) ( Sigma-Aldrich ) for 72 hours to induce differentiation into macrophages and with Ac-LDL ( 50 µg/mL ) for 48 hours to generate foam cells . Thereafter , for each master regulator , cells were transfected with siRNA ( one at a time ) ( Ambion , Life Technologies , Table S17 ) using Lipofectamine 2000 as recommended by the manufacturer ( Invitrogen ) , in medium without FCS , PEST , or PMA . Forty-eight hours after siRNA transfection , cells were examined for effects on the expression of network genes ( see section Gene Expression Measurements and Hypergeometric Testing for Network Specificity ) and CE accumulation ( see section Lipid and Protein Measurements ) . Ac-LDL was prepared as described [58] . The samples were then dialyzed against PBS at 4°C . Ac-LDL protein concentration was determined by the Bradford method . LDL was isolated from plasma of healthy donors by sequential ultracentrifugation [59] . For differential expression analyses and to determine the degree of silencing by siRNA ( Table S17 ) , total RNA was isolated from the targeted THP-1 foam cells with the RNeasy Mini-kit ( Qiagen ) . The concentration was determined by NanoDrop ( Thermo Scientific ) . The degree of siRNA silencing was determined by TaqMan analyses ( Table S17 ) . cDNA was synthesized from 0 . 4 µg of total RNA with Superscript III ( Invitrogen ) . Diluted cDNA was amplified by real-time PCR with 1×TaqMan universal PCR master mix ( Applied Biosystems ) according to the manufacturer's protocol . Assay-On-Demand kits with corresponding primers and probes from Applied Biosystems were used ( Table S17 ) ; samples were normalized with the comparative Ct method . mRNA expression profiles of targeted THP-1 foam cells were generated with Agilent Human Custom Gene Expression Microarray 8×15K , containing network genes ( 556 unique genes from the 30 , 40 , and 50 week networks ) and TEML genes ( 116 TEML genes from DAVID ( two of these 116 genes are present among the network genes ) as well as three macrophage emigration genes ( CCR7 , LXR , and NTN1 ) , for a total 673 of unique genes ( spotted in duplicate ) , according to the manufacturer's instructions . The R package “limma” was used to normalize the Agilent array data and to identify differentially expressed genes ( FDR<0 . 1 ) with the Benjamini-Hochberg procedure . The probability that a master regulator was specific for its time-point network rather than expected by chance was calculated by using hypergeometric distribution P values as follows:When sampling X genes of M genes ( M = 673 array genes ) , what is the probability ( P ) that x or more of these genes belong to a time-point-specific network K ( K = 53 , 185 , or 379 genes for the 30- , 40- , or 50-week network , respectively ) , shared by n of the M genes ( Table 4 ) . Lipids from siRNA-targeted THP-1-derived foam cells were isolated by extraction with hexane/isopropanol ( 3∶2 ) at room temperature for 1 hour and then with 0 . 5 ml of chloroform for 15 min [60] . The lipids were dried and resuspended in isopropanol with 1% Triton-X-100 ( Sigma-Aldrich ) . The lipid content of the foam cells was determined by enzymatic assays using the Infinity kit for total cholesterol ( Thermo Scientific ) and a kit for free cholesterol ( Wako Chemicals ) . After lipid extraction , proteins were extracted from the same wells by incubation with 0 . 5 M sodium hydroxide for 5 hours at 37°C . Protein concentration was determined by the Bradford method . CE accumulation in the targeted THP-1 foam cells was calculated as total cholesterol – free cholesterol/protein concentration , relative to control .
The main underlying cause of heart attacks and strokes is atherosclerosis . One strategy to prevent these often deadly clinical events is therefore either to slow atherosclerosis progression or better , induce regression of atherosclerotic plaques making them more stable . Plasma cholesterol lowering ( PCL ) is the most efficient way to induce atherosclerosis regression but sometimes fails to do so . In our study , we used a mouse model with elevated LDL cholesterol levels , similar to humans who develop early atherosclerosis , and a genetic switch to lower plasma cholesterol at any time during atherosclerosis progression . In this model , we examined atherosclerosis gene expression and regression in response to PCL at three different stages of atherosclerosis progression . PCL led to complete regression in mice with early lesions but was incomplete in mice with mature and advanced lesions , indicating that early prevention with PCL in individuals with increased risk for heart attack or stroke would be particularly useful . In addition , by inferring PCL-responsive gene networks in early , mature and advanced atherosclerotic lesions , we identified key drivers specific for regression of early ( PPARG ) , mature ( MLL5 ) and advanced ( SRSF10/XRN2 ) atherosclerosis . These key drivers should be interesting therapeutic targets to enhance PCL-mediated regression of atherosclerosis .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "animal", "models", "medicine", "model", "organisms", "gene", "expression", "genetics", "mouse", "molecular", "genetics", "atherosclerosis", "biology", "computational", "biology", "gene", "networks", "cardiovascular" ]
2014
Plasma Cholesterol–Induced Lesion Networks Activated before Regression of Early, Mature, and Advanced Atherosclerosis
Position determination in biological systems is often achieved through protein concentration gradients . Measuring the local concentration of such a protein with a spatially varying distribution allows the measurement of position within the system . For these systems to work effectively , position determination must be robust to noise . Here , we calculate fundamental limits to the precision of position determination by concentration gradients due to unavoidable biochemical noise perturbing the gradients . We focus on gradient proteins with first-order reaction kinetics . Systems of this type have been experimentally characterised in both developmental and cell biology settings . For a single gradient we show that , through time-averaging , great precision potentially can be achieved even with very low protein copy numbers . As a second example , we investigate the ability of a system with oppositely directed gradients to find its centre . With this mechanism , positional precision close to the centre improves more slowly with increasing averaging time , and so longer averaging times or higher copy numbers are required for high precision . For both single and double gradients , we demonstrate the existence of optimal length scales for the gradients for which precision is maximized , as well as analyze how precision depends on the size of the concentration-measuring apparatus . These results provide fundamental constraints on the positional precision supplied by concentration gradients in various contexts , including both in developmental biology and also within a single cell . To determine position in a biological system , some component within the system must have a nonuniform spatial distribution . Often , this is achieved through the formation of gradients of protein concentration . Typically , a gradient forms when a protein is manufactured/injected within a small region and subsequently spreads and decays [1] . By measuring the local concentration , position relative to the source can be determined . In developmental biology , where such gradients are used to control patterns of gene expression , gradient proteins are called morphogens . However , intracellular concentration gradients are also thought to be important for organisation inside single cells . For a gradient mechanism to be biologically viable , position determination must be precise and therefore robust to noise . Variability from one copy of the system to another ( e . g . , from cell to cell or embryo to embryo ) will certainly compromise positional precision . Production and degradation rates can vary ( e . g . , due to different copy numbers of transcription factors or proteases ) . The physical size of the system will also vary , and this may affect proper positioning . Most previous analyses of morphogen gradients have focused on robustness to changes in these extrinsic factors [2–4] between different copies of the system . However , there will also be intrinsic noise affecting the gradient within a single copy of the system , for example due to the unavoidably noisy nature of the biochemical reactions involved . This dissection of the fluctuations into extrinsic or intrinsic components mirrors that introduced into the analysis of stochastic gene expression [5–7] . However , here , intrinsic noise alters not only the overall protein copy numbers ( similar to [5] ) , but also crucially the spatiotemporal protein distribution . Even if all extrinsic variation could be eliminated , intrinsic biochemical noise would still lead to a fundamental limit to the precision of position determination , in a similar way to limits on the precision of protein concentration measurement [8 , 9] . In this paper , we therefore address the question of how precisely a concentration gradient can specify positional information , and calculate the limits on positional precision for a simple , but biologically relevant , gradient formation mechanism with first-order reaction kinetics . Quantitative measurements , for example on the Bicoid–Hunchback system in Drosophila [10] , have shown that remarkable positional precision can sometimes be obtained . For this reason , understanding the fundamental limits to the precision of concentration gradients is clearly an important issue in developmental biology . Our results will be equally relevant to gradients that form within single cells , where protein copy numbers of a few thousand [11–13] will lead to large density fluctuations . The properties of intracellular protein gradients have been studied by Brown and Kholodenko [14] . Recently , a number of these gradients have been observed experimentally in both prokaryotic and eukaryotic systems . The bacterial virulence factor IcsA forms a polar gradient on the cell membrane of Shigella flexneri [15] . MipZ in Caulobacter crescentus forms polar gradients to aid division site selection [11] . In Bacillus subtilis , the MinCD complex also forms polar gradients in order to direct division site selection to the mid-plane of the cell [16 , 17] . In Escherichia coli , the oscillatory dynamics of the Min proteins creates a time-averaged gradient that directs cell division placement [18–24] . Using mechanisms of this sort , division site placement in bacteria can achieve an impressive precision of ±1% of the cell length [25 , 26] . Cell division in eukaryotic cells is also believed to be regulated by concentration gradients . For example , in fission yeast , the protein Pom1p forms a cortical concentration gradient emanating from a cell tip , thereby restricting the cell division protein Mid1p to the cell centre [27 , 28] . In eukaryotic cells , gradients of the Ran and HURP proteins aid the formation of the mitotic spindle by biasing microtubule growth toward the chromosomes [29–33] . Gradients may also play a role in the localization of Cdc42 activation , thereby permitting a coupling between cell shape and protein activation [34 , 35] . Suppose that a biological system needs to identify a particular position along its length , such as the mid-plane to ensure symmetrical cell division . As concrete examples , MipZ and the MinCD complex act by displacing the essential cell division protein FtsZ from the cell membrane . Since the concentrations of MipZ/MinCD are higher near the cell poles , FtsZ accumulates near the cell centre . Below some critical threshold of MinCD or MipZ concentration , enough FtsZ will presumably accumulate to form the division apparatus . The locations where the concentration gradient crosses these thresholds mark positions within the cell . In our analysis , we simply postulate the existence of such well-defined critical thresholds , where the gradient sharply switches a downstream signal from on to off . Clearly , any real gradient cannot act as such a sharp switch—in reality , a certain amount of smearing is inevitable . Furthermore , there will be additional noise in the process of actually measuring the concentration due both to the binding of the gradient proteins to the receptor molecules [8 , 9] , and also to the downstream reactions that process this incoming signal [5–7 , 36–38] . In general , the noise of the output signal of a processing network can be written as the sum of a contribution from the noise in the input signal plus a contribution from the reactions that constitute the processing network . We assume here that the detector and the processing network are ideal and do not add any noise to the gradient input signal . As a result , our calculated variation constitutes a lower bound; any real gradient-signalling system will inevitably have a lower precision . We first considered a system with a single planar morphogen source and linear degradation , thereby producing an exponentially decaying average concentration profile . While this model is very simple , it remains biologically relevant in both developmental and intracellular contexts . Gradients of Bicoid in Drosophila and IcsA in Shigella have been quantitatively measured and shown to fit this exponential decay profile on average to high accuracy [10 , 15] . We then calculated the expected distribution of positions where a noisy gradient crosses a concentration threshold . With typical cellular copy numbers of a few thousand proteins , the system would be unable to identify the correct threshold position from a single measurement . To achieve reliable position determination , the concentration must be averaged over time . We show that by averaging measurements , a biological system is able to achieve precision in position determination of a few percent of the system size even with hundreds of protein copies , a result we verified with computer simulations . Furthermore , we find that the precision of position determination is maximised when a particular choice of the gradient decay length is made . We also show how the precision depends on the detector size ( i . e . , the volume over which the density measurement is made ) . For a 2-D gradient ( e . g . , on a membrane ) , the precision possible after a certain averaging time depends only very weakly on the detector size . We relate all these results to experimental measurements of gradients in Shigella and fission yeast . We also considered the ability of gradients from two poles to identify the centre of the system , as in the MipZ and Pom1p gradients discussed above . Related designs have also been proposed for the control of hunchback positioning in Drosophila [3 , 4 , 39] . As before , we find that the precision of the system can be optimised by a particular choice of the decay length . However , if the threshold position is set at the system centre , time-averaging improves precision more slowly than in the single-source model . For subcellular gradients , we find that a few thousand copies of the gradient proteins may therefore be required for high precision . Our results strongly constrain the possible concentrations of gradient proteins in two gradient systems . We considered a protein gradient that is used to determine a particular position along the length of a cylindrical system . The system will have dimension d = 2 if the gradient is restricted to the membrane , or d = 3 if the gradient is in the cytoplasm . We chose the x-axis along the long axis of the system . Position in the remaining coordinates is denoted by the vector y . For a membrane system , periodic boundary conditions are appropriate in the y direction . Otherwise , zero-flux boundaries are used throughout . The system length is L , and the size of the system in the remaining directions is taken to be L⊥ ( so L⊥ = 2πr , where r is the system radius , for the d = 2 membrane case ) . A source on the x = 0 plane produces proteins at rate J per unit area , which then diffuse with diffusion constant D , and are degraded uniformly at rate μ . Neglecting fluctuations , the protein concentration ρ ( x , y , t ) is described by If L ≫ λ = ( D/μ ) 1/2 , the characteristic decay length of the gradient , we find that , at steady state , the density is Symmetry dictates that the average density is independent of y . Gradients with the form of Equation 2 have been found to accurately fit quantitatively measured concentration profiles in both developmental [10] and subcellular [15] systems . While we have outlined the model in terms of production and degradation , Equation 1 could equally apply to other mechanisms in which the active protein originates in a single location , but deactivation occurs uniformly throughout the system . The same equation would therefore describe a protein that is phosphorylated by a polar-localised kinase and dephosphorylated by a uniformly distributed phosphatase , or a protein that is activated by being injected into the membrane at a pole and deactivated when it dissociates . These biochemical details do not affect the behaviour of the model . We suppose that signalling is active where the local gradient protein concentration is above some threshold value , ρT , and inactive otherwise . The average concentration profile for a single gradient , Equation 2 , suggests that the system will be divided into a region 0 ≤ x < xT where signalling is active , and a region xT ≤ x ≤ L where signalling is not active , with ρT = ρ ( xT ) . However , noise in the local protein concentration will cause this threshold position to fluctuate . This noise may come from intrinsic fluctuations in the diffusion , injection , and decay processes , or from extrinsic factors that produce systematic changes in the boundary position when comparing one copy of the system to another . Here we consider only intrinsic biochemical fluctuations . Protein production and degradation events were considered to be single-molecule reactions with a fixed probability per unit time , and hence were Poisson processes . We also assumed that the hopping of proteins in or out of a particular region of space is governed by Poisson statistics , thereby generating a diffusive process for molecular transport . Since the system is linear , the instantaneous fluctuations in molecular number , n , within a volume ( Δx ) d centred on the position ( x , y ) should also obey Poisson statistics , with In terms of protein density , this becomes This relation can also be established using more elaborate field theoretic techniques ( see [40] ) . From this expression for the variation in the density , we can compute the width of the threshold position distribution by expanding the average threshold position xT . To leading order , this width is given by where ρ′ ( xT ) denotes the first derivative of the density at x = xT . Here we identify ( Δx ) d as the size of the region in which the concentration is being measured . For subcellular gradients involved in positional information , this volume is determined by the size of an individual receptor or protein with which the gradient protein interacts , an example being the interaction between the MinCD and FtsZ proteins in B . subtilis . The size of the detector , Δx , will then be on a molecular scale . This conclusion still holds even if the gradient proteins bind cooperatively to the “detection” protein/receptor due to the close physical proximity of the bound molecules . In contrast , however , the cellular length scale will be much larger , 1μm or bigger . Throughout the following analysis we focus on subcellular gradients . However , our model can equally be applied to developmental biology , and we consider these systems further in the Discussion . As concrete examples , we first consider the IcsA polar gradient on the membrane of the rod-shaped bacterium Shigella ( L ≈ 3 μm , L⊥ ≈ 3 μm ) [15] . IcsA is exported to the outer membrane at a single pole , after which it diffuses and undergoes uniform proteolysis by the protease IcsP , thereby forming an exponential gradient exactly as in our model [15] . Outer membrane IcsA is then able to recruit actin nucleation factors . However , a critical concentration of IcsA is likely needed for actin nucleation: in this way a comet-like actin tail is generated at only one cell pole , thereby generating unidirectional motility of the pathogen . A cell will typically have a few thousand copies of IcsA [12] , forming a gradient with λ ≈ 0 . 5 μm [15] . We take the detector size to be Δx = 0 . 01 μm , consistent with an interaction between IcsA and actin nucleation proteins . For diffusion on the cell membrane , we take D = 1 μm2s−1 . On the membrane of a cell of this size , there would be approximately LL⊥/ ( Δx ) 2 ∼ 105 potential detector sites , many more than the typical copy number . Even near to the source pole , detector sites will typically be unoccupied . A detector region at a distance x = 0 . 5 μm from the highly occupied pole will have average occupancy of <n> ∼ 10−1 . In the cytoplasm of a similarly sized bacterium , the number of potential detector sites will be ∼106 , again much larger than the protein copy numbers typically supported by bacteria . Similar estimates can be made for single polar gradients in fission yeast ( L = 10 μm , L⊥ = 6 μm ) , such as for Pom1p [27 , 28] . Here we assume a total of 2 , 000 protein copies ( this concentration has not yet been measured but this number is plausible [28] ) . We also take D = 1 μm2s−1 and a decay length of λ = 2 μm , parameters that are approximately consistent with the Pom1p gradient imaged by Padte et al . [28] . We again assume that Δx = 0 . 01 μm , corresponding to a molecular-sized detector , as would be the case if the gradient protein interacted with other membrane proteins ( such as Mid1p ) [27 , 28] . The typical occupancy of a Δx = 0 . 01 μm site is then <n> ∼ 10−2 at x = 2 μm from the source . As we have seen for both fission yeast and Shigella , average detector site occupancies that are very much less than one protein per site ensure that the threshold occupancy must necessarily be less than one . Since most regions will be devoid of any copies of the protein , a single instantaneous measurement of the protein density cannot give a good estimate of the local average concentration . In addition , multiple positions where the concentration crosses ρT would be observed simultaneously in such a measurement since the concentration would be above the threshold everywhere there is a protein molecule present , and below the threshold where there is no protein molecule . To reliably determine the average concentration profile , the system must therefore integrate the measured concentration over time . The noisy concentration profile provided by the gradient protein forms the input signal that is then time-averaged by a downstream signal-processing network . In general , the mechanism for time-averaging is provided by the lifetimes of the states in the processing network . For instance , in the case of gene expression , fluctuations in the occupancy of the promoter by a gene regulatory protein can be filtered by the lifetime of the mRNA transcript , provided that lifetime is much longer than the timescale of fluctuations in the promoter occupancy [7 , 9] . Similarly , for subcellular gradients , as in Shigella , fluctuations in the gradient can be filtered by the lifetime of activated receptors/detector proteins or their downstream products . Provided this timescale is much longer than the sub-millisecond timescale of the gradient fluctuations , good time-averaging can then be achieved . Importantly , the reactions in the downstream network not only time-average the noise of the input signal , but also add further noise to the signal [5–7 , 36–38] . Here , we focus exclusively on noise in the concentration gradient and do not model the downstream reactions explicitly , but simply assume they are noiseless and model them with an effective averaging time . In essence , we assume that the detector and the network that process the gradient signal are ideal and do not add further noise , and are thus able to time-average the gradient signal in the best possible way . Our results thus provide a lower bound to the output noise set by the Poissonian fluctuations of the signalling molecules . We suppose that averaging over a time interval τ we can take Nτ = τ/τind independent measurements of the concentration . In our ideal case , we then expect that the fluctuations in the concentration would decrease according to Nτ−1/2 . Since the width varies linearly with Δρ according to Equation 5 , the width will also decrease as The timescale τind on which independent measurements can be made is set in our ideal case solely by the reaction–diffusion dynamics of the gradient proteins , as discussed in Methods . For cellular parameter values , the typical reaction timescale , 1/μ , will be much longer than the typical timescale for diffusion between detector sites , ( Δx ) 2/D . Assuming a molecular-sized detector , this latter timescale would be on the order of 10−4 s , whereas effective protein lifetimes will typically be seconds or longer . The Damkohler number for the system , the ratio of the diffusive and reaction timescales , would therefore be Da ∼ ( Δx ) 2/λ2 ∼ 10−4 . Since Da ≪ 1 , the averaging timescale is dominated by diffusive motion . In d = 3 we find τind ∼ ( Δx ) 2/D . However , in d = 2 , density correlations decay away more slowly , leading to the appearance of logarithmic corrections that are weakly dependent on the parameters λ and Δx . For long averaging times , τ ≫ 1/μ , the width determined from time-averaged measurements would be in d = 2 , and for d = 3 where k2d , k3d , and α are constants . As we have discussed above , Δx will be set by the concentration detection mechanism . However , in a subcellular context , Δx also sets the highest possible resolution of the system . Once w ≈ Δx , the cell cannot resolve the target position with any higher precision . Equation 7 suggests that in d = 2 , precision depends only very weakly on the detector size , through the logarithmic correction factor . Reducing the detector size would increase the number of independent measurements made in a given averaging time . However , since fewer proteins would be measured by each detector over one averaging period , reducing Δx would therefore increase the instantaneous density fluctuations . In d = 2 , these two effects largely cancel . Hence , even if we have over/underestimated the detector volume , this will have little effect on the precision of gradients in d = 2 dimensions , such as IcsA in Shigella or Pom1p in fission yeast . In three dimensions , however , w varies as ( Δx ) −1/2 . Since increasing Δx reduces w in both d = 2 and d = 3 , an optimal strategy would be to choose Δx to match the desired precision in order to minimise the required averaging time . Intriguingly , from Equations 7 and 8 we find that there exists an optimal decay length such that precision is maximised . This result can be understood as follows: for fixed xT , and for λ ≫ xT , the value of |<ρ′ ( xT ) >| tends to a constant J/D , independent of xT . However , as λ increases , <ρ ( xT ) > increases and therefore the absolute size of the fluctuations in the density also increases . Therefore , for large and increasing values of λ , w ∝ <ρ ( xT ) 1/2> / |<ρ′ ( xT ) >| must be increasing . Now if λ is smaller than xT and decreasing , when computing the width ∝ <ρ ( xT ) 1/2> / |<ρ′ ( xT ) >| , the presence of the square root means that the numerator decreases much more slowly than the denominator . Hence , the width must again increase as λ is decreased for small λ . Combining these results for small and large λ , the width must have a minimum , optimum value as a function of λ . This occurs at λ = xT in d = 3 . In d = 2 , the optimal decay length is given approximately by in which we have retained the first-order logarithmic correction . To examine the biological impact of Equation 7 we again considered the Pom1p membrane gradient in fission yeast [27 , 28] using the parameters described earlier . Simulations of this example system were performed as described in Methods , with on average 100 proteins in the system . Figures 1A and 1B show how the measured threshold position , x̄ , and width , w , vary with averaging time . For long averaging times , the simulation data gives excellent agreement with Equation 7 , with the constants k2d = 0 . 40 ± 0 . 02 and α = 2 . 5 ± 0 . 8 . Figure 1C shows the w ∼ τ−1/2 behaviour predicted in Equation 7 , and Figure 1D confirms that the width has a minimum as a function of λ . The simulation results are consistent with the position of the minimum predicted by Equation 9 . Figure 1E shows that the distribution of measured threshold positions is Gaussian to a good approximation . Since the averaging timescale τind in a subcellular system is on the order of ∼10−4 s , time-averaging over a period of minutes can achieve great precision even with very few copies of the gradient protein . With the parameter values given above , Equation 7 predicts that the position xT = 2 μm can be located to within ±0 . 5 μm within an averaging time τ = 60 s even if the system contains on average only about 20 copies of the protein . A precision of ±0 . 1 μm can be achieved in the same averaging time with about 400 copies of the protein , a remarkably high level of precision for such a low concentration . In vivo Pom1p gradients may be formed by a few thousand protein copies , allowing for even greater precision . However , we can see in Figure 1B that for averaging times of less than about a second , the simulation results are not consistent with Equation 7 . In this regime both w and x̄; are equal to λ . As discussed above , at very short averaging times the presence of a particle at any position will cause the time-averaged concentration to be above ρT at that point and hence generally will generate a threshold crossing . The probability distribution of threshold measurements , p ( x ) , will therefore follow the probability distribution of particles . Assuming L ≫ λ , we have The cell will on average estimate the threshold position to be and measurements will be distributed about this position with variance The system is therefore unable to resolve the correct threshold position at these short timescales if this is different from λ . Associated with the average concentration at the threshold is a length scale , l ∼ ρT−1/d , the typical distance between proteins at this position . The average time for a protein to diffuse this distance will scale as l2 / D . In two dimensions , this time is given by Since τ× is the timescale on which a diffusing particle first arrives at xT , if τ ≪ τ× , there will generally be no particles detected at xT in the averaging period . The system therefore cannot reliably estimate the mean concentration at xT , and hence cannot precisely identify the threshold position . For averaging times much greater than τ× , on average at least one particle will be detected at xT . The time-averaged concentration profile will then approach Equation 2 , and x̄ will approach xT . Hence τ× determines the crossover time between the two observed regimes of constant w and w ∝ τ−1/2 . Figure 1F shows that the scaling in Equation 13 is also reproduced in our simulations . For the parameter values above , τ× = 0 . 3 s , and for a more realistic copy number of 1 , 000 , τ× = 0 . 03 s . These timescales are extremely short compared with cell-cycle timescales , but do nevertheless show that some sort of time-averaging is probably essential: a single instantaneous measurement is unlikely to provide precise positional information . In fact , as we have seen , averaging over much longer times ( tens of seconds ) may be necessary if very high ( 1% ) precision is required . Simulations of the model in d = 3 were also performed ( unpublished data ) . Similar behaviour was observed in this case , and Equation 8 gave good agreement with the observed width at long averaging times . To reliably locate the centre of a system , the mechanism responsible must incorporate information about the overall system size so that the identified position can scale correctly . A single gradient characterised by a fixed decay length cannot achieve this . We therefore examined a system where protein gradients are produced by sources at both ends , and where the central position is identified as a concentration minimum . We modified our earlier model by adding an additional planar source at x = L . This addition is appropriate for modelling cell division inhibitors , such as MipZ in Caulobacter , that are injected into the membrane near both cell poles . However , our model would apply equally if the two sources were of different repressor proteins ( as may be the case in fission yeast [27 , 28] ) , although we do assume that J , D , and μ are the same for both gradients . In this scenario , signalling activity would be determined by the total concentration . Without fluctuations , this would be described by The steady-state solution is now which has the expected minimum at x = L/2 . We then supposed that the cell compares the concentration to a threshold value corresponding to the minimum of the average profile , ρmin = ρ ( L/2 ) = ρT . Positions where the concentration is at or below the threshold are identified as being at the centre of the cell . While the average steady-state density profile would never extend below ρmin , fluctuations ensure that the concentration in the region around the centre spends a significant amount of time at or below the threshold . Around point ( s ) where <ρ ( x ) > = ρT , noise in the protein concentration would lead to a distribution of threshold-crossing positions . We considered an expansion of the density fluctuations about xT = L/2 , giving , to leading order since any first-order term proportional to <ρ′> vanishes at xT = L/2 . The width is therefore given by Substituting in Equation 15 gives As in the single-gradient model , the typical occupancy of the threshold region would be much less than one . For example , if we take the parameter values considered previously for the Pom1p gradient in fission yeast , with 2 , 000 protein copies , the average occupancy of a detector site at x = L/2 would be <n ( L/2 ) > ∼ 10−3 . We assume here that Pom1p forms a gradient from both poles . In fact , it may only form a single gradient , with another hitherto unidentified protein forming the second polar gradient [27 , 28] . However , as discussed earlier , this detail does not affect our calculations . As a second example , MipZ in Caulobacter ( L = 2 . 5 μm , L⊥ = 2 μm ) is typically present at about 1 , 000 copies , and forms two polar gradients with a decay length λ ≈ 0 . 25 μm [11] . The average occupancy at the centre of this system would be approximately <n ( L/2 ) > ∼ 10−3 . Averaging measurements of the concentration over time is therefore required in both cases to obtain precise positional information . Since the width now goes as ( Δρ ) 1/2 , as shown in Equation 17 , we expect where , , and Α~ are constants . Averaging proceeds much more slowly than previously , with a τ−1/4 dependence . This follows directly from the vanishing of the first derivative at the average threshold position . In d = 3 , and for λ ≪ L , Equation 19 predicts that w will be minimised when λ ≈ L/6 is chosen . In d = 2 , logarithmic corrections again alter this result slightly , with the optimal decay length now occurring at in which we have included the leading logarithmic correction . This optimal length scale arises for similar reasons as in the single-gradient model . For the Pom1p gradient imaged by Padte et al [28] , the decay length is observed to be 1–1 . 5 μm , comparable with this optimal decay length of about 1 . 5 μm for a 10-μm cell . We simulated our model in d = 2 with representative parameter values for fission yeast membrane gradients . We used μ = 0 . 36 s−1 , chosen to give λ = 1 . 67 μm , and J = 6 μm−1s−1 , giving , on average , 200 protein copies in total . Figure 2 shows the results of these simulations . Again , we observe two distinct regimes . At averaging times longer than about a second , there is excellent agreement with Equation 19 , as we can see in Figure 2C . Fitting to the simulation results , we find = 0 . 63 ± 0 . 02 and Α~ = 2 . 5 ± 1 . 0 . Figure 2D confirms the existence of the optimal decay length in our simulations . Since the width decays as τ−1/4 for this system , longer averaging times and/or higher protein copy numbers are required than in the single-gradient model to achieve high precision . Intrinsic biochemical noise may therefore strongly constrain systems of this type . For the yeast-membrane gradient considered above to achieve a precision of ±5% of the cell length after averaging for 1 min , about 800 protein copies are required . Therefore , in the absence of any other positioning mechanisms , the Pom1p gradient will require ∼1 , 000 protein copies or more to precisely direct the location of cell division . We estimate that the MipZ gradient in Caulobacter , with 1 , 000 protein copies , would be able to locate the cell centre to within ±5% of L after approximately τ = 2 s . However , since precision only improves as τ−1/4 , averaging over τ = 20 min would be required for the same system to achieve ±1% accuracy . Noise in biochemical processes within a cell will lead to fluctuations in protein concentration gradients , and hence also to variation in the position where these gradients cross a particular threshold value . These fluctuations therefore place a limit on the potential precision of position determination mechanisms relying on concentration gradients alone . In subcellular systems with protein copy numbers in the thousands , this noise will be sufficiently large that position cannot be determined reliably from a single measurement of the density profile . To determine position to within a few percent of the system length , a precision achieved by some subcellular systems , the protein concentration must be averaged over time . For a single subcellular membrane gradient , we have seen that by averaging over a period of a minute , excellent precision can potentially be achieved with only a few hundred protein copies . This remarkable precision is due to the sub-millisecond diffusive timescale on which time-averaging occurs . Precise identification of the cell mid-plane by gradients emanating from both poles requires longer averaging times or higher copy numbers , since larger fluctuations result from the vanishing first derivative of the average concentration at the system centre . Intrinsic biochemical noise may therefore be a strong constraint on subcellular two-gradient positioning systems , dictating that the copy numbers be sufficiently high to suppress fluctuations . So far we have focused almost exclusively on fluctuations in subcellular gradients; however , our results are also applicable to developmental biology , and we wish to comment briefly on this application . Here , the appropriate length scales are usually much longer , on the order of hundreds of micrometers in Drosophila . Moreover , the gradients affect patterns of gene expression through the binding of gradient molecules to DNA regulatory sequences inside individual nuclei . For example , in Drosophila , where exponential gradients have been quantitatively measured for Bicoid [10] , Bicoid binds cooperatively to hunchback regulatory DNA . In this case we again expect molecular-scale effective measuring volumes , with Δx ∼0 . 01 μm as a reasonable order of magnitude . We next assume purely Poisson statistics for the fluctuations: this is a stronger assumption than for our earlier subcellular gradients , as there will be additional complications arising , for example , from the import/export of morphogens from nuclear compartments . However , if diffusive noise is dominant , then Poisson statistics would be retained , and we could expect our earlier analysis to apply , although with one important distinction . Instead of Δx setting the maximal possible precision , this would now be set by the size of individual nuclei ( prior to cellularization ) , since we expect relatively homogeneous gene expression within a single nuclear volume . A single nucleus in Drosophila has a length scale of about 10 μm , still much smaller than the decay length of the gradient of λ ∼100 μm , allowing for high-precision gene expression [10] . Using the Drosophila Bicoid gradient as an example , we use L = 500 μm , L⊥ = 100 μm , and estimate D = 10 μm2s−1 and μ = 10−3s−1 , giving λ = 100 μm , consistent with experimental measurements [10] . Assuming a high copy number of 107 per embryo ( we are not aware of experimental constraints on this figure ) gives J ∼ 1 μm−2s−1 . For a single gradient in d = 3 , we find that about a 5-min averaging time is required to bring the error down to ±1 nuclear length . For a two-gradient model in d = 3 , longer averaging times on the order of an hour are required to reduce the centre-finding positional error to about ±2 nuclear lengths . Since gene expression may need to be controlled on shorter timescales than this , other designs ( e . g . , using interacting gradients [3 , 4] ) may be required for high-precision centre-finding ( see also below ) . The effects of the optimum gradient length scale will also be interesting to probe in a developmental biology context . However , our simple analysis may be complicated by the multiple roles played by many morphogens: for example , Bicoid not only activates hunchback , but it also helps to regulate pair-rule genes , such as Even-skipped . Nevertheless , it is interesting to note that the Bicoid gradient length scale λ ∼ 100 μm [10] is not too far away from the L/6 optimum for a two-gradient system , and in a single-gradient context will offer maximal precision well into the anterior half of the embryo . Up to this point we have only considered systems with first-order degradation . Morphogen gradients with nonlinear decay have also been proposed [2] . This nonlinearity will lead to non-Poissonian density fluctuations , which may significantly change the observed behaviour . England and Cardy [41] have previously calculated the response of a gradient with nonlinear decay to one source of biochemical noise , namely a fluctuating production rate . However , they calculated the change to the average gradient , while fluctuations about this average may also be important . It would certainly be of interest to compare the performance of linear and nonlinear degradation mechanisms in more detail . Centre-finding mechanisms with interactions have also been proposed [3 , 4] . In these models , position is determined from the combined gradient of two proteins , which would be steep around the system centre due to an interaction between the two gradients . These mechanisms may therefore be able to achieve greater precision for midpoint determination than the noninteracting mechanism considered here . Throughout this work we have assumed that the gradient protein concentration fluctuates about a steady-state profile , and hence averaging over a longer time will give a more precise estimate of the average profile . For a subcellular system , the steady-state gradient will develop over timescales of less than about a minute , due to the micrometer length scales involved . This timescale is short compared with the cell-cycle time , which ranges from tens of minutes up to many hours . For this reason we expect that subcellular gradients will be in steady state , and therefore that our analysis will be directly applicable . However , in developmental biology , the effective lifetimes will likely be much longer , and the gradient may take hours to fully reach steady state . Moreover , a number of developmental biology systems are known to respond to a morphogen gradient that has not reached steady state [42–44] . A further complication is the possibility of gradient formation by non-Fickian diffusion [45] , where there is no steady state at all . The model considered in this paper does not take into account time-varying average gradients . If the average gradient is evolving , a longer averaging period will not necessarily lead to improved precision . Clearly , more work will be required to understand how such dynamically evolving systems are able to yield precise positional information and filter out fluctuations . Nevertheless , we do note that two-gradient systems of the kind analyzed here are naturally able to locate the system centre even without being in steady state , due to the symmetry of the system [3] . The positional variations in such a non–steady-state scenario will not be the same as calculated here , but our analysis does form a first step toward the analysis of these more complex systems . We have assumed in our analysis that during the time-averaging process we are taking independent measurements at intervals of τind . However , in both real biological systems and our simulations , measurements can generally be taken at much shorter intervals than this , leading to correlations between consecutive measurements . For a series of correlated measurements taken at time intervals δt over a period 0 ≤ t ≤ τ , with τ ≫ δt , the expected error for the time-averaged concentration at position x , ( Δρ ( x , τ ) ) 2 , is given by [46] where ( Δρ ( x , 0 ) ) 2 is the variance of a single measurement , and C ( t ) is the normalized density correlation function , We therefore define the timescale τind to be and assuming τind ≫ δt , we recover For N independent measurements of the density , we would expect the error to decline as N–1/2 . For large enough values of τind ( τ ) , where τind becomes independent of τ , we can therefore interpret τind as the time interval required for successive measurements to be independent . The next step of the calculation is to compute the correlation function C ( t ) appropriate for our model . For pure diffusion , we expect: On timescales t ≪ ( Δx ) 2/D , the system remains perfectly correlated , as there has been insufficient time for particles to hop away to neighbouring sites . However , for t ≫ ( Δx ) 2/D , an algebraically decaying correlation function is found , characteristic of diffusion . However , we also need to incorporate the effects of spontaneous decay that occur independently of the diffusive motion . Adding decay to the system simply alters the correlation functions by a multiplicative factor of exp ( −μt ) . We now substitute this full form into the definition of τind ( Equation 24 ) . In the biologically relevant limits where τ ≫ ( Δx ) 2/D and 1/μ ≫ ( Δx ) 2/D , we find , for d = 2 In d = 3 , we find For the parameter values considered in our simulations , we do not observe the logarithmic τ dependence in the width predicted by Equation 28 . In the single-gradient simulations , this is because at short times τ ≪ τ× , we enter the constant w ∼ λ regime . For the parameter values used , the transition from w ∼ λ at τ ≪ τ× ≈ 0 . 3 s to the long-time behaviour ( Equation 7 ) for τ ≫ 1/μ ≈ 4 s overwhelms the small logarithmic effect . If the production rate J were increased significantly , τ× ∝ J−1 would be reduced and the ln ( τ ) regime would become accessible since the τ× and 1/μ timescales would then become better separated . However , even in this case , the logarithmic variation in Equation 28 is intrinsically weak , and would likely have a negligible effect in a biological context . Stochastic simulations were performed on a 2-D square lattice with Nx = L/δx sites in the x direction and Ny = L⊥/δx sites in the y direction , where δx = 0 . 01 μm is the lattice spacing . The detector size Δx was normally set equal to δx except for cases where the detector size was varied , in which case Δx was set to be a multiple of δx . Zero-flux boundaries were implemented at x = 0 and x = L , and a periodic boundary was used to connect y = 0 with y = L⊥ . A fixed time step , δt = 2 . 5 × 10−5 s , was chosen so that for the given diffusion constant the total probability of diffusion out of a site in all directions approached 1 . However , a time step five times smaller was also tested with no effect on any of the results . For each x = 0 site , particles were injected at each time step in a Poisson process with mean j = Jδxδt . In the two-gradient model , particles were also added at x = L in an identical but uncorrelated process . Diffusion and decay were also treated as Poisson processes , with hopping and decay probabilities of Dδt/ ( δx ) 2 and μδt per particle , respectively . Simulations were initialised with the mean number of particles in the system , JL⊥/μ for the one-gradient model or twice this value for the two-gradient model , with a probability distribution that followed the average density distribution . The mean occupancy for each detector site was calculated over the averaging period , τ . For each site this mean occupancy was compared with each neighbouring site . If one occupancy was above the threshold and the other below , this boundary was identified as a threshold-crossing position . This process was repeated for many averaging periods , ranging from 105 repeats for short averaging times to 500 repeats for very long averaging times , to generate a distribution of crossing positions throughout the system . Threshold crossings in both the x and y directions were observed . We found that the distributions as a function of x position of these two types of crossing were the same . For each row of sites , x = 0 to x = L at a fixed y , the mean ( “measured threshold” ) and root-mean–squared deviation ( “width” ) of the threshold distribution from many averaging periods were calculated independently . In Figures 1 and 2 , we plot the mean of these two quantities across the different y values within the system , with error bars of one standard deviation . For the single-source model , the standard parameter values used in the simulations were as follows: L = 10 μm , L⊥ = 6 μm , D = 1 μm2s−1 , μ = 0 . 25 s−1 , J = 4 . 17 μm−1s−1 , Δx = 0 . 01 μm , and xT = 2 μm . To generate the data collapse in Figures 1C and 1F , simulations were also performed with the following parameter values: D = 0 . 5 μm2s−1; J = 6 . 25 μm−1s−1; Δx = 0 . 02 μm; μ = 1 s−1; μ = 0 . 11 s−1; xT = 1 μm and xT = 3 μm . For the two-source model , standard parameters were the same as above except μ = 0 . 36 s−1 and J = 6 μm−1s−1 . In Figure 2C , data are also shown with the following parameter values: D = 0 . 5 μm2s−1; μ = 1 s−1; μ = 0 . 25 s−1; J = 9 μm−1s−1; Δx = 0 . 02 μm; L = 7 . 5 μm; L = 15 μm , and Δx = 0 . 02 μm .
Many biological systems require precise positional information to function correctly . Examples include positioning of the site of cell division and determination of cell fate during embryonic development . This positional information often is encoded in concentration gradients . A specific protein is produced only within a small region , and subsequently spreads into the surrounding space . This leads to a spatial concentration gradient , with the highest protein concentration near the source . By switching on a signal only where the local concentration is above a certain threshold , this gradient can provide positional information . However , intrinsic randomness in biochemical reactions will lead to unavoidable fluctuations in the concentration profile , which in turn will lead to fluctuations in the identified position . We therefore investigated how precisely a noisy concentration gradient can specify positional information . We found that time-averaging of concentration measurements potentially allows for great precision to be achieved even with remarkably low protein copy numbers . We applied our results to a number of examples in both cell and developmental biology , including positioning of the site of cell division in bacteria and yeast , as well as gene expression in the developing Drosophila embryo .
[ "Abstract", "Introduction", "Results", "Discussion", "Methods" ]
[ "biophysics", "cell", "biology", "developmental", "biology", "none" ]
2007
Fundamental Limits to Position Determination by Concentration Gradients
Histone modifiers are critical regulators of chromatin-based processes in eukaryotes . The histone methyltransferase Set1 , a component of the Set1C/COMPASS complex , catalyzes the methylation at lysine 4 of histone H3 ( H3K4me ) , a hallmark of euchromatin . Here , we show that the fission yeast Schizosaccharomyces pombe Set1 utilizes distinct domain modules to regulate disparate classes of repetitive elements associated with euchromatin and heterochromatin via H3K4me-dependent and -independent pathways . Set1 employs its RNA-binding RRM2 and catalytic SET domains to repress Tf2 retrotransposons and pericentromeric repeats while relying on its H3K4me function to maintain transcriptional repression at the silent mating type ( mat ) locus and subtelomeric regions . These repressive functions of Set1 correlate with the requirement of Set1C components to maintain repression at the mat locus and subtelomeres while dispensing Set1C in repressing Tf2s and pericentromeric repeats . We show that the contributions of several Set1C subunits to the states of H3K4me diverge considerably from those of Saccharomyces cerevisiae orthologs . Moreover , unlike S . cerevisiae , the regulation of Set1 protein level is not coupled to the status of H3K4me or histone H2B ubiquitination by the HULC complex . Intriguingly , we uncover a genome organization role for Set1C and H3K4me in mediating the clustering of Tf2s into Tf bodies by antagonizing the acetyltransferase Mst1-mediated H3K4 acetylation . Our study provides unexpected insights into the regulatory intricacies of a highly conserved chromatin-modifying complex with diverse roles in genome control . In eukaryotic cells , DNA-based processes operate within the context of a chromatin template [1] , [2] . Chromatin-modifying complexes targeting select residues of histones for posttranslational modifications exert various levels of genome control including chromatin assembly , transcription , DNA repair , replication and recombination [1] , [3] . Furthermore , modified histone marks contribute to shaping the genome landscape into distinct chromatin domains . Most notable is the methylation of histone H3 at lysine 4 ( H3K4me ) , which distinguishes euchromatin from heterochromatin , which is marked by H3 lysine 9 methylation ( H3K9me ) [4] , [5] . H3K4me can exist as mono- ( H3Kme1 ) , di- ( H3K4me2 ) , or tri- ( H3K4me3 ) methylation , and is catalyzed by a number of SET-containing histone methyltransferases that are parts of Set1C/COMPASS and MLL complexes [6] , [7] . The roles of individual Set1C/COMPASS subunits have been revealed through studies primarily in the budding yeast Saccharomyces cerevisiae , with loss of individual subunits of Set1C having different effects on the stability of the complex and the states of H3K4me [8] , [9] , [10] , [11] . Interestingly , there is a positive codependency between the levels of H3K4me and those of Set1 proteins [12] . Consistent with the prevalent enrichment of H3K4me2 and H3K4me3 throughout the gene-rich euchromatin [13] , [14] Set1C has been shown to localize to active RNA Polymerase II ( Pol II ) genes [15] , [16] . However , accumulating evidence implicates a variety of genetic elements under the repressive control of Set1 and H3K4me . In budding yeast the silencing of Ty1 retrotransposons [17] , long noncoding RNAs [18] , and antisense regulatory noncoding RNAs [19] requires Set1 and H3K4me . In addition , transcriptional profiling analysis of Set1C/COMPASS mutants supports repressive roles for H3K4me3 at ribosomal genes during multiple stresses [20] and for H3K4me2 and H3K4me3 through promotion of 3'end antisense transcription [21] . In the fission yeast Schizosaccharomyces pombe , Set1 ( KMT2 ) is the sole histone methyltransferase responsible for H3K4me [22] , [23] . Biochemical purification identified Set1 as the core subunit of the Set1C complex whose components have orthologs in budding yeast and humans [24] . However , the contributions of Set1 and individual Set1C subunits to H3K4me and their roles in transcriptional repression are not well-characterized in S . pombe . Previous genome-wide mapping shows that the S . pombe genome is dominated by a euchromatin landscape marked with H3K4me2 [14] . Heterochromatin domains distinguished by H3K9me are restricted to prominent genome landmarks including pericentromeres , subtelomeres , ribosomal DNA arrays , and the silent mating-type locus ( mat ) [14] . These domains contain repetitive elements that help direct RNAi-mediated heterochromatin assembly [25] . The S . pombe genome also contains repetitive elements in the forms of long terminal repeat ( LTR ) Tf2 retrotransposons and their LTR remnants interspersed across euchromatin and not normally targeted for heterochromatic silencing [14] , [26] . Instead , repression of Tf2 retrotransposons , which are enriched for H3K4me2 [14] , requires Set1 [27] . In this study , we investigate the contributions of various protein domains of Set1 and its associated Set1C subunits to H3K4me and their roles in the regulation of repetitive elements associated with euchromatin and heterochromatin . We find that S . pombe Set1 possesses multiple modes of regulation that are dependent and independent of H3K4me and Set1C . Set1-mediated repression of Tf2s and pericentromeric repeats is maintained in mutant cells deficient in Set1C subunits or Set1 domain mutants with defects in H3K4me activity . In contrast , intact H3K4me by the Set1C complex is required to maintain repression at the mat and subtelomeric regions . We show that the contributions of several individual Set1C subunits to the levels of H3K4me and Set1 proteins are notably different from those of S . cerevisiae orthologs . Whereas a recent study identifies a feedback mechanism between H3K4me and Set1 protein levels in S . cerevisiae , we find that the stability of Set1 proteins is not coupled to the levels of H3K4me or HULC complex-mediated H2B ubiquitination . Finally , we describe a surprising role for the Set1C complex in the nuclear organization of Tf2 elements into Tf bodies . Set1C employs H3K4me to limit the levels of H3K4 acetylation at Tf2s by antagonizing the function of the histone H3K4 acetyltransferase Mst1 . Our study considerably expands the regulatory repertoire of an important histone modifier and highlights the multifaceted function by a highly conserved chromatin-modifying complex with diverse roles in genome control . The protein architecture of fission yeast Set1 is highly conserved [22] , [24] , containing two putative RNA-recognition motifs ( RRMs ) termed RRM1 and RRM2 near the N-terminus [28] , [29] , an nSET domain responsible for interaction with certain COMPASS subunits [11] , [30] , a catalytic SET domain and a short post-SET ( pSET ) domain near its C-terminus [28] ( Figure 1A ) . Previous studies from budding yeast have shown that H3K4me is affected by the loss of various domains of Set1 [11] , [28] , [29] , [31] . We examined the status of H3K4me in S . pombe mutant strains lacking individual domains of set1 . Loss of RRM1 abolished H3K4me3 , substantially diminished H3K4me2 [22] , and slightly decreased H3K4me1 compared to wildtype ( Figures 1B and S1 ) . An RRM2 deletion resulted in no appreciable decrease in H3K4me levels . Cells expressing Set1 with a deleted nSET , SET or pSET domain displayed a complete loss of H3K4me . S . cerevisiae cells expressing Set1 with a C-terminal TAP epitope showed reduced H3K4me levels [32] while an affinity-purified S . pombe equivalent Set1-TAP protein retained in vitro H3K4me activity [24] . We found that H3K4me was completely abolished in cells containing a FLAG ( 3× ) epitope attached to the C-terminus of Set1 ( set1FH3K4me- ) , likely a result of the epitope interfering with the interaction of the SET or pSET domain with the H3K4 substrate [33] , [34] . Deletion of a protein domain could affect the stability of Set1 proteins [11] , [12] . To examine this possibility , we constructed strains expressing either full-length or domain-deleted Set1 that contains a fused FLAG epitope at the N-terminus of Set1 . Unlike certain S . cerevisiae domain mutants in which Set1 protein level was undetectable [12] , our western blot analysis readily detected Set1 expression of all domain mutants ( Figure S2 ) . However , there were noticeably reduced levels of Set1 proteins lacking either the RRM1 or nSET domain , suggesting that H3K4me defects observed in RRM1 and nSET mutants could partly be due to reduced amount of Set1 proteins in these mutants . Slight decreases in Set1 protein levels were seen in mutants deficient in RRM2 , SET , and pSET domains ( Figure S2 ) . We next performed reverse transcription followed by realtime PCR ( qRT-PCR ) analysis to examine the effect of various set1 mutations on Tf2 expression . Cells deficient in H3K4me ( set1FH3K4me- ) or lacking either the RRM1 , nSET or pSET domain exhibited little change in transcript levels of Tf2s ( Figure 1C ) . However , Tf2 expression was substantially increased in cells lacking either the RRM2 or SET domain . These results support a catalytic mode of Set1-mediated repression independent of H3K4me that requires intact RRM2 and SET domains . Loss of set1 has been shown to compromise centromeric and telomeric silencing of a reporter gene [23] . We performed qRT-PCR in set1 mutant strains to assess the status of transcription at known heterochromatic regions . Intriguingly , similar to Tf2s , derepression of pericentromeric repeats was observed only in set1Δ and in cells lacking either the RRM2 or SET domain ( Figure 1D ) , suggesting a common mode of Set1-mediated repression for retrotransposons and pericentromeric repeats . In contrast , derepression at the mat locus and subtelomeres was observed in all mutants with defects in H3K4me including set1FH3K4me- ( Figures 1E and 1F ) . The S . pombe genome encodes three functional copies of histone H3 at distinct loci [35] , [36] . We previously observed that repression of Tf2s was maintained in H3K4 mutants in which lysine 4 on all three copies of histone H3 was substituted for either alanine ( H3K4A ) or arginine ( H3K4R ) [27] . A previous study reported upregulation of pericentromeric repeats in a H3K4R mutant containing only one functional copy of histone H3 [37] . However , we saw little change in repression at the three major heterochromatin domains in our H3K4 mutants ( Figure S3 ) , suggesting a role for histone gene dosage acting together with modifications at certain histone residues ( i . e . , H3K4 ) to maintain heterochromatic silencing . Collectively , these results suggest that Set1 utilizes distinct modes to repress different classes of repetitive elements via H3K4me -dependent and -independent pathways . We have previously shown that Set1 localizes at Tf2s [27] . Whether Set1 localizes at heterochromatic repeats is not known . We utilized chromatin immunoprecipitation ( ChIP ) to monitor Set1 enrichment at known euchromatin and heterochromatin targets in set1 mutant strains . In strains deficient in either the RRM1 or nSET domain , there was reduced Set1 enrichment at the housekeeping actin gene act1 and Tf2s ( Figures 2A and 2B ) . Loss of the RRM2 , SET domain or H3K4me function did not appear to hamper Set1 localization at these elements . Surprisingly , we detected Set1 localization at pericentromeric repeats ( Figure 2C ) . Unlike the two examined euchromatic targets ( i . e . , act1 and Tf2s ) , Set1 localization at pericentromeric repeats was generally not affected in strains deficient in any one of the domains . We did not detect Set1 enrichment at the mat locus or subtelomeric repeats either in wildtype or domain mutants ( Figures 2D and 2E ) . These results suggest that the absence of H3K4me does not adversely affect Set1 localization at certain euchromatic and heterochromatic targets and that H3K4me-dependent repression might not require stable interaction of Set1 with its target loci . The contribution of individual Set1C subunits to H3K4me has been well characterized in S . cerevisiae [8] , [9] , [10] , [38] , [39] , [40] , [41] . However , aside from H3K4me2 [24] , the roles of Set1C subunits in S . pombe in H3K4 methylation are not well explored . We therefore assessed the status of H3K4me in cells deficient for individual Set1C components . Cells deficient for set1 , swd1 or swd3 exhibit a complete loss of H3K4me ( Figures 3A and S3 ) , similar to results observed in S . cerevisiae mutant orthologs [41] . spp1 ( spf1 ) is essential for all three forms of H3K4me in S . pombe . In contrast , the loss of S . cerevisiae SPP1 only diminishes [9] , [31] or abolishes H3K4me3 [8] . Loss of swd2 , which is lethal in S . cerevisiae [42] , abolished H3K4me3 and diminished the levels of H3K4me2 and H3K4me1 in S . pombe . Cells lacking ash2 exhibited relatively intact levels of H3K4me2 and H3K4me1 . Although H3K4me3 was detectable within individual cells in the ash2 mutant ( Figure S4 ) , we were unable to detect H3K4me3 at bulk histone levels ( Figure 3A ) . In the sdc1 mutant , only H3K4me3 level was slightly diminished , while H3K4me2 and H3K4me1 levels were largely unaffected . These results differ from budding yeast findings , in which loss of ASH2 or SDC1 reduced all three states of H3K4me [9] , [10] or completely abolished H3K4me2 [40] . All three forms of H3K4me appeared to be intact in cells deficient for shg1 . Collectively , our results show that several components of S . pombe Set1C make different contributions to H3K4me compared to their orthologs in S . cerevisiae . To gain insights into potential mechanisms underlying the repressive function of Set1 , we investigated the role of individual Set1C subunits in the repression of Tf2s and heterochromatic repeats . We found that loss of repression at Tf2s and pericentromeric repeats was observed only in the set1Δ mutant ( Figures 3B and 3C ) . However , at the mating-type and subtelomeric regions , cells deficient for any of the Set1C components except shg1 displayed a noticeable derepression ( Figures 3D and 3E ) . These findings indicate that depending on genomic context , Set1 can dispense or act together with Set1C components to repress different classes of repetitive elements . In budding yeast , reduced levels of Set1 proteins have been observed in mutants deficient in SWD1 , SWD2 , SWD3 , or SPP1 [10] , [11] , [38] , [43] , [44] . We examined the levels of Set1 proteins in S . pombe strains lacking each of the other Set1C components . The levels of Set1 were minimally affected by the loss of ash2 , sdc1 and shg1 , somewhat diminished in swd1Δ and swd3Δ , and most noticeably reduced in swd2Δ and spp1Δ mutants ( Figure 4A ) . The loss of individual Set1C subunits on Set1 protein stability is likely to occur at the posttranslational level , as Set1 transcript levels were relatively unaffected in Set1C mutants ( Figure S5 ) . Soares et al . recently reported that there is feedback control linking the stability of Set1 proteins to the status of H3K4me in S . cerevisiae [12] . Our analysis showed that despite the complete loss of H3K4me in set1F H3K4me- or set1 mutants lacking either the SET or pSET domain , the levels of these Set1 mutant proteins remain comparable to that of wildtype ( Figure S2 ) . However , except for ash2Δ , reduced Set1 protein levels in Set1C mutants ( i . e . , spp1Δ , swd1Δ , swd2Δ , swd3Δ ) generally correlated with a loss of H3K4me , in particular , H3K4me2/3 ( see Figure 3A ) . To disentangle the effects of Set1C component deficiency or Set1 mutations on the stability of Set1 proteins from their contributions to Set1 activity toward H3K4me , we analyzed Set1 proteins in H3K4 mutant cells . We found that Set1 proteins are readily detectable in either H3K4A or H3K4R strains though with apparently reduced levels in H3K4A mutant ( Figure 4B ) . Mono-ubiquitination of histone H2B ( H2Bub ) has been shown to contribute to H3K4 methylation [45] , [46] , [47] , [48] , [49] . In S . pombe H2Bub is mediated by a histone H2B-conjugating complex termed HULC , consisting of a rad6 ortholog Rhp6 , two RING finger proteins Rfp1 and Rfp2 similar to budding yeast Bre1 , and a serine-rich protein Shf1 [50] , [51] . Deficiency of HULC components resulted in drastic reduction in H3K4me levels [50] , [51] . We analyzed the loss of H2Bub or HULC subunits on the abundance of Set1 proteins . We could detect Set1 proteins in all HULC/H2Bub mutants . Similar to H3K4A , Set1 levels were reduced in rhp6Δ ( Figure 4C ) . Our results suggest that in contrast to what has been observed in S . cerevisiae , the regulation of Set1 protein abundance in S . pombe is largely independent of the status of H3K4 methylation and H2B ubiquitination . We have previously identified a novel role for Set1 in the nuclear organization of Tf2s into Tf bodies [27] . To dissect potential mechanisms underlying Set1-mediated clustering of Tf2s , we performed fluorescence in situ hybridization ( FISH ) analysis to monitor the status of Tf bodies in various set1 mutants . In contrast to wildtype cells with intact Tf bodies , set1 mutants lacking the RRM1 , nSET , SET or pSET domain exhibited defects in Tf bodies to the same extent as set1Δ ( Figure 5A ) . Because H3K4me is severely compromised in these set1 mutants ( see Figure 1B and . S1 ) , H3K4me might be required to maintain the integrity of Tf bodies . Indeed , Tf2 elements were observed to decluster in the set1FH3K4me- cells which lack H3K4me altogether . In the set1 mutant containing the RRM2 deletion , which resulted in intermediately increased levels of Tf2 expression ( see Figure 1C ) , the integrity of Tf bodies was modestly affected . These results suggest that Set1 relies on disparate domains and possibly different catalytic activities ( see discussion below ) to exert control over different aspects of Tf2 regulation . The dependence on H3K4 methylation to maintain the integrity of Tf bodies suggests a role for Set1C subunits in the organization of Tf2s within the nucleus . We utilized Tf2 FISH analysis to monitor the status of Tf bodies in strains with null mutations for each of the Set1C components . Similar to what was observed in set1 mutants with impaired H3K4me , Set1C mutants with gross defects in H3K4me displayed declustering of Tf2s compared to wildtype ( Figure 5B ) . Only in shg1 mutant cells did we observe a disruption of Tf body integrity that did not correspond with the loss of H3K4me ( Figure 3A ) , suggesting a distinct role for Shg1 in nuclear organization . The MYST family histone acetyltransferase Mst1 has been shown to acetylate lysine 4 of histone H3 ( H3K4ac ) [37] . Declustering of Tf2 elements in H3K4me mutants could be due to inappropriate Mst1 activity such as heightened H3K4ac at Tf2s . Consistent with this idea , mutation of mst1 in cells lacking H3K4me abrogated defects in the integrity of Tf bodies ( Figure 6A ) and diminished the elevated H3K4ac levels at Tf2s and the housekeeping gene act1 observed in the absence of H3K4me ( Figure 6B and 6C ) . Together , these results reveal that Set1 controls Tf2 repression independent of the Set1C complex , but relies on Set1C-mediated H3K4me to maintain Tf body integrity by antagonizing the H3K4 acetyltransferase activity of Mst1 . Unlike other chromatin-modifying enzymes such as the Clr4/Suv39h H3K9 methyltransferase not universally present in eukaryotes , the protein architecture of Set1 and its associated complex subunits are remarkably conserved across known eukaryotic lineages [7] . The multi-domain structure of Set1 suggests its ability to interact with multiple proteins and integrate opposing inputs . Works from budding yeast have yielded many insights into the roles of various Set1 domains in H3K4me and domain interactions with various components of the Set1C/COMPASS complex [10] , [11] , [28] , [29] , [30] . Our analysis of Set1C in S . pombe reveals additional insights . We found that the nSET , SET , and pSET domains are essential for all three states of H3K4me , similar to results previously noted in budding yeast [12] , [28] . In the RRM1 mutant , we observed defects in H3K4me2 as previously reported [22] , in addition to the complete loss of H3K4me3 and slightly reduced H3K4me1 , though the degree to which these defects are due to reduced Set1 abundance in this mutant is unclear . Reduced levels of Set1 proteins in RRM1 and nSET mutants could also account for the relatively lower enrichment of these mutant proteins at euchromatic targets observed in ChIP experiments . In contrast , the loss of RRM2 did not appear to affect the protein levels of Set1 or hamper the ability of Set1 to methylate all three states of H3K4me . In fact , we detected slight increases in overall H3K4me levels in the RRM2 mutant ( Figure 1B ) , suggesting it might have an inhibitory role against H3K4me , similar to the roles of the central autoinhibitory region noted in budding yeast Set1 [28] . The contributions of several S . pombe Set1C subunits to H3K4me diverge from those in S . cerevisiae . Among these are Ash2 , Sdc1 , Swd2 , and Spp1 ( Spf1 ) . S . pombe spp1 is essential not only for H3K4me2 [24] , but H3K4me3 and H3K4me1 . This pattern is somewhat similar to that seen in S . cerevisiae spp1Δ mutant expressing a Set1 C-terminal fragment containing only the nSET , SET , and pSET domains [11] , [30] . These data point to a more critical role for Spp1 in conferring the H3K4me activity within the Set1C complex in S . pombe compared to S . cerevisiae , perhaps by more effectively countering the inhibitory effect of the RRM2 domain on H3K4me and/or by influencing the conformation of the nSET domain within the Set1C complex to effect H3K4me [11] , [30] . However , the substantially reduced amount of Set1 protein levels in S . pombe spp1 null cells could also contribute to the complete loss of all three forms of H3K4me . Differences in the role of the Swd2 subunit in previous reports in budding yeast and our study were also noted . S . cerevisiae mutant cells carrying temperature-sensitive alleles of SWD2 exhibit severe reductions in H3K4me2/3 [43] , [52] , and an S . cerevisiae swd2Δ mutant overexpressing a C-terminal fragment of Sen1 that suppresses the lethality of swd2Δ displays similar defects in H3K4me2/3 but no significant change to H3K4me1 [44] . In contrast , we found reduction for all three states of H3K4me in S . pombe swd2Δ cells , with H3K4me3 most affected , followed by H3K4me2 and H3K4me1 . Similar H3K4me2/3 defects in both yeast species deficient in swd2 could be due to the requirement of swd2 to maintain sufficient levels of Set1 protein abundance [43] , [44] . However , H3K4me1 defects seen in S . pombe swd2Δ might reflect a more dedicated role for Swd2 in Set1C function compared to its S . cerevisiae counterpart , which is also a subunit of the essential transcription termination factor APT [53] , and proposed to be needed to overcome antagonism by Set1C [54] . Despite discrepancies in findings from several groups [9] , [10] , [11] , [40] , [44] , S . cerevisiae ash2Δ and sdc1Δ mutants exhibit similar H3K4me defects , likely reflecting their function as a heterodimer within the Set1C complex [10] , [11] . Our study suggests a more extensive role for S . pombe Ash2 than Sdc1 in maintaining various states of H3K4me , in particular H3K4me3 and H3K4me2 . In addition , loss of either ash2 or sdc1 did not produce H3K4me defects as severe as those seen in the equivalent budding yeast mutants . These differences might reflect divergence in the functions of Ash2 and Sdc1 in S . pombe , likely due to their associations with the histone H3K4 demethylase Lid2 complex , which is not present in S . cerevisiae [24] , [55] . Set1 has been documented to be a highly unstable protein in budding yeast [44] . This instability of Set1 has been shown to be coupled to the levels of H3K4me [12] , complicating efforts to untangle the specific roles of various Set1 domains and Set1C subunits to H3K4me from their direct contributions to the stability of Set1 . Our study indicates that Set1 is inherently more stable in S . pombe , and is readily detectable in whole cell extracts from wildtype and mutant strains deficient for either the individual Set1 domains or Set1C complex components . It is worth noting that the RRM1 and nSET domains and certain Set1C subunits ( Swd1 , Swd2 , Swd3 , Spp1 ) appear to contribute to Set1 stability . However , the regulation of Set1 protein levels in S . pombe is largely independent of H3K4me abundance and H2Bub levels , further highlighting the considerable divergence in the regulation of Set1 in S . pombe versus that of S . cerevisiae . The role of Set1 as a transcriptional repressor has been widely documented in budding yeast [17] , [18] , [20] , [32] , [46] , [56] , [57] , [58] . However , these studies ascribed Set1 repressor function solely to H3K4me2 and/or H3K4me3 [19] , [20] , [21] , [58] . We have previously shown that Set1 mediates repression of Tf2 retrotransposons independent of H3K4me [27] . Our current study reveals an unanticipated complexity in the repressive function of Set1 , in that the requirement of H3K4me in transcriptional silencing depends upon the genomic context ( Figure 7 ) . The complete loss of H3K4me does not appear to hamper the ability of Set1 to localize to and maintain repression at Tf2s and pericentromeric repeats , while at the mat locus and subtelomeric repeats Set1-mediated H3K4me contributes to repression . These findings were consistent with analyses using Set1C subunit deletion mutants . Loci that depend on H3K4me-mediated repression ( mat and subtelomeric repeats ) also require Set1C components needed for maintaining proper H3K4me ( all Set1C subunits except Shg1 ) . Repression of Tf2s and pericentromeric repeats , on the other hand , is maintained in all Set1C mutants ( except set1Δ ) . Our study identifies a novel mode of Set1 function that does not depend on H3K4me and an intact Set1C complex . The requirement of the SET domain but not H3K4me activity suggests that Set1 mediates repression of Tf2s and pericentromeric repeats via methylation of a novel substrate ( s ) . In budding yeast , Set1 has been shown to methylate Dam1 , a component of the kinetochore DASH complex [59] . Although methylation of Dam1 is independent of H3K4me , its methylation requires other Set1C subunits [40] . S . pombe encodes a dam1 ortholog , though it appears not to contain conserved Set1 methylation sites [60] . Considering that Set1 represses Tf2s and pericentromeric repeats independent of H3K4me and other Set1C subunits , it is possible that repression of these repeats might involve Set1 binding to RNA via its RRM2 domain and methylation of targets associated with either transcription and/or RNA processing . Heterochromatic repeats and Tf2s in certain genetic backgrounds are targeted for RNAi-mediated heterochromatic and exosome-mediated silencing [26] , [61] , [62] . It has been shown that histone deacetylases ( HDACs ) cooperate with RNAi to assemble heterochromatin at pericentromeres [63] . Even though loss of set1 does not appear to affect the levels of H3K9 methylation and siRNAs at pericentromeric heterochromatin ( Figure S6A ) [37] , there were noticeable increased levels of H3K9 acetylation at that region ( Figure S6B ) . Several HDAC mutants ( i . e . , sir2 , clr3 ) are known to retain robust levels of siRNAs and H3K9me and yet exhibit increased levels of certain histone acetylation marks at pericentromeres [63] , [64] , [65] . Thus , it is likely that in the absence of set1 , HDACs , RNAi and exosome act in redundant pathways to help maintain heterochromatin . We previously reported that Set1 has a novel role in genome organization by clustering interspersed Tf2 elements into Tf bodies [27] . Even though declustering of Tf2s was not observed in H3K4 mutant strains ( H3K4A , H3K4R ) [27] , a role for H3K4me in Tf2 clustering could not be excluded due to the loss of both H3K4 acetylation and methylation in those H3K4 mutants . Our current study supports an active role for the Set1C complex in maintaining the integrity of Tf bodies by antagonizing the function of the H3K4 acetyltransferase Mst1 . Set1 has been shown to limit the abundance of H3K4ac at gene promoters in S . cerevisiae [66] . Thus , H3K4me catalyzed by Set1C could compete with Mst1-mediated H3K4ac at Tf2s to maintain the integrity of Tf bodies . However , as loss of H3K4me also results in increased H3K4ac at the house keeping gene act1 , Tf2 declustering in set1 mutants could reflect global changes in genome organization due to heightened levels of H3K4ac across multiple loci . HDACs recruited by CENP-B proteins to Tf2s have also been shown to contribute to Tf2 clustering [27] , [67] . Cells may therefore exploit dynamic competition between Set1C , HATs , and HDACs to regulate the various states of H3K4 , which could in turn facilitate rapid genome reorganization in response to acute environmental changes [68] . Null mutant and C-terminal FLAG ( 3× ) strains were constructed using a Kanamycin cassette [69] . Double mutants were generated by standard genetic cross methods [70] . Full-length and domain mutants of set1 containing an N-terminal FLAG ( 3× ) epitope were generated by a two-step site-directed mutagenesis ( SDM ) . First , the set1 gene was replaced with a ura5 lys7 cassette [71] . Second , an SDM PCR fragment containing either full-length or domain deleted FLAG-Set1 was transformed into the above set1 null strain ( set1Δ::ura5 lys7 ura5-14 lys7-2 ) , and transformants were scored by growth on the uracil counter selective agent 5-Fluoroorotic acid ( 5-FOA ) and sensitivity to lysine minus media [71] . Proper insertions were confirmed by PCR and DNA sequencing . Liquid cultures were grown at 30°C in standard rich media supplemented with 225 mg/L adenine ( YEA ) . RNA was isolated by a hot acid phenol method [72] and converted to cDNA with Superscript III reverse transcriptase and anchored oligo-dT primer ( Life Technologies ) . cDNA was subjected to qPCR analysis using DyNAzyme™ II PCR Master Mix ( Finnzymes ) with SYBR green on the Applied Biosystems 7500 Fast Real-Time PCR System . Fold expression changes of mutant versus wildtype cells relative to act1 gene were determined using the 2−ΔΔCt method in Microsoft Excel . Cells from 50 ml culture ( OD∼0 . 5 ) were washed in 10 ml NIB buffer ( 15 mM PIPES pH 6 . 8 , 0 . 25M sucrose , 60 mM KCl , 15 mM NaCl , 5 mM MgCl2 , 1 mM CaCl2 , 0 . 8% Triton X-100 , 10 ng/µl TSA , 1 mM PMSF , Roche protease inhibitor mini tablet ) , lysed with acid-washed glass beads in a bead beater , and centrifuged at 11 , 000× g for 10 min [22] . Cell extract pellets were resuspended in 0 . 4M H2SO4 , incubated on ice for 1 h with occasional mixing , and the supernatant was collected following centrifugation at 8 , 000× g for 5 min . Histone extract was concentrated by trichloroacetic acid ( TCA ) precipitation , washed in acetone and resuspended in 100 µl LDS buffer ( Life Technologies ) and quantitated using the BCA method ( Pierce ) . 5 µg of histone extracts were resolved on 14–22% Tricine SDS PAGE and transferred to a nitrocellulose membrane using the iBlot system ( Life Technologies ) . Histone H3 and modified residues were detected with antibodies against H3 ( Abcam , ab1791 ) , H3K4me1 ( Abcam , ab8895 ) , H3K4me2 ( Fisher , 07030MI ) , or H3K4me3 ( Fisher , 07-473MI ) . S . pombe cells ( OD 1–2 ) were lysed in HCS buffer ( 150 mM HEPES pH 7 . 2 , 250 mM NaCl , 0 . 1% NP-40 , 1 mM EDTA , 1 mM dithiothreitol , 1 mM PMSF ) and protein inhibitor tablet ( Roche ) by acid-washed beads in a bead beater ( three times 30 s with 2 min interval on ice ) . 50 µg of protein extracts were run on a PAGE gel ( Express Plus 4–20% Bis-tris ( MOPS ) , Genescript ) and subjected to overnight western blot transfer at 4°C . Set1 was detected using anti-FLAG antibody ( Genescript , A00187 ) . ChIP assays were performed as previously described [27] . qPCR was performed using Phire Hot Start II DNA Polymerase ( Thermo Scientific ) supplemented with SYBR green ( Life Technologies ) on the Applied Biosystems 7500 Fast Real-Time PCR System . Enrichment of ChIP vs . input DNA was determined using the 2−ΔΔCt method in Microsoft Excel . IF and FISH assays were performed as previously described [27] , [68] . Briefly , S . pombe cells were grown in 10 ml YEA media until OD595 ∼0 . 5–1 . 10 ml of 2 . 4 M sorbitol YEA solution was added to culture , and cells were immediately cross-linked with 2 . 9 ml of freshly made 30% paraformaldehyde/YEA solution for 30 min in a 18°C water bath shaker . Cross-linked reaction was quenched with 1 . 2 ml of 2 . 5 M glycine . Cells were transferred to a microcentrifuge tube , subjected to cell wall digestion in 0 . 5 mg/ml zymolyase solution ( Associated of Cape Cod , 100T ) at 37°C for 30 min , blocked with PEMBAL ( 100 mM PIPES pH 6 . 9 , 1 mM EGTA , 1 mM MgSO4 , 1% BSA , 0 . 1 M L-lysine ) for 1 hr and subjected to either IF or FISH analysis . For IF analysis , cells were incubated overnight at room temperature with antibodies against either H3K4me1 ( Abcam , ab8895 ) , H3K4me2 ( Fisher , 07030MI ) , or H3K4me3 ( Fisher , 07-473MI ) . Cells were then incubated with anti-mouse Alexa Fluor 488 ( Invitrogen ) followed by DAPI staining to visualize H3K4me signal in the nucleus . For FISH analysis , PEMBAL-treated cells were treated with RNase A ( 0 . 1 mg/ml ) at 37°C for 3 h . Hybridization was carried out at 40°C for 12–14 h with 100–150 ng of Tf2-orf probes in 100 µl hybridization buffer ( 50% formamide , 2× SSC , 5× Denhart's solution , 10% dextran sulfate ) . Cells were washed three times in 100 ml 2× SSC for 30 min each . Images were obtained using a Zeiss Axioplan 2 microscope . The chi-square test of homogeneity was used to determine whether declustering of Tf2 elements seen in mutant cells relative to wildtype was significant .
Methylation of histone H3 at lysine 4 ( H3K4me ) is a well-documented mark associated with euchromatin . In this study , we investigate the contributions of the histone methyltransferase Set1 ( KMT2 ) and its associated Set1C/COMPASS complex in the fission yeast Schizosaccharomyces pombe to histone H3 lysine 4 methylation ( H3K4me ) , transcriptional repression , and genome organization . We show that Set1 exhibits multiple modes of transcriptional repression at different types of repetitive elements , requiring distinct domains of Set1 and other Set1C subunits . Despite high conservation of subunits between the S . pombe and S . cerevisiae Set1C complexes , there are considerable differences in contributions to H3K4me by several individual subunits . Furthermore , unlike a recent report in S . cerevisiae , the abundance of Set1 proteins in S . pombe is generally not coupled to either the status of H3K4 methylation or H2B ubiquitination , further highlighting critical differences in Set1 regulation between the two yeast species . We describe a role for the Set1C complex in the nuclear organization of dispersed retrotransposons into Tf bodies . Set1C maintains Tf body integrity by employing H3K4me to antagonize the activities of the H3K4 acetyltransferase Mst1 . Collectively , our findings dramatically expand the regulatory landscape controlled by the Set1C complex , an important and highly conserved chromatin-modifying complex with diverse roles in genome control and development .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "biochemistry", "histone", "modification", "cell", "biology", "post-translational", "modification", "proteins", "acetylation", "chromosome", "biology", "gene", "expression", "genetics", "biology", "and", "life", "sciences", "epigenetics", "histone", "acetylation", "molecular", "genetics", "euchromatin", "chromatin", "heterochromatin" ]
2014
Multifaceted Genome Control by Set1 Dependent and Independent of H3K4 Methylation and the Set1C/COMPASS Complex
Distinct phylogenetic lineages of Mycobacterium tuberculosis ( MTB ) cause disease in patients of particular genetic ancestry , and elicit different patterns of cytokine and chemokine secretion when cultured with human macrophages in vitro . Circulating and antigen-stimulated concentrations of these inflammatory mediators might therefore be expected to vary significantly between tuberculosis patients of different ethnic origin . Studies to characterise such variation , and to determine whether it relates to host or bacillary factors , have not been conducted . We therefore compared circulating and antigen-stimulated concentrations of 43 inflammatory mediators and 14 haematological parameters ( inflammatory profile ) in 45 pulmonary tuberculosis patients of African ancestry vs . 83 patients of Eurasian ancestry in London , UK , and investigated the influence of bacillary and host genotype on these profiles . Despite having similar demographic and clinical characteristics , patients of differing ancestry exhibited distinct inflammatory profiles at presentation: those of African ancestry had lower neutrophil counts , lower serum concentrations of CCL2 , CCL11 and vitamin D binding protein ( DBP ) but higher serum CCL5 concentrations and higher antigen-stimulated IL-1 receptor antagonist and IL-12 secretion . These differences associated with ethnic variation in host DBP genotype , but not with ethnic variation in MTB strain . Ethnic differences in inflammatory profile became more marked following initiation of antimicrobial therapy , and immunological correlates of speed of elimination of MTB from the sputum differed between patients of African vs . Eurasian ancestry . Our study demonstrates a hitherto unappreciated degree of ethnic heterogeneity in inflammatory profile in tuberculosis patients that associates primarily with ethnic variation in host , rather than bacillary , genotype . Candidate immunodiagnostics and immunological biomarkers of response to antimicrobial therapy should be derived and validated in tuberculosis patients of different ethnic origin . Mycobacterium tuberculosis ( MTB ) , the causative agent of tuberculosis ( TB ) , emerged as a pathogen in Africa and has co-evolved with humans following migration to Europe and Asia some 70 , 000 years ago [1] . Distinct phylogenetic lineages of MTB consistently associate with human populations of different genetic ancestry in a variety of settings [2]–[5] and elicit differing immune responses from antigen-presenting cells of healthy donors in vitro [6]–[11] . Antimycobacterial immune responses might therefore be expected to vary between TB patients of different ethnic origin; however , studies investigating this question have not been conducted . Demonstration of significant ethnic variation in inflammatory responses at presentation and after initiation of treatment would have implications for the development of immunodiagnostics and for the identification of surrogate endpoints for trials of antituberculous drugs . We therefore conducted a study to characterise ethnic variation in circulating and antigen-stimulated concentrations of a panel of 43 soluble inflammatory mediators and 14 haematological parameters ( collectively termed ‘inflammatory profile’ ) before and after intensive-phase antituberculous therapy in a multi-ethnic cohort of patients with pulmonary tuberculosis ( PTB ) who participated in a clinical trial of adjunctive vitamin D supplementation conducted in London , UK [12] . The primary comparison was between patients of African vs . Eurasian ancestry , on the grounds of the distinct genetic structure of these populations [13] , and because TB patients of African ancestry are recognised to have delayed clearance of MTB from the sputum in comparison to non-African patients [14] , [15] – a phenomenon that might be immunologically mediated_ENREF_17 . We found that patients of African and Eurasian ancestry had significantly different inflammatory profiles at presentation , and that these differences associated primarily with variation in host , but not bacillary , genotype . Ethnic differences in inflammatory profile became more marked after intensive-phase treatment , and immunological correlates of time to sputum culture conversion between patients of African vs . Eurasian ancestry were distinct . A total of 141 patients were eligible to participate in the study ( Study Profile , Figure S1 ) . Self-defined ethnic origin was used to attribute ancestry as African ( n = 45 ) , Eurasian ( n = 83 ) , East Asian ( n = 9 ) , Latin American ( n = 3 ) or mixed ( n = 1 ) according to Rosenberg's five-region classification [13] . Due to small numbers in other groups , analyses were confined to participants of African and Eurasian ancestry . These two groups had similar demographic and clinical characteristics at presentation , the only statistically significant difference being a slightly shorter duration of symptoms pre-diagnosis in patients of African vs . Eurasian ancestry ( median 2 . 0 vs . 2 . 5 months respectively , p = 0 . 03; Table 1 ) . Forty-three soluble factors and 14 haematological parameters detailed in Table S1 were measured in samples of serum , plasma or whole blood taken at baseline . The median circulating concentrations of 7 soluble factors ( Interleukin [IL]-2 , IL-5 , IL-13 , IL-17 , tumour necrosis factor [TNF] , basic fibroblast growth factor [FGF-β] and matrix metalloproteinase-7 [MMP-7] ) were below the limit of detection ( LOD ) at baseline and were excluded from further analyses; median values , ranges and LODs for these analytes at baseline are presented in Table S2 . The remaining 50 parameters were then analysed using the t-test for general linear models ( GLM ) with statistical adjustment for covariates with potential to influence inflammatory profile ( age , sex , duration of symptoms pre-diagnosis , duration of antimicrobial therapy pre-sampling and baseline serum 25-hydroxyvitamin D [25 ( OH ) D] concentration ) . Five parameters were identified as having concentrations which were significantly different ( false discovery rate [q-value]≤0 . 05 ) between participants of African vs . Eurasian ancestry . Four ( peripheral blood neutrophil count and serum concentrations of CC chemokine ligand [CCL] 2 , CCL11 and vitamin D binding protein [DBP] ) were lower in participants of African vs . Eurasian ancestry ( p≤0 . 0018 ) , and one ( serum CCL5 concentration ) was higher ( p = 2 . 15×10−5; Table 2; Figure 1 ) . These parameters were then assessed using principal component analysis ( PCA ) , a well-established mathematical technique for reducing the dimensionality of complex datasets by transforming the data to a new coordinate system [16] . This provided a visual representation of how well the identified parameters differentiated individuals from the two ethnic groups ( Figure 2 ) . Despite the relative homogeneity in genetic structure in populations of Asian and European ancestry [13] , healthy Asians and Europeans have previously been reported to have differing inflammatory profiles that associate with increased risk of coronary heart disease [17] . In order to explore whether combining these groups concealed significant heterogeneity in inflammatory profile in patients with TB , we stratified the analysis above , subdividing the Eurasian group into European and Middle Eastern vs . Central and South Asian . The resultant PCA plot showed that inflammatory profiles of these two groups clustered together , and were separated from those of patients of African ancestry ( Figure 2 ) . In keeping with this observation , no significant differences in circulating concentrations of inflammatory mediators were found between Eurasian sub-groups ( Figure 3 ) . Our decision to combine data for Europeans and Asians in subsequent analyses was further justified by the finding that allele frequencies of two single nucleotide polymorphisms investigated in the DBP gene ( rs 4588 and rs 7041 ) did not differ between participants of European/Middle Eastern vs . Central/South Asian ancestry ( p≥0 . 32 ) , but that they were different between Eurasians and Africans ( p<0 . 001 , Table 1 ) In order to determine whether antigen-stimulated responses also differed between patients of African vs . Eurasian ancestry , whole blood samples taken from a sub-group of 42 patients ( 13 of African ancestry , and 29 of Eurasian ancestry ) were stimulated ex vivo with the recombinant MTB antigen culture filtrate protein , 10 kDa ( rCFP-10 ) . The concentrations of 39 soluble factors listed in Table S1 were assayed in supernatants of whole blood samples taken at baseline and stimulated with rCFP-10 for 72 hours . The median concentrations of six soluble factors ( IL-2 , IL-5 , IL-13 , epidermal growth factor [EGF] , FGF-β and MMP-7 ) were below the LOD at baseline and were excluded from further analyses; median values , ranges and LODs for these analytes are presented in Table S2 . The remaining 33 parameters were analysed using the t-test for GLM with the same adjustment for covariates as conducted for circulating responses . Those that were different between groups were visualised by PCA . Two such parameters were found: antigen-stimulated concentrations of IL-1 receptor antagonist [IL-1RA] and IL-12 were both higher in participants of African vs . Eurasian ancestry ( p≤0 . 0030; Table 2; Figure 1 ) . As before , we conducted a sensitivity analysis to determine whether patients of European/Middle Eastern vs . Central/South Asian ancestry differed in their antigen-stimulated inflammatory profile: both the PCA plot ( Figure 2 ) and scatter plots ( Figure 3 ) showed similar patterns between these sub-groups . Moreover , conducting a t-test for GLM analysis did not identify any significant differences in inflammatory profile between the Eurasian sub-groups , further strengthening the rationale to pool data for patients of European/Middle Eastern and Central/South Asian ancestry together in subsequent analyses . MTB has co-evolved with humans , and different bacillary strains associate with different ethnic groups [2]; moreover , MTB strains of different lineage elicit differing immune responses in vitro [6]–[11] _ENREF_8 . Ethnic variation in inflammatory profile in PTB might therefore be explained by differential representation of MTB strain lineages between ethnic groups . To investigate this possibility , genetic lineages of isolates from sputum of study participants were determined using multilocus Mycobacterial Interspersed Repetitive Units – Variable Number of Tandem Repeats ( MIRU-VNTR ) analysis [18] , and frequencies of isolates of different lineage were compared between patient groups . Isolates of Indo-Oceanic ( Lineage 1 ) , East Asian ( Lineage 2 ) and East African-Indian ( Lineage 3 ) lineages tended to be under-represented , and isolates of Euro-American ( Lineage 4 ) lineage over-represented , in participants of African vs . Eurasian ancestry ( Table 1; p = 0 . 08 ) . We therefore repeated the analyses of ethnic differences in inflammatory profile above , including additional statistical adjustment for MTB strain lineage: the set of parameters identified as being significantly different between patients of African vs . Eurasian ancestry was unchanged ( Table 2 ) , suggesting that MTB strain lineage was not a determinant of ethnic differences in inflammatory profile that we had observed . As a further test of the influence of MTB strain lineage on immune responses in the host , we conducted stratified analyses to compare inflammatory profiles associated with different MTB strain lineages in patients of African and Eurasian ancestry separately . Among patients of Eurasian ancestry , no statistically significant differences in either circulating or antigen-stimulated immune responses were observed between patients infected with organisms of different strain lineage . Among patients of African ancestry , serum concentrations of prostaglandin E2 ( PGE2 ) were significantly lower in patients infected with MTB of East African-Indian lineage compared to those infected by other lineages ( p = 0 . 0008; Figure 4 ) , but no inter-lineage differences were seen for any other circulating parameter , or for any antigen-stimulated parameter investigated . Since ethnic variation in the distribution of MTB strain lineages did not associate with differences in inflammatory profile observed between participants of African vs . Eurasian ancestry , we proceeded to investigate whether these differences might arise as a result of genetic variation in the host – a hypothesis suggested by results of human genome scans identifying chromosomal regions that influence immune responses to M . tuberculosis [19] , [20] . To explore this possibility , we investigated two common functional single nucleotide polymorphisms in the DBP gene ( rs4588 and rs7041 ) , combinations of which form three haplotypes ( Gc1F , Gc1S and Gc2 ) . These polymorphisms were selected for investigation on the basis that they have been shown to influence antimycobacterial immune responses; that their frequency varies between people of African vs . Eurasian ancestry [21]; and that we had identified a significant difference in DBP concentration between ethnic groups . Rs4588 and rs7041 genotypes were determined , and haplotype frequencies were compared between ethnic groups: Gc1F carriers were over-represented , and Gc2 carriers under-represented , in patients of African vs . Eurasian ancestry ( p<0 . 0001 , Table 1 ) . Moreover , serum DBP concentration in newly-diagnosed TB patients varied with DBP genotype , with those of Gc1F/1F genotype having the lowest concentrations and those with Gc1S/1S genotype having the highest concentrations , irrespective of ethnic group ( p<0 . 0001 for comparison by genotype; p>0 . 05 for ethnic comparison within each genotype; Figure 5 ) . We therefore repeated the analysis of ethnic differences in inflammatory profiles , this time including statistical adjustment for DBP genotype in addition to the phenotypic characteristics previously incorporated in the model . Ethnic differences in neutrophil count , in serum DBP concentration , and in antigen-stimulated responses that had previously attained statistical significance in the original model were rendered non-significant by this adjustment ( Table 2 ) . The effect of the adjustment for DBP genotype is further illustrated in Figure 6 , which shows a reduction in separation of samples from patients of African vs . Eurasian ancestry in a PCA plot after incorporation of DBP genotype in the model . We conclude that ethnic variation in DBP genotype associates with variation in inflammatory profiles observed between PTB patients of African vs . Eurasian ancestry . We next proceeded to investigate whether ethnic differences in inflammatory profiles persisted after completion of intensive-phase antituberculous therapy . Concentrations of the same immunological parameters described above were assayed in samples of serum , plasma and whole blood taken after 8 weeks of antituberculous therapy from a cohort of 85 patients ( 30 of African ancestry and 55 of Eurasian ancestry ) who fulfilled pre-defined criteria for inclusion in the per-protocol analysis of the clinical trial in which they were participating ( Study Profile , Figure S1 ) . Patients of different ethnic origin whose samples contributed to this analysis had similar demographic and clinical characteristics , the only statistically significant difference being a shorter duration of symptoms pre-diagnosis in patients of African vs . Eurasian ancestry ( median 1 . 9 vs . 3 . 0 months respectively , p = 0 . 001 ) . As before , parameters whose concentration was significantly different between participants of African vs . Eurasian ancestry were identified using the t-test for GLM , with adjustment for clinical and demographic covariates with potential to influence the effects of antimicrobial therapy on immune responses ( age , sex , duration of symptoms pre-diagnosis , duration of antimicrobial therapy pre-sampling , isoniazid sensitivity vs . resistance , and allocation to vitamin D vs . placebo in trial ) . The effect of significant parameters was then assessed visually using PCA . This analysis revealed that ethnic differences in neutrophil count and serum concentrations of CCL2 , CCL5 , CCL11 and DBP persisted at 8 weeks ( p≤7 . 84×10−6 , Figure 1 ) and that an additional parameter , serum C-X-C chemokine ligand 8 ( CXCL8 ) concentration , was lower in participants of African vs . Eurasian ancestry at this time point ( p = 0 . 0018; Table 2 ) . PCA plots of circulating inflammatory parameters sampled at different time points show that samples from patients of African vs . Eurasian ancestry were more widely separated at 8 weeks compared to baseline ( Figure 6 ) , indicating that ethnic variation in circulating inflammatory profile was more marked at 8 weeks than at baseline . Ethnic variation in antigen-stimulated responses was also observed in 8-week samples , with supernatant concentrations of antigen-stimulated CCL11 and hepatic growth factor ( HGF ) being significantly lower in patients of African vs . Eurasian ancestry after completion of intensive-phase therapy ( p≤0 . 0023; Table 2 , Figure 1 ) . As an additional check to determine whether any of this variation could be attributed to the effects of adjunctive vitamin D supplementation , which we have previously shown to be immunomodulatory [22] , we repeated the analyses above in the sub-group of 47 participants allocated to the placebo arm of the clinical trial in which they were participating . Near-identical results were obtained in this smaller cohort ( Table S4 , Figure S2 ) , confirming that ethnic differences in 8-week inflammatory profile observed in the analysis of all participants did not arise as a result of confounding by differential allocation to vitamin D vs . placebo in patients of African vs . Eurasian ancestry . Given that differences in immune response have been reported to associate with differences in microbiological clearance among patients with PTB [23]–[25] , and that speed of sputum culture conversion has been reported to vary between TB patients of African vs . Eurasian ancestry [14] , [15] we wished to determine whether ethnic differences in inflammatory response associated with variation in microbiological response to therapy . To this end , we compared time to sputum culture conversion between participants of African vs . Eurasian ancestry in our cohort , and found no significant difference ( p = 0 . 41 ) . Since ethnic differences in inflammatory profile persisted throughout intensive-phase treatment , we reasoned that profiles associated with fast vs . slow sputum culture conversion might therefore differ between ethnic groups . To test this hypothesis , we classified each participant for whom sputum culture conversion data were available ( n = 82 ) according to their time to sputum culture conversion from positive to negative , denoting those with time greater than or equal to the median value of 37 . 25 days ‘slow converters’ ( n = 41 ) , and those with time less than this value ‘fast converters’ ( n = 41 ) . Clinical characteristics of patients having fast vs . slow sputum culture conversion , stratified by ethnicity , are compared in Table S3 . Slow sputum culture conversion was associated with older age and higher baseline sputum bacillary load among Eurasians ( p≤0 . 01 ) ; similar trends were seen among Africans ( p≤0 . 45 ) . We then compared inflammatory profiles measured during the course of intensive-phase therapy between fast and slow sputum culture converters for patients of African vs . Eurasian ancestry . The interaction analysis for ethnic group was conducted using rank regression on the interaction term ‘week of sampling*speed of sputum culture conversion’ with adjustment for the same covariates as for the analysis of 8-week samples above , plus week of sampling and subject ID . Baseline bacillary load was not adjusted for as this is likely to be a significant driver of differences in inflammatory profiles between fast vs . slow converters . The kinetics of circulating inflammatory responses differed markedly between patients with fast vs . slow sputum culture conversion , and immunological correlates of speed of sputum culture conversion differed between patients of African vs . Eurasian ancestry . Circulating immunological correlates of fast vs . slow sputum culture conversion in patients of African vs . Eurasian origin are summarised schematically in Figure 7 , and presented in detail in Table 3 and Figure 8 . Of the 50 parameters investigated , 27 were associated with speed of sputum culture conversion in one or both ethnic groups . Twelve of these parameters associated with speed of sputum conversion in patients of Eurasian ancestry only; nine associated with speed of sputum culture conversion in patients of African ancestry only; four associated similarly with speed of response in both ethnic groups; and two were differentially associated with speed of sputum culture conversion in patients of African vs . Eurasian ancestry , i . e . elevated levels of these markers associated with slower sputum culture conversion in one ethnic group and faster conversion in another . As a further step to validate our findings , we applied network PCA to the parameters listed in Table 3 in order to investigate the relationship between changes in inflammatory parameters observed during treatment ( Figure 9 ) . For both ethnic groups , acute phase reactants erythrocyte sedimentation rate ( ESR ) and C-reactive protein ( CRP ) were linked and red cell parameters were linked to each other . In the African network MMP-1 was linked to its inducer PGE2 , and chemokines CCL2 and CCL11 were closely linked , while in the Eurasian network , monocyte and neutrophil counts were closely linked . These linkages are consistent with biological understanding of the regulation of these inflammatory mediators and cell populations , validating results of the PCA . Kinetics of antigen-stimulated responses were not compared between groups due to small numbers within each sub-group . Clinically significant ethnic differences in immune responses to Plasmodium falciparum and human immunodeficiency virus have previously been described [26] , [27] , but to our knowledge , this study is the first to address the question of whether inflammatory responses vary between TB patients of different ethnic origin . We report that inflammatory profiles vary significantly between TB patients of African and Eurasian ancestry having similar clinical and demographic characteristics , and that these differences associate primarily with ethnic variation in host rather than bacillary genotype . We also show that ethnic differences in inflammatory profiles observed at presentation persist after completion of intensive-phase therapy , and that immunological correlates of speed of sputum bacillary clearance differ markedly between patients of African vs . Eurasian ancestry . These findings have important implications for the design of studies investigating immunological biomarkers of response to antituberculous therapy . African patients living in Africa have previously been reported to have more extensive disease at diagnosis than Europeans living in Europe , and to have lower rates of sputum conversion after intensive-phase antimicrobial therapy [14] , [15] , but controversy remains as to whether this reflects ethnic variation in host-pathogen interactions or geographical variation in laboratory practice and/or access to effective therapy . In our study – where patients of different ethnic origin were recruited in a single city , and where all microbiological samples were analysed in a single laboratory – we observed no difference in rates of cavitation or 2-month sputum culture conversion between patients of African vs . Eurasian ancestry . Despite this , we did observe significant ethnic variation in inflammatory profile between groups . Many of these differences were associated with variation in host DBP genotype , supporting the findings of an in vitro study reporting that DBP has broad influences on the antimycobacterial response [28] . This is plausible , given that this protein modulates macrophage activation and neutrophil chemotaxis , as well as performing its classical role in transport of vitamin D metabolites in the circulation [29] . We also observed ethnic variation in circulating and/or antigen-stimulated concentrations of cytokines ( IL-1RA , IL-12 ) and chemokines ( CCL2 , CCL5 , CCL11 , CXCL8 ) , many of which play key roles in the antimycobacterial immune response . The genes encoding these mediators are all polymorphic , and in some cases , ethnic variation in frequency of alleles influencing antimycobacterial responses has been reported [30] , [31] . Study of functional associations of polymorphisms in these genes might yield insights into the genetic basis for ethnic variation in immune responses to MTB . Further investigation in other populations is also required to validate the ethnic differences in inflammatory profile that we report , as the large number of analyses and relatively modest sample size of our study could have led to Type I and Type II errors regarding specific parameters . Nevertheless , our main conclusions regarding strong ethnic group differences appear solid given the highly statistically significant differences found after stringent adjustment for multiple comparisons . In contrast to the variation in inflammatory response between patients of different DBP genotype , relatively little difference in circulating and antigen-stimulated responses was seen between individuals infected with MTB strains of different lineage when multivariate analysis of the full cohort of 128 patients was performed . Secondary stratified analyses within the two main ethnic groups were conducted as a ‘belt and braces’ validation , to ensure that multivariate analysis had been successful in adjusting for potential ethnicity-related confounders of the relationship between MTB strain and immune profile . This secondary analysis identified only one analyte which was affected by lineage , and only in one ethnic group . The fact that the main analysis and the validation analyses yielded the same result - i . e . minimal effect of MTB strain on immune profile - lends considerable weight to our conclusion that MTB strain is not a major determinant of immune profile in tuberculosis . This finding complements that of Pareek and colleagues , who recently reported that ethnicity is a powerful determinant of clinical TB phenotype independently of mycobacterial lineage [32] . Other investigators have reported that ‘modern’ strains elicit lower inflammatory responses than ‘ancient’ strains in macrophages , but that no difference in responses was seen in peripheral blood leukocytes [11] , the population of cells investigated here . Further study is required to determine whether macrophages isolated from TB patients of different ethnic origin vary in their response to different MTB strains . Nevertheless , our observation that ethnic differences in inflammatory profile persisted after the several log-fold reduction in bacillary load induced by intensive-phase therapy tends to support the hypothesis that host , rather than bacillary , factors are the major determinants of ethnic variation in inflammatory profile . Such variation in inflammatory responses to antimicrobial treatment might reflect ethnic differences in allele frequency of polymorphisms of drug transporter genes that have been shown to associate with pharmacokinetic response to rifampicin [33] . However , our observations that sputum conversion rates were similar in patients of African vs . Eurasian ancestry , and that ethnic differences in inflammatory responses post-therapy were qualitative rather than quantitative , does not support this hypothesis . It is more plausible that , as at baseline , ethnic differences in inflammatory profile after treatment represent ethnic variation in alleles encoding components of the inflammatory response . Such variation may have arisen as a result of differences in selective pressures on the immune response between populations that remained in Africa vs . those that migrated out of the continent some 70 , 000 years ago [1] . Whatever the underlying reasons for these differences , our observation that immunological correlates of fast vs . slow sputum culture conversion differ between patients of African vs . Eurasian ancestry has practical implications for the design of studies to identify immunological correlates of response to intensive-phase antituberculous therapy . Studies evaluating candidate biomarkers published to date have been relatively small , and have tended to investigate fewer parameters in smaller numbers of patients of homogeneous ancestry than in the current study . Our finding that high CRP and ESR associate with slow sputum culture conversion is in keeping with other reports [25] , [34] . Larger studies are now needed; our findings indicate that the validity of candidate biomarkers of treatment response identified by such studies will need to be evaluated in patients of different ancestry , as the inflammatory response in TB is ethnically heterogeneous . The patients included in this study were participants in the AdjuVIT study - a double-blind randomised placebo-controlled trial of high-dose vitamin D during intensive-phase antimicrobial treatment of pulmonary TB , conducted in London , UK . Recruitment commenced on January 25th 2007 , and ended on July 3rd 2009 . A detailed account of study design has previously been given [12] . Participants self-defined their ethnic origin using the UK Office of National Statistics classification [35] and this information was used to attribute ancestry into one of five groups: African , Eurasian ( incorporating participants of European , Middle Eastern , Central or South Asian ethnic origin ) , East Asian , Oceanic and American [13] . Baseline assessment included collection of a sputum sample for microscopy and culture and a blood sample . Fresh whole blood was sent for determination of full blood count and ESR and ex vivo stimulation with a mycobacterial antigen as described below . Aliquots of serum , plasma and whole blood were also stored at −80°C until completion of the trial . Participants were reviewed at 14 , 28 , 42 and 56 days after starting antituberculous therapy to assess clinical status and to monitor for adverse events . Blood and sputum samples were collected at each time-point and processed as above . Full characterisation of inflammatory profile was performed in the sub-set of participants who fulfilled pre-defined criteria for per-protocol analysis ( i . e . those infected with a rifampicin-sensitive isolate of M . tuberculosis who received at least three doses of study preparation , who were compliant with antituberculous therapy , who did not take second-line antituberculous therapy or oral corticosteroids , who completed all study visits and who were not HIV sero-positive ) . The study was approved by East London and The City Research Ethics Committee ( ref 06/Q0605/83 ) , and registered with ClinicalTrials . gov ( NCT00419068 ) . Written informed consent was obtained from all participants before enrolment . For all participants recruited on or after May 15th 2008 , fresh whole blood was diluted 1∶10 in RPMI 1640 medium ( Sigma-Aldrich , Gillingham , UK ) and duplicate 180 µl aliquots were stimulated in 96-well plates at 37°C in the presence of 5% CO2 with rCFP-10 ( Rv3874 , Proteix Biotechnologies , Vestek , Czech Republic; final concentration 2 . 5 µg/ml ) or 2% bovine serum albumin in phosphate buffered saline ( negative control ) . Plates were centrifuged after 72 hours' incubation , and cell-free supernatants were aspirated and frozen at −80°C pending immunological analysis . rCFP-10 was tested for presence of endotoxin: concentration was found to be 260 IU ( EU ) /mg , working concentration 63 pg/ml . Addition of this concentration of endotoxin to TB patients' whole blood in control experiments did not stimulate cytokine or chemokine secretion . Immunological parameters were selected on the basis that they played a role in host defence against MTB and/or that they were recognised biomarkers of disease activity [36] . Concentrations of 43 soluble factors listed in Table S1 were determined in serum/plasma as follows . Serum CRP and albumin concentrations were assayed using an Architect ci8200 analyser ( Abbott Diagnostics , Chicago , IL , USA ) . Serum concentrations of IL-1β , IL-1RA , IL-2 , IL-2R , IL-4 , IL-5 , IL-6 , IL-7 , IL-10 , IL-12 ( p40/p70 ) , IL-13 , IL-15 , IL-17 , G-CSF , GM-CSF , IFN-α , IFN-γ , TNF , CXCL8 , CXCL9 , CXCL10 , CCL2 , CCL3 , CCL4 , CCL5 , CCL11 , EGF , FGF-β , HGF and vascular endothelial growth factor ( VEGF ) were quantified using a human 30-plex bead immunoassay panel ( sensitivity [sens . ] according to Lot #617361 , Invitrogen , Camarillo , CA , USA ) . Serum samples required high dilution for accurate determination of CCL5 concentration and all were re-assayed using a single-plex bead assay ( Invitrogen ) . Serum PGE2 concentration was analysed by high sensitivity competitive enzyme immunoassay ( EIA; Assay Designs , Ann Arbo37 . 25r , MI , USA; sens . 13 . 4 pg/ml ) . Plasma concentrations of antimicrobial peptides ( AMP ) LL-37 ( sens . 31 pg/ml ) , HNP1-3 ( sens . 156 pg/ml ) and NGAL ( sens . 400 pg/ml ) were analysed by ELISA ( Hycult Biotechnology , Uden , The Netherlands ) . Plasma concentrations of MMP-1 , -2 , -3 , -7 and -8 were determined by Fluorokine MAP multianlalyte profiling ( sens . according to Lot #273379 , R&D systems ) ; plasma concentration of MMP-9 was determined by DuoSet ELISA ( sens . 3 pg/ml , R&D systems ) . Serum concentration of DBP was determined by ELISA ( sens . 0 . 65 ng/ml , R&D systems ) . Multi-plex bead assays were performed on a Luminex 200 anlayzer ( Luminex Corporation , Austin , TX , USA ) . ELISA and EIA absorbances were measured using a Benchmark Plus microplate spectrophotometer ( Bio-Rad Laboratories , Hertfordshire , UK ) . The concentrations of 39 of these analytes ( all of the above except DBP , PGE2 , CRP and albumin; listed in Table S1 ) were also determined in WBA supernatants . Antigen-stimulated AMP and MMP concentrations were corrected by subtraction of unstimulated values . For MMP-2 , -3 , -8 and HNP and NGAL , unstimulated values were generally greater than stimulated values and this was the case sometimes for MMP-9 and LL-37 . Cytokine/chemokine values were generally undetectable in unstimulated samples and a correction was not applied . Fourteen haematological parameters listed in Table S1 were also measured in fresh whole blood . Full blood counts were performed using a LH750 haematology analyser ( Beckman Coulter , Brea , CA , USA ) . ESR was measured by the Wintrobe method using a s2000 analyser ( Desaga , Wiseloch , Germany ) . Human DNA was extracted from whole blood using the Promega Wizard SV 96 Genomic DNA Purification System on the Biomek FX robot ( Beckman Coulter ) , quantified using the Nanodrop spectrophotometer and normalised to 5 ng/ml . 10 ng DNA was used as template for 5 ml pre-developed TaqMan assays ( Applied Biosystems , Foster City , CA , USA ) to type the StyI ( rs4588 ) and HaeIII ( rs7041 ) polymorphisms of the vitamin D binding protein . These assays were performed on the ABI 7900HT platform in 384-well format , and data were analysed with Autocaller software . DBP haplotypes were deduced from StyI and HaeIII genotypes as previously described [21] . Mycobacterial DNA was extracted and genotyped using automated 15 mycobacterial interspersed repetitive unit–VNTR as previously described [18] . Contingency tables were analysed using chi-square tests , unless more than 20% of cells in a table had an expected frequency of <5 , when Fisher's exact tests were employed . Median serum DBP concentration was compared between groups using a Kruskal-Wallis test with Dunn's post hoc test to correct for multiple comparisons . Time to sputum culture conversion was compared between groups using a logrank test . Analyte concentrations were calculated from raw luminex , ELISA and EIA data using Masterplex ReaderFit software ( Hitachi Solutions America , San Francisco , CA , USA ) and these calculated values were plotted using GraphPad Prism 5 software ( La Jolla , CA , USA ) . Linear modelling and PCA was conducted using Qlucore Omics Explorer 2 . 2 software ( Qlucore AB , Lund , Sweden ) . Analyte concentrations were log2 converted and the variance was normalized to 1 . For analytes that were undetectable in at least one sample , the ‘limit of detection’ value was added to every measured value for that analyte prior to log2 conversion . Missing values were imputed by K nearest neighbours ( k-NN ) [37]: for circulating parameters , 2 . 5% of data points were missing; for CFP-10-stimulated parameters , 3% of data points were missing . Parameters whose concentration differed significantly between patients of African vs . Eurasian ancestry were identified using the t-test for GLM with adjustment for covariates with potential to influence the inflammatory profile using the eliminated factors approach . This fits a multiple regression model to all covariates , and subtracts the expression values predicted by this model from the observed values in order to remove covariate effects between patients [38] . An F-test for GLM with adjustment for covariates with potential to influence the inflammatory profile was performed to identify parameters whose concentration varied according to MTB strain lineage within each ethnic group . Parameters associating with slow vs . fast sputum culture conversion within each ethnic group were identified by rank regression analysis on the interaction term ‘week of sampling*speed of sputum culture conversion’ with adjustment for covariates with potential to influence the inflammatory profile , week of sampling ( to correct for effects of treatment duration alone ) and subject ID ( to correct for repeated measures ) . Rank regression was conducted by replacing the ordinal interaction categorical predictors with numerical predictors , followed by a normal linear regression . Samples were ordered alternately fast , then slow , with increasing time since treatment initiation [39] . These analyses yield t-statistics ( calculated as the regression co-efficient for each parameter divided by its standard deviation ) representing the magnitude of difference in concentration of a given parameter between groups being compared; p values , representing the probability that such differences could have arisen by chance alone; and q values , which define the lowest false discovery rate ( FDR ) for which the hypothesis would be accepted under the Benjamini-Hochberg procedure for multiple testing correction [40] . Thresholds of 0 . 05 were applied for p and q values throughout . PCA networks were created using one connection , i . e . by connecting each analyte to the other analyte that it shares the most similar pattern of change with over time; the distance between analytes in the network represents their Pearson correlation coefficients . Points in the network are coloured according to the value of the R-statistic generated for each analyte from the rank regression interaction analysis , which identified variables that had a significantly different pattern of change between slow and fast responders over time . The value of the R-statistic indicates the proportion of the total variation of that variable which is explained by the model tested . It is calculated as the square root of the R2-statistic , and the sign indicates the direction of the observed effect . A positive R-statistic indicates a higher concentration of that analyte in slow vs . fast culture converters , and vice versa .
Mycobacterium tuberculosis ( MTB ) is the causative agent of tuberculosis . Genetically distinct strains of MTB cause disease in particular ethnic groups , and these strains vary in their ability to elicit inflammatory responses from antigen-presenting cells in vitro . Circulating and antigen-stimulated concentrations of inflammatory mediators ( ‘inflammatory profile’ ) might therefore be expected to differ between tuberculosis patients of different ethnic origin; however , this question has not previously been addressed . We therefore conducted a study to characterise ethnic variation in inflammatory profiles in a cohort of 128 newly-diagnosed tuberculosis patients in London , UK . Patients of African vs . Eurasian ancestry had distinct inflammatory profiles at presentation; differences did not relate to MTB strain variation between groups , but they did associate with ethnic variation in host genotype . Moreover , immunological correlates of the rate of MTB clearance from sputum differed between patients of African vs . Eurasian ancestry . Our findings provide insight into the mechanisms underlying ethnic variation in inflammatory profile in tuberculosis patients , and indicate that candidate immunodiagnostics and immunological biomarkers of response to tuberculosis therapy should be derived and validated in tuberculosis patients of different ethnic origin .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "bacteriology", "medicine", "immunopathology", "infectious", "diseases", "bacterial", "diseases", "inflammation", "immune", "cells", "genetics", "of", "the", "immune", "system", "clinical", "immunology", "immunity", "bacterial", "taxonomy", "tuberculosis", "host-pathogen", "interaction", "biology", "microbiology", "pulmonology", "immune", "response" ]
2013
Ethnic Variation in Inflammatory Profile in Tuberculosis
Progressive multifocal leukoencephalopathy ( PML ) induced by JC virus ( JCV ) is a risk for natalizumab-treated multiple sclerosis ( MS ) patients . Here we characterize the JCV-specific T cell responses in healthy donors and natalizumab-treated MS patients to reveal functional differences that may account for the development of natalizumab-associated PML . CD4 and CD8 T cell responses specific for all JCV proteins were readily identified in MS patients and healthy volunteers . The magnitude and quality of responses to JCV and cytomegalovirus ( CMV ) did not change from baseline through several months of natalizumab therapy . However , the frequency of T cells producing IL-10 upon mitogenic stimulation transiently increased after the first dose . In addition , MS patients with natalizumab-associated PML were distinguished from all other subjects in that they either had no detectable JCV-specific T cell response or had JCV-specific CD4 T cell responses uniquely dominated by IL-10 production . Additionally , IL-10 levels were higher in the CSF of individuals with recently diagnosed PML . Thus , natalizumab-treated MS patients with PML have absent or aberrant JCV-specific T cell responses compared with non-PML patients , and changes in T cell-mediated control of JCV replication may be a risk factor for developing PML . Our data suggest further approaches to improved monitoring , treatment and prevention of PML in natalizumab-treated patients . Progressive Multifocal Leukoencephalopathy ( PML ) is a demyelinating disease of the central nervous system caused by JC virus ( JCV ) [1] . PML occurs most often in the setting of immunodeficiency , such as AIDS , leukemia , organ transplantation and idiopathic CD4 lymphopenia . However , cases of PML have recently been reported after immunotherapy with monoclonal antibodies , including approximately 260 cases in MS patients treated with natalizumab ( Tysabri ) as of July 30 , 2012 . The risk of PML increases with the number of natalizumab doses administered with highest incidence after 24 [2]–[4] . The 2 . 1/1000 overall risk of PML [2] is a major consideration in the decision to treat with natalizumab . The mechanism underlying JCV reactivation in natalizumab-treated individuals with MS is actively being investigated , but may involve both reduced immune surveillance of the central nervous system due to attenuated extravasation of leukocytes out of the bloodstream and into tissues [5]–[7] , and also latent viral infection in CD34+ cells that migrate from the bone marrow to the peripheral circulation [8] . In this context , characterization of the effects of natalizumab on T cell immune control of JCV replication might shed light on the host and viral factors that determine the risk for the development of PML . Previous studies have measured CD4 and CD8 T cell responses to JCV in individuals with PML who had not received natalizumab therapy [9]–[14] . CD8 T cell responses to particular JCV epitopes were found to be associated with longer survival times after early PML in HIV-positive subjects [15] , and a recent study showed that CD4 and CD8 T cell responses to JCV were more likely to be detected in PML survivors than in PML progressors [16] . Most studies have focused solely on the magnitude of JCV-specific T cell responses but the quality of the response also may be important [17] . In one study , HIV-related PML was associated with a unique increase in JCV-specific interleukin 10 ( IL-10 ) production by bulk cultures of peripheral blood mononuclear cells ( PBMC ) , compared with non-HIV PML samples [18] . Little is known about the quality of the JCV-specific response in natalizumab-treated individuals . It has been reported that after natalizumab treatment , the levels of mRNA for the cytokines interferon gamma ( IFNγ ) and IL-10 change , with IFNγ levels increasing in PBMC and decreasing in CSF cells and IL-10 levels increasing in CSF cells [19] . However , as these levels were measured in bulk cultures from each compartment , it is not possible to determine whether this finding simply reflects the altered frequencies of different cell types in the blood and CSF that are known to occur with treatment [7] , [20]–[22] . Two recent studies yielded disparate results when examining longitudinal T cell responses to JCV in individuals with MS after natalizumab treatment [23] , [24] . Notably , these studies measured JCV-specific T cell responses directed against the VP1 protein by interferon gamma production . We hypothesized that immune control of JCV viremia in natalizumab-treated individuals might depend on T cells directed at other portions of the virus and/or on the range of the cytokines produced . To investigate the JCV-specific T cell response in greater depth , we performed a detailed characterization of the functional T cell response to the entire JC virus proteome longitudinally in individuals initiating treatment with natalizumab , and in individuals who had developed PML after treatment with natalizumab . We characterized the T cell immune response to JCV in eight patients with MS initiating natalizumab and ten healthy volunteers . As T cell responses have not been described to JCV proteins other than VP1 , we sought to determine whether either CD4 or CD8 T cells were directed against the other portions of the virus . We stimulated PBMC with 5 pools of peptides covering the entire JCV proteome: large T antigen , small t antigen , VP1 , VP2 and agnoprotein . The peptides were 15mers overlapping by 11 amino acids to optimize coverage of both CD4 and CD8 T cell epitopes , and included additional peptides to cover JCV sequence variants identified in the literature ( as described in the materials and methods ) . Responses were measured by intracellular cytokine staining ( ICS ) and polychromatic flow cytometry for cytokines associated with effective control of viral infections: IFNγ , tumor necrosis factor ( TNF ) and interleukin 2 ( IL-2 ) , and for a cytokine associated with poor control of viral infections [25]–[27] , interleukin 10 ( IL-10 ) . Both CD4 and CD8 memory T cell responses were readily measured ex vivo from cryopreserved PBMC samples . Responses were observed in all MS patients that variously targeted each of the JCV proteins: large T antigen , small t antigen , VP1 , VP2 and agnoprotein ( Figure 1 ) . Notably , different viral proteins were immunodominant in different individuals and most individuals targeted more than one protein ( Figure 1 ) . Both CD4 and CD8 T cells specific for JCV were observed in most subjects ( Figure 1 ) . None of the responding T cells produced IL-10 , but each produced one or more of the other three cytokines , with a significant fraction expressing two or three cytokines ( Figure 1 , Figure 2A and data not shown ) . The specificity , magnitude and functional profile of these responses varied among individuals . Similar JCV-specific T cell responses were observed in all ten healthy subjects ( Figure S1 ) . To determine the impact of natalizumab after short-term treatment , when risk of PML is low , we compared T cell responses to JCV and cytomegalovirus ( CMV ) before and after the initiation of natalizumab . The eight individuals with MS were examined at baseline ( month 0 ) before natalizumab therapy , one month after the first infusion of natalizumab ( month 1 ) , and at the latest timepoint available ( after 3–7 monthly infusions ) . The total CD4 and CD8 T cell responses to JCV , calculated as the sum of the memory response to each of the five JCV peptide pools , did not change significantly at different timepoints ( Figure 2A ) . There was also no significant difference in the magnitude of the responses to each individual peptide pool ( data not shown ) . The functional profile of the CD4 and CD8 T cell responses to JCV , defined by production of a combination of IFNγ , TNF and IL-2 , did not vary significantly over time ( Figure 2A ) . Although the relative sizes of the slices of the pie charts representing different combinations of cytokines produced by the T cells varied across timepoints , these apparent differences were not statistically significant as determined by the chi squared and permutation tests which were used to calculate P values [28] . We therefore observed no change in the magnitude , quality , or targeting of the JCV-specific immune response early after initiation of natalizumab treatment . CMV was chosen as a control antigen for JCV because it is a prevalent DNA virus that , like JCV , is neurotropic and establishes latent infection . The CMV pp65-specific CD4 and CD8 memory T cell responses did not change in magnitude or functional profile between the three timepoints ( Figure 2B ) . None of the T cells directed against JCV or CMV produced IL-10 at any timepoint . These findings suggest that short-term natalizumab therapy does not alter these antiviral immune responses . To gauge the impact of natalizumab on the overall memory T cell response to stimulation we measured cytokine production after stimulation with the mitogen SEB . No difference was observed in the magnitude or profile of IFNγ , TNF and IL-2 produced at the different time points in response to SEB stimulation ( data not shown ) . We also measured IL-10 production because this regulatory cytokine is associated with poor control of persistent viral infections [25]–[27] . Upon mitogenic stimulation with SEB , a small number of CD4 T cells produced IL-10 . The frequency of these cells increased after one dose of natalizumab , although this effect was not sustained at later timepoints ( Figure 3A ) . This increase in the frequency of IL-10-producing memory CD4 T cells is unlikely to result from a change in the frequency of the parent population ( total memory CD4 T cells ) as the frequency of these cells did not change across timepoints ( data not shown ) . The increased frequency of T cells that produce IL-10 after one dose of natalizumab may indicate its potential to skew the functional profiles of T cells . In order to determine whether MS patients with natalizumab-associated PML had similar T cell responses , we also examined the response to mitogenic stimulation in T cells from four patients whose CSF samples were previously tested in our laboratory for JCV DNA . One of these patients , PML-4 , was one of the first cases diagnosed in 2005 , who was followed for more than 5 years and never cleared JCV from the brain [29] . Importantly , in three out of the four subjects ( PML-1 , PML-3 and PML-4 ) , a high frequency of CD4 T cells produced IL-10 in response to mitogenic stimulation with SEB , despite the discontinuation of natalizumab in these individuals ( Figure 3B ) . T cells from subject PML-2 did not produce IL-10 in response to JCV , CMV or SEB stimulation and this finding was confirmed in a second sample from PML-2 ( data not shown ) . The observation that the frequency of IL-10 producing memory CD4 T cells was high in subjects with natalizumab-associated PML raises the intriguing possibility that the transient increase in these cells after treatment initiation might indicate a potential role for natalizumab in skewing the immune response toward a cytokine associated with PML . Approximately 2 . 1/1000 MS patients treated with natalizumab develop PML [2] . We hypothesized that such individuals might have an aberrant T cell response to JCV . Given our findings that JCV-specific T cells may target many of the JCV antigens and are varied in functional profile among individuals , we sought to identify whether there were differences in the antiviral immune responses of individuals who developed PML after treatment with natalizumab . We therefore characterized T cell responses in four individuals with natalizumab-associated PML by measuring JCV-specific production of IFNγ , TNF , IL-2 and IL-10 . In two subjects , PML-1 and PML-2 , no JCV-specific CD4 or CD8 T cell responses were observed that were significantly above background ( Figure 4A ) . These were the only subjects in whom no JCV-specific T cell responses were observed , as all the MS patients and healthy volunteers had detectable responses . The sample from PML-1 was taken 2 weeks after diagnosis with PML , and the sample from PML-2 was taken 2 months after diagnosis . Both subjects were still being followed 2 years after diagnosis . In PML-3 and PML-4 , the JCV-specific T cell response had a markedly different functional profile than was observed in any of the non-PML or healthy subjects . These subjects , who were diagnosed with PML 4 months and 5 years prior to sampling , respectively , had JCV-specific CD4 T cells that produced IL-10 ( Figure 4B ) . The specificity of the IL-10 response for PML-4 was confirmed by double staining with the same antibody conjugated to two different fluorophores ( Figure S2 ) . The antigen specificity of the subjects' responses differed . In subject PML-3 , 1 . 5% of the CD4 memory T cells produced IL-10 and were specific for the small t antigen ( Figure 4A , grey bar ) , while the sum of the IFNγ responses to all of the JCV peptide pools was 0 . 10% ( Figure 4A , red bar ) . The total IFNγ-producing memory CD8 T cell response was 0 . 26% . This subject died 1 month after sampling . In subject PML-4 , 0 . 34% of the CD4 memory T cells produced IL-10 and were specific for VP1 , while the sum of the IFNγ responses to all of the JCV peptide pools was 0 . 12% of CD4 memory cells . The total IFNγ-producing CD8 memory T cell response was 0 . 44% . Subject PML-4 died 1 year after sampling . Importantly , these were the only IL-10-producing virus-specific T cell responses we observed among all the subjects examined in this study . Thus , in contrast to non-PML MS patients or healthy volunteers , individuals who developed PML had JCV-specific T cell responses that were either uniquely dominated by IL-10 producing cells or were undetectable . As JCV-specific T cells in the blood of the four individuals with PML were either absent or of unusual functionality , we next examined the cytokine profile within CSF samples from 10 individuals with natalizumab-associated PML , including subjects PML 1–4 , at the time of initial diagnosis . In addition , a second CSF sample from a later time point was available for 8 of these subjects . The samples were tested for the presence of 27 cytokines and other markers and were compared to diagnostic CSF samples from 10 individuals who had suspected natalizumab-associated PML which was subsequently ruled out by a negative PCR for JCV . Twelve of the molecules were undetectable in the vast majority of CSF samples , including IL-1β , IL-2 , IL-4 , IL-12p70 , IL-17 , Eotaxin , FGF basic , GM-CSF , IFNγ , MIP1α , RANTES , and TNF . An additional 12 molecules , IL-1ra , IL-6 , IL-7 , IL-8 , IL-9 , IL-13 , G-CSF , IP-10 , MCP-1 , PDGF-β , MIP1β and VEGF , were measured in the CSF samples but did not vary in PML and non-PML samples . Although the assay was not sensitive enough for accurate quantification of low levels of IL-10 , it could readily distinguish whether IL-10 was detectable above background . Thus , we found that 50% of the PML CSF samples had detectable IL-10 , while none of the non-PML samples had detectable IL-10 ( P = 0 . 0325 , Figure 5A ) . Similarly , for IL-5 , 60% of the PML samples had IL-5 levels above the limit of quantification , while none of the non-PML samples had IL-5 levels that could be quantitatively measured ( P = 0 . 0108 , Figure 5A ) . Thus , higher levels of IL-10 and IL-5 can be detected in the CSF of individuals with natalizumab-associated PML . Finally , in the samples from early in PML disease , levels of IL-15 in the CSF were significantly higher than in later samples from the same subjects ( P = 0 . 02 ) , or in the samples from non-PML subjects ( P = 0 . 004 ) . These results reveal a PML-specific CSF cytokine profile that may reflect the altered cytokine profile we observed in JCV-specific T cells in the blood . The factors that lead to the development of PML in individuals treated with natalizumab need to be investigated in more detail , particularly immune responses to the virus . Consequently , we investigated the T cell immune response to the entire JC virus proteome longitudinally in subjects with MS who were initiating therapy with natalizumab and in subjects who had natalizumab-associated PML . The principal findings were: 1 ) T cell responses were identified against all JC virus proteins and could be measured ex vivo in the peripheral blood of individuals treated with natalizumab and healthy subjects; 2 ) the magnitude and quality of T cell responses to JCV and CMV did not change from baseline through the first several months of natalizumab treatment; 3 ) the frequency of T cells producing the cytokine IL-10 in response to mitogenic stimulation temporarily increased after the first dose of natalizumab; 4 ) individuals with PML either made no detectable T cell responses to JCV or had JCV-specific CD4 T cell responses uniquely dominated by IL-10 production rather than IFNγ; and 5 ) individuals shortly after PML diagnosis had higher levels of IL-10 , IL-5 and IL-15 in the CSF . Whether JCV-specific T cell responses can be reliably measured ex vivo [30] , [31] and the effect of natalizumab on these responses [23] , [24] have both been the subject of some debate . Using ex vivo stimulation with overlapping peptides , we readily detected CD4 and CD8 T cell responses to JCV by multiparameter ICS in healthy subjects , subjects with MS and in some subjects with PML . This approach could be useful in monitoring JCV-specific T cell function in natalizumab-treated individuals in the context of a vaccine or a risk stratification protocol . Our findings that JCV-specific T cells are directed against each of the viral proteins and that the specificity and immunodominance of the response varies among individuals strongly suggest that measuring responses to all viral proteins rather than VP1 alone is essential to obtaining a complete picture of JCV-specific immunity . Our results also suggest that measuring multiple cytokines rather than IFNγ alone allows for the identification of associations that would otherwise be missed , and in particular highlight the potential importance of IL-10 in evaluating T cell responses to JCV . In our longitudinal study we found no difference in either the magnitude or functional profile of the total JCV-specific T cell response during short-term natalizumab treatment . Although the sample size of our study was limited in power to detect longitudinal differences , the fact that no difference was observed in any of the cytokines measured , or in their relative contribution to the response at different time points , suggests that JCV-specific T cell responses are not altered simply by the initiation of natalizumab . However , in the subjects with PML , JCV-specific CD4 T cell responses were either undetectable or uniquely dominated by IL-10 . Importantly , it has been shown that IL-10 is detrimental to the clearance of lymphocytic choriomeningitis virus ( LCMV ) infection because of its inhibitory effect on virus-specific memory CD4 T cells [25] , [26] . Furthermore , vaccines which stimulate CD4 T cell IL-10 production can limit the elicitation of protective polyfunctional CD4 T cells [32] . Thus , the production of IL-10 but not IFNγ , TNF or IL-2 by JCV-specific CD4 T cells may interfere with antiviral activity to the detriment of control of JCV replication , either locally in the CNS or in peripheral tissues , and may consequently be causative of PML . Another interpretation is that IL-10 production , which was not detected until >4 months after the subjects had developed PML , is a later-stage response to the inflammation often associated with PML ( PML-IRIS ) . This sample size does not allow us definitively to link the time since diagnosis to the presence of an IL-10 response . However , our finding that IL-10 was detectable in 50% of early CSF samples suggests that IL-10 production may occur early in natalizumab-associated PML disease . Importantly , these two interpretations are not mutually exclusive . Although the role of IL-10 in PML associated with other conditions has not been thoroughly characterized , one study found increased JCV-specific IL-10 production by total PBMC from HIV+ PML cases , but not from non-HIV PML cases [18] . This result suggests that IL-10 may be associated with PML resulting from causes other than natalizumab therapy . Notably , we found that the frequency of memory CD4 T cells that produce IL-10 upon mitogenic stimulation is transiently increased after the first dose of natalizumab . It is tempting , therefore , to speculate that natalizumab may skew the CD4 T cell response toward IL-10 production and away from production of IFNγ , TNF , and IL-2 . This suggests a possible mechanism by which natalizumab treatment could lead to PML , as 50% of the subjects with natalizumab-associated PML that we studied produced IL-10 in response to JCV , and the other 50% had no measurable T cell response to the virus . There are a number of mechanisms by which natalizumab treatment could potentially skew the CD4 T cell response toward IL-10 production , including increasing antigen load through mobilization of infected CD34+ cells [8] , which may affect the cytokine profile [32] , altering the antigen-presenting cells ( APCs ) that interact with the T cells [25] , or altering T cell trafficking and thus with which APCs the T cells interact [5]–[7] . There is also the possibility that a direct effect of natalizumab on T cells could affect cytokine production , as may occur with ribavirin treatment for hepatitis C virus [33] . The low probability that any of the natalizumab-treated MS study subjects could go on to develop PML is consistent with the lack of JCV-specific IL-10 production in those subjects who did not have PML . Furthermore , in this study we were only able to measure T cell responses in the peripheral blood and were not able to sample the CNS of subjects without PML . A previous study showed that after 12 months of natalizumab treatment , levels of IL-10 mRNA were increased in bulk CSF cells , while remaining unchanged in PBMC [19] . Although this finding may be due to altered CSF cell subset composition after treatment rather than upregulation of IL-10 , it supports the hypothesis that natalizumab may alter the cytokine milieu in the CNS . Indeed , we found increased levels of IL-10 and IL-5 in CSF samples from individuals with natalizumab-associated PML . Although these cytokines are not typically associated with control of virus replication or with each other , their aberrant production may be indicative of an immune response that fails to control JCV replication at the site of disease . The increased CSF IL-15 we observed early in PML disease is consistent with inflammatory CNS disease [34]–[37] but is unlikely to be due solely to MS disease-associated inflammation [36]–[38] as the PML and non-PML groups both consisted of individuals with MS who were treated with natalizumab and had neurological symptoms consistent with PML . Thus far , it is not possible to demonstrate a causative link between IL-10 production and PML as the very low incidence of natalizumab-associated PML makes a prospective study unlikely . However , we believe that the potential mechanism suggested by these data should inform future work . Taken together , our data provide a framework for understanding immune control of JC viremia and the development of PML and suggest avenues of investigation to allow the better monitoring , treatment and prevention of PML in natalizumab-treated people . First , our finding that subjects with PML lacked JCV-specific T cell responses or produced IL-10 in response to stimulation suggests that immune monitoring might identify natalizumab-treated individuals who are at risk of developing PML , by screening subjects prior to treatment or while on treatment . None of the MS patients without PML or healthy subjects included in our study showed an absent or IL-10 producing T cell response similar to that observed in the subjects with PML , and this suggests that individuals with these phenotypes are relatively rare and could be identified by immune monitoring prior to treatment . The potential of such screening of JCV-specific T cell responses to identify a small number of individuals at risk for the development of PML could be complementary to stratification strategies based on antibody levels that are currently being tested to identify approximately 50% of treated individuals who are at increased risk [2] , [39] . Second , the unique IL-10 response to JCV in two PML cases and the increased levels of IL-10 in the CSF of subjects with PML suggests that IL-10 or the IL-10 receptor may be potential therapeutic targets in natalizumab-associated PML [26] . Finally , the poor magnitude or quality of the memory T cell response to JCV in subjects with PML suggests that a vaccine which boosts JCV-specific T cells that produce IFNγ , TNF and IL-2 could play a role in the prevention of natalizumab-associated PML . The study was approved by the IRB of the University of Texas Southwestern Medical Center . Written informed consent was obtained from all study subjects . Peripheral blood mononuclear cells ( PBMC ) were isolated from whole blood by ficoll-hypaque density centrigugation ( GE Heathcare Life Sciences ) . PBMC were cryopreserved in freezing media containing 90% fetal bovine serum and 10% DMSO for use in T cell assays . Frozen PBMC were thawed and washed twice with RPMI 1640 supplemented with 10% heat inactivated fetal calf serum , 100 U/ml penicillin G , 100 U/ml streptomycin sulfate , and 1 . 7 mm sodium glutamine ( R-10 ) containing 10 U/ml DNase I ( Roche Diagnostics ) . Cells were then rested for two hours before being washed and then plated in 96-well plates in 200 uL final volume of R10 with DNase I . All experiments were done at 5×106 PBMC/ml in the presence of 1 µg/ml each of αCD28 and αCD49d ( BD Bioscience ) , in the absence or presence of peptide antigens or SEB . Cells were stimulated for 6 hours , with 10 µg/ml brefeldin A ( BFA ) ( Sigma Chemical Company ) added after 1 hour . For subjects PML-1 , PML-2 and PML-3 , freshly isolated PBMC were stimulated for 16 hours with BFA added after 1 hour . For subject PML-4 frozen PBMC were stimulated for 6 hours as described in parallel with MS and healthy subjects . Directly conjugated monoclonal antibodies ( mAbs ) specific for the molecules listed were obtained from the following: CD3 APC-H7 , TNF Cy7PE , IFNγ V450 , IL-10 APC , IL-10 PE , IL-2 FITC , CD3 Alexa700 , IFNγ FITC , IL-2 APC from BD Biosciences; CD45RO-TRPE , CD27-Cy5PE from Beckman Coulter; CD4-Cy55PE from Caltag , CD8 QD705 from Invitrogen , and IL-2 Alexa700 from BioLegend . The following antibodies were conjugated in our laboratory according to standard protocols ( http://drmr . com/abcon/index . html ) : CD57-QD585 , CD14-QD605 , CD14 PacificBlue and CD19-QD655 . Stimulated PBMC used for intracellular cytokine staining were washed and pre-stained for 10 minutes with a pre-titered amount of LIVE/DEAD fixable aqua dead cell stain ( Molecular Probes ) . Cells were then surface stained with a mixture of pre-titered amounts of directly conjugated antibodies to CD27 , CD45RO , CD57 , CD4 , CD8 , CD19 , and CD14 made to a total volume 100 µl with Delbecco's phosphate buffered saline ( PBS ) . Cells were stained for 30 min at 4°C in the dark . Cells were then washed and permeabilized using the cytofix/cytoperm kit ( BD Biosciences ) according to the manufacturer's instructions . After intracellular staining for CD3 , IFNγ , TNF , IL-2 and IL-10 cells were washed one final time and fixed in PBS containing 1% paraformaldehyde and then stored at 4°C . Flow cytometric analysis was done within 24 h of staining . Cells were analyzed with a modified LSRII ( BD Immunocytometry Systems ) equipped for the detection of 18 fluorescence parameters . Between 500 , 000 and 1 , 000 , 000 events were collected for each sample . Electronic compensation was conducted with antibody capture beads ( BD Biosciences ) stained separately with individual mAbs used in test samples . All analytical gating was performed using FlowJo version 9 . 0 . 1 ( Tree Star , Inc . Ashland , OR ) . CD4 and CD8 memory T cells were identified by sequential gating , using the same gating scheme for all analyzed samples . Cells were identified as lymphocytes by Side Scatter Area ( SSC-A ) and Forward Scatter Area ( FSC-A ) , and as singlets by Forward Scatter Area ( FSC-A ) and Forward Scatter Height ( FSC-H ) . Memory CD4 and CD8 cells were defined as Aqua LIVE/DEAD stain− , CD14− , CD19− , CD3+ , CD4+ and CD8− or CD8+ and CD4− , and memory cells were defined as CD45RO+ or CD45RO− and CD27− . Cells positive for IFNγ , TNF , IL-2 and IL-10 were expressed as a percentage of either memory CD4 or memory CD8 T cells . In all samples other than that from subject PML-3 , IL-10+ cells were defined as cells that were positive for both anti-IL-10 PE and anti-IL-10 APC . In PML-3 , IL-10+ cells were defined by anti-IL-10 PE alone . An amino acid sequence for the entire JCV coding region was constructed based on Mad1 that also included any common variants from NCBI and the literature [40] , in order to cover the vast majority of variation in the viral epitopes that are presented in vivo in our patient population ( particularly focusing on genotypes 1 , 3 , 4 and 6 ) . 15mers overlapping by 11 amino acids were generated from this sequence , to create 381 peptides , plus 162 additional peptides containing variant amino acids , for a total of 543 peptides . These were dissolved in DMSO and pooled to create 5 peptide pools: the VP1 , large T antigen and agnoprotein pools contained the entire sequence of these proteins , while the VP2 pool contained only peptides that were not shared with VP1 , and the small t antigen pool contained peptides not shared with large T plus 5 additional peptides that cover the T′ splice variants [41] . The number of peptides in each pool was Agnoprotein – 27 , VP1 – 149 , VP2 – 109 , large T – 222 , small t – 36 . Because the peptide stimulations were done in pools rather than individually , and contained multiple variants of the same peptide , it is theoretically possible that competition amongst peptides in the same pool for the same MHC protein decreased sensitivity of the assay to detect a response . Because we use high concentrations of each peptide ( 2 µg/ml each ) , this should only occur if a pool contains a peptide with at least 100-fold higher affinity for MHC than typical peptides , and thus should be a very rare event . Limitations on the number of cells available from samples prevents the assessment of all peptides individually and necessitates the commonly-used pooling approach . Peptides were obtained from New England Peptide , and were >70% pure . For measurement of CMV-specific T cells , 138 15mers overlapping by 11 amino acids corresponding to the entire CMV pp65 protein sequence were pooled and dissolved in DMSO . CMV pp65 peptides were obtained from JPT peptide technology , and were >70% pure . In pooled peptide mixes , each peptide was at a concentration of 400 µg/ml . Five µl were added for each ml of assay volume . Final concentration of peptides was 2 µg/ml . Statistical comparisons were performed using Prism ( GraphPad Software , San Diego , CA ) . Experimental variables were analyzed using Fisher's Exact test , Mann-Whitney U test or Wilcoxon matched-pairs signed rank test . Bars depict median values . P-values <0 . 05 were considered significant . Analysis and graphical representation of cytokine production was conducted by using the data analysis program Simplified Presentation of Incredibly Complex Evaluations ( SPICE version 5 . 05013 Beta ) [28] .
Progressive multifocal leukoencephalopathy ( PML ) is a complication of treatment with natalizumab in patients with multiple sclerosis ( MS ) and Crohn's disease . PML results from a failure of the immune system to control replication of JC virus ( JCV ) in the brain . We studied the T cell responses of 8 patients with MS who were starting treatment with natalizumab , 10 healthy volunteers , and 4 patients with natalizumab-associated PML . The magnitude and quality of JCV-specific immune responses remained unchanged after starting natalizumab . However , applying the same methods and antigens , we found that immune responses in the individuals who developed PML differed from those in the MS patients and healthy volunteers . In the four patients with PML from whom the laboratory had identified JCV DNA in the cerebrospinal fluid ( CSF ) , two had no measurable T cell response to JCV and two had T cells that produced IL-10 , an anti-inflammatory mediator . Furthermore , we studied the CSF of 10 patients with natalizumab-associated PML and 10 patients on natalizumab who had similar symptoms but did not have PML . We found that IL-10 was detectable in the CSF of half of the individuals with PML but none of the control group . These findings shed light on the mechanisms that lead to PML in a subset of patients treated with natalizumab and have implications for therapeutic and preventative measures .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "medicine", "infectious", "diseases", "adaptive", "immunity", "demyelinating", "disorders", "immunity", "multiple", "sclerosis", "neurology", "neurological", "disorders", "immunology", "biology", "microbiology", "immune", "response", "immunotherapy" ]
2012
Changes in JC Virus-Specific T Cell Responses during Natalizumab Treatment and in Natalizumab-Associated Progressive Multifocal Leukoencephalopathy
Taenia solium , a parasite that affects humans and pigs , is the leading cause of preventable epilepsy in the developing world . Geographic hotspots of pigs testing positive for serologic markers of T . solium exposure have been observed surrounding the locations of human tapeworm carriers . This clustered pattern of seropositivity in endemic areas formed the basis for geographically targeted control interventions , which have been effective at reducing transmission . In this study , we further explore the spatial relationship between human tapeworm carriers and infected pigs using necroscopic examination as a quantitative gold-standard diagnostic to detect viable T . solium cyst infection in pigs . We performed necroscopic examinations on pigs from 7 villages in northern Peru to determine the number of viable T . solium cysts in each pig . Participating humans in the study villages were tested for T . solium tapeworm infection ( i . e . , taeniasis ) with an ELISA coproantigen assay , and the distances from each pig to its nearest human tapeworm carrier were calculated . We assessed the relationship between proximity to a tapeworm carrier and the prevalence of light , moderate , and heavy cyst burden in pigs . The prevalence of pig infection was greatest within 50 meters of a tapeworm carrier and decreased monotonically as distance increased . Pigs living less than 50 meters from a human tapeworm carrier were 4 . 6 times more likely to be infected with at least one cyst than more distant pigs . Heavier cyst burdens , however , were not more strongly associated with proximity to tapeworm carriers than light cyst burdens . Our study shows that human tapeworm carriers and pigs with viable T . solium cyst infection are geographically correlated in endemic areas . This finding supports control strategies that treat humans and pigs based on their proximity to other infected individuals . We did not , however , find sufficient evidence that heavier cyst burdens in pigs would serve as improved targets for geographically focused control interventions . Taenia solium , the pork tapeworm , is a parasite that affects 50 million people worldwide [1] . When the parasite infects the human central nervous system , the result is a severe neurological condition called neurocysticercosis ( NCC ) , which may lead to seizures , headaches , and stroke . In Latin America alone , 1 . 3 million people have epilepsy from NCC [2] , and , in rural Peru , 1 in 200 people suffer from epilepsy caused by NCC [3] . T . solium is transmitted between humans and pigs , and is commonly found in rural areas of low income countries where access to sanitation is limited and free-roaming pigs have access to human feces . An adult tapeworm residing in the human gut produces millions of infectious eggs over its lifespan that are expelled through the feces of the infected human host ( a condition called taeniasis ) . When infected humans defecate outside , T . solium eggs may be consumed by free-ranging pigs and develop into larval cysts that lodge in the soft tissue of the pigs ( a condition called porcine cysticercosis ) . Humans may , in turn , be infected with the intestinal tapeworm by consuming these cysts in undercooked pork . Transmission of the T . solium parasite varies considerably by location , as significant variations in prevalence have been observed both at a regional scale [3–6] , and among households within a community [7 , 8] . Detecting these spatial patterns of T . solium infection , whether on a regional scale or at the household level , is an important step in the development of effective control strategies . The most common spatial pattern analysis that has been used to study T . solium has been the detection of clusters of a single type of infection ( e . g . , porcine cysticercosis ) . To this end , studies in both Latin America [4 , 9] and Africa [7 , 10] have found that cases of porcine cystiercosis tend to be clustered within the same households and among neighboring households within the same communities . Such studies that identify univariate clusters of infection are important first steps in understanding disease distribution , and may be used to prioritize the allocation of scarce resources for prevention . Other studies have sought to examine these clusters of cysticercosis in relation to the locations of human tapeworm carriers as potential sources of infection . Examining the precise spatial relationship between human and porcine hosts allows for the investigation of physical and biologic mechanisms dictating T . solium transmission , which can then be used to design spatially explicit control strategies . In separate studies conducted in endemic regions of Peru , Garcia et al . [4] and Lescano et al . [11] found that living in the same household as a tapeworm carrier was an important risk factor for cysticercosis seropositivity among humans . Similarly , clusters of porcine cysticercosis have been found to occur in hotspots surrounding human tapeworm carriers . Lescano et al . found that pigs living less than 50 meters from a tapeworm carrier were much more likely to be seropositive than more distant pigs [12] , and O’Neal et al . found that the prevalence of human taeniasis was significantly increased among individuals residing within 100 meters of an infected pig [13] . The results of these latest distance analyses directly led to the development of a control methodology known as “ring strategy” . Ring strategy targets anti-helminthic treatment to only those humans and pigs that reside within 100 meters of a positively identified pig . This targeted approach to treatment was developed as an alternative to mass anti-helminthic treatment in Peru , and was based on the assumption that pig and human disease are spatially dependent and likely be found in close proximity to each other . Ring interventions have now been trialed in endemic communities of Peru , and have shown early success , with significant reductions in pig seroincidence observed in intervention communities [14] . The strong associations observed between cysticercosis infection and tapeworm carriers in previous spatial analyses , together with the early success of ring strategies , suggests that location and proximity are important determinants of T . solium transmission . Despite this knowledge , important gaps remain in our understanding of the spatial dynamics of T . solium transmission that impede our ability to understand transmission mechanisms , and improve upon existing control strategies . First , most studies that have investigated the spatial association between human tapeworm carriers and infected pigs have relied on testing pig sera for the presence of antibodies against T . solium , which does not distinguish active cyst infection from cleared infection or exposure to T . solium eggs without infection [15] . Necroscopic examination of pigs , which provides a count of the total number of viable T . solium cysts in pigs , is the most sensitive and specific diagnostic currently available for T . solium cyst infection in pigs , and would allow us to draw more confident conclusions about the spatial relationships that have been observed . In addition , previous studies have been limited to only assessing the presence or absence of pig infection based on serologic markers . Counting the total number of T . solium cysts in necropsied pigs provides a quantitative measure of the degree of infection . A spatial analysis of cyst burden would allow us to detect a biologic gradient between the degree of infection ( i . e . , number of cysts counted on pig necropsy ) and their proximity to a tapeworm carrier . This association could provide important insight into the environmental and biologic mechanisms driving T . solium egg dispersion and cyst infection , and may lead to the identification of more specific diagnostic targets ( e . g . , pigs of a specific cyst burden ) for ring strategies . In order fill these knowledge gaps we performed a distance analysis examining the relationship between T . solium cyst infection in pigs and their distance to infected human tapeworm carriers in an endemic region of northern Peru . Specifically , we assessed this spatial relationship at different burdens of cyst infection and at different distances . Our objectives were to determine if pigs with heavier cyst burdens were more likely to be found in close proximity to tapeworm carriers , and to determine if a critical distance threshold could be identified at which the relationship between human tapeworm carriers and infected pigs could no longer be observed . Based on the positive findings of previous studies , we hypothesized that a strong association between infection in pigs and their proximity to human tapeworm carriers would exist , and would become stronger at heavier burdens of infection . We performed a door-to-door survey of all households in the 7 villages and attempted to recruit all human residents older than 2 years of age for participation . Consenting participants were interviewed for household and demographic characteristics . Survey questions included the age and sex of each pig , the presence and condition of a pig corral on the property , the source of household drinking water , and human waste disposal ( open field defecation or latrine ) . We used handheld GPS receivers ( GeoExplorer II; Trimble , Sunnyvale , CA ) with post-processed differential correction for sub-meter accuracy to record a single set of coordinates in front of each household . These coordinates were used to represent the locations of both human and pig participants in each household . At the conclusion of the trial period , all participants were presumptively treated for taeniasis with a single oral dose of niclosamide according to their weight ( 11–34 kg received 1 g; 35–50 kg received 1 . 5 g; > 50 kg received 2 g ) , and were instructed to collect their next stool . Niclosamide was chosen for mass treatment because it is highly effective against taeniasis [16 , 17] , and does not affect the cystic stage of T . solium like other available chemotherapies , which could cause neurological symptoms in undiagnosed cases of NCC [18] . Post-treatment stool samples were first tested with enzyme-linked immunosorbent assays for T . solium coproantigens ( CoAg-ELISA ) as previously described [19] . Reactive samples ( optical density ratio ( ODR ) > 7 . 5% ) were examined microscopically for the presence of Taenia spp . eggs in stool using the test tube spontaneous sedimentation technique [20] , and humans with reactive samples were followed up after two weeks with further testing and treatment to confirm clearance . Other intestinal parasites that were detected during stool screening were provided appropriate treatment through the local health center . For this analysis , we considered humans to be positive for T . solium taeniasis if Taenia spp . eggs were visualized in stool or the CoAg-ELISA test produced an ODR greater than 20% . We choose to use ODR > 20% as a case definition in this analysis to reduce the rate of false positives due to non-specific binding and cross-reaction with other Taenia spp . , which may occur at low ODR values [21 , 22] . All pigs older than four weeks of age were eligible for participation in this study . Serum samples were collected from all eligible pigs in the study villages at the conclusion of the year-long trial , and were analyzed by enzyme-linked immunoelectrotransfer blot ( EITB ) to detect the presence serum antibodies that indicate exposure to T . solium eggs . Briefly , the EITB assay measures reactivity of pig serum to seven lentil-lectin purified glycoprotein antigens isolated from native cysts [23] . Reaction to 1 or more of these glycoprotein antigens bands is highly sensitive for detecting active cyst infection ( 89% ) , however lacks specificity ( 48% ) ; while reaction to 4 or more bands is less sensitive ( 61% ) , but has improved specificity ( 92% ) [15] . Given that the expected prevalence of active cyst infection among pigs in this region of Peru is around 5–10% , the predictive value of a negative EITB assay is high ( Garcia et al . found that 99% ( 144 out of 146 ) of seronegative pigs in this region were necropsy-negative [17] ) . Results from the EITB assay were used to select pigs for necroscopic examination . In order to prevent the unnecessary sacrifice of uninfected pigs , we attempted to purchase only pigs with one or more positive EITB bands for necropsy . Pigs with negative serologic results were assumed to contain zero cysts , as negative EITB results are highly predictive of negative necropsy results [17] . Of the 791 pigs tested from the seven study villages , 419 ( 53% ) seropositive pigs were identified . Study staff attempted to purchase all seropositive pigs for necroscopic examination , however , due to reluctance of villagers to sell their animals , only 146 ( 35% ) of these seropositive pigs were able to be purchased . Purchased pigs were anesthetized and humanely euthanized . To determine the number of viable T . solium cysts in each necropsied pig , the entire carcass was dissected and systematically inspected using fine tissue slices of less than 0 . 5 cm . Viable cysts were those with well-delineated thin-walled cystic structures containing clear vesicular fluid and a visible white protoscolex , however a formal bile test was not conducted to confirm viability . Degenerated and calcified cysts , while enumerated by examiners , were not included in this analysis . For pigs with particularly dense cyst burdens , a weighed sample of forelimb muscle was counted for cysts and extrapolated to estimate the total body burden . Our final analysis was carried out on a sample of 515 pigs ( 65% of 791 total pigs ) . This sample was composed of the 146 ( 28% ) seropositive pigs that study staff purchased from pig-owners for necroscopic examination and 369 ( 72% ) seronegative pigs for which a cyst count of zero was imputed . The remaining 272 serepositive pigs in our sample were excluded because necroscopic examination was not performed and cyst burden could not be estimated . In ArcMap 10 . 3 ( ESRI; Redland , CA ) , we plotted the household locations of study participants ( humans and pigs ) using a transverse Mercator projection ( UTM Peru 17S , 1996 ) . We then calculated the Euclidean distance in meters from each pig’s household to the nearest human tapeworm carrier household . Pigs living in the same household as a human tapeworm carrier were given a distance value of zero . Distances were categorized into bins of < 50 meters , 50–500 meters and > 500 meters . These groupings were chosen because they produced a well-delineated gradient of infection prevalence at increasing distances . The 100 meter distance threshold was not included in our results because no effect was observed among pigs 50–100 meters from a tapeworm carrier . Due to the lack of normality in the dependent variable ( cyst burden ) , we elected to categorize this variable into bins based on the following schema: heavy infection ( ≥ 100 viable cysts ) , moderate infection ( 10–99 viable cysts ) , light infection ( 1–9 viable cysts ) and no infection ( zero viable cysts or negative EITB serology ) . We used logistic regression models with binary outcomes to examine predictors for three different cyst burden thresholds ( ≥ 1 cyst , ≥ 10 cysts , and ≥ 100 cysts ) , using pigs with no infection ( zero viable cysts or negative EITB serology ) as a reference group in all models . Logistic regression models with robust sandwich estimators from the generalized estimating equations ( GEE ) family were used to account for household clustering ( i . e . , dependence between pigs from the same household ) . We first created bivariate models for pig- and household-level predictors and selected covariates to include in our final multivariable models if they were significant ( α = 0 . 05 ) in any of the three cyst burden models . This study was reviewed and approved at Oregon Health and Science University , Portland , Oregon , USA , by the Institutional Review Board ( protocol #10116 ) and the Institutional Animal Care and Use Committee ( protocol #2843 ) . It was also reviewed and approved at Universidad Peruana Cayetano Heredia , Lima , Peru , by the Institutional Ethics Committee ( protocol #61326 ) , and the Institutional Committee for the Ethical Use of Animals ( protocol #61326 ) . Written informed consent was obtained from all human participants . The consent of an adult or guardian was required for the participation of children <18 years old . Treatment of animals adhered to the Council for International Organizations of Medical Sciences ( CIOMS ) International Guiding Principles for Biomedical Research Involving Animals . Pigs were humanely euthanized by administering 0 . 1 mg/kg of xylazine with 5 mg/kg of ketamine intravenously to achieve deep anesthesia , followed by injection of 100 mg/kg of sodium pentobartital . The 7 participating villages ranged in population from 130 to 596 human inhabitants , for a total population of 1 , 890 individuals ( Table 1 ) . 32% of the population reported practicing open field defecation , and 63% of the population reported raising pigs . In total , 1 , 420 ( 75% ) participants submitted stool samples for parasite testing . Residents who declined to submit stool samples were more likely to be male , younger , and practice open defecation ( S1 Table ) . A geographic analysis of participating and non-participating households across the 7 study villages revealed no concerning spatial patterns of non-participation ( Ripley’s K1-K2 test for random labelling [24 , 25] , S1 Appendix ) . Among human participants , 34 ( 2 . 4% ) showed evidence of a T . solium taeniasis . The prevalence of taeniasis ranged from 0 . 9% to 5 . 3% among the seven study villages . Of the 34 taeniasis cases , 26 ( 76% ) tested positive on CoAg-ELISA with ODR ≥ 40% , while 6 ( 18% ) had CoAg-ELISA ODR between 20% and 40% , and 2 ( 6% ) were diagnosed by microscopy alone ( ODR < 20% ) . A sensitivity analysis showing the possible impact of these different case definitions on the observed associations is presented in S2 Appendix . Serum samples were collected from all eligible pigs in the study villages ( n = 791 pigs ) . Overall , 53% tested positive for antibodies against T . solium cyst ( at least one positive EITB band ) , and seropositivity ranged from 38% to 69% among the seven study villages ( Table 1 ) . 9% of the pigs had 4 or more positive bands , and the prevalence of 4 or more bands ranged from 1% to 20% among the study villages . The 515 pigs included in this study consisted of 146 ( 28% ) pigs that were necropsied , and 369 ( 72% ) seronegative pigs for which a cyst count of zero was imputed . Among study pigs , the median age was 8 months and 54% were female . In terms of T . solium cyst burden , 471 ( 92% ) of the study pigs were uninfected , 26 ( 5% ) pigs had light infection ( 1–9 cysts ) , 8 ( 2% ) pigs had moderate infection ( 10–99 cysts ) , and 10 ( 2% ) pigs had heavy infection ( ≥100 cysts ) . Fig 1 shows the geographical distribution of human tapeworm carriers and infected pigs in the study villages . The prevalence of T . solium cysticercosis ( at least one viable cyst on necropsy ) was greatest among pigs living within 50 meters of a tapeworm carrier , and decreased proportionally at greater distances . The prevalence of at least one viable cyst was 15 . 6% ( 12 out of 77 ) at < 50 meters from a tapeworm carrier , 8 . 3% ( 27 out of 325 ) between 50 and 500 meters , and 4 . 4% ( 5 out of 113 ) at > 500 meters ( p < 0 . 01 for trend ) ( Fig 2 ) . Of the 12 infected pigs living within 50 meters of a tapeworm carrier , 3 ( 25% ) pigs resided in the same household as the tapeworm carrier . Overall , the prevalence of T . solium infection among pigs owned by tapeworm carriers was 12 . 5% ( 3 out of 24 ) . This was not significantly different from the prevalence of pig infection among all pigs living within 50 meters of a tapeworm carrier . The prevalence of moderate-to-heavy cyst infection ( ≥ 10 viable cysts ) and heavy infection cyst infection ( ≥ 100 viable cysts ) showed similar trends of increasing prevalence at closer distances to tapeworm carriers , however the distance trend for heavy infection was non-significant . The prevalence of pigs with ≥ 10 viable cysts was 6 . 5% ( 5 out of 77 ) at < 50 meters , 3 . 7% ( 12 out of 325 ) between 50 and 500 meters , and 0 . 9% ( 1 out of 113 ) at > 500 meters ( p = 0 . 04 for trend ) , while the prevalence of heavy infection ( ≥100 viable cysts ) was 3 . 9% ( 3 out of 77 ) at < 50 meters , 1 . 8% ( 6 out of 325 ) between 50 and 500 meters , and 0 . 9% ( 1 out of 113 ) at > 500 meters ( p = 0 . 15 for trend ) . When examined in bivariate logistic regression , only two predictors , distance to the nearest human tapeworm carrier and the age of the pig , were significantly associated with cyst infection . Pigs residing within 50 meters of a tapeworm carrier were significantly more likely to be infected than pigs living more than 500 meters from a tapeworm carrier ( Table 2 ) . This association increased in strength between pigs with at least one viable cyst ( OR = 4 . 6; 95% CI: 1 . 4 , 15 . 4 ) and 10 or more viable cysts ( OR = 8 . 7 , 95% CI: 1 . 0 , 76 . 1 ) . The 50 meter distance threshold , however , was not significant when tested for heavily infected pigs ( 100 or more cysts ) . Similarly , residing in the same households as a tapeworm carrier did not significantly increase the odds of T . solium cyst infection , regardless of the cyst burden tested . Pigs residing 50 to 500 meters from tapeworm carriers did not have a significantly greater odds of infection ( light , moderate , or heavy infection ) compared to pigs residing more than 500 meters from a tapeworm carrier ( OR = 1 . 99 for ≥ 1 viable cyst , 95% CI: 0 . 64 , 6 . 2 ) . Variables that were not significant and thus excluded from the final multivariable model were household defecation practice ( latrine versus open field ) , water source , number of human occupants , the number of pigs owned , the presence of tapeworm carriers in the household , pig sex and the presence of a corral for the pig . Based on our findings from the bivariate analysis , only distance to nearest tapeworm carrier and pig age were included in the final adjusted GEE logistic regression models ( Table 3 ) . After adjusting for pig age , pigs living less than 50 meters from a human tapeworm carrier were 4 . 56 times ( 95% CI: 1 . 33 , 15 . 6 ) more likely to be infected with at least one cyst than pigs living more than 500 meters from a tapeworm carrier . In the two models that assessed the effect of distance at heavier cyst burdens , we found strong but non-significant effects of living less than 50 meters from a tapeworm carrier ( OR = 7 . 27 , p = 0 . 07 for ≥10 cysts; OR = 4 . 25 , p = 0 . 21 for ≥ 100 cysts ) . Similar to our findings in the unadjusted analysis , distances greater than 50 meters from a tapeworm carrier , including pigs living 50 to 100 meters from a tapeworm carrier , were not significantly associated with increased pig infection at any cyst burden . The distance bins of <50 meters , 50–500 meters , and >500 meters from a tapeworm carrier were chosen for logistic regression models above because of the strong positive association we found among pigs residing <50 meters from a tapeworm carrier . In order to compare our results with previously trialed ring interventions , which initiated targeted interventions within 100 meters of infected pigs [13 , 14] , we also evaluated the odds of cyst infection among pigs living < 100 meters from a tapeworm carrier . We found that pigs residing <100 meters from a tapeworm carrier had a significantly increased odds of cyst infection compared to pigs living > 500 meters from a tapeworm carrier ( OR = 3 . 54 , 95%: 1 . 09 , 11 . 6 ) ; however this association was driven by the strong association among pigs residing < 50 meters from tapeworm carriers , and was not significant for moderate or heavy cyst burdens . Overall , there were few infected pigs residing 50 to 100 meters from tapeworm carriers ( 8% , 3 out of 36 pigs ) , and pigs residing only in the distance band of 50 to 100 meters from tapeworm carriers did not have significantly increased odds of infection compared to the reference distance of >500 meters ( OR = 2 . 01 , 95% CI: 0 . 31 , 12 . 9 ) ( S2 Table ) . In this analysis , we investigated the association between T . solium cysts burden and proximity to human tapeworm carriers in villages of northern Peru where T . solium is endemic . There were a few key findings to highlight in our analysis . First , consistent with our hypothesis , the locations of human tapeworm carriers and pigs infected with viable T . solium cysts were geographically correlated in the study communities . Prevalence of T . solium cysticercosis decreased monotonically as distance from a human tapeworm carrier increased ( 15 . 6% , 8 . 3% , and 4 . 4% for pigs living < 50 meter , 50–500 meters , and > 500 meters from a tapeworm carrier , respectively ) . Our second hypothesis was that proximity to human tapeworm carriers would show a stronger association with pig infection when examined at heavier cyst burdens , thus representing a gradient effect between distance and cyst burden . However , the only statistically significant association observed in the final adjusted models was the comparison of all infected pigs ( at least one cyst ) with uninfected pigs . At moderate ( ≥ 10 cysts ) and heavy ( ≥ 100 cysts ) cyst burdens , where we expected to find stronger associations , we found that the associations with proximity to human tapeworm carriers became non-significant . Therefore , we were unable to detect any significant biologic gradient between the burden of infection and proximity to tapeworm carriers . Finally , we found that distances less than 50 meters from human tapeworm carriers were associated with an increased prevalence of viable T . solium cyst infection in pigs . Pigs living less than 50 meters from a human tapeworm carrier were 4 . 6 times more likely to be infected with at least one cyst than pigs living more than 500 meters from a tapeworm carrier . Pigs living more than 50 meters from a tapeworm carrier , including pigs living between 50 and 100 meters from a tapeworm carrier , did not have an increased odds of infection at any cyst burden analyzed . These findings are consistent with a previous study that examined the effect of proximity to human tapeworm carriers on the prevalence of pig seropositivity ( as measured by EITB serology ) in a similar rural region of Peru [12] . Lescano et al . found that the prevalence of T . solium seropositivity in pigs decreased as distance from a human tapeworm carrier increased ( 69% , 36% , and 18% among pigs living < 50 meters , 50–500 meters , and > 500 meters from a tapeworm carrier , respectively ) . Additionally , they concluded that the 50 meter areas surrounding human tapeworm carriers represented significant foci of transmission , with pigs living in these rings 3 . 7 times more likely to be seropositive than pigs living more than 500 meters from a tapeworm carrier . Our study , therefore , contributes additional evidence that pigs living within 50 meters from a human tapeworm carrier in this region are at increased risk for T . solium infection , and uses the gold-standard diagnostic for pig infection to demonstrate the proclivity for tapeworm carriers to shed infectious T . solium eggs in the areas immediately surrounding their homes . Neither our study nor previous work provide evidence that distances greater than 50 meters ( e . g . , 100 meter rings used in ring strategies ) are associated with an increased risk of T . solium infection . While a distance gradient was observed in both our study and the study referenced above , neither found a significant independent effect of distances greater than 50 meters on pig infection . O’Neal et al . found that 100 meter rings represented significant foci of T . solium transmission in rural Peru; however , this study did not specifically evaluate 50 meter rings to determine which distance was responsible for the increased level of transmission [13] . The idea that 50 meters is a critical distance at which pigs are exposed to increased risk of T . solium infection in this region is consistent with our understanding of pig range and behavior . A GPS tracking study of pigs in rural Peru found that pigs spent an average of 70% of their time within 50 meters of their residence and interacted with human defecation areas nearly 30 minutes per day inside these 50 meters rings ( compared to just 7 minutes per day outside of 50 meters ) [26] . Based on these findings , we propose that 50 meter rings accurately represent T . solium transmission foci in endemic areas of rural Peru . Our finding that the association between proximity to tapeworm carriers and pig infection did not strengthen at heavier cyst burdens was unexpected . While we observed a strong gradient of infection among heavily infected pigs ( prevalence of heavy infection was 3 . 9% , 1 . 8% , and 0 . 9% at < 50 meters , 50–500 meters , and > 500 meters , respectively ) , we expected to also find an increase in the strength of the proximity effect at higher cyst burdens . The independent effect of proximity to tapeworm carriers on the odds of moderate ( ≥ 10 cysts ) and heavy ( ≥ 100 cysts ) infection , however , were not statistically significant . There are a few possible explanations for this unexpected finding . First , it is possible that pigs living in close proximity to tapeworm carriers are , in fact , exposed to greater concentrations of T . solium eggs in their residential environments , but that the burden of established cyst infection in pigs is mediated by host factors such as differential immune responses , rather than being driven purely by exposure dose . It is also possible that the lack of an observed dose-response distance effect in this analysis could simply be explained by the small numbers of infected pigs that were represented in our sample . Only 1 . 9% ( 10 out of 515 ) of pigs in our sample were heavily infected , and 3 . 5% ( 18 out of 515 ) had more than 10 cysts . These low cell counts likely made it difficult to observe a significant effect in these groups . Although distance to human tapeworm carriers was an important predictor for T . solium infection among pigs , many infected pigs in our study did not reside in close proximity to a tapeworm carrier . In fact , only 27% ( 12 out of 44 ) of the infected pigs in this study lived within 50 meters of a tapeworm carrier , and some infected pigs lived more than 1 km from it’s the nearest identified tapeworm carrier . There are a number of possible explanations for this unexpected finding that should be investigated with future studies . First , due to the cross-sectional nature of this study , we were only able to detect prevalent cases of porcine cysticercosis , meaning that cyst infection in older pigs could have been caused by previously treated or recovered tapeworm carriers that were not detected . This could have caused pigs with older infections to appear further from tapeworm carriers than they were at the time of infection . It is also possible that pigs appearing distant from tapeworm carriers were infected through egg dispersion mechanisms , such as dung beetles and flies , which have been identified as possible mechanical vectors capable of dispersing T . solium eggs over long distances [27–31] . Finally , it is possible that we did not identify all tapeworm carriers in the study area . 25% of human inhabitants did not provide stool specimens for testing , which could represent a significant number of undetected human tapeworm carriers , and could explain the appearance of large distance values for some pigs . There are a few additional limitations to our study that must be noted . First , while the CoAg-ELISA is the most sensitive and specific diagnostic available to detect human taeniasis [22] , cross-reaction with other Taenia species and non-specific binding of the CoAg-ELISA assay with host factors are known to occur [21 , 22] . Therefore , it is possible that our use of the CoAg-ELISA assay for T . solium tapeworm detection could have allowed for false positive diagnoses , which may have diluted the observed spatial relationship . Our sensitivity analysis , however , found that this scenario is unlikely to have occurred . Additionally , our use of household coordinates to represent human and pig locations could have misrepresented the true location of transmission events . For example , it is possible that some free-roaming pigs in our study were infected by consuming infected human feces distant from their household location . Previous studies of human and pig behavior in this region , however , have shown that pigs tend to roam in close proximity to their owner’s homes , and that open human defecation areas are concentration near household locations [26] , suggesting that transmission events are most likely to occur in the immediate proximity of household locations used in this study . Finally , in order to reduce unnecessary animal sacrifice , we imputed cyst burdens of zero for seronegative pigs without performing full necropsies on these animals . Based on our knowledge of the sensitivity of EITB serologic tests [15 , 17] , it is possible that a small proportion of these seronegative pigs were truly infected , likely biasing our estimates towards observing weaker associations . Cysticercosis has a substantial economic and health burden on populations in endemic rural areas of Peru . The results of this study provide an important first step in understanding the spatial dynamics of cysticercosis infection to support the use of ring strategy in Peru . In order to advance control efforts , however , more research must be done to improve diagnostic tests and improve our understanding of factors affecting the transmission of T . solium between humans and pigs . Answering these questions and optimizing ring strategy could lead to profound reductions in the burden of cysticercosis and ultimately contribute to elimination in the region .
Taenia solium , the pork tapeworm , is a parasite transmitted between humans and pigs . The disease is most common in developing countries where access to sanitation is limited and domestic pigs are allowed to roam freely . Humans infected with the intestinal tapeworm release T . solium eggs into the environment when they defecate outside , and pigs become infected with the larval stage of the disease , cysticercosis , upon consuming these eggs in human feces . Prior work using serologic testing of pigs has shown that proximity to human tapeworm carriers is a possible risk factor for porcine cysticercosis . Our study investigated if proximity to human tapeworm carriers was associated with established cyst infection in pigs , and if proximity to tapeworm carriers also increased the degree of cyst infection in pigs ( i . e . , the number of T . solium cysts a pig was infected with ) . The results confirmed that human tapeworm carriers and infected pigs are geographically correlated , but did not uncover a stronger relationship for heavily infected pigs . It is important to continue to investigate this spatial relationship as some control strategies rely on the assumption that human tapeworm carriers will be found in close proximity to heavily infected pigs . Future control strategies would benefit from a precise knowledge of the degree of pig infection that best predicts for nearby tapeworm carriers .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "medicine", "and", "health", "sciences", "pathology", "and", "laboratory", "medicine", "pig", "models", "tropical", "diseases", "geographical", "locations", "vertebrates", "parasitic", "diseases", "animals", "mammals", "peru", "animal", "models", "physiological", "processes", "experimental", "organism", "systems", "neglected", "tropical", "diseases", "research", "and", "analysis", "methods", "infectious", "diseases", "swine", "zoonoses", "serology", "south", "america", "defecation", "taeniasis", "people", "and", "places", "helminth", "infections", "diagnostic", "medicine", "physiology", "cysticercosis", "biology", "and", "life", "sciences", "amniotes", "organisms" ]
2017
Spatial relationship between Taenia solium tapeworm carriers and necropsy cyst burden in pigs
We have previously identified and characterized the phenomenon of ectopic human centromeres , known as neocentromeres . Human neocentromeres form epigenetically at euchromatic chromosomal sites and are structurally and functionally similar to normal human centromeres . Recent studies have indicated that neocentromere formation provides a major mechanism for centromere repositioning , karyotype evolution , and speciation . Using a marker chromosome mardel ( 10 ) containing a neocentromere formed at the normal chromosomal 10q25 region , we have previously mapped a 330-kb CENP-A–binding domain and described an increased prevalence of L1 retrotransposons in the underlying DNA sequences of the CENP-A–binding clusters . Here , we investigated the potential role of the L1 retrotransposons in the regulation of neocentromere activity . Determination of the transcriptional activity of a panel of full-length L1s ( FL-L1s ) across a 6-Mb region spanning the 10q25 neocentromere chromatin identified one of the FL-L1 retrotransposons , designated FL-L1b and residing centrally within the CENP-A–binding clusters , to be transcriptionally active . We demonstrated the direct incorporation of the FL-L1b RNA transcripts into the CENP-A–associated chromatin . RNAi-mediated knockdown of the FL-L1b RNA transcripts led to a reduction in CENP-A binding and an impaired mitotic function of the 10q25 neocentromere . These results indicate that LINE retrotransposon RNA is a previously undescribed essential structural and functional component of the neocentromeric chromatin and that retrotransposable elements may serve as a critical epigenetic determinant in the chromatin remodelling events leading to neocentromere formation . Despite the fact that the functional role of the centromere in mitotic and meiotic cell divisions is evolutionarily conserved , the underlying DNA sequences of the centromeres are highly variable across the phylogeny and show no obvious conservation [1] , [2] . Thus , a conundrum remains as to whether there are any specific sequence requirements for the different types of , primarily tandemly repeated , DNA in providing the template for centromere formation . In recent years , accumulating evidence has pointed to epigenetic factors including DNA methylation and histone modifications as having important roles in the establishment of centromeric chromatin [3] , [4] . In addition , the discovery of fully functional human neocentromeres that arise ectopically from non-tandemly repetitive chromosomal sites further supports the fundamental roles of epigenetic phenomena in the regulation of centromere activity [5] . This class of variant centromeres not only represents an apparently sequence-independent epigenetic model for centromerization but also serves as an excellent tool for the detailed mapping of centromeric chromatin domains – an undertaking that has previously been hampered by the repetitive nature of the mammalian centromeric DNA [6] . The core neocentromeric chromatin is fundamentally defined by the presence of specialized centromere-specific histone H3 variant CENP-A nucleosomes; however , the exact molecular mechanisms involved in the formation of a neocentromere have yet to be defined [7] , [8] , [9] , [10] . To date , approaching one hundred cases of neocentromere emergence have been reported on all the human chromosomes except for chromosomes 7 , 19 , and 22 [6] . Interestingly , some genomic regions , such as the terminal chromosomal segments of 3q , 8p , 13q , and 15q , are more prevalent in neocentromere cases , with these ‘hotspots’ collectively accounting for approximately half of all the cases reported [5] , [11] . Although the ectopic emergence of neocentromeres in hitherto non-centromeric genomic sites suggests the involvement of epigenetic mechanisms of formation , it remains possible that the underlying genomic DNA sequences exert a specific role in the establishment and/or maintenance of the functional integrity of the neocentromeric chromatin . For example , such a possibility is suggested by the universal observation of an elevated AT content , an increase in the density of LINEs ( Long Interspersed Nuclear Elements ) , and a decrease in the density of SINEs ( Short Interspersed Nuclear Elements ) for the six different neocentromeric domains that have been mapped to date [7] , [8] , [9] , [10] . The first human neocentromere was identified at position 10q25 on the derivative marker chromosome mardel ( 10 ) following a de novo interstitial pericentric deletion that has removed the presiding centromere of a normal chromosome 10 [12] . Despite the lack of detectable α-satellite DNA , the 10q25 neocentromere was able to form a mitotically stable kinetochore that binds over 40 of the known functionally important centromere-associated proteins tested [13] , [14] , [15] , [16] . Using a combined BAC ( Bacterial Artificial chromosome ) -array/ChIP ( Chromatin Immunoprecipitation ) technique , the CENP-A-associated domain was mapped to a 330-kb genomic segment along the 10q25 neocentromeric chromatin [9] . Subsequently , other centromere protein-binding domains such as those of HP1 and CENP-H , and an increased scaffold/matrix attachment region ( S/MAR ) , were mapped , defining an overall neocentromeric chromatin region of approximately 4 . 0 Mb in size [17] . To further define the finer structural organization of the core neocentromeic chromatin , we have recently performed high-resolution chromatin mapping using PCR fragment-array/ChIP analysis . The CENP-A domain was found to be assembled as multiple clusters ( seven in total ) along the 10q25 neocentromeric chromatin [18] . Interestingly , in silico sequence analysis indicated that these CENP-A-binding clusters contain a 2 . 5-fold increase in the prevalence of L1 retrotransposon sequences ( which belong to the only active subfamily of LINEs ) when compared to the surrounding non-CENP-A-binding regions or the genome average [18] , [19] , [20] . L1 retrotransposon is a major group of interspersed repetitive elements that comprise 17% of the human genome . Although the great majority of L1s are inactive due to 5′ end truncations , active transcription and translation of these retrotransposons has recently been detected in a variety of cell types and implicated to be a potential regulator for cellular processes [19] , [20] . However , detailed investigations on the functional role of individual L1 retrotransposon in the human genome have been limited by technical difficulties associated with its repetitive nature . In this study , we present an in-depth bioinformatic analysis and the experimental investigation of the possible functional roles of the L1 retrotransposons in the regulation of neocentromere activity . Our previous in silico analysis of the various types of DNA motifs and sequence properties revealed a significant , 2 . 5-fold , increase in the prevalence of L1 retrotransposons within the CENP-A-binding domain of the 10q25 neocentromere [18] . Here , we extended the analysis to the investigation of the genomic distribution and sequence characteristics of L1 retrotransposons across a 6-Mb genomic region spanning the 10q25 neocentromere using the RepeatMasker track of the UCSC genome browser . Besides an enrichment of L1 retrotransposons , the CENP-A-binding clusters of the 10q25 neocentromere were also associated with a higher number of intact L1 genomic segments ( Figure 1A ) . These CENP-A-binding clusters contained 56 L1s per 100 kb DNA , whereas the flanking non-CENP-A-binding regions contained only 26 L1s per 100 kb DNA , with an overall 2 . 1-fold increase in L1 content in the CENP-A-binding regions ( Table S1 ) . In addition to the bioinformatics analysis , ChIP/quantitative PCR analysis using a specific antibody against CENP-A also showed a specific enrichment of L1 genomic sequences in the CENP-A-associated chromatin of 10q25 neocentromere ( Figure S1 ) . Although there was no significant difference in term of the rate of divergence , deletion , and insertion between the L1 retrotransposons within the CENP-A and non-CENP-A-associated regions across the 6-Mb region of the 10q25 neocentromere ( Table S1 ) , the average length of the L1 retrotransposons located within the CENP-A-binding regions ( average length of 865 bp ) was significantly longer ( increased by 2 folds ) compared with those found within the non-CENP-A-binding regions ( average length of 440 bp ) ( Figure 1A; Table S1 ) . Such a difference was attributed to an increase in the proportion of the primate-specific L1 subfamily , as shown by a higher L1P/L1M ratio ( L1P , primate-specific; L1M , mammalian-wide ) , within the region . Given the L1P subfamily included active full-length L1 ( FL-L1 ) retrotranposons , we next searched for the presence of FL-L1 at this region . Functional annotation of the FL-L1 retrotransposons spanning across the 6-Mb region of the 10q25 neocentromere using the online L1Base program ( http://l1base . molgen . mpg . de/ ) identified six FL-L1s , four of which ( L1a–d ) residing within or close to the CENP-A-associated clusters , while the remaining two ( L1e–f ) were located >1 . 5 Mb away from the CENP-A-associated domain ( Figure 1B and Figure 2 ) . Although the functional role of L1s in the regulation of genomic architecture is not well defined , it is of significant interest that L1s can be transcribed into RNA and subsequently translated into proteins for retrotransposition activity [20] , [21] , [22] , [23] . Recent reports indicate that L1 RNAs are actively transcribed in a variety of cell types from full-length L1 elements ( ∼6 kb in size ) that contain an internal promoter , two ORFs , and a poly-A tail at the 3′ UTR [20] , [21] , [22] , [23] . To address if any of the six FL-L1s at the 10q25 neocentormere chromatin were transcriptionally active , RT-PCR primers were designed to specifically target each of the elements ( L1a–f ) in monochromosomal CHO-human hybrid lines , CHOK1-M10 and CHOK1-N10 ( containing the human neocentromeric mardel ( 10 ) and the progenitor normal human chromosome 10 , respectively ) ( Figure 3 ) . The specificity of each primer was confirmed by direct sequencing of the PCR products , which established that only the desired target sites were amplified . No transcripts from FL-L1a , FL-L1c , FL-L1d , FL-L1e and FL-L1f were detected . However , as shown in Figure 3A–C , based on the use of three independent primer sets that targeted to a combined genomic segment of 415 bp within the 5′ UTR , transcripts for FL-L1b were clearly detected in CHOK1-M10 and CHOK1-N10 cells . Further analysis of four additional monochromosomal hybrid cell lines – two human/hamster hybrids CHOK1#8 and GM10926 ( each containing an unrelated normal human chromosome 10 ) and two human/mouse hybrids GM11688 ( containing a unrelated normal human chromosome 10 ) and ES-M10 ( containing the mardel ( 10 ) chromosome ) – showed positive transcription activities of FL-L1b in three of the hybrid lines ( GM10926 , GM11688 , and ES-M10 ) but not in CHOK1#8 ( Figure 3B; Table S2 ) . No detectable transcriptional activity was detected for FL-L1a , FL-L1c , FL-L1d , FL-L1e and FL-L1f in any of these cell lines . These results indicated that the FL-L1b locus was actively transcribed both before and following neocentromere formation . In addition , it was of interest to note that FL-L1b was located within the central and largest CENP-A cluster ( Figure 2B ) , and belonged to the active L1PA2 subfamily [20] , [24] , [25] , [26] . To investigate whether the FL-L1b locus is the only active L1 element within the 10q25 neocentromeric chromatin , additional primers were specifically designed to target those truncated L1s that contained intact promoter sequences and also others that were greater than 4 kb in size identified within the 6-Mb genomic region ( Figure 2A ) . These targets included five long truncated L1s with or without the promoter sequence ( L1g–k ) and other short orphan L1 promoter sequences ( L1l–m ) . The results of RT-PCR analysis indicated no detectable transcripts from any of these L1 targets in the three monochromosomal hybrid cell lines assayed - CHOK1#8 , CHOK1-N10 , and CHOK1-M10 ( Table S3 ) . Given that antisense transcription has been detected from the 5′ UTR of L1 elements [26] , [27] , we performed RT-PCR analysis on all promoter-containing FL-L1s and truncated L1s ( L1a–f , i , k , l , m; Figure 2 ) within the 6-Mb region using primer sets each targeted to the 5′ upstream flanking sequence at one end and to the 5′ UTR of L1 at the other end . No antisense transcript could be detected for all promoter-containing L1s across the 6-Mb genomic region ( Table S3 ) . These results showed that , across the 6-Mb neocentromeric domain , active transcription was found only at the FL-L1b locus , and that the resulting RNA products were predominantly long sense transcripts of at least 415 bp in size . Next we investigated if the corresponding FL-L1b RNA transcripts were incorporated into the 10q25 neocentromeric chromatin . Chromatin immunoprecipitation was performed using a specific anti-CENP-A antibody . RNAs from both the input and immunoprecipitated fractions were isolated , reverse transcribed into cDNAs , and subjected to real-time quantitative PCR analysis using three independent primer sets targeted to the 5′ UTR of FL-L1b . A significant enrichment ( P<0 . 001 ) of FL-L1b RNA in the CENP-A bound fractions was observed , as indicated by a 4 to 5 fold increase in the yield of PCR products ( Figure 3D ) . In contrast , none of the negative control sequences , 18S , 5S , and β-actin , was enriched in the immunoprecipitated fractions . We have also performed similar RNA-ChIP experiments and analyzed the RNA-ChIP products using primers targeting to the other L1s ( FL-L1a , -L1c , -L1d ) as well as four genes ( KIAA1600 , TRUB1 , GFRA1 ) that reside around the CENP-A-binding domain and detected no enrichment of any of these transcripts in the CENP-A chromatin of the CHOK1-M10 cells ( Figure S2 ) . Together , these results indicated the specific incorporation of the FL-L1b RNA into the CENP-A-associated chromatin of the 10q25 neocentromere . To study the potential role of the FL-L1b RNA at the 10q25 neocentromere , we designed two sets of siRNA oligonucleotide duplexes ( Figure S3 ) for the specific transcriptional knockdown of FL-L1b in the monochromosomal CHOK1-M10 hybrid line; the study of RNAi knockdown in a CHO background offered the advantage of minimizing any potential off-target RNAi knockdown effects because the CHO genome contained significantly diverged L1 elements . The transfection conditions for RNAi knockdown were optimized to achieve >80% reduction in the FL-L1b transcripts as compared to the transfection-reagent-only and Stealth siRNA negative controls ( Figure 4A ) . Similar efficiency of FL-L1b transcriptional knockdown was also achieved in the other mouse/human and hamster/human somatic hybrids described above ( data not shown ) . To determine the cellular effects of the FL-L1b knockdown , a kill-curve analysis was performed on a CHOK1-M10 hybrid cell line containing a mardel ( 10 ) chromosome that had been tagged with a Zeocin resistance gene [13] , [17] . At the optimal concentration of 200 µg/ml of Zeocin , the majority ( >80% ) of non-mardel ( 10 ) -containing CHOK1-N10 cells were killed 48 hours post Zeocin treatment , whereas the normal growth of CHOK1-M10 cells was not affected ( Figure 4C-i ) . A significant loss of cell viability was observed in CHOK1-M10 following FL-L1b RNAi-knockdown , with the percentage of surviving CHOK1-M10 cells being reduced to approximately 50% compared to the transfection-reagent-only and Stealth siRNA negative controls 48 hours post Zeocin selection ( Figure 4C ) . These results indicated a presumed FL-L1b-induced impairment of neocentromere function that has led to the loss of the Zeocin-resistant mardel ( 10 ) chromosome . To further extend the Zeocin kill-curve results , a direct assessment of the loss of the mardel ( 10 ) chromosome following FL-L1b knockdown was determined by FISH ( Fluorescence In Situ Hybridization ) analysis using a mardel ( 10 ) -specific BAC probe . The stability of mardel ( 10 ) was greatly affected 48 hours post FL-L1b RNAi-knockdown , with a significant reduction from ∼95% to ∼55% in the CHOK1-M10 cell line , and from ∼100% to ∼60% in the mouse-human hybrid cell line ES-M10 ( Figure 4D ) . Under similar conditions , the stability of the normal human chromosome 10 in control CHO-human ( GM10926 , CHOK1-N10 ) and mouse-human ( GM11688 ) hybrid lines were not affected after FL-L1b transcriptional knockdown , suggesting that the loss of mardel ( 10 ) was directly linked to the effect of the FL-L1b knockdown on the neocentromere activity . In order to further investigate the structural integrity of the neocentromere after FL-L1b transcriptional knockdown , a combined immunofluorescence and FISH analysis was performed on metaphase CHOK1-M10 cells using an anti-CENP-A antiserum ( CREST6 ) and a BAC DNA probe ( RP11-359H22 ) that hybridized to the 10q25 neocentromeric region of mardel ( 10 ) . Cells were harvested at 24 hours following RNAi-knockdown in order to capture the early to intermediate stages of the disruption of neocentromere function prior to the complete loss of the mardel ( 10 ) chromosome . The mean fluorescence intensity of the CREST6 signals on the 10q25 neocentromere was reduced by 20 to 30% ( P<0 . 001 ) after the FL-L1b transcriptional knockdown using either siRNA#1 or siRNA#2 ( Figure 4B; examples of reduced CENP-A levels on 10q25 neocentromere post FL-L1b RNAi knockdown are shown in Figure S5 ) . In some cases , the CREST signals on the 10q25 neocentromere were as low as 20% that of the control cells . In addition to the quantitative immunofluorescence data , ChIP and real-time PCR analysis was also performed using an anti-CENP-A antibody for analysis comparing the enrichments of CENP-A at the 10q25 neocentromere with and without FL-L1b RNAi knockdown in CHOK1-M10 cells ( Figure S6 ) . Consistently , the ChIP/PCR results showed a reduction of CENP-A protein at 10q25 neocentormere following RNAi knockdown of FL-L1b transcript , providing independent confirmation of the importance of FL-L1 transcript in regulating the structural integrity of 10q25 neocentromere . We have previously reported that genes located across the 10q25 neocentromere region are transcriptionally competent [17] . Here , we used the transcription status of these genes as a measure to determine the effect of FL-L1b knockdown on the overall neocentromeric chromatin environment . The transcriptional levels of 13 actively transcribed genes within the 6-Mb 10q25 neocentromere region ( see Figure 2 ) were determined by qRT-PCR analysis at 24 hours post FL-L1b RNAi-knockdown . While most of the genes were unaffected , the transcriptional activities of 2 genes , ATRNL1 ( which spanned the CENP-A-binding domain ) and TRUB1 ( located outside the CENP-A domain , with its 5′-end CpG island being ∼410 kb away from the FL-L1b locus ) , were significantly reduced ( by approximately 60–70%; P<0 . 05 ) after the FL-L1b transcriptional knockdown ( Figure 4E ) . To ensure that the FL-L1b RNAi knockdown-mediated mardel ( 10 ) chromosomal instability was not attributed to a reduction in the level of TRUB1 and/or ATRNL1 transcripts , siRNA oligonucleotide duplexes were designed to target these and two other immediately surrounding genes , KIAA1600 and GFRA1 . Approximately 70–90% transcriptional knockdown was achieved for each of these genes in the CHOK1-M10 cells ( Figure S4 ) . No significant difference in the percentage cell survival was observed in the Zeocin kill-curve analysis , providing support for a specific role of FL-L1b rather than these genes in the maintenance of the mardel ( 10 ) stability ( Figure S4 ) . Our earlier bioinformatic analysis revealed a >2 . 5-fold increase in the prevalence of L1 retrotransposons in the underlying DNA sequence of the 10q25 CENP-A-binding clusters [18] . In this study , we described the increased frequency of intact L1 segments and average length of L1 DNA within the 330-kb CENP-A domain . Across the 6-Mb region of the 10q25 neocentromeric chromatin , a concentrated cluster of four FL-L1s was found at the CENP-A-binding domain of the 10q25 neocentromere [18] . Furthermore , in silico analysis of other neocentromere sites ( Figure S7 ) has revealed the presence of at least one FL-L1 element at the CENP-A-binding domain of five out of the six neocentromeres mapped to date [7] , [8] , [10] . The average FL-L1 density across these neocentromeres was also higher by 1 . 5 times compared to that of the human genome . These observations indicated a potential role of the L1 retrotransposon , particularly the full-length members ( FL-L1s ) , in the regulation of neocentromeric chromatin . In humans , active transcription and translation of L1 retrotransposons has been detected in a wide-range of cell types , including germ cells , tumours and transformed cell lines , and a smaller number of non-malignant somatic cells [21] , [22] , [28] , [29] , [30] , [31] , [32] . Importantly , multiple lines of evidence indicated that L1 RNAs are actively transcribed from full-length elements ( ∼6 kb in size ) that contain an internal promoter , two ORFs , and a poly-A tail at the 3′ UTR [20] , [21] , [22] , [23] . However , a detailed investigation of the transcriptional status of a single FL-L1 has not been described due to the technical difficulties associated with its repetitive nature . However , unlike tandemly-repeated satellite DNAs , which are highly homogeneous , L1 interspersed repeats are comparatively more diverged in sequence . Here , we took advantage of sequence divergence amongst the L1 repeats and designed oligonucleotide primers that targeted the diverged sites within a single FL-L1 retrotransposable element for RT-PCR and RNAi-knockdown analysis in monochromosomal somatic cell hybrids to determine its transcriptional activity and associated function – an undertaking that has not been previously described . We determined the transcriptional status of all six FL-L1s and other non-full-length L1 targets within the 6-Mb genomic window spanning the core neocentromere . Interestingly , only one of them ( i . e . FL-L1b ) was actively transcribed from the mardel ( 10 ) in CHOK1-M10 , although all six FL-L1s contained the internal promoter sequences ( for sequence comparisons between transcriptionally active and silent FL-L1s assayed in this study , see Tables S4 and S5 ) . Our previous study has described the active transcription of multiple genes within the broader 10q25 neocentromeric domain , including ATRNL1 that spanned the entire length of the CENP-A-associated chromatin [17] . However , it was uncertain if the core neocentromeric chromatin was permissive to active transcription given that the putative promoter of ATRNL1 was located outside the CENP-A domain . Here , based on the active transcription status of FL-L1b that is located within the central CENP-A-binding cluster at the 10q25 neocentromere , our study provided clear evidence for the permissibility of transcription within the core neocentromeric chromatin . More recently , this phenomenon of active transcription through the core centromere has also been demonstrated in α-satellite-containing human artificial chromosomes , where the CENP-A-associated domain was shown to spread into the adjacent transcriptionally active selectable marker gene [33] , [34] . Furthermore , transcriptional competence of the core centromeric chromatin has also been described in Oryza sativa ( rice ) and Zea mays ( maize ) [35] , [36] . These studies , including our current data , clearly show that CENP-A-associated chromatin is permissive to the transcription of genes and non-genic retrotransposable elements . The pattern of FL-L1 transcription within the 6-Mb domain in the hamster-human hybrids CHOK1-N10 ( containing the progenitor normal human chromosome 10 ) and GM10926 ( containing an unrelated normal human chromosome 10 ) was identical to that found in CHOK1-M10 . The formation of the 10q25 neocentromere did not significantly change the transcription level of FL-L1b , in consistent with our previous finding on the transcription competence of multiple genes located within this region [17] . Similar results were obtained from mouse-human hybrids GM11688 ( containing an unrelated normal human chromosome 10 ) and ES-M10 ( containing the neocentromeric mardel ( 10 ) chromosome ) , indicating that the active transcription of FL-L1b was not affected by differences in species background . Interestingly , FL-L1b transcription was not detected in one of the normal human chromosome 10 in the CHOK1#8 cell line – an observation that may be explained by differential epigenetic silencing or by mutations at the promoter or upstream regulatory sequences of the CHOK1#8 FL-L1b DNA . Using RNA-ChIP-qPCR analysis , we showed that FL-L1b single-stranded RNA transcripts were incorporated as part of the ribonucleoprotein component of the CENP-A-associated domain . Interestingly , the presence of long single-stranded centromeric RNA transcripts including CentC satellite repeats and CRM retrotransposons in Zea mays [36] , 160B/Athila2 retrotransposon in Arabidopsis thaliana [37] , PRAT satellite repeats in Palorus ratzeburgi [38] , and α-satellite repeats in humans [39] were also reported in recent studies . Furthermore , chromatin immunoprecipitation experiments in Zea mays and humans independently showed that these centromeric RNA transcripts were associated with the core centromeric chromatin [36] , [39] . Together , these results indicated that a pool of single-stranded RNA could be directly transcribed from the satellite repeats ( and centromere-specific retrotransposons ) of the normal centromeres or the L1 retrotransposon of a neocentromere and subsequently incorporated into the core centromeric/neocentromeric chromatin . The functional role of FL-L1b RNA at the 10q25 neocentromere was determined by RNAi knockdown of FL-L1b in human/mouse and human/hamster monochromosomal hybrid lines . FISH and/or Zeocin kill-curve analysis indicated that FL-L1b knockdown led to a significant reduction ( by ∼40–50% ) of the mitotic stability of mardel ( 10 ) and the compromised structural integrity of the 10q25 neocentromere . These FL-L1b knockdown-mediated mitotic effects at the 10q25 neocentromere were fast and similar to the rapid response previously described in RNAi knockdown or conditional knockout of core centromere proteins , such as CENP-A [40] , CENP-H [41] , [42] , [43] and CENP-K [44] . Our results therefore demonstrate a functional significance of L1 RNA transcripts at the core neocentromere region which has not been fully defined in previous studies . In addition to the two FL-L1b siRNA duplexes , we have included the analysis of siRNA duplexes that targeted four genes spanning and surrounding the CENP-A-associated region . None of these siRNAs exerted any effect on mardel ( 10 ) stability , as indicated by the cell viability assay ( Figure S4 ) . More specifically , RNAi knockdown of ATRNL1 , a gene that spanned across the CENP-A-associated domain , did not result in any compromise in the functional integrity of the 10q5 neocentromere . These data indicate that the FL-L1b RNAi-induced mardel ( 10 ) instability is likely to be a result of the depletion of FL-L1b RNA transcripts rather than due to indirect effects arising from the recruitment of chromatin remodelling or modifying complexes to the 10q25 neocentromere via the RNAi pathway . The precise functional role ( s ) of FL-L1 RNA transcripts at the core neocentromeric chromatin remains to be delineated . Transcription at the FL-L1 locus and/or the L1 transcript itself may act as an early-specification epigenetic signal for the recruitment of CENP-A nucleosomes . Interestingly , the transcriptional knockdown of FL-L1b leads to a more ‘closed’ local chromatin state , as indicated by the reduction in the transcription of two surrounding genes ATRNL1 and TRUB1 . At the 10q25 neocentromere , the transcription activity may facilitate the process of histone replacement by partially disassembling the nucleosomes to provide a more ‘open’ chromatin structure [45] for subsequent deposition of CENP-A nucleosomes . The recent identification of GATA-type transcription factor Ams2 , which promotes the centromere localization of CENP-A in Schizosaccharomyces pombe , also provides supports toward a role of transcription in defining a centromere state [46] . Rather than the transcriptional activity itself , it is also possible that the FL-L1b RNA transcript may serve as a specific epigenetic signal at the 10q25 neocentromere since by RNAi knockdown several neighbouring genes did not affect the mitotic stability of mardel ( 10 ) . Although this hypothesis remains to be tested , the underlying process may be similar to the function of long Xist RNA in promoting the establishment of a specialized chromatin state such as the incorporation of macroH2A during X-inactivation [47] , [48] . Alternatively , the chromatin-bound FL-L1b RNA at the 10q25 neocentromere may be involved in the formation of a flexible ribonucleoprotein complex that brings together and/or stabilizes the proteins of the core neocentromere , as suggested by the observed CENP-A delocalization after FL-L1b RNAi knockdown . Nonetheless , it is interesting to note that the FL-L1b locus is being actively transcribed from both the progenitor chromosome 10 and the neocentric mardel ( 10 ) . The absence of active CENP-A recruitment to the FL-L1b locus on the progenitor chromosome suggests that the FL-L1b transcript is unlikely the sole epigenetic specification determinant for CENP-A recruitment . The transcribed FL-L1b locus and/or FL-L1b RNA-bound chromatin may require additional players ( e . g . specific RNA-binding proteins ) in recruiting CENP-A for the formation of a neocentromere . The reduction in the transcriptional activity of the two genes surrounding CENP-A domain ( ie . ATRNL1 and TRUB1 ) following FL-L1b knockdown indicated that the FL-L1b RNA could be regulating a larger genomic domain than that of the CENP-A-associated chromatin . It is unknown how these FL-L1 RNA transcripts mediate such long-range chromosomal effects , however , it is interesting that this extended genomic domain overlaps with a region of high L1 DNA content ( using the human genome average as the baseline threshold ) ( Figure 2B ) . The incorporation of FL-L1b RNA into the neocentromeric chromatin may potentially involve a simple base pair recognition mechanism [49] , similar to what has been described for the assembly of the telomerase complex by telomerase RNA or the formation of heterochromatin structure by short interfering siRNA [50] , [51] . In future studies , the identification of potential chromatin remodelling proteins that interact with the centromeric or neocentromeric RNA transcripts should shed new light on the epigenetic mechanisms of regulation of centromere/neocentromere architecture and function . Increasing evidence now point to neocentromere formation as the underlying mechanism for centromere repositioning that underpins karyotype evolution and speciation [6] , [52] . The elucidation of the molecular mechanisms of neocentromere formation will not only provide important insights into the inherent epigenetic determinants that initiate de novo centromere assembly , but will also provide a better understanding of the operating mechanisms for centromere repositioning and karyotype evolution . The somatic hybrid cell lines were cultured as previously described [17] , [53] . These include ( i ) human/hamster monochromosomal hybrid CHOK1#8 , CHOK1-N10 and CHOK1-M10 , containing unrelated chromosome 10 , progenitor chromosome 10 , and mardel ( 10 ) respectively; ( ii ) human/mouse mardel ( 10 ) -containing monochromosomal hybrid ES-M10 [14] , [17] . Two additional somatic hybrid cell lines , GM10926 ( CHOK1 background ) and GM11688 ( mouse A9 background ) , each containing an unrelated human chromosome 10 , were obtained from the Human Genetic Cell Repository of Coriell Institute of Medical Research and were cultured in Ham's Kao and Michayluk medium ( KAO ) supplemented with 10% dialyzed FCS ( Gibco BRL ) at 37°C and Ham's F12 Medium/DMEM ( 1∶1 mixture ) 2 mM L-glutamine , 10% FCS with 500 µg/ml Geneticin ( Gibco ) at 37°C . 200 µg/ml Zeocin ( Invitrogen ) was added into the media for selection of the mardel ( 10 ) chromosome in CHOK1-M10 and ES-M10 . A time-course experiment was first performed to determine the time duration required to kill the non-resistant CHOK1 cells ( CHOK1-N10 ) at 200 µg/ml of Zeocin . The transcription-knockdowns of FL-L1b and other genes of interest were performed by siRNA transfection of the relevant siRNA oligonucleotide duplexes for 48 hours at 25 nM in CHOK1-M10 . Subsequently , cells were incubated with 200 µg/ml of Zeocin for an additional 48 hours following RNAi knockdown . The number of viable cells was determined by staining with Trypan Blue ( 0 . 8 mM Trypan Blue in 1× PBS ) for 5 minutes at room temperature and counting with a hemocytometer under the microscope . The mitotic stability of the mardel ( 10 ) was calculated as the ratio of the percentage of viable cells under Zeocin selection to the number of viable cells without Zeocin selection . The genomic location of each chromatin domain and the sequence characteristics were determined using the UCSC Genome Browser ( http://genome . ucsc . edu . au ) May 2004 builds and its in-build RepeatMasker track [54] . Full-length L1s were identified and annotated using the online L1Base software package ( http://l1base . molgen . mpg . de/ ) [55] . Specifically , several key features were analyzed and these included ( 1 ) general characteristics , such as the GC content , target site duplications , intactness scores , the polyadenylation signal , and the presence of poly-A tails; ( 2 ) classifications of L1s; ( 3 ) 5′UTR promoter features and the conservations of transcription factor binding sites; ( 4 ) the conservation of amino acid residues in the two ORFs ( Table S4 ) . In the L1Base program , the ‘intactness score’ was calculated for the query FL-L1 sequence . One point was awarded to every conserved sequence feature ( according to the consensus L1 sequence ) that was known to affect the transcriptional and/or translational activity [55] . The transcriptionally active FL-L1b had an intactness score of 25 , being the highest of the six FL-L1s ( Table S5A ) . As for the 100 bp internal promoter [56] , [57] within the 5′ UTR , the nucleotide sequence conservation of the six FL-L1s ( FL-L1a–g ) ranges from 72 . 3 to 91 . 6% and FL-L1b ranked the equal highest of the six FL-L1s ( Table S5B ) . FL-L1b also contained all of the known conserved transcription factor binding sites within the 5′ UTR , while more than one mutation was found within the 5′ UTR of the other FL-L1s . In addition , a CpG island that was potentially important for transcriptional regulation was present within FL-L1b . Other noted sequence features of FL-L1b that could contribute to its transcription and functional activities were listed following: ( 1 ) FL-L1b contained an intact polyadenylation signal and a relatively long poly-A tail , which were important for mRNA maturation and subsequent protein translation; ( 2 ) FL-L1b was the only FL-L1 of the 6 FL-L1s with no ORF frame shifts or mutations at the important amino acid residues analysed; ( 3 ) FL-L1b belonged to the retrotranspositionally competent Ta subfamily and was flanked by 15 bp target-site duplications ( Table S5 C to F ) . RNA chromatin immunoprecipitation was performed as described in [58] with slight modifications . RIPA Buffer ( 50 mM Tris-Cl , pH 7 . 5 , 1% NP-40 , 0 . 5% sodium deoxycholate , 0 . 05% SDS , 1 mM EDTA , 150 mM NaCl , 1 tablet of Roche Complete Protease Inhibitor per 10 ml of RIPA buffer ) was used for cell lysis and immunoprecipitation was performed using a rabbit polyclonal anti-mouse CENP-A antibody at 1∶500 dilution [15] . Immunocomplex recovery was achieved following two washes with RIPA High Stringency Wash Buffer ( 50 mM Tris-HCl pH 7 . 5 , 1% Nonidet P-40 , 1% sodium deoxycholate , 0 . 1% SDS , 1 mM EDTA , 0 . 1 mM PMSF , 1 tablet of Roche Complete™ Protease Inhibitor per 10 ml buffer ) containing 250 mM and 500 mM NaCl in stepwise manner . Elution of RNA was performed with RNA-ChIP Elution Buffer ( 50 mM Tris-HCl pH 7 . 5 , 5 mM EDTA , 1% SDS , 10 mM dithiothreitol ) and reverse cross-linked at 70°C for 45 minutes . Total RNA was then isolated and subsequently subjected to quantitative PCR analysis . Total RNA was extracted using either the RNeasy Mini Kit ( Qiagen ) for transcription detection assays or TRIZOL reagent ( Invitrogen ) for RNA-ChIP . TURBO DNA-free Kit ( Ambion ) was used to remove possible contaminating DNA . cDNA synthesis was performed using Transcriptor First Strand cDNA Synthesis Kit ( Roche ) . Quantitative RT-PCR was carried out using SYBR Green PCR Master Mix ( Applied Biosystems ) on 7300 or 7900HT Real-Time PCR System ( Applied Biosystems ) according to manufacturer's instructions . cDNA equivalent to 10 ng RNA was amplified with 150 nM forward and reverse primers in a 25 µL reaction ( for primer sequences , see Table S6 ) . Dissociation curves were performed to confirm specific amplifications without primer dimer formation . Samples were also subjected to gel electrophoresis analysis to confirm that the PCR products were of expected size . For the transcription assay of the FL-L1s , sequencing experiments were also performed to confirm the identity of each RT-PCR product . For calculations and statistics in the analysis , see below . The comparative CT method was used for data analysis in transcription detection assay and quantitative ChIP-PCR analysis . The ΔCT value was calculated as [ΔCT = CT ( test gene/genomic segment ) −CT ( control gene/genomic segment ) ] . The CT value of each test gene/segment was normalized against the CT value of control gene/segment , either 5S ( for DNA-ChIP-qPCR analysis ) or β-actin ( for transcription assay and RNA-ChIP-qPCR analysis ) to give the ΔCT value . The ΔΔCT value was calculated as [ΔΔCT = ΔCT ( test cell line ) −ΔCT ( control cell line ) ] for transcription analysis , or [ΔΔCT = ΔCT ( before siRNA knockdown ) −ΔCT ( after siRNA knockdown ) ] for transcription knockdown assay , and [ΔΔCT = ΔCT ( input ) −ΔCT ( bound ) ] for ChIP-qPCR analysis , respectively . Relative fold-difference in transcription activity was expressed as in transcription analysis and transcription knockdown assays . Relative-binding value in ChIP-qPCR analysis was calculated by . Two sets of Stealth siRNA oligonucleotide duplexes targeting FL-L1b were designed using the online BLOCK-iT RNAi Designer software ( Invitrogen ) . In contrast , siRNA oligonucleotide duplexes targeting genes KIAA1600 , TRUB1 , ATRNL1 , and GFRA1 were obtained as pre-designed Stealth Select siRNA ( Invitrogen ) . Sequences of the siRNA oligonucleotide duplexes are listed in Table S6 . CHOK1-M10 cells were seeded in 6-well culture plates without antibiotic selection at a density of 2×104 cells/well , 24 hours prior to siRNA transfection . Transcriptional knockdown was performed by transfecting cells with Stealth siRNA oligonucleotide duplexes ( Invitrogen ) at a final concentration of 25 nM in DMEM ( Dulbecco's Modified Eagle's Medium ) using 2 . 5 ng/µl Lipofectamine 2000 ( Invitrogen ) for a period of 24 to 48 hours according to the manufacturer's instructions . The effects of RNAi knockdown of FL-L1b and other target genes were assayed by quantitative RT-PCR . Stealth siRNA Negative Control Low GC Duplex ( Invitrogen ) was also included as control for sequence independent RNAi knockdown effects . Immunofluorescence [59] and FISH [13] were performed as previously described . Anti-centromere autoimmune serum CREST6 ( which predominantly recognize CENP-A protein ) and RP11-359H22 BAC were used for the identification of 10q25 neocentromere on mardel ( 10 ) [13] . Metaphase spreads were visualized using an Imager M1 microscope ( Zeiss ) and the digital images were captured by the AxioCam MRm camera ( Zeiss ) . CREST6 immunofluorescence signals on 10q25 neocentromere were quantified and normalized against CHO centromeres in CHOK1-M10 cells using AxioVision software version V4 . 6 . 1 . 0 ( Zeiss ) . The quantification of CREST6 immunofluorescence signals was performed following FL-L1b RNAi knockdown . A circular area of defined size ( diameter of 2 µm ) was selected around the centromere of interest . Total intensity ( I ) of each pixel within the delineated area was determined and defined as arbitrary fluorescence unit ( a . f . u . ) . Digital images obtained from immunofluorescence analysis were nonsaturating and auto-corrected for background removal . Non-specific background signal ( IBK ) for each metaphase spread was calculated by the average arm intensity from five chromosomes and subsequently subtracted from the total intensity ( I ) . Average signal intensity of 15–20 endogenous CHO centromeres ( ICHO ) from each spread was calculated and used as normalization control to correct for the variation in hybridization between spreads . The ratio of CREST6 fluorescence intensities ( R ) on 10q25 neocentromere to CHO centromeres was calculated using the following equation: R = ( IM10−IBK ) / ( ICHO−IBK ) . The mean fluorescence intensity of CREST6 ( M ) on 10q25 neocentromere was calculated using the following equation: M = R×ICHOALL . ICHOALL represents the average intensity for all CHO centromeres ( ∼750 ) calculated for each treatment in the RNAi knockdown experiments .
The centromere is an essential chromosomal structure for the correct segregation of chromosomes during cell division . Normal human centromeres comprise a 171-bp α-satellite DNA arranged into tandem and higher-order arrays . Neocentromeres are fully functional centromeres that form epigenetically on noncentromeric regions of the chromosomes , with recent evidence indicating an important role they play in centromere repositioning , karyotype evolution , and speciation . Neocentromeres contain fully definable DNA sequences and provide a tractable system for the molecular analysis of the centromere chromatin . Here , the authors investigate the role of epigenetic determinants in the regulation of neocentromere structure and function . They identify that a retrotransposable DNA element found within the neocentromere domain is actively transcribed and that the transcribed RNA is essential for the structural and functional integrity of the neocentromere . This study defines a previously undescribed epigenetic determinant that regulates the neocentromeric chromatin and provides insight into the mechanism of neocentromere formation and centromere repositioning .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "molecular", "biology/centromeres", "molecular", "biology/chromatin", "structure", "genetics", "and", "genomics/chromosome", "biology" ]
2009
LINE Retrotransposon RNA Is an Essential Structural and Functional Epigenetic Component of a Core Neocentromeric Chromatin
Liver fluke infection of livestock causes economic losses of over US$ 3 billion worldwide per annum . The disease is increasing in livestock worldwide and is a re-emerging human disease . There are currently no commercial vaccines , and only one drug with significant efficacy against adult worms and juveniles . A liver fluke vaccine is deemed essential as short-lived chemotherapy , which is prone to resistance , is an unsustainable option in both developed and developing countries . Protein superfamilies have provided a number of leading liver fluke vaccine candidates . A new form of glutathione transferase ( GST ) family , Sigma class GST , closely related to a leading Schistosome vaccine candidate ( Sm28 ) , has previously been revealed by proteomics in the liver fluke but not functionally characterised . In this manuscript we show that a purified recombinant form of the F . hepatica Sigma class GST possesses prostaglandin synthase activity and influences activity of host immune cells . Immunocytochemistry and western blotting have shown the protein is present near the surface of the fluke and expressed in eggs and newly excysted juveniles , and present in the excretory/secretory fraction of adults . We have assessed the potential to use F . hepatica Sigma class GST as a vaccine in a goat-based vaccine trial . No significant reduction of worm burden was found but we show significant reduction in the pathology normally associated with liver fluke infection . We have shown that F . hepatica Sigma class GST has likely multi-functional roles in the host-parasite interaction from general detoxification and bile acid sequestration to PGD synthase activity . The liver flukes , Fasciola hepatica and Fasciola gigantica are the causative agents of fasciolosis , a foodborne zoonotic disease affecting grazing animals and humans worldwide [1] . Liver fluke causes economic losses of over US$ 3 billion worldwide per annum to livestock via mortality , reduction in host fecundity , susceptibility to other infections , decrease in meat , milk and wool production and condemnation of livers [1] . The disease is increasing in livestock worldwide with contributing factors such as climate change ( warmer winters and wetter summers supporting larger intermediate mud snail host populations ) ; fragmented disease management ( only treating sheep not cattle and limiting veterinary interaction ) ; encouragement of wet-lands; livestock movement; and/or failure/resistance of chemical control treatments in the absence of commercial vaccines [1] , [2] . Fasciolosis is also a re-emerging human disease with estimates of between 2 . 4 and 17 million people infected worldwide [3] . In response , the World Health Organisation have added fasciolosis to the preventative chemotherapy concept [4] . There are currently no commercial vaccines and triclabendazole ( TCBZ ) is the most important fasciolicide , as the only drug with significant efficacy against adult worms and juveniles [5] . Evidence from developed countries where TCBZ has been used widely exposes the reliance on this drug as an Achilles heel of liver fluke chemotherapeutic control , with well-established evidence of drug-resistance [5] . Therefore , TCBZ does not offer a long-term sustainable option for livestock farmers worldwide . The need for a liver fluke vaccine is further underscored by the fact that the costs associated with anthelmintic intervention for fluke control make short-lived chemotherapy an unsustainable option in developing countries . Protein superfamily studies in liver fluke have provided a number of leading vaccine candidates . High quality one-gene based vaccine discovery research has identified several vaccine candidates from protein superfamilies that provide significant , but often variable protection rates in challenge animal trials against liver fluke . For example , Mu class Glutathione transferase ( GSTs ) have been widely investigated as vaccine candidates for fasciolosis [6]–[9] . The Mu class GSTs have established roles in general Phase II detoxification of xenobiotic and endogenously derived toxins in F . hepatica within the host bile environment [10] . The general detoxification role is supported by GSTs contributing to 4% of the total soluble protein in F . hepatica , with a widespread tissue distribution . Proteomics and EST sequencing approaches have now delineated what members of the GST family are expressed in F . hepatica and two new classes of GST , Sigma and Omega , have been uncovered [11] . In the related trematode , Schistosoma mansoni , the Sigma class GST ( Sm28 ) has generally shown more robust protection in vaccine trials against schistosome infection [12] , than the F . hepatica Mu GSTs against F . hepatica infection . Sigma class GSTs , unlike Mu Class GSTs , have been characterized as GSH-dependent hematopoietic prostaglandin synthases responsible for the production of prostaglandins in both mammals and parasitic worms [13]–[18] . Prostaglandins have been extensively studied in mammals and are shown to be involved in a range of physiological and pathological responses [19]–[23] . Parasite-produced prostaglandins may be involved in parasite development and reproduction as well as the modulation of host immunity , allergy and inflammation during establishment and maintenance of a host infection [16] , [24]–[28] . The host protection success of Sigma GST based vaccinations in schistosomiasis may therefore be related to neutralising specific functions in host-parasite interplay , such as prostaglandin synthase activity . In this manuscript we follow four work pathways to functionally characterise the newly identified Sigma GST from F . hepatica . 1 ) We confirm its designation as a Sigma class GST using substrate profiling , 2 ) we assess prostaglandin synthase activity and its effect on host immune cells , 3 ) we localise the Sigma GST within adult fluke and between ontogenic stages and 4 ) assess its potential as a vaccine candidate . GST proteins representative of recognised GST superfamily classes were obtained from European Bioinformatics Institute Interpro database ( http://www . ebi . ac . uk/interpro/ ) , and from non-redundant databases at NCBI ( http://www . ncbi . nlm . nih . gov/ ) . A mammalian and a helminth or invertebrate GST sequence were selected for each GST class where available . Sequences were aligned via ClustalW program [29] in BioEdit Sequence Alignment Editor Version 7 . 0 . 5 . 2 . [30] and sequence identity matrices produced from multiple alignments . Phylogenetic bootstrap neighbour-joining trees were produced as PHYLIP output files in ClustalX Version 1 . 83 [31] according to the neighbour-joining method of Saitou and Nei [32] . ClustalX default settings for alignments were accepted using the GONNET protein weight matrices with PHYLIP tree format files viewed within TREEVIEW [33] . Full-length cDNA for FhGST-S1 was available in the form of an expressed sequence tag ( EST ) clone Fhep24h03 , details of which can be obtained from the previously published Sigma class GST [11] and is identical to the submitted GenBank accession No . DQ974116 . 1 ( NCBI http://www . ncbi . nlm . nih . gov/ ) . FhGST-S1 was amplified via PCR using the following primer pair: rFhGST-S1 forward primer , 5′ GGAATTCCATATGGACAAACAGCATTTCAAGTT 3′;rFhGST-S1 reverse primer , 5′ ATAAGAATGCGGCCGCCTAGAATGGAGTTTTTGCACGTTTTTT 3′ . Restriction enzyme sites ( in bold type and underlined ) for NdeI ( forward primer ) and NotI ( reverse primer ) were included so that the entire ORF could be directionally cloned into the pET23a ( Novagen ) vector . Recombinant protein was produced in Escherichia coli BL21 ( DE3 ) cells ( Novagen ) . rFhGST-S1 protein was purified according to the glutathione affinity chromatography method of Simons and Vander Jagt [34] from transformed E . coli cytosol following protein expression . Native GSTs were purified from F . hepatica soluble cytosolic supernatants as previously described [11] . Purity of rFhGST-S1 was assessed by electrospray ionisation ( ESI ) mass spectrometry , sodium dodecyl sulphate polyacrylamide gel electrophoresis ( SDS-PAGE ) and 2DE according to LaCourse et al . [35] . A range of model and natural substrates ( see Table 1 for details ) were used to profile the Sigma GST . A number of ligands were also assessed for their ability to inhibit GST activity with 1-chloro-2 , 4-dinitrobenzene ( CDNB ) as the second substrate [36] . Values were reported as the concentration of inhibitor required to bring GST specific activity to 50% of its original activity ( IC50 ) . At least six different inhibitor concentrations were used in each IC50 determination in triplicate . Inhibitors were pre-incubated for 5 minutes prior to starting reactions . IC50 values were estimated graphically [37] . Prostaglandin synthase activity was assessed via an adapted method based upon those of Sommer et al . [26] and Meyer et al . [16] , [38] , with extraction modifications based upon Schmidt et al . [39] . In brief , reactions were performed in glass vials in 2 mM sodium phosphate buffer , pH 7 . 4 , containing 10 mM glutathione , 50 mM NaCl , 0 . 5 mM tryptophan , 1 µM hematin , 1 U COX-1 enzyme , 100 µM arachidonic acid ( All Sigma , UK . COX-1 [C0733] ) and rFhGST-S1 at final concentration ranges of 0 . 1–100 . 0 µg/ml . Negative control reactions lacking either GST or COX-1 were also prepared . Reactions were incubated for 5–10 min in a water bath at 37°C . This was followed by 4 minutes incubation at 25°C in a shaking water bath . Prostaglandins were extracted by adding 860 µL of ice-cold ethyl acetate . Reactions were vortexed for 30 s then centrifuged briefly at 10 , 000× g at 4°C for 2 min . The upper ethyl acetate layer was retained and solvent was evaporated under a nitrogen stream at 45°C . The remaining residue was reconstituted in 50 µl of methanol/water/fomic acid ( 25∶75∶0 , 1 ) mix at pH 2 . 8 and stored at −80°C until ready for mass spectrometry analysis . Standards of prostaglandins D2 , E2 and F2α ( Cayman , Ltd ) were also prepared in methanol/water/formic acid mix for analysis . The nano LC-MS analyses were performed using a Waters Q-Tof micro mass spectrometer ( Waters ) coupled to a LC-Packings Ultimate nano LC system ( Dionex ) . The pre-column used was a LC Packings C18 PepMap 100 and the nano LC column used was a LC Packings 15 cm PepMap 100 C18 ( both Dionex ) . Samples were loaded on the pre-column with mobile phase A ( 25% methanol with 0 . 1% formic acid added ) . Loading flow rate was 0 . 03 ml/min for 6 min . The samples were eluted on to the nano LC column using mobile phases B ( 60% acetonitrile ) and C ( 100% methanol ) . A typical gradient profile was 100% B to 100% C in 10 min ( flow rate of 0 . 2 µl/min ) with the column held at 100% C for 1 hour . The mass spectrometer was operated in the negative ion nano electrospray mode with a source temperature of 80°C and capillary voltage 2 . 8 kV . The scan range was 40 to 400 Da for 1 . 5 s . F . hepatica adults were collected , cultured in vitro for 4 h and the ES products collected and prepared as previously described [40] . Newly excysted juveniles ( NEJ ) were excysted from metacercariea in vitro and cultured in Fasciola saline for 4 h post excystment as previously described [41] . F . hepatica ( adult and NEJ ) soluble fractions were obtained by homogenisation of frozen fluke at 4°C in a glass grinder in lysis buffer ( 20 mM KHPO4 , pH 7 . 0 , 0 . 1% Triton-X100 and a cocktail of protease inhibitors [Roche , Complete-Mini , EDTA-free] ) . Homogenates were centrifuged at 100 , 000× g for 1 h at 4°C . Supernatants were considered as the soluble cytosolic fraction . Cytosolic protein extracts were treated and resolved by 2DE as described previously [11] . F . hepatica eggs were isolated , cultured and protein extracted as previously described [42] . Recombinant F . hepatica Sigma GST ( rFhGST-S1 ) , and native F . hepatica S-hexylGSH-affinity purified GST samples ( and human/rat recombinant PGD-synthase ) were subjected to standard SDS-PAGE and 2DE , electro-transferred to membranes [43] , [44] and western blotted with a polyclonal antibody ( 1∶20 , 000 dilution ) raised in rabbits to the recombinant F . hepatica Sigma GST by Lampire Biological Laboratories , USA . Membranes were also probed with Mu class GST antibody ( represented by the anti-Schistosoma japonicum GST26 Mu class antibody [1∶1 , 000 dilution] and an anti-rat PGD-synthase antibody [1∶1 , 000 dilution] , Pharmacia-Biotech 27-4577 ) . F . hepatica eggs , NEJs ( somatic and ES preparations ) and adults ( somatic and ES preparations ) were subjected to SDS-PAGE and also electro-transferred as described above and probed with the polyclonal antibody raised in rabbits to the recombinant F . hepatica Sigma GST . All western blots were developed as described previously [11] . F . hepatica Sigma class GST ( FhGST-S1 ) was detected by immunohistology in tissue sections of whole adult F . hepatica extracted from bile ducts of sheep liver and also in situ from sections of liver . Staining for FhGST-S1 was performed on formalin-fixed and paraffin-embedded tissue sections according to the method described previously [45] . Sections were washed in Tris-buffered saline ( TBS; 0 . 1 M Tris-HCl with 0 . 9% NaCl [pH 7 . 2] ) , treated with 0 . 05% ( w/v ) protease ( type XXIV , bacterial: Sigma ) in TBS for 5 min at 37°C for antigen retrieval , before three further 5 min washes in ice-cold TBS . Following TBS washes , sections were incubated for 10 min in 50% ( v/v ) swine serum in TBS followed by incubation for 15–18 h at 4°C in rFhGST-S1 polyclonal antibody ( diluted at 1∶500 in 20% swine serum in TBS ) . Sections were again washed in TBS before further incubation at ambient temperature ( approximately 20°C+/−3°C ) with anti-rabbit peroxidise anti-peroxidase ( PAP; diluted at 1∶100 in 20% swine serum in TBS ) . Following washes with TBS , sections were incubated , with stirring , for 10 min , with 3 , 3-diaminobenzidine tetrahydrochloride ( DAB; Fluka , Buchs , Switzerland ) with 0 . 01% v/v hydrogen peroxide in 0 . 1 M imidazole buffer pH 7 . 1 , before counterstaining with Papanicolaou's hematoxylin for 30 s . Sections were then rinsed , dehydrated in alcohol , cleared in xylene , and mounted . Consecutive sections from each tissue were used as negative controls in which the rFhGST-S1 polyclonal antibody was replaced by TBS . Aligning Sigma class GSTs of trematodes shows the extent of identity and similarity across this class of GSTs ( Figure S1 ) . An amino acid sequence comparison of FhGST-S1 with other trematode GSTs places FhGST-S1 into the Sigma class of GSTs , with identities averaging approximately 45% . Comparison with the most closely matching mammalian GSTs shows sequence identities averaging only approximately 28% ( Table S1 ) . Despite phylogenetic neighbour-joining trees place mammalian and trematode GSTs within the same broad Sigma class ( Figure S1 ) there remains a distinct separation of the trematode and mammalian clusters . Full sequence length recombinant F . hepatica Sigma Class GST ( rFhGST-S1 ) was shown to be purified to a high level from transformed E . coli cytosol following expression yielding 57 . 3 mg of rFhGST-S1 from a 1 litre culture of BL21 ( DE3 ) cells . Purity was judged by the presence of a single band upon SDS-PAGE at the estimated size and a dominating single peak via ESI MS at the precise calculated theoretical mass for the complete protein sequence ( Figure 1 ) . Analysing this fraction by 2D SDS-PAGE revealed a single protein resolving into 3 protein spots . Western blotting of the 2DE profile with anti-rFhGST-S1 antibody confirmed all 3 resolved protein spots as rFhGST-S1 ( 2DE and western blot data not shown ) . No recognition was seen probing the 3 spots with an anti-Mu class antibody . rFhGST-S1 was produced as an active protein , displaying significant enzymic activity towards the model GST substrate 1-chloro-2 , 4-dinitrobenzene ( CDNB ) and a range of substrates commonly used to characterise GSTs ( Table 1 ) . F . hepatica GST is very similar in terms of its enzymatic profile to the GST of S . japonicum currently undergoing clinical vaccine trials . FhGST-S1 also displays higher glutathione-dependent lipid peroxidase activity compared to both Sm28GST and Sj26GST [47] . Interestingly , ligand inhibition studies on rFhGST-S1 showed the enzymic activity of rFhGST-S1 with CDNB was inhibited by the major pro-active form of the main liver fluke drug Triclabendazole . The sulphoxide derivative ( TCBZ SO ) gave an IC50 ( 50% enzyme inhibition ) of 57±5 µM ( 5 replicates ) . Bile acids , potentially natural ligands for liver fluke tegumental associated proteins in the host bile environment , were also assessed for activity inhibition . The rFhGST-S1 interacted with all three bile acids tested using five replicate assays: Cholic acid ( IC50 302±73 µM ) ; Deoxycholic acid ( IC50 223±21 µM ) and Chenodeoxycholic acid ( IC50 64±9 µM ) . Previous studies on the Sigma class GSTs from both mammals and helminth parasites have revealed a capacity to synthesise Prostaglandin D2 ( PGD2 ) and PGE2 . Since prostaglandin synthase activity may be a conserved role of Sigma class GSTs , we also tested the ability of rFhGST-S1 to synthesise prostaglandin eicosanoids using a coupled assay with COX-1 . COX-1 catalyses the conversion of arachidonic acid to the H2 form before the prostaglandin isomer is converted to either the D or E form . Nano-LC/MS analysis enabled us to detect the presence of both PGD2 and PGE2 in the assay mixture with the PGD2 form being the more abundant of the two prostanoids ( Figure 2 ) . While some PGE2 in the mixture could have arisen from rapid degradation of the unstable PGH2 , nano-LC-MS was unable to detect either PGD2 or PGE2 in negative control reactions lacking either COX-1 or GST . The rFhGST-S1 catalyses PGD2 formation in a concentration-dependent manner as previously described for rOvGST-1 [26] . PGD2 was also detected in coupled assays with rFhGST-S1 and COX-1 using an Enzyme Immno Assay ( EIA ) detection kit ( Cayman ) and showed similar results ( results not shown ) . FhGST-S1 was first identified in adult liver fluke in S-hexyl-GSH affinity isolated fractions of cytosol [11] . Western blots confirmed the presence of FhGST-S1 in NEJs and adult flukes and further enabled us to identify the Sigma GST in relative abundance in egg extracts , suggesting that it may play a metabolic role in embryogenesis/reproduction ( Figure 3 ) . Western blot analyses demonstrate that FhGST-S1 is consistently expressed during the course of in vitro parasite embryonation ( days 1–9 , only data for days 2 , 7 and 9 shown in Figure 3 ) . In contrast , immunoblot analysis of freshly voided ( day 0 ) eggs reveals that expression of the Sigma class GST is greatly reduced at the time of voiding from the host ( Figure 3 ) . However , immunolocalisation studies of adult parasites revealed an abundance of FhGST-S1 in the vitelline cells and eggs , emphasising the likely importance of this enzyme in egg formation and development . Some staining was also found in the parasite parenchyma and tegument , also suggesting a role at the host-parasite interface ( Figure 4 ) . Indeed , FhGST-S1 was detected in ES products of adult fluke cultured in vitro ( Figure 3 ) suggesting that the protein could , in principle , come into contact with the host immune system as it is released from the tegument during tegumental turnover and sloughing of the fluke body surface . rFhGST-S1 exhibited prostaglandin synthase activity producing PGE2 and PGD2 . In addition , it has been shown previously that rFhGST-S1 activates DCs in vitro [48] . Therefore , an attempt to determine if rFhGST-S1 could induce the secretion of total prostaglandin , PGE2 and PGD2 from DCs was performed . Prior to experimentation , endotoxin levels in rFhGST-S1 were assessed and were similar to that of the media alone . Both of which were below the lower limit of detection ( <0 . 01 EU/ml ) . When examining prostaglandin induction DCs stimulated with rFhGST-S1 secreted total prostaglandin and PGE2 ( DC ( WT ) ; Figure 5 ) but not PGD2 ( data not shown ) . Since it has been previously determined that the activation of DCs by rFhGST-S1 was dependent upon TLR4 [48] we repeated the experiment in DCs from TLR4KO mice and in keeping with previous findings demonstrated that the secretion of total prostaglandin and PGE2 by rFhGST-S1 was significantly reduced in the absence of the TLR4 receptor ( DC ( TLR4KO ) ; Figure 5 ) . rFhGST-S1 was then further assessed for its potential to induce prostaglandin secretion from macrophages by exposing two macrophage cell lines with rFhGST-S1 . After 18 hours the levels of total prostaglandin , PGE2 and PGD2 were measured . In this assay , both macrophage cells lines stimulated with rFhGST-S1 secreted total prostaglandin , PGE2 and PGD2 ( Figure 6 ) . However , the levels secreted by J744 cell line were higher when compared to the amount secreted by RAW264 . 7 cell line . In these experiments we included medium only as a negative control and LPS as a positive control . In all experiments the levels of prostaglandin in response to rFhGST-S1 was comparable to the levels secreted in responses to LPS . Following the completion of the vaccine trial , liver fluke were recovered and the livers scored . The resulting data is summarised in Table 2 . When assessing fluke burdens , length , weight and fecal egg counts , no significant differences between rFhGST-S1 immunised and Quil A immunised groups were observed . Despite this lack of significance , at 7–9 days post-infection ( dpi ) the number of gross hepatic lesions appeared reduced in rFhGST-S1 immunised groups compared to the Quil A control group . At 15 weeks post-infection ( wpi ) , a similar outcome is observed . Liver hepatic lesion scoring appeared to show reductions in the severity of damage occurred in the rFhGST-S1 immunised group compared to the Quil A only group , despite no significant differences in the aforementioned morphometric data . Microscopically , at 7–9 dpi animals from the Quil A group showed tortuous necrotic tracts surrounded by a scarce inflammatory infiltration with occasional eosinophils ( Figure 7A ) . Older necrotic areas were surrounded by macrophages , epithelioid cells and multinucleate giant cells and lymphocytes . Some migrating larvae were found in the liver parenchyma without inflammatory infiltrate associated to them . In goats immunised with rFhGST-S1 smaller necrotic areas associated to a heavy infiltration of eosinophils ( Figure 7B ) were seen . Unlike the Quil A immunised group , all migrating larvae found were surrounded by a heavy infiltration of eosinophils . A significant increase of IgG anti-rFhGST-S1 was observed two weeks after vaccination with a strong increase after the second injection at week 4 in immunised animals ( Figure 8 ) . The Quil A control group did not show any specific IgG response until 2 weeks after infection . Specific IgG titres increased during infection in both groups , but they were consistently higher in the immunised group throughout the duration of the the experiment . Previous studies have highlighted the importance of parasite GSTs , including Sigma class GSTs , in host-parasite interactions and as potential vaccination candidates . With this in mind , we have studied the relatively newly identified Sigma class GST from F . hepatica to both enhance our understanding of this important enzyme in Fasciola and the Sigma class of GSTs as a whole . Alignments and phylogenetics classified FhGST-S1 alongside trematode and mammalian Sigma class GSTs , yet there remains a distinct divide between the parasites and their hosts , a phenomenon also observed for the recently reclassified ‘Nu’ class of GSTs from nematodes [49] . Therefore , it may be that trematode GSTs are sufficiently distinct to support a sub-classification within the broad Sigma class . The distinction of FhGST-S1 from fasciolosis host Sigma class GSTs enhances its potential as a therapeutic target . Substrate activity profiling of rFhGST-S1 using model substrates showed the enzyme to have comparable activity to other trematode Sigma class GSTs such as Sm28GST [47] . However , rFhGST-S1 exhibits relatively high GSH-conjugating activity towards the potentially natural reactive aldehyde , 4-hydroxy-nonenal ( 4-HNE ) toxin and high GSH-dependent peroxidase activity towards the tested lipid peroxides which includes the endogenous substrate linoleic acid hydroperoxide . 4-HNE is the major aldehydic end-product of lipid peroxidation that is involved in signalling of host immune cells leading to apoptosis of T- and B-cells [50] . Assessing the inhibition of rFhGST-S1 activity with CDNB revealed that both bile acids and the flukicide TCBZ appear to bind to the enzyme . In particular , the interaction of the bile acid cholate with rFhGST-S1 is approximately ten fold higher than GSTs from the sheep intestinal cestode Moniezia expansa [51] . Host bile acids are known as triggers of physiological processes in trematodes including Fasciola sp . [52] , [53] . Therefore , molecular interaction of bile acids with FhGST-S1 warrants further investigation especially , given that FhGST-S1 is localised to near the body surface of the fluke , where it could potentially bind cholate and other free bile acids found in abundance in host bile ( cholate is found at approximately 100 mM in sheep bile ) [54] . The hydroxy-TCBZ SO levels in the bile have been shown to be in excess of 100 µM [55] thus , the IC50 of 57±5 µM for TCBZ SO suggests the abundant FhGST-S1 could be involved in TCBZ response in phase III sequestration based detoxification . This finding warrants further investigation to understand the role of FhGST-S1 in TCBZ action or detoxification . Sigma class GSTs from both parasites and mammals have been known to exhibit prostaglandin synthase activity . To this end , the Sigma GST from F . hepatica shares a high sequence identity with recognised Sigma class GSTs with prostaglandin synthase activity , including rOvGST-1 from the filarial parasite , Onchocerca volvulus . Using a coupled assay with COX-1 we have shown that rFhGST-S1 is capable of synthesizing both PGD2 and PGE2 , with PGD2 being the predominant prostanoid . Parasite-derived eicosanoids , including prostaglandins , are known to be important in the establishment of parasitic infection and the survival and proliferation within the host . Therefore , eicosanoids produced by parasitic helminths may play a role in pathophysiological changes during helminth infections . For example , chronic fasciolosis is associated with fever and changes in liver biochemistry , both of which could be associated with parasite-derived eicosanoids thromboxane B2 ( TXB2 ) , PGI2 , PGE2 and leukotriene B4 ( LTB4 ) , detected in the ES products and homogenates of adult F . hepatica worms [56] . In addition , the migration of host epidermal Langerhans cells , which play a key part in immune defence mechanisms , has been shown to be inhibited by parasite-derived PGD2 in the Schistosoma mansoni-mouse model of human infection , thus allowing schistosomes to manipulate the host immune system [57] . Earlier studies have revealed the presence of eicosanoids produced by S . mansoni cercariae which could also play a role in establishment of infections through loss of the cercarial tail following penetration of the skin [58] . It therefore seems likely that prostaglandins synthesised via FhGST-S1 will have a role in establishing the infection within the host . In general , prostaglandins and eicosanoids have potent biological activities in reproduction . For example in the zebrafish egg , high levels of PGE2 were seen post fertilisation coupled with high PGD2 synthase transcript levels during the early stages of egg development concomitant with an exponential decrease of PGD2 levels over the next 120 h post fertilisation [59] . However , in F . hepatica , eggs in gravid adults are released in an immature state in the bile duct , where they pass to the external environment via the host's excretory system and complete embryogenesis ex-host . Therefore , FhGST-S1 may have a secondary , or indeed primary , function in egg development and embyrogenesis . A role in egg development is further supported by proteomic studies of F . hepatica ontogenic stages which reveal the presence of FhGST-S1 in eggs ( [42] and the current study ) . FhGST-S1 appears to be highly abundant in eggs with western blotting showing FhGST-S1 to be constitutively expressed , despite its association with a large spot consisting of multiple co-migrating proteins unresolved via 2DE ( for association see [42] ) . Immunolocalisation studies revealed that FhGST-S1 is closely associated with vitelline cells of mature adult worms . Given the importance of PGs in reproduction , we hypothesize that PG synthase activity exhibited by rFhGST-S1 contributes to developmental cues during egg formation . Interestingly , no FhGST-S1 was seen in day 0 , un-embryonated , eggs by western blotting yet in situ immunlocalisation showed freshly voided eggs , equivalent to day 0 eggs , to contain copious amounts of FhGST-S1 . While it is most likely that FhGST-S1 is present in day 0 eggs , albeit at a reduced expression , the discrepancy seen between the two techniques is probably related to the antibody dilutions used for each method; in total a 40-fold difference in favour of immunolocalisation . FhGST-S1 was also identified in both NEJs and adult worms using western blotting . This finding emphasises the multi-functionality of FhGST-S1 , where in NEJs egg productions is not yet in process , suggesting its main function is in PG synthesis for host modulation or as a detoxification enzyme . In the adult worm , FhGST-S1 could also be localised , to a smaller extent , in the parenchyma and tegument . Given the high activity of FhGST-S1 towards the toxic 4-HNE and to lipid hydroperoxides this suggests a detoxification role at the host-parasite interface . With near surface expression of FhGST-S1 , in the parenchyma and tegument , there is the potential for this enzyme to be readily released into the host environment . Indeed , we have identified FhGST-S1 in the ES products of adult worms . With this in mind , previous studies have highlighted the importance of parasite Sigma class GSTs in immunomodulation of the host immune response . This includes our recent study implicating rFhGST-S1 in chronic inflammation through the activation of dendritic cells ( DCs ) [48] . While active rFhGST-S1 was able to induce levels of IL-12p40 and IL-6 cytokines in DCs in a dose-dependent manner , the previously described F . hepatica Mu-class GSTs failed to induce any cytokine secretion . Since denatured rFhGST-S1 also failed to induce any cytokines in DCs , activation of DCs is likely related to the structure and activity of the enzyme . However , inhibition of nitric oxide production , involved in driving a Th2 immune response , may also be a contributing factor in skewing the host response to fasciolosis [60] . F . hepatica infections are associated with a T-helper-cell type 2 ( Th2 ) immune response dominating during the chronic phases of infection [61] , but pro-inflammatory responses are suppressed [62] . Suppression of allergic responses during chronic parasitic worm infections has a mutually beneficial effect on the parasites' proliferation and the hosts' survival . Prostanoids , including PGD2 , are important in mediating these allergic inflammatory responses . While generally regarded as pro-inflammatory molecules , these important lipid molecules are also involved in mediating anti-inflammatory responses [63] . Helminth-derived molecules are thought to be involved in driving the Th2 response stereotypical of parasitic worm infections . DC and macrophage cell cultures exposed to rFhGST-S1 showed elevated levels of Th2 cytokines after 24 h [48] . In this study , the effects of rFhGST-S1 exposure onprostanoid synthesis in host immune cells was investigated . The results of which show the stimulation of PGD2 and PGE2 in both DCs and macrophage cell lines suggesting FhGST-S1 is one such helminth derived molecule capable of driving the Th2 response . As we have shown FhGST-S1 to have key roles in F . hepatica , both in NEJs and adult worms , coupled with the near surface expression and release of the enzyme via the ES products , we assessed the potential of FhGST-S1 to be used as a vaccine candidate . This was especially poignant given that the S . mansoni Sigma GST homologue ( Sm28 ) is in phase II clinical trials [12] . Unfortunately , the current goat based vaccine trial did not show any significant differences in fluke burdens between the rFhGST-S1 immunised and Quil A control group . However , a high individual variability was recorded , particularly in the vaccinated group also reported in previous trials using goats vaccinated with alternative candidates such as cathepsin L1 [64] and Sm14 [65] . The vaccine trial shown here using a target species with an acceptable adjuvant may have been adversely affected by the strain of F . hepatica used to challenge goats . Here we have shown an unusually high infectivity rate with the strain of F . hepatica used; which we have reported in a previous trial using goats [64] . Using an alternative strain of F . hepatica for experimental infections in this species has given normal infectivity rates ranging from 14% to 26 . 5% [65] . In the present trial it appeared that goats immunised with rFhGST-S1 , despite no variations in fluke burdens or morphometrics , showed reduced gross hepatic lesions during early infection , up to day 9 post infection , which continued to week 15 post infection where liver scores for hepatic lesions appeared reduced for rFhGST-S1 immunised animals . These results suggest that animals from the immunised group produced an early response to migrating larvae that has induced some partial protection from liver damage . The early and consistent specific IgG response found in the present work also agrees with the results obtained in a previous trial using naïve FhGST [46] . However , in both studies high levels of specific IgG did not induced a protective response reducing worm burdens . A promising aspect of producing anti-helminth vaccines is developing multivalent vaccines . In many cases the greatest protection from challenge is by vaccinating with a combination of Fasciola antigens [66] , [67] . Therefore , based on the immunisation with FhGST-S1 showing an early response reducing hepatica damage , could be considered for inclusion into a multivalent vaccine against Fasciolosis . In addition , in light of our findings showing FhGST-S1 to be highly prominent in egg production and the egg itself , as with previous vaccination trials [67] , it will be important to investigate the ability of eggs voided from vaccinated animals to embryonate . The potential to reduce pasture contamination by inhibiting egg embryonation , combined with the demonstrated reduction in liver damage , warrants further exploration using rFhGST-S1 as a vaccine candidate . In summary , we have further promoted the concept that FhGST-S1 clearly demonstrates key host-parasite roles in synthesising PGs and stimulating PG release from host innate immune cells . In addition we have shown FhGST-S1 to be a key protein for detoxification , which may well be involved in TCBZ response . In line with current vaccine development theory we have shown FhGST-S1 to have multi-functional roles in the liver fluke physiology . Furthermore , we have shown FhGST-S1 to be expressed across ontogenic stages , localised to the fluke surface , and to the egg , both characteristics vital for vaccine development and success . Whilst no protection from fluke burden was seen in trials , the inclusion of rFhGST-S1 as a multivalent vaccine component should be investigated . However , it is important to fully characterise the host immune response during the early stages post-infection to better understand the mechanism mediating an effective host response . This will be essential to improve any future vaccine formulation . [36 , 51 , 68–71] Table 2 Refs .
Combating neglected parasitic diseases is of paramount importance to improve the health of human populations and/or their domestic animals . Uncovering key roles in host-parasite interactions may support the vaccine potential portfolio of a parasite protein . Fasciola hepatica causes global disease in humans and their livestock but no commercial vaccines are available . Members of the Sigma class glutathione transferase ( GST ) family have long been highlighted as vaccine candidates towards parasitic flatworms . To this end , a Sigma class GST is currently undergoing phase II clinical trials to protect against infection from the schistosomes . In this study we characterise the protein from F . hepatica following four work pathways that 1 ) confirm its designation as a Sigma class GST using substrate profiling , 2 ) assess prostaglandin synthase activity and its effect on host immune cells , 3 ) localise the Sigma GST within adult fluke and between ontogenic stages and 4 ) measure its potential as a vaccine candidate . The work presented here shows F . hepatica Sigma class GST to have key host-parasite roles and we suggest , warrants further investigation for inclusion into vaccine formulations .
[ "Abstract", "Introduction", "Materials", "and", "Methods", "Results", "Discussion" ]
[ "medicine", "immunology", "microbiology", "parasitology", "veterinary", "science", "vaccination", "veterinary", "diseases", "veterinary", "parasitology", "cytochemistry", "proteins", "biology", "recombinant", "proteins", "biochemistry", "histochemistry", "clinical", "immunology", "immunity", "vaccine", "development" ]
2012
The Sigma Class Glutathione Transferase from the Liver Fluke Fasciola hepatica
Animals learn to make predictions , such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting . How predictions are realized on the neuronal level and what plasticity rule underlies their learning is not well understood . Here we propose a biologically plausible synaptic plasticity rule to learn predictions on a single neuron level on a timescale of seconds . The learning rule allows a spiking two-compartment neuron to match its current firing rate to its own expected future discounted firing rate . For instance , if an originally neutral event is repeatedly followed by an event that elevates the firing rate of a neuron , the originally neutral event will eventually also elevate the neuron’s firing rate . The plasticity rule is a form of spike timing dependent plasticity in which a presynaptic spike followed by a postsynaptic spike leads to potentiation . Even if the plasticity window has a width of 20 milliseconds , associations on the time scale of seconds can be learned . We illustrate prospective coding with three examples: learning to predict a time varying input , learning to predict the next stimulus in a delayed paired-associate task and learning with a recurrent network to reproduce a temporally compressed version of a sequence . We discuss the potential role of the learning mechanism in classical trace conditioning . In the special case that the signal to be predicted encodes reward , the neuron learns to predict the discounted future reward and learning is closely related to the temporal difference learning algorithm TD ( λ ) . Animals can learn to predict upcoming stimuli . In delayed paired-associate tasks , animals learn to respond to pairs of stimuli ( e . g . images A1-B1 and A2-B2 ) separated by a delay . These tasks can be solved by either keeping a memory of the first stimulus ( A1 or A2 ) during the delay period ( retrospective coding ) or anticipating the second stimulus ( B1 or B2 ) during the delay period ( prospective coding ) . Monkeys seem to use both coding schemes [1] . Recordings in the prefrontal cortex of monkeys performing a delayed paired-associate task revealed single neurons with decreasing firing rate in response to a specific first stimulus ( A1 or A2 ) and other neurons with ramping activity in trials where a specific second stimulus ( B1 or B2 ) is anticipated [1 , 2] . Thus , the firing rate of a neuron may encode not only past and current events , but also prospective events . Learning to anticipate a future stimulus can also be observed in classical trace conditioning , where a conditioned stimulus ( CS , e . g . sound of a bell ) is followed after a delay by an unconditioned stimulus US ( e . g . a sausage ) that causes a response R ( e . g . salivation ) [3 , 4] . After several repetitions of this protocol , the conditioned stimulus CS can elicit response R already before the onset of the unconditioned stimulus US . A common experimental finding in these examples is the slowly ramping neuronal activity prior to the predicted event . In an experiment where mice choose to lick left or right in response to a tactile cue , the neural activity in the anterior lateral motor cortex ramps up in the waiting period before the response [5] . This activity pattern implements prospective coding as it indicates whether the animal will lick left or right . Serotonergic neurons in the dorsal raphe nucleus of mice show an activity ramp in a delay period between a predictive odor cue and the availability of a sucrose reward [6] . In rats that navigate a maze towards the learned position of a chocolate milk reward , the activity of striatal neurons increases while the rat approaches the reward position [7 , 8] . In visual delayed paired associate tasks in which monkeys are trained to select a specific choice object that is associated with a previously shown cue object , increasing activity in the delay period was measured for neurons in the prefrontal cortex [1 , 9 , 10] and in the inferior temporal cortex [2 , 11] . It is unclear how prospective coding emerges . The cue and the associated predictable event are typically separated by an interval of some seconds . On the other hand , synaptic plasticity , that is presumably involved in learning new associations , typically requires presynaptic and postsynaptic activity to coincide in a much shorter interval . Some tens of milliseconds is , for example , the size of the ‘plasticity window’ in spike-timing dependent plasticity; no synaptic change occurs , if presynaptic and postsynaptic spike are separated by more than the size of this plasticity window [12 , 13] . This mismatch between the behavioral and the neuronal timescales begs the question how a neuronal system can learn to make predictions more than a second ahead . There are also plasticity mechanisms that can correlate pre- and postsynaptic spiking events that are separated by seconds [14 , 15] . Yet , assuming many simultaneously active afferents , it remains unclear how the behaviourally relevant pair of pre- and postsynaptic spikes can be selected out of hundreds behaviourally irrelevant pairs . In normative models of synaptic plasticity , the shape of the causal part of the plasticity window matches the shape of the postsynaptic potential ( PSP ) , if the objective is to reproduce precise spike timings [16–18] . However , if the objective is to reproduce future activity , this specific learning rule is insufficient . Yet , as we demonstrate in this article , the same plasticity rule with only a slightly wider window also allows for learning a prospective code . With this mechanism , it is possible to learn an activity ramp towards a specific event in time , or to learn predicting a time-varying signal or a sequence of activities well ahead in time . In a 2-compartment neuron model , this mechanism leads to the dendritic prediction of future somatic spiking . The mechanism stands in contrast to the work of Urbanczik & Senn , where the current somatic spiking is predicted [18] . Despite this fundamental difference , the plasticity rules only differ in the width of the potentiation part of the plasticity window . Before defining the learning rule in detail , we provide an intuitive description . In a neuron with both static synapses ( green connection in Fig 1A and 1B ) and plastic synapses ( blue in Fig 1A and 1B ) , we propose a learning mechanism for the plastic synapses that relies on two basic ingredients: spike-timing dependent synaptic potentiation and balancing synaptic depression . The synaptic connections are strengthened if a presynaptic spike is followed by a postsynaptic spike within a ‘plasticity window of potentiation’ ( red in Fig 1A and 1B ) . The size of this plasticity window turns out to have a strong influence on the timing of spikes that are caused by strengthened dendritic synapses . If the plasticity window has the same shape as a postsynaptic potential ( PSP ) , learned spikes are fired at roughly the same time as target spikes [16–18] . But if the plasticity window is slightly longer than the postsynaptic potential , learned spikes tend to be fired earlier than target spikes . More precisely , because of the slightly wider plasticity window of potentiation , presynaptic spikes may elicit postsynaptic spikes through newly strengthened connections ( thick blue arrow in Fig 1B ) even before the onset of the input through static synapses . These earlier postsynaptic spikes allow to strengthen the input of presynaptic neurons that spike even earlier . We refer to this as the bootstrapping effect of predicting the own predictions . As a result , a postsynaptic activity induced by the input through static synapses will be preceded by an activity ramp produced by appropriately tuned dendritic input . The neuron learns a prospective code that predicts an upcoming event . We consider a 2-compartment neuron model that captures important functional details of spiking neurons and is well suited for analytical analysis [18] . In this model ( Fig 1C ) , a dendritic compartment receives input through plastic synapses with strength w . The voltage U of the somatic compartment is coupled to the dendritic voltage Vw and receives additional input I through static synapses , C U ˙ = - g L U + g D ( V w - U ) + I , ( 1 ) where gL is the leak conductance , gD is the coupling conductance between soma and dendrite and C , the somatic capacitance . The dendritic potential Vw is given by a weighted sum of presynaptic inputs , i . e . V w ( t ) = ∑ i w i PSP i ( t ) = ∑ i w i ∑ t f ∈ T i κ t - t f ( 2 ) with plastic synaptic weights wi , postsynaptic potentials PSPi that model the depolarization of the postsynaptic membrane potential due to the arrival of a presynaptic spikes at synapse i , set T i of spike arrival times at synapse i and spike response kernel κ . Spiking of the postsynaptic neuron is modeled as an inhomogeneous Poisson process with rate φ ( U ) . We model the input with time varying excitatory and inhibitory conductances gE and gI proximal to the soma such that I ( t ) = g E ( t ) ( E E - U ) + g I ( t ) ( E I - U ) ( 3 ) as proposed by Urbanczik & Senn [18] . For large total conductance and slowly varying input , the somatic membrane potential U ( t ) is well approximated ( see Methods ) by its steady state solution U ( t ) ≈ λ ( t ) V w * ( t ) + U * ( t ) , ( 4 ) where we introduced the attenuated dendritic potential V w * ( t ) = g D g L + g D V w ( t ) , ( 5 ) the attenuated somatic input U * ( t ) = g E ( t ) E E + g I ( t ) E I g tot ( t ) ( 6 ) and the ‘nudging’ factor λ ( t ) = g L + g D g tot ( t ) , ( 7 ) with gtot ( t ) = gL + gD + gE ( t ) + gI ( t ) , to be in accordance with Urbanczik & Senn [18] . The nudging factor λ ( t ) ∈ ( 0 , 1] is close to 1 for small somatic input and equal to 1 if gE ( t ) + gI ( t ) = 0 . The plasticity rule we consider for the dendritic synapses can be seen as differential Hebbian in the sense that both the potentiation and depression term are a product of a post- and presynaptic term . The strength of synapse i is assumed to change continuously according to the dynamics w ˙ i = η α φ U PSP ˜ i - φ V w * PSP i , ( 8 ) where PSP ˜ i ( t ) = 1 τ ∫ 0 ∞ d s e - s τ PSP i ( t - s ) ( 9 ) is the low-pass filtered postsynaptic potential at synapse i , φ ( U ) and φ ( V w * ) are the instantaneous firing rates based on the somatic potential and the attenuated dendritic potential , respectively , and η is the learning rate . The factor of potentiation α that scales the potentiation term is positive but smaller than the inverse of the largest nudging factor 1/maxt λ ( t ) to prevent the unbounded growth of synaptic strengths . Under the assumption of a periodic environment , rich dendritic input dynamics , constant nudging factor λ and linear φ ( Methods ) , the weight dynamics in Eq 8 leads to prospective coding by making the dendritic rate φ ( V w * ( t ) ) approach the expected future discounted somatic input rate , i . e . φ ( V w * ( t ) ) = α τ ∫ 0 ∞ d s e - s τ eff φ U * ( t + s ) , ( 10 ) where the effective discount time constant τeff is given by τ eff = τ 1 - λ α . ( 11 ) Depending on the factor of potentiation α and the nudging factor λ , the effective time constant τeff can be much larger than the biophysical time constant τ of low-pass filtering and match behavioral timescales of seconds . In particular , if the somatic input is strong and hence λ close to 0 ( close to ‘clamping’ ) , the effective discount time constant is short , τeff ≈ τ . But when nudging is weak ( λ close to 1 ) , the synapses on the dendrite learn to predict their self-generated somatic firing rate and the effective discount time constant is extended up to τ eff ≈ τ 1 - α . The case of weak nudging is also the case when the neuron’s somatic firing rate is roughly determined by the dendritic input , φ ( U ( t ) ) ≈ φ ( V w * ( t ) ) , see Eq 4 . In particular , if after learning the somatic input is transiently silenced , the neuron’s firing rate φ ( U ( t ) ) , according to Eq 10 , represents the discounted future rate of the somatic input U* ( t ) applied during the previous learning period , even if this was only slightly nudging the somatic potential U ( t ) itself . Periodic inputs are unrealistic in a natural setting . But a similar result holds also in more general settings , where a neuron is occasionally exposed to correlated dendritic and somatic inputs . In this more general stochastic setting we derive the main result under the assumption that dendritic and somatic inputs depend on the state of a stationary latent Markov chain X0 , X1 , … . The dependence on a stationary latent Markov chain assures that the neuron is occasionally exposed to correlated dendritic and somatic inputs . The main result in this setting is ( cf . Eq 48 ) φ ( V w * ( x ) ) = α 1 - λ α ∑ k = 0 ∞ γ eff k E φ U * ( X k ) | X 0 = x , ( 12 ) where γ eff = e - δ τ eff is a large discount factor that leads to a similar discount behavior as in the time-continuous case , if t = kδ . It is important to note that in the stochastic case the dendritic rate is only informative about expected future somatic inputs . Metaphorically speaking , a neuron can learn to predict the expected win in a lottery , but obviously it cannot learn to predict single lottery draws . In the limit , τ → 0 we find that PSP ˜ = PSP and with α = 1 we recover the learning rule of Urbanczik & Senn [18] . This rule adapts the dendritic synapses such that the dendritic input matches the somatic input Fig 2B . On the other hand , the learning rule with a slightly larger potentiation window leads to dendritic input that ramps up long before the onset of somatic input Fig 2C . By looking at Eqs 4 and 8 we can now obtain a better intuition for the bootstrapping effect of predicting the own predictions . If at the beginning of learning all synaptic weights wi are zero , the dendritic potential Vw is at rest ( = 0 ) all the time and the somatic membrane potential U ( t ) follows the somatic input U* ( t ) ( see Eq 4 ) . In this case , the learning rule in Eq 8 contains only the potentiation term w ˙ = η α φ ( U * ) PSP ˜ i . ( 13 ) In the example in Fig 2C , the somatic input U* and consequently φ ( U* ) is non-zero only after 1800 ms . Therefore , synapse i is potentiated only if a presynaptic spike arrives shortly before the onset of the somatic input . The next time a presynaptic spike arrives at synapse i , the somatic membrane potential is depolarized by the dendritic input already before the onset of the somatic input and the learning rule contains at this moment ( e . g . at 1780 ms in Fig 2C ) the terms w ˙ = η α φ ( λ V w * ) PSP ˜ i - φ ( V w * ) PSP i . ( 14 ) These terms would cancel each other in the case of Urbanczik & Senn [18] where α = λ = 1 and PSP ˜ i = PSP i . But if PSP ˜ i is the low-pass filtered version of the postsynaptic potential ( as in Fig 2C ) they do not cancel . Instead , synapses are potentiated , if a presynaptic spike arrives shortly before the somatic potential was depolarized due to dendritic input through already potentiated synapses . The consequence of this bootstrapping effect appears in Fig 2C in the gray curves . After 100 training sessions , the dendritic input starts to rise around 1200 ms , but synapses with earlier presynaptic spikes are not yet strengthened . With each further training session the dendritic input rises earlier . The dendritic and the somatic inputs are deterministic periodic functions , in the example in Fig 2C . Therefore we can directly compare the simulation to the theoretical results of the previous section . For the interval without somatic input ( 0–1800 ms ) , where φ ( U ) = φ ( V w * ) , we find a good agreement ( dashed red and thick black line in Fig 2C ) . Small differences are to be expected , because in the theoretical derivations a constant nudging factor λ is assumed and the steady-state solution of the somatic membrane potential dynamics is used ( see Eq 4 ) . The dendritic rate φ ( V w * ) is only slightly below the somatic rate φ ( U ) in the interval with somatic input ( 1800–2000 ms ) , because the somatic input is small . The input pattern in Fig 2A is a particularly simple example of a deterministic , periodic pattern with rich enough structure . Enough structure to learn a prospective code exists also in sufficiently many randomly generated ( frozen ) spike trains that are deterministically repeated , if there is always at least one presynaptic spike within the duration of a PSP and the probability of repeating a nearly identical presynaptic spike pattern is low ( see Fig 3A ) . We did not systematically search for the minimal number of required dendritic synapses . But for the example in Fig 3A we found empirically that a few hundred synapses are necessary . If the presynaptic firing frequency is only 2 Hz , we found that 1000 presynaptic neurons are enough to learn the ramp in 100 trials , whenever the learning rate is larger than in the 20 Hz case . At the end of learning , the time course of the somatic potential matches the one of the previous example ( black lines in Figs 2C and 3A ) . But during learning , the time course of the somatic potential is different in the two examples ( gray lines in Figs 2C and 3A ) . This is a consequence of the influence of correlations in the dendritic input . For the frozen spike trains , the presynaptic auto-correlation E [ PSP i ( t ) PSP i ( t + s ) ] ≠ 0 is non-vanishing for all s and i . This causes the average firing rate to increase early during learning ( Fig 3A; gray lines in interval 0–1500 ms in contrast to gray lines in the same interval in Fig 2C ) . In the examples given so far , the dendritic and the somatic inputs are deterministic , but deterministic repetitions of the exact same spike trains are unrealistic . In Fig 3B we consider the more realistic case of random spiking . In each trial , the spikes are sampled from an inhomogeneous Poisson process , with periodically repeating rates . The resulting activity ramp is noisier but in good agreement with the theoretical result . It is important that the rates of the Poisson process have sufficiently rich structure . In Fig 3D the firing rate of the Poisson process is kept constant for one third of the trial . In this case , the temporal structure is not sufficiently rich to learn a smooth ramp and a stepwise activity ramp is learned instead . In Fig 3C , the target event occurs only with a 50% chance , i . e . the somatic input is given only in half the trials . This results in an activity ramp with smaller amplitude , which is consistent with the theoretical finding that the dendritic rate depends linearly on the average somatic input rate ( see Eq 12 ) . Prospective coding in neurons of the prefrontal cortex was observed in an experiment with monkeys performing a delayed paired-associate task [1] . In this experiment , monkeys learned to associate a visual sample to a visual target presented one second later . Our learning rule allows for learning a prospective code in such a task . During training , sample A1 is always followed by target B1 after a delay of 1s , and sample A2 is followed by target B2 ( Fig 4A ) . In the simulation we assume that the sample ( first stimulus ) leaves a memory trace in form of a spatio-temporal activity pattern that projects through dendritic synapses , while the target ( second stimulus ) drives somatic synapses ( Fig 4B ) . In order to have sufficiently rich presynaptic activity ( c . f . Fig 3B ) , the memory trace of the sample is modeled by an inhomogeneous Poisson process with sample dependent rate trajectories ( Fig 4C ) , i . e . during the presentation of the first stimulus the rate trajectory of each neuron approaches a previously chosen template trajectory that depends on the sample ( see Methods ) . These memory traces are inspired by liquid state machines ( see Discussion ) . If a neuron receives strong somatic input only in the presence of a specific target ( neurons 1 and 2 in Fig 4B ) , its firing rate ramps up exclusively in anticipation of this target ( neurons 1 and 2 in Fig 4D ) . In contrast to such a ‘grandmother-cell coding’ ( one neuron for one target ) , a set of neurons could encode the target in a distributed manner , where the target is identified by the overall activity pattern and single neurons respond differently to different target stimuli . Such a distributed code can be learned with neurons that receive somatic input of target-specific strengths ( neuron 3 in Fig 4B; B1 stronger than B2 ) . After learning , the amplitude of the activity ramp reflects this target specificity ( neuron 3 in Fig 4D ) . In Figs 2 to 4 the somatic target input was silent most of the time and active only during a short interval . This simple time course of the somatic input is , however , not a requirement and learning also converges for more complex trajectories of somatic input . In general , a time varying input through ( static ) somatic synapses induces plasticity that advances the postsynaptic firing rate φ ( U ( t ) ) relative to the firing rate φ ( U* ( t ) ) determined by the somatic input alone . Fig 5A shows an example with an advancement of roughly 50ms that has been achieved with a shorter time window ( ∼20 ms ) for synaptic potentiation . As in Fig 3A , the dendritic input was a periodically repeated random spike train that could also be replaced by stochastic spiking with time dependent firing rates as in Fig 3B . Since the learning rule converges to a point where the dendritic input is proportional to the future discounted somatic input ( Eq 10 ) , the advanced sequence ( black in Fig 5A ) is not simply a forward shifted version of the somatic input ( green in Fig 5A ) . This becomes clearly apparent at the center of the figure , where the somatic input is symmetric around 1000 ms , but the advanced sequence is decaying , because the somatic input has a strong dip around 1100 ms . Despite this , the advancement can be characterized by the peak time of the correlation function between φ ( U ( t ) ) and φ ( U* ( t ) ) that , as the effective discount time constant τeff , diverges with increasing potentiation factor α ( Fig 5B–5D ) . Time series prediction is a fundamental operation of the brain that is , for instance , involved in motor planning . In our context , the activity time course that has to be reproduced may be provided by proprioceptive feedback from muscles as somatic input U* to neurons in the primary motor cortex [19] . This feedback can be weak , delayed and sparse . The dendritic input V* , in turn , may be conveyed by a higher visual area or a premotor planning area . This dendritic input learns to predict the discounted future firing rate caused by the somatic input , and hence learns to produce the muscle activity that feeds back again as a delayed proprioceptive signal . Lastly , we consider a recurrently connected network of 200 neurons that receive external input only at the soma and no external input at the dendrites . The input at the dendrites is given by the output spikes of the network neurons , where we consider all-to-all connectivity ( Fig 6A ) . In contrast to the examples in Figs 2 to 5 , there is no external control to assure the richness of the dendritic input and there are no guarantees that learning converges in the sense of Eq 10 . Still , we observe the interesting result that learning changes synaptic strengths to allow fast replay of slow experienced sequences . For sequentially and periodically repeated stimulations on a slow timescale ( green shading in Fig 6 ) , the recurrent dendritic connections between subsequently stimulated groups of neurons are strengthened . After 300 repetitions of the same sequence , a brief initial stimulation is sufficient to evoke an activity sequence that has the same ordering as the original sequence ( Fig 6B after 2400 ms ) . However , the replay dynamics can be much faster than the dynamics of the stimulation . Replay depends on the internal dynamics of the network , notably the time constants of the PSP and the membrane time constant . Due to prospective coding , the sequence becomes advanced in time while repeatedly presenting the stimuli , and due to the recurrent connectivity the advanced sequence can be recalled with a brief stimulation of the first group of neurons ( Fig 6B ) . Note that there is no need to explicitly distinguish between a training and recall session . Recall differs from training only in the somatic input , which consists of a brief activation of the first group of neurons during recall and slow , sequential activation during training . The learning rule is active all the time . The proposed learning mechanism of prospective coding is related to a well studied version of temporal difference ( TD ) learning . Using our notation for a stochastic and time discrete setting , the goal in TD learning is to estimate a value function V ( x ) = α 1 - α λ ∑ t = 0 ∞ γ TD t E φ U * ( X t ) | X 0 = x , ( 15 ) where 0 < γTD < 1 is a discount factor and the expectation is taken over the Markov chain X0 , X1 , … . We assume that this value function can be approximated by a linear function of the form V ^ ( x ) = φ V w * ( x ) , ( 16 ) where φ is linear . In TD ( λ ) with linear function approximation , the weights w evolve according to the learning rule [20–22] Δ w t , i = w t + 1 , i - w t , i = η δ t PSP ^ t , i , ( 17 ) with learning rate η , eligibility trace PSP ^ t , i = ∑ s = 0 ∞ ( λ TD γ TD ) s PSP i ( X t - s ) , 0 ≤ λTD ≤ 1 , and delta error δ t = α 1 - α λ φ U * ( X t ) + γ TD φ V w t * ( X t + 1 ) - φ V w t * ( X t ) . ( 18 ) This delta error is zero on average if the approximation φ ( V w t * ( x ) ) is equal to the value function V ( x ) in Eq 15 . Furthermore , φ ( V w t * ( x ) ) converges to V ( x ) under the learning rule of TD ( λ ) in Eq 17 [20] . The discrete time version of our learning rule ( Eq 42 ) , implemented in the 2-compartment model , converges to Eq 12 which is identical to the value function in Eq 15 if γTD = γeff . Therefore , this form of TD ( λ ) and our learning mechanism converge to the same value . It is also interesting to see that both methods use an eligibility trace PSP ^ and PSP ˜ that are the same if λTD γTD = γ , i . e . λTD = γ/γeff . But despite the convergence to the same point and the use of the same eligibility trace , learning moves in general along different trajectories under this form of TD ( λ ) and the learning mechanism we propose . So far we compared the learning mechanism of prospective coding to the plain TD ( λ ) that has access to the PSP and U* . If only access to U = λV*+U* is available , it is also possible to combine TD ( λ ) with the bootstrapping effect of predicting the own predictions by implementing a variant of TD ( λ ) in the dendritic compartment of the 2-compartment model . If the delta error is defined as δ t = α φ U ( X t ) + γ φ V w t * ( X t + 1 ) - φ V w t * ( X t ) , ( 19 ) one can show that the learning rule in Eq 8 is almost identical to the TD learning rule in Eq 17 with λTD = 1 ( Methods ) . In this case , the weights during learning move along similar trajectories , irrespective of whether this form of TD ( 1 ) or our learning rule is used . If this form of TD ( 1 ) were not implemented in the 2-compartment model , i . e . if the first term in the delta error in Eq 19 would be replaced by φ ( U* ( Xt ) ) , the time constant of future discounting would be γ instead of γeff . But since the first term in the delta error in Eq 19 depends on the full somatic potential U = λV* + U* the bootstrapping effect of predicting the own predictions applies and the large time constant γeff arises . As a simple and biologically plausible explanation for how animals can learn to predict future events , we have proposed a local plasticity mechanism that leads to prospective coding in spiking neurons , i . e . the plastic synapses change such that the neuron’s current firing rate depends on its expected , future discounted firing rate . Our model proposes a partial solution to the problem of learning associations on a behavioral timescale without using slow intrinsic processes . Even with a plasticity window that is only slightly larger than the duration of a postsynaptic potential , the effective time constant of discounting the expected future firing rate can be on the order of seconds , thanks to the bootstrapping effect of predicting the own predictions . This effect arises because already predictive inputs influence the activity of a neuron . This is captured by the 2-compartment model of Urbanczik & Senn [18] , where the output depends on both the dendritic ( student ) and the somatic ( target ) input . For clarity , we presented the model with target input through static ( i . e . non-plastic ) somatic synapses and in the examples of ramping activity in Figs 2 and 3 the somatic input was non-zero only during a short period . This simple form of the target input is not a requirement . First , the learning mechanism also applies to arbitrary time courses of the somatic input , as we show in the example of time series prediction in Fig 5 , where an advanced and smoothed version of a complex somatic input is learned . Second , the somatic synapses do not need to be static . Yet , they should change slower than the dendritic synapses in order to get a separation of plasticity timescales . And third , the target input could also arrive at another dendritic branch instead of the soma ( see generality of the results in Methods ) . We focused solely on learning temporal associations and neglected important aspects of learning in animals . However , the proposed learning mechanism can easily be extended to include , for example , a weighting based on behavioral relevance . In the delayed paired-associate task , our model learns the associations between sample and target irrespective of the behavioral relevance of this association . In animal training , however , reward or punishment is crucial; for example the monkeys in the study of Rainer et al . [1] received juice rewards . The learning rate in our learning mechanism is a free parameter that could incorporate a weighting by behavioral relevance . Biophysically , a neuromodulator like dopamine could implement this modulation of the learning rate . It is also possible to postpone the weight update in Eq 1 and use reward modulated eligibility traces instead ( see e . g . [23–25] for theory and [15 , 26] for experiments ) . The proposed learning mechanism could also be involved in classical trace conditioning , where the first stimulus ( CS ) is separated from the second stimulus ( US ) and the response ( R ) by a delay period , similar to the situation in the delayed paired-associate task . Let us assume that neuron 1 in Fig 4 is involved in initiating response R ( e . g . salivation ) . If the unconditioned stimulus causes somatic input to this neuron and a memory trace of the conditioned stimulus arrives at the dendritic synapses , our learning mechanism would lead to ramping activity and salivation prior to the onset of the unconditioned stimulus that originally triggered the salivation . To our knowledge , there is no conclusive experimental data to support or discard the hypothesis that prospective coding is involved in classical trace conditioning . In the cited studies on ramping activity [1 , 2 , 6–11] , the animals were actively engaged in a task ( operant conditioning ) . It is unlikely , however , that the ramping activity is merely a side-effect of movement preparation , since Rainer et al . [1] found it to be stimulus-specific but not action-specific . In our model of delayed paired-associate tasks , activity ramps rely on temporally structured input from short-term memory neurons . The usage of these short-term memory neurons is motivated by the observation that hippocampal activity is needed to overcome the temporal discontiguity in trace conditioning [4 , 27] . We modeled the dynamics of the recurrent short-term memory network with a stochastic process . The parameter choice of this stochastic process is inspired by the widespread experimental observation that stimulus onset quenches the neural variability [28 , 29] . It should also be possible to model the memory traces with “dynamical attractors” in recurrent networks of rate neurons [30] or with long and stable transient dynamics in balanced networks [31] . Since these memory traces are not the main focus of this study we generated them in a simpler way with the stochastic process , which still feels more natural than the delay-line like traces used in a study on trace conditioning [32] . In recurrent neural networks the learning rule of prospective coding allows fast replay of slow input sequences ( Fig 6 ) . Fast replay could be valuable for planning , where it is important to quickly assess the likely successors of a given state . The same fast replay of a previously induced slower activity sequence was also observed in the rat primary visual cortex [33] and it is as well studied as compressed hippocampal replay of a spatial sequence [34] . In rats these replay events can be observed minutes or hours after the spatial experience . In contrast , the simple form of the plasticity rule in Eq 8 does not have any consolidation properties and ongoing pre- and postsynaptic activity would quickly change the learned weight patterns and thus overwrite the memories . It is , however , straightforward to extend the plasticity model by a consolidation mechanism . In the three state consolidation model of Ziegler et al . [35 , 36] , early long-term potentiation ( LTP ) is induced by a triplet rule [37] . Replacing the triplet rule by the plasticity rule in Eq 8 would endow the learning rule of prospective coding with a consolidation mechanism . Such a consolidation mechanism would allow to replay sequences a long time after the training session . Aiming at a better understanding of biological implementations of prediction learning , our model allows to speculate about physiological realizations of the model variables . Similar to previously proposed plasticity rules [16 , 18] , our learning mechanism depends on the postsynaptic firing rate φ ( U ) , a function of the dendritic potential φ ( V w * ) , the postsynaptic potential PSP and , as a new ingredient compared to previous propositions [16 , 18]: a low-pass filtered version of the postsynaptic potential PSP ˜ . A plasticity window that is slightly larger than the duration of a postsynaptic potential is in agreement with experimentally measured plasticity window sizes [13 , 38] . In particular , an increased level of dopamine was observed to expand the effective time window of potentiation to at least ∼45 ms [38] . Importantly , even with a plasticity window on this timescale , predictions can be learned on a timescale of seconds due to the bootstrapping effect of predicting the own predictions . We have shown that the proposed learning mechanism is closely related to temporal difference learning with eligibility traces TD ( λ ) . As discussed in the previous paragraph , a local biological implementation of our learning rule seems straightforward . In contrast , it seems more challenging to locally implement the delta error of TD learning . Potjans et al . and Kolodziejski et al . propose a local implementation that depends either on differential Hebbian plasticity [39] or on two postsynaptic activity traces with different time constants to approximate the difference in the delta error [40] . Both methods require a gating mechanism that allows plasticity only shortly after the onset of a new state and they require transition intervals between states of fixed duration . Furthermore , “state neurons” are only highly active when the agent is in a certain state , which requires the segmentation of the sensory input stream into discrete states . The learning rule we propose does not require these strong assumptions . Frémaux et al . [41] speculate about a non-local implementation of TD learning with spiking neurons , where the TD error is represented by the firing rate of dopaminergic neurons that receive input from three groups of neurons that encode reward , value function and derivative of the value function . In the simulations , however , Frémaux et al . did not use the proposed network implementation of the TD error and they mention that it remains to be seen whether such a circuit can effectively be used to compute a useful TD error . A non-local implementation of the TD error appears compelling in a actor-critic setting , since the actor and the critic can be learned with the same TD signal . However , if the task is to predict more than a scalar quantity like reward , it seems inefficient to use a non-local implementation of the TD error for each quantity to be predicted . Already in our simple example of prospective coding in a recurrent neural network , four TD error networks would be needed in such a non-local implementation . Generally , associating temporally separated events requires some memory of the first event until the second event is present . Possible neural implementations of this memory rely on long spiking activity traces or on long synaptic eligibility traces . Our model of the delayed paired-associate task relies on long spiking activity traces . The short-term memory network can be seen as a liquid state machine [42] or echo state machine [43] and the ramping activity is learned as readout from this activity traces . Alternatively , the activity trace could be represented by slowly , exponentially decaying spiking activity after strong stimulation of a cell [44] . This proposition , however , fails to explain the experimentally observed activity ramps prior to predictable events [1 , 2 , 6–11] The origin of the ramping activity observed in experiments is not yet fully understood . An alternative to our proposition can be found in recurrent neural network dynamics , where slowly ramping or decaying activity arises with appropriately tuned synaptic weights [2 , 25] . In a reinforcement learning setting the time constant of the ramp can be learned by adjusting the recurrent weights with reward modulated Hebbian plasticity [45] . Data analysis of recordings in the macaque lateral intraparietal area revealed yet another candidate explanation: single neuron activity profiles could follow a step-like time course , while the averaged activity is a ramp , if the steps occur at different points in time [46] . Despite the formal link of our prospective coding algorithm to TD learning , the learning we consider is purely supervised on the level of the neuron . Yet , the same learning rule can also be used to explain conditioning experiments . Instead of the multiplicative modulation by a global reward signal , the reward signal could directly nudge the somatic compartment of the neurons and act as a teaching signal . But the learning rule would also allow for combining the somatic nudging signal with an additional modulatory factor , and nudging and modulatory signals could even be sparse and interleaved . For instance , the rule may explain the simultaneous shaping of predictive motor circuitries by sensory feedback and reward [5] . Fluctuating somatic inputs may cause behavioral variations and feedback signals may gate dendritic plasticity such that only rewarded fluctuations act as a target signal for prospective coding . It is also possible to adapt the somatic input connections directly with reinforcement learning , and a ramping activity could arise from learning a prospective code with stimulus-dependent dendritic input . Since reward is an intrinsic component in animal training , we acquired an advanced knowledge about the neuronal bases of reward prediction . But predictions are not restricted to reward , and predicting the identity of stimuli yields more versatile information . We speculate that prospective coding is more abundant than previously thought and , as we showed , it could easily be implemented on the level of an individual neuron . This view is also consistent with the recently observed future-predicting encoding in the retina [47] . To this end , a potentiation window slightly larger than a PSP , together with the bootstrapping effect of predicting the own predictions , is a parsimonious mechanism for learning prospective codes by neurons . A characterisitics of these neurons is that their current firing rate matches their own expected future discounted rate . The spike response kernel κ in Eq 2 is given by κ ( t ) = c H ( t ) ( e - t / τ m - e - t / τ s ) , ( 20 ) with Heaviside function H ( t ) = 0 if t < 0 and H ( t ) = 1 otherwise , τm = 10 ms , τs = 10/3 ms and c - 1 = ∫ - ∞ ∞ d t H ( t ) ( e - t / τ m - e - t / τ s ) . We set the somatic capacitance C = 1 nF , the leak conductance gL = 100 nS , the coupling conductance gD = 1 . 8 μS , and the excitatory and inhibitory reversal potential EE = 14/3 and EI = -1/3 , respectively . The description of the excitatory conductance gE ( t ) is given in the figure captions . The inhibitory nudging conductance gI ( t ) was equal to 0 except for simulations with PSP ˜ = PSP in Fig 2 , where gI ( t ) = 4gE ( t ) . The resting potential is 0 for both , the dendritic potential Vw and the somatic potential U . If one takes our unitless resting potential of 0 to correspond to -70 mV , and a potential of 1 to correspond to -55 mV , the above choices for EE and EI correspond to reversal potentials of 0 mV ( excitation ) and -75 mV ( inhibition ) . The instantaneous firing rate of the neuron is assumed to depend on the somatic membrane potential through function φ ( U ) , which in the simulations has the form φ ( U ) =φ max if U > 1 0 if U < 0 φ max · U otherwise , ( 21 ) with φmax = 0 . 06 kHz . In simulations with spiking , the firing rate multiplied by the simulation time step serves as instantaneous rate of an inhomogeneous Bernoulli process . For slowly enough changing Itot ( t ) and gtot ( t ) , U ( t ) is well approximated by Itot ( t ) /gtot ( t ) . To see this , we use the ansatz U ( t ) = Itot ( t ) /gtot ( t ) + ϵ ( t ) in Eq 1 and find ϵ ˙ = - g tot C ϵ - I ˙ tot g tot + g ˙ tot I tot g tot 2 , ( 22 ) which leads to the conclusion that the error ϵ is small if | I ˙ tot g tot + g ˙ tot I tot g tot 2 | ≪ 1 during at least an interval of approximate duration C g tot . Under these assumptions we write U ( t ) ≈ I tot ( t ) g tot ( t ) = λ ( t ) V w * ( t ) + U * ( t ) , ( 23 ) where we introduce the ‘nudging’ factor λ ( t ) = g L + g D g tot ( t ) , the attenuated dendritic potential V w * ( t ) = g D g L + g D V w ( t ) , and the attenuated somatic input U * ( t ) = g E ( t ) E E + g I ( t ) E I g tot ( t ) . Our main results are robust to variations of the model . For example , the target input I could be given by the input through static synapses on another dendritic branch instead of synapses at the soma , i . e . I ( t ) = gD ( Vs ( t ) − U ) . In this case , the nudging factor becomes λ = g L + g D g L + 2 g D and is constant in time . Modifying the depression term of the learning rule has an effect on the effective time scale τeff , but large effective time constants are achievable in any case . If the depression term in Eq 8 would be replaced by - φ ( λ V w * ) PSP i , the effective time constant would be τeff ≈ τ/ ( 1 − α ) , i . e . τeff would be independent of λ but still diverge when α → 1 . Similarly , for a depression term given by −φ ( Vw ) PSPi , the effective time constant would be τeff ≈ τ/ ( 1 − λ2 α ) , with λ2 = gD/gtot . In the current writing of the learning rule , Eq 8 , the postsynaptic term arises as instantaneous firing rate φ ( U ) . But this rate could also be replaced by a postsynaptic sample spike train S ( t ) that averages out to this same rate , 〈S ( t ) 〉 = φ ( U ( t ) ) . Since learning becomes slower by this sampling , we run our simulations in the form of Eq 8 . For each STM neuron i we first choose template rate trajectories r i 1 ( t ) for stimulus A1 and r i 2 ( t ) for stimulus A2 by sampling from a mean-zero Ornstein-Uhlenbeck process d r i s ( t ) = - θ 1 r i s ( t ) d t + σ 1 d W ( t ) , ( 24 ) where W is a Wiener process , 1/θ1 = 1000 ms , σ1 = 1 and s ∈ {1 , 2} . Actual rate trajectories ri ( t ) were sampled from a process with trial dependent mean and time dependent variance , i . e . d r i ( t ) = - θ 2 ( r i ( t ) - μ s ( t ) ) d t + σ ( t ) d W ( t ) , ( 25 ) where 1/θ2 = 100 ms , μ s ( t ) = ( 1 - σ ( t ) ) r i s ( t ) - σ ( t ) / 2 ( 26 ) and σ2 ( t ) = 1 if t < 0 s or t > 3 s , σ2 ( t ) = 0 . 1 otherwise . This assures that in each trial the rate trajectories approach the template trajectories during the presentation of the sample . In between trials , the rate trajectories are independent of the template trajectories . Spike times are determined by sampling from an inhomogeneous Bernoulli process with rate φ ( ri ( t ) ) Δt , where Δt is the simulation time step . The differential equations were integrated with the Euler forward method with step size 0 . 1 ms . We choose the learning rate η = 0 . 5 in all simulations except for the simulation in Fig 2B and 2C where η = 50 , since the presynaptic firing rate is low . All simulations are written in C . The plots are generated with Mathematica . The source code is publicly available at https://github . com/jbrea/prospectiveCoding . We assume a stationary environment and rich dendritic input dynamics , such that the dendritic inputs can potentially be predictive of the somatic input . There are different ways to model stationarity of the environment . One way is to restrict the inputs to depend on a stationary latent Markov chain . We consider this case in detail in the next section . Here , to present the main ideas in a mathematically simple form , we look at the artificial case , where stationarity enters through deterministic and periodic functions PSPi ( t ) and U* ( t ) with period T . Under this assumption , learning is at a stationary point when the changes of the weights in Eq 8 integrated over one period vanish , i . e . 0 = ∫ 0 T d t w ˙ i ( t ) = η ∫ 0 T d t α φ U ( t ) PSP ˜ i ( t ) - φ V w * ( t ) PSP i ( t ) . ( 27 ) Using the definition of PSP ˜ in Eq 9 we find 0 = ∫ 0 T d t α φ U ( t ) 1 τ ∫ 0 ∞ d s e - s τ PSP i ( t - s ) - φ V w * ( t ) PSP i ( t ) ( 28 ) = ∫ 0 T d t α τ ∫ 0 ∞ d s e - s τ φ U ( t + s ) - φ V w * ( t ) PSP i ( t ) , ( 29 ) where Eq 29 is obtained by changing the order of integration , changing the integration variable t to t + s and using ∫ - s T - s d t f ( t ) = ∫ 0 T d t f ( t ) , which holds for any T-periodic function f ( t ) . The puzzling transition from an integral that depends on the past values of PSPi in Eq 28 to an integral that depends on the future values of U in Eq 29 is a result of the assumed stationarity of the environment , which here is expressed in the periodicity of the functions PSPi ( t ) and U* ( t ) . Eq 29 holds for all synapses i , if φ V w * ( t ) = α τ ∫ 0 ∞ d s e - s τ φ U ( t + s ) . ( 30 ) Strictly , Eq 30 follows from Eq 29 only if the inputs PSPi ( t ) span the space of square integrable , T-periodic functions . In actual implementations the number of synapses is limited , but we find empirically that Eq 30 holds approximately at the stationary point if , loosely speaking , the inputs PSPi ( t ) at individual synapses are sufficiently rich and different from each other . The right-hand side of Eq 30 also depends on the dendritic potential V w * , since the membrane potential U depends both on the dendritic input V w * and the somatic input U* ( see Eq 4 ) . Assuming a linear transfer function φ , Eq 30 becomes φ V w * ( t ) = α τ ∫ 0 ∞ d s e - s τ λ φ V w * ( t + s ) + φ U * ( t + s ) . ( 31 ) With a Fourier transform and assuming a constant nudging factor λ we can solve this equation for φ ( V w * ( t ) ) . The Fourier coefficients h ^ k , k ∈ Z , of the T-periodic function h ( t ) = ∫ 0 ∞ d s e - s τ f ( t + s ) are given by h ^ k = ∫ 0 T d t e - i 2 π k t T ∫ 0 ∞ d s e - s τ f ( t + s ) = ∫ 0 T d t e - i 2 π k t T f ( t ) ∫ 0 ∞ d s e i 2 π k s T e - s τ ( 32 ) = f ^ k ∫ 0 ∞ d s e s i 2 π k T - 1 τ ( 33 ) = f ^ k 1 1 τ - i 2 π k T , ( 34 ) where , in the first line , we changed the order of integration , changed the variable t to t − s and used the periodicity of the integrand to obtain ∫ - s T - s d t e - i 2 π k t T f ( t ) = ∫ 0 T d t e - i 2 π k t T f ( t ) . In the second line we introduced the Fourier coefficients f ^ k . With f ( t ) = φ ( V w * ( t ) ) and g ( t ) = φ ( U* ( t ) ) we rewrite Eq 31 f ( t ) = α τ ∫ 0 ∞ d s e - s τ λ f ( t + s ) + g ( t + s ) ( 35 ) and Fourier transform both sides to obtain f ^ k = α τ λ f ^ k + g ^ k 1 1 τ - i 2 π k T . ( 36 ) Solving for f ^ k leads to f ^ k = α τ g ^ k 1 1 τ - i 2 π k T 1 - α λ τ 1 1 τ - i 2 π k T ( 37 ) = α τ g ^ k 1 1 - α λ τ - i 2 π k T . ( 38 ) This equation has the same structure as Eq 34 . With the inverse Fourier transform and assuming αλ < 1 we find Eq 10 , i . e . f ( t ) = α τ ∫ 0 ∞ d s e - s τ eff g ( t + s ) , ( 39 ) where τ eff = τ 1 - α λ . We formalize the notion of a stationary environment by introducing a stationary latent Markov chain and restricting the dendritic input PSPi ( t ) = PSPi ( Xt ) and the somatic input U* ( t ) = U* ( Xt ) to depend on the state Xt of the Markov chain . An alternative way to formalize the notion of stationarity would be to define stationary dynamics of the dendritic inputs and define the correlation between dendritic and somatic input . As it is always possible to reformulate the stationary dendritic input dynamics and the correlation between dendritic and somatic input in terms of a stationary latent Markov chain—with potentially large state space—we stick to the description with a latent Markov chain . Formally , for time t ∈ Z , states Xt in a finite set X = ( s 1 , s 2 , … , s N ) evolve according to a stationary , irreducible Markov chain with transition probabilities T ( si , sj ) = Pr ( Xt+1 = sj|Xt = si ) and stationary distribution π ( si ) = Pr ( Xt = si ) . Note that the case of deterministic periodic input is readily formulated in terms of a stationary latent Markov chain that cycles deterministically through the state space , e . g . T ( si , sj ) = 1 if j = i + 1 or j = 1 and i = N and T ( si , sj ) = 0 otherwise . Functions that depend only on the state of the Markov chain are thus cyclic with period N , e . g . PSPi ( Xt ) = PSPi ( Xt+N ) . In order to switch to matrix notation in the rest of this section , we introduce the following terms: In the following we sketch the proof for the equivalents of Eqs 30 , 12 and 11 in the Markov chain setting . We will make use of the following basic facts about conditional expectations: E f ( X t ) | X 0 = x = ∑ x 0 , x 1 , … , x t δ ( x , x 0 ) ∏ s = 1 t T ( x s - 1 , x s ) f ( x t ) = ( T t f ) ( x ) , ( 40 ) E f ( X - t ) | X 0 = x = 1 π ( x ) ∑ x - t f ( x - t ) π ( x - t ) Pr ( X 0 = x | X - t = x - t ) = 1 π ( x ) ( f ′ Π T t ) ( x ) , ( 41 ) where t > 0 , Tt denotes the matrix power of T , Π = diag ( π ( s1 ) , π ( s2 ) , … , π ( sN ) ) is the diagonal “stationary distribution matrix” , column vector f = ( f ( s1 ) , f ( s2 ) , … , f ( sN ) ) ′ and row vector f′ , the transposed of f . For λTD = 1 and therefore PSP ^ = PSP ˜ , we can rewrite Eq 17 by expanding the delta error in Eq 19 and using the identity γ PSP ˜ t , i = PSP ˜ t + 1 , i - PSP i ( X t + 1 ) to find δtPSP˜t , i=αφ ( U ( Xt ) ) PSP˜t , i+φ ( Vwt* ( Xt+1 ) ) ( PSP˜t+1 , i−PSPi ( Xt+1 ) ) −φ ( Vwt* ( Xt ) ) PSP˜t , i=αφ ( U ( Xt ) ) PSP˜t , i−φ ( Vwt* ( Xt+1 ) ) PSPi ( Xt+1 ) ( 57 ) + φ V w t * ( X t + 1 ) PSP ˜ t + 1 , i - φ V w t * ( X t ) PSP ˜ t , i . ( 58 ) With small parameter updates in each time step , the terms in Eq 58 approximately cancel each other when summing over subsequent terms: δ t PSP ˜ t , i contributes + φ ( V w t * ( X t + 1 ) ) PSP ˜ t + 1 , i and δ t + 1 PSP ˜ t + 1 , i contributes - φ ( V w t + 1 * ( X t + 1 ) ) PSP ˜ t + 1 , i . What remains are the terms in Eq 57 , which resemble the terms in the learning rule in Eq 42 .
Sensory inputs are often predictable . Lightning is followed by thunder , a falling object causes noise when hitting the ground , our skin gets wet when we jump into the water . Humans learn regularities like these without effort . Learned predictions allow to cover the ears in anticipation of thunder or close the eyes just before an object hits the ground and breaks into pieces . What changes in the brain when new predictions are learned ? In this article , we present a mathematical model and computer simulations of the idea that the activity of a single neuron represents expected future events . Such a prospective coding can be learned in a neuron that receives input from the memory trace of a first event ( e . g . lightning ) and also input from the second event ( e . g . thunder ) . Synaptic input connections from the memory trace are potentiated such that the spiking activity ramps up towards the onset of the second event . This deviates from the classical Hebbian learning that merely associates two events that are coincident in time . Learning in our model associates a current event to future events .
[ "Abstract", "Introduction", "Results", "Discussion", "Methods" ]
[ "learning", "action", "potentials", "medicine", "and", "health", "sciences", "nervous", "system", "membrane", "potential", "social", "sciences", "electrophysiology", "neuroscience", "learning", "and", "memory", "synaptic", "plasticity", "cognitive", "psychology", "computational", "neuroscience", "neuronal", "dendrites", "neuronal", "plasticity", "coding", "mechanisms", "developmental", "neuroscience", "animal", "cells", "cellular", "neuroscience", "psychology", "cell", "biology", "anatomy", "synapses", "physiology", "neurons", "biology", "and", "life", "sciences", "cellular", "types", "computational", "biology", "cognitive", "science", "neurophysiology" ]
2016
Prospective Coding by Spiking Neurons
Type 2 diabetes mellitus ( DM ) is a major risk factor for developing tuberculosis ( TB ) . TB-DM comorbidity is expected to pose a serious future health problem due to the alarming rise in global DM incidence . At present , the causal underlying mechanisms linking DM and TB remain unclear . DM is associated with elevated levels of oxidized low-density lipoprotein ( oxLDL ) , a pathologically modified lipoprotein which plays a key role during atherosclerosis development through the formation of lipid-loaded foamy macrophages , an event which also occurs during progression of the TB granuloma . We therefore hypothesized that oxLDL could be a common factor connecting DM to TB . To study this , we measured oxLDL levels in plasma samples of healthy controls , TB , DM and TB-DM patients , and subsequently investigated the effect of oxLDL treatment on human macrophage infection with Mycobacterium tuberculosis ( Mtb ) . Plasma oxLDL levels were significantly elevated in DM patients and associated with high triglyceride levels in TB-DM . Strikingly , incubation with oxLDL strongly increased macrophage Mtb load compared to native or acetylated LDL ( acLDL ) . Mechanistically , oxLDL -but not acLDL- treatment induced macrophage lysosomal cholesterol accumulation and increased protein levels of lysosomal and autophagy markers , while reducing Mtb colocalization with lysosomes . Importantly , combined treatment of acLDL and intracellular cholesterol transport inhibitor ( U18666A ) mimicked the oxLDL-induced lysosomal phenotype and impaired macrophage Mtb control , illustrating that the localization of lipid accumulation is critical . Collectively , these results demonstrate that oxLDL could be an important DM-associated TB-risk factor by causing lysosomal dysfunction and impaired control of Mtb infection in human macrophages . Type 2 diabetes mellitus ( DM ) has been recognized as a major risk factor for tuberculosis ( TB ) for decades [1] . Recent epidemiological studies have demonstrated that DM triples the risk of developing active TB [2] , and approximately 15% of global TB cases can be attributed to DM comorbidity [3] . The precise mechanisms through which DM enhances the risk of active TB disease progression are unknown , however it has been hypothesized that metabolic changes associated with DM attenuate the immune response towards Mycobacterium tuberculosis ( Mtb ) , the causative pathogen of TB . As the global incidence of DM has been rising at an alarming rate [4] , including more recently in TB endemic regions of African and Asia , it is of great importance to identify the molecular and cellular mechanisms underlying TB-DM comorbidity . DM patients often suffer from dyslipidemia and oxidative stress , conditions which can contribute to the formation of oxidized low density lipoprotein ( oxLDL ) [5] . LDL can be oxidized by free radicals and reactive products of oxygenases , a process which has been mostly studied in the context of atherosclerosis during which oxLDL is generated in the subendothelial space of the arterial wall [6 , 7] . High levels of circulating oxLDL were shown to be associated with DM , insulin resistance and decreased glucose tolerance [8–11] . oxLDL is recognized as a damage-associated molecular pattern ( DAMP ) by macrophages and is a ligand for various scavenger receptors on the cell surface , including CD36 , scavenger receptor A ( SR-A ) and lectin-type oxidized LDL receptor 1 ( LOX-1 ) [12] . The uptake of oxLDL by macrophages plays a major role during the pathophysiology of atherosclerosis as it leads to the generation of pro-inflammatory lipid-loaded foam cells in the arterial vessel wall [13 , 14] . These macrophages exhibit increased scavenger receptor expression , cytokine secretion and production of oxidizing agents , supporting both immune cell infiltration and further generation of oxLDL which can culminate in atherosclerotic plaque formation [15] . Foamy macrophages also occur during TB progression and are thought to be of great importance for the development of TB granulomas and persisting Mtb infection , since the bacterium relies on host-derived lipids and cholesterol as a source of carbon for its survival [16–18] . Infection of alveolar macrophages with Mtb initiates the formation of the early TB granuloma , which progresses from a core of infected foam cells to an enclosed structure with a thick fibrous capsule and a lipid-rich caseous center of necrotic macrophages [16] . Various studies have demonstrated that Mtb and other mycobacteria are able to utilize host-derived lipids and even reprogram lipid metabolism in infected macrophages to induce foam cell formation , in part through the effects of mycobacterial cell wall lipids [19–24] . Interestingly , oxLDL was also found to accumulate in granulomas and alveolar macrophages of Mtb infected guinea pigs and to enhance bacterial replication [25] , suggesting that local oxLDL production could play a role in foam cell formation and Mtb persistence during TB disease . OxLDL-derived lipids have been demonstrated to be resistant to lysosomal esterases which are normally responsible for lipid breakdown . This results in lipid accumulation inside lysosomes after initial uptake by macrophages [26 , 27] , as well as to dysfunctions in the trafficking and efflux of intracellular cholesterol which mimic those observed in the lysosomal storage disorder Niemann Pick disease type C ( NPC ) . During NPC disease , mutations in the lysosomal cholesterol transporters NPC1 or NPC2 result in severe neurological defects due to excessive intralysosomal storage of cholesterol and sphingolipids [28] . Cholesterol accumulation due to oxLDL uptake or NPC1-deficiency induces lysosomal dysfunction in macrophages , as it can interfere with phagolysosomal trafficking , maturation and fusion [29 , 30]; inhibit autophagy [31 , 32] , an important cellular pathway which is simultaneously involved in lipid and cholesterol metabolism [33] and Mtb killing [34] in macrophages; increase lysosomal pH [35]; directly damage lysosomal membranes [36 , 37]; and trigger various downstream inflammatory pathways such as formation of the NLRP3 inflammasome [38] . A recent paper demonstrated that both infection with live M . smegmatis or M . bovis BCG and treatment with mycobacterial cell wall lipids induced a NPC-like phenotype in macrophages with associated defects in lysosomal function [39] , indicating that cholesterol accumulation could provide a permissive environment for mycobacteria in addition to being a nutritional source . To investigate whether oxLDL is a molecular component in the interplay between TB and DM , we measured oxLDL concentrations in plasma samples of DM , TB and TB-DM patients and analyzed the effect of oxLDL on in vitro Mtb infection in primary human macrophages . We found that oxLDL is elevated in the plasma of DM patients and supported Mtb intracellular survival in vitro by inducing lysosomal dysfunction . Collectively , our findings provide a proof of concept for a contribution of oxLDL as a risk factor for TB during DM . First , we sought to confirm the presence of high levels of circulating oxLDL in DM patients from a TB endemic setting and to assess the relative impact of TB-DM comorbidity on circulating oxLDL levels . OxLDL concentrations were determined in plasma samples from healthy endemic controls ( HC ) , TB , DM and TB-DM patients of a South-African cohort , previously used in a lipidomic biomarker analysis [40] , by sandwich ELISA using a monoclonal antibody against a conformational epitope in oxidized ApoB-100 [41] . Patient characteristics are described in S1 Table . Plasma oxLDL levels were significantly higher in DM patients ( median: 65 . 8 [interquartile range: 39 . 2–83 . 2] U/l ) compared to both HC ( 42 . 3 [35 . 3–82 . 2] U/l , p < 0 . 05 ) and TB-DM patients ( 44 . 4 [30 . 3–56 . 7] U/l , p < 0 . 05 ) ( Fig 1A ) , but not significantly different in patients with TB-DM compared to TB alone ( 44 . 3 [29 . 6–50 . 0] U/l ) . However , a clear dichotomy was distinguishable in the TB-DM patient group: our previous analysis of these samples [40] had demonstrated that both DM and TB-DM patients displayed characteristics of dyslipidemia , as evidenced by high levels of serum triglycerides ( TG ) ( Fig 1B ) . Furthermore , serum triglyceride levels were positively correlated with oxLDL across all measured samples ( r2: 0 . 4189 , p = 1 . 155−10 ) ( Fig 1C ) . To investigate whether oxLDL levels were related to the severity of dyslipidemia in TB-DM patients , we subdivided the groups according to serum TG-concentrations ( TG-high and TG-low , Fig 1D ) . DM and TB-DM patients with TG-high had increased oxLDL levels compared to those with TG-low ( DM: 72 . 0 [61 . 7–87 . 1] vs 46 . 8 [33 . 0–76 . 3] U/l , p = 0 . 053; TB-DM: 56 . 4 [52 . 1–59 . 4] vs 32 . 7 [27 . 2–39 . 2] U/l , p < 0 . 05 ) . Taken together , the results validate that DM patients have increased levels of circulating oxLDL and that plasma oxLDL concentrations are elevated in DM and TB-DM patients with concomitant hypertriglyceridemia . As oxLDL was clearly elevated in DM patients and has been described to have profound effects on macrophage function , we hypothesized that oxLDL treatment could compromise the capacity of macrophages to control Mtb infection . To investigate this , macrophages were treated with 1 , 10 or 25 μg/ml oxLDL or native LDL overnight . Oil Red O staining indicated a dose-dependent increase in intracellular lipid levels after oxLDL treatment , while native LDL did not induce foam cells ( Figs 2A and S1B ) . These macrophages were subsequently infected for 24 h with Mtb H37Rv and intracellular bacterial loads were assessed by bacterial colony forming unit ( CFU ) assay . OxLDL treatment significantly increased Mtb load compared to native LDL at all tested concentrations ( 1 μg/ml: 136% [113% - 171%] vs 97% [78% - 128%] , p < 0 . 01; 10 μg/ml: 143% [115% - 167%] vs 110% [102% - 121%] , p < 0 . 01; 25 μg/ml: 230% [179% - 248%] vs 115% [94 . 8% - 127%] , p < 0 . 01 ) , and this effect was dose-dependent ( 25 μg/ml oxLDL vs 1 μg/ml: p < 0 . 01; vs 10 μg/ml: p < 0 . 01 ) ( Fig 2B ) . The magnitude of the increase in bacterial load was not correlated with small fluctuations in infectious load ( MOI ) ( S1C Fig ) . While these experiments demonstrated that oxLDL treatment supported Mtb persistence in human macrophages , it was unclear whether this was the result of increased phagocytosis , reduced intracellular mycobacterial control or enhanced replication . To gain a better understanding on the cellular processes affected by oxLDL treatment , we explored the functional consequences of oxLDL-induced foam cell formation . Firstly , the phagocytic capacity of oxLDL-treated macrophages was assessed to investigate whether the increased mycobacterial load might be related to enhanced Mtb uptake . Macrophages treated with either native LDL or oxLDL were incubated with fluorescent polystyrene beads and bead phagocytosis was quantified by flow cytometry ( Fig 3A ) . Although a small but significant decrease in bead uptake was observed in macrophages incubated with 25 μg/ml oxLDL compared to LDL ( p < 0 . 05 ) ( Fig 3B ) , overall macrophage phagocytic capacity was unaffected by oxLDL treatment , indicating that the increased mycobacterial burden in oxLDL-derived foam cells was probably not the result of increased phagocytic uptake . To confirm this , we investigated the intracellular bacterial load of oxLDL-treated macrophages directly after 1 h of infection and found no significant differences compared to control conditions ( S2C Fig ) . Next , we explored the cytokine response of oxLDL-derived foam cells to Mtb-infection as earlier studies had reported potent oxLDL-induced pro-inflammatory cytokine production . In contrast to these studies , oxLDL-treatment in our experiments significantly decreased the secretion of TNF-α compared to treatment with LDL ( 47 [20 – 186] vs 128 [54 – 453] pg/ml , p < 0 . 05 ) or PBS ( 146 [62 – 466] pg/ml , p < 0 . 05 ) . Similar results were obtained for IL-6 after oxLDL treatment versus LDL ( 11 [0–226] vs 66 [0–553] pg/ml , p < 0 . 05 ) or PBS ( 73 [0–412] pg/ml , p < 0 . 05 ) , although some inter-individual variation was observed ( Fig 3C ) . IL-10 levels were not significantly affected by oxLDL , while IL-1β levels were very low . Finally , oxLDL-derived macrophages were co-cultured with a HLA-DR2-restricted CD4+ T cell clone ( R2F10 ) and its cognate peptide ( Mlep hsp65 p418–427 ) and T cell proliferation was measured to determine macrophage dependent antigen presentation . OxLDL treatment dose-dependently diminished the antigen presentation capacity of macrophages , especially at suboptimal peptide concentrations ( Fig 3D ) . Similar results were obtained using a second , HLA-DR3-restricted CD4+ T cell clone ( Rp15 1–1 ) ( S2A Fig ) , both after loading with its cognate peptide or purified protein derivative ( PPD ) . This diminished antigen presentation capacity was independent of cell surface expression of HLA-DR and co-stimulatory molecules CD80 and CD86 ( S2B Fig ) . Taken together , oxLDL treatment impaired several macrophage functions , including antigen presentation and pro-inflammatory cytokine secretion , but not their phagocytic capacity . OxLDL-derived free and esterified cholesterol have been demonstrated to be sequestered in lysosomes in macrophages [26 , 27] , which potentially leads to lysosomal dysfunction . To investigate whether lysosomal localization of oxLDL lipids is required for its effect on Mtb load , oxLDL treatment was compared to acLDL , a non-naturally occurring modified lipoprotein which is endocytosed through identical scavenger receptor pathways as oxLDL , but does not induce lysosomal cholesterol accumulation [26 , 42] . In resemblance to oxLDL , acLDL treatment of macrophages resulted in foam cell formation . However , while lipid staining intensities were similar ( S1B Fig ) , clear differences in intracellular lipid localization and droplet structure were observed between both types of lipoproteins: in general , acLDL-induced intracellular lipid droplets were darker in color and appeared more granular than those resulting from oxLDL treatment ( Fig 4A ) . Most importantly , however , acLDL did not affect macrophage Mtb load compared to untreated macrophages while oxLDL treatment significantly increased mycobacterial load ( Fig 4B: oxLDL: 232% [194%– 278%] vs acLDL: 108% [88% - 126%]; p < 0 . 0001 ) . This effect was not restricted to Mtb , as comparable results were obtained after macrophage infection with Salmonella enterica serovar Typhimurium ( Stm ) ( Fig 4D: 179% [162% - 183%] vs 124% [88% - 136%]; p < 0 . 05 ) and M . bovis BCG ( Fig 4C: 178% [133% - 254%] vs 97% [82% - 123%]; p < 0 . 05 ) . To examine whether the observed difference between oxLDL and acLDL could be related to lysosomal function , their effect on lysosomal and autophagy markers during Mtb infection was analyzed by Western blot ( Fig 4E ) . OxLDL treatment increased protein levels of lysosomal markers compared to PBS and acLDL , as demonstrated by higher levels of lysosomal membrane glycoproteins ( LAMP1 & LAMP2 ) and proteases ( Cathepsin D & L ) ( Fig 4F ) , also including the 48 kDa processing intermediate pro-cathepsin D ( S3A Fig ) . Furthermore , oxLDL but not acLDL treatment led to an increased accumulation of LC3-II in the presence of vacuolar type H+-ATPase inhibitor bafilomycin A1 ( 10 nM ) to block vesicle breakdown , indicative of increased autophagic flux . In contrast , levels of autophagosome cargo protein p62 , a mediator of selective autophagy , were not elevated by oxLDL ( Fig 4F ) . Collectively , these results indicate that oxLDL induces a general defect in macrophage antimicrobial function which is dependent on intracellular lipid localization . To further substantiate this hypothesis , macrophages were treated with PBS , oxLDL or acLDL in the absence or presence of U18666A ( 3 μg/ml ) , an inhibitor of intracellular cholesterol transport [43] . Lysosomal cholesterol sequestration was visualized using confocal microscopy by staining with fluorescent probes for neutral lipids ( LipidTOX ) , lysosomes ( Lysotracker ) and cholesterol ( filipin ) ( Fig 4G ) . OxLDL treatment induced a marked accumulation of cholesterol inside lysosomal vesicles as indicated by filipin and Lysotracker colocalization , which was not observed in macrophages treated with PBS or acLDL . Strikingly , when combined with U18666A , acLDL-treated macrophages showed identical lysosomal cholesterol sequestration as oxLDL . The absence of an effect of acLDL treatment alone on Mtb load suggested that the localization of cholesterol inside lysosomes might be a causative factor in the increased Mtb growth phenotype of oxLDL-treated macrophages . To test this , we investigated whether combined treatment of acLDL with U18666A could mimic the effect of oxLDL on macrophage Mtb control . Indeed , while U18666A alone or in combination with oxLDL did not significantly alter macrophage phenotype ( Fig 4G ) and Mtb load , it increased mycobacterial burden when applied in conjunction with acLDL compared to DMSO control ( Fig 4H: 169% ± 62% vs 107% ± 32%; p < 0 . 05 ) . Similar to oxLDL , U18666A treatment alone and in combination with acLDL increased protein levels of lysosome and autophagy markers in Mtb-infected macrophages , most notably Cathepsin L and when combined with bafilomycin A1 ( 10 nM ) , p62 and LC3-II ( S3B Fig ) . Macrophage viability was unaffected by oxLDL and/or U18666A treatment in combination with Mtb infection as determined by combined Hoechst/propidium iodide ( PI ) staining ( S3C and S3D Fig ) . Collectively , these results indicate that not simply the presence , but the specific accumulation of cholesterol inside lysosomes is crucial for the oxLDL- and U18666A-induced increase in Mtb survival in human macrophages . While the above model proposes that oxLDL can interfere with macrophage mycobacterial control , we could not yet exclude whether oxLDL-induced foam cell formation also supported Mtb replication , possibly by providing increased nutrients . To gain a better understanding of overall kinetics of oxLDL-induced increased Mtb load and its associated cytokine response , infected macrophages treated with PBS control , oxLDL or acLDL were infected with Mtb and the intracellular bacterial load and concentrations of 29 cytokines and chemokines in supernatants were determined at 0 ( uptake control ) , 4 , 24 , 48 , 72 and 144 h post-infection . OxLDL treatment showed increased Mtb survival compared to PBS as early as 4 h post-infection , and versus both PBS and acLDL at all later time points ( 24–144 h ) ( S4A Fig ) . For all treatment conditions the intracellular Mtb load decreased with time , ranging from 1 . 3 to 12 . 4% of original bacterial uptake after 144 h of infection , which is supportive of a model in which the effect of oxLDL is the result of inhibited bacterial killing and not of increased bacterial outgrowth . The multiplex results were congruent with the ELISA data from Fig 3C , as oxLDL-treated macrophages produced significantly lower levels of TNF-α and IL-6 after 24 h of Mtb infection compared to PBS control ( S4B Fig ) . Many cyto- and chemokine concentrations were lower in oxLDL-treated macrophages between 4–48 h of Mtb infection , while supernatants from acLDL-treated macrophages often showed intermediate levels compared to PBS and oxLDL ( IL-10 , IL-6 , TNF-α , IL-8 , CCL3 , CCL4 , G-CSF , GM-CSF ) . We did not find significant differences at 72 and 144 h post-infection after FDR correction . IL-1RA was the only cytokine which showed increased production as a result of oxLDL , although the magnitude of this response varied between donors . Concentrations of CXCL10 , IFNα2 , CCL2 and VEGF increased as a result of Mtb infection , however no differences were observed between treatment conditions for these factors . Levels of Epidermal Growth Factor ( EGF ) , Eotaxin , IFNγ , IL-12p40 , IL-12p70 , IL-1β , IL-13 , IL-15 , IL-17A , IL-1α , IL-2 , IL-3 , IL-4 , IL-5 , IL-7 and TNF-β were measured but not shown as their concentrations were either very low in all samples ( <100 pg/ml ) or not detectable . Taken together , these experiments provide further evidence for an overall diminished cytokine response as a result of oxLDL treatment during Mtb infection . To identify the relevant molecular processes which are deregulated by lysosomal cholesterol accumulation , oxLDL-treated macrophages infected with Mtb were treated with compounds targeting various cell signaling pathways which are known to be affected by oxLDL in an attempt to rescue their antimicrobial capacity . Firstly , infected foamy macrophages were treated with rapamycin , an inhibitor of mammalian target of rapamycin complex 1 ( mTORC1 ) . mTOR is a master regulator of various cellular pathways including autophagy , and rapamycin-induced autophagy was reported to ameliorate foam cell formation [44 , 45] . Rapamycin ( 2 μM ) slightly but significantly reduced Mtb load compared to DMSO in PBS-treated macrophages ( 80 ± 15% of PBS/DMSO , p < 0 . 05 ) , but did not affect bacterial burden in either oxLDL or acLDL-induced foamy macrophages ( Fig 5A ) . Secondly , lysosomal storage disorders such as NPC disease are associated with defects in lysosomal Ca2+ homeostasis [46] , and activation of the lysosomal ion channel transient receptor potential channel 1 ( TRPML1 ) by small-molecule activator ML-SA1 was shown to rescue lysosomal trafficking in NPC-/--macrophages [30] . However , ML-SA1 treatment ( 10 μM ) did not affect Mtb infection in any of our conditions ( Fig 5B ) . Finally , oxLDL can induce endoplasmic reticulum ( ER ) stress in macrophages [47] , a state of disturbed ER homeostasis due to accumulation of unfolded proteins and/or disrupted Ca2+ handling which plays a role in the apoptotic response in atherosclerotic plaques and the TB granuloma [48 , 49] . Treatment of Mtb-infected macrophages with three established reducers of the ER stress response , namely chemical chaperone 4-phenylbutyrate ( 4-PBA; 3 mM ) and downstream kinase inhibitors 4μ8c ( 10 μM ) and GSK2656157 ( 10 μM ) ( respectively targeting inositol-requiring enzyme 1-α ( IRE1-α ) and protein kinase RNA-like endoplasmic reticulum kinase ( PERK ) ) , did not alleviate the oxLDL-induced increase in mycobacterial survival ( Fig 5C ) . In conclusion , chemical modulation of mTOR signaling , lysosomal Ca2+ homeostasis or ER stress did not reverse the oxLDL-induced increased mycobacterial load in human macrophages . The above experiments demonstrated that the endolysosomal system is pivotal for oxLDL-induced increased mycobacterial survival . As earlier studies have reported that cholesterol accumulation impaired proper lysosomal trafficking [29 , 30] , we hypothesized that Mtb trafficking to functional lysosomes was inhibited by oxLDL treatment . To investigate this , oxLDL-treated macrophages infected with fluorescent DsRed-expressing H37Rv were stained for functional lysosomes with Lysotracker ( Fig 6A ) , and lysosomal colocalization was determined for each intracellular mycobacterium individually ( Fig 6B ) . OxLDL significantly decreased the average colocalization between Mtb and Lysotracker ( 39 ± 9% ) compared to acLDL ( 51 ± 12% , p < 0 . 05 ) or PBS treatment ( 60 ± 6% , p < 0 . 05 ) ( Fig 6C ) , indicating that oxLDL inhibits phagolysosomal fusion in Mtb-infected macrophages . In an attempt to identify the specific lysosomal pathways affected by oxLDL treatment , we investigated colocalization of Mtb with galectin-3 and NDP52 . Galectins are carbohydrate-binding proteins which play a role in targeting damaged endomembrane structures for autophagy [50] , including phagolysosomes damaged by Stm or Mtb [51–53] , and galectin-3 colocalization with lysosomes is an established measure of lysosomal damage [54] . NDP52 is an autophagy adaptor which has previously been implicated in the autophagic clearance of both Stm and Mtb [51 , 55 , 56] . Although colocalization events with Mtb were observed for both galectin-3 and NDP52 , this occurred for a minority of intracellular bacteria ( range 2–8% of bacteria ) and no significant differences were found between oxLDL and control conditions ( Fig 6D–6G ) . The looming epidemic of concurrent TB-DM poses a serious global health problem . Identification of the causal molecular and cellular mechanisms underlying the increased risk of TB in DM patients is paramount for adequate treatment . Previously , we have demonstrated that TB-DM patients have a blood lipid profile with pro-atherogenic properties [40] , which could have implications for TB-DM pathogenesis . We now identify oxLDL as a potential risk factor for TB . OxLDL levels were found to be increased in plasma samples of DM patients from a TB endemic region , who represent the specific population at increased risk for disease . Although both triglyceride and oxLDL levels were lower in the TB-DM group compared to DM , this might well be related to the duration and severity of DM disease as the majority of TB-DM patients were recently diagnosed diabetics compared to the DM alone group ( S1 Table ) . Furthermore , TB was associated with wasting syndrome and therefore with low levels of many circulating metabolites in this patient population , including LDL [40] . As these patients were not merely at increased risk of TB at the moment of blood collection but had already developed active disease , it is not unlikely that oxLDL levels are decreased since onset of TB . Nonetheless , a clear dichotomy in oxLDL concentrations was visible based on triglyceride-status in TB-DM patients , implying that diabetes-associated dyslipidemia was a factor associated with increased oxLDL levels in this population . Importantly , oxLDL- , but not acLDL- , induced foamy macrophage formation supported intracellular Mtb survival through lysosomal cholesterol accumulation and subsequent dysfunction . This effect was not limited to Mtb as similarly enhanced bacterial loads were observed for Stm and M . bovis BCG , which reside in different intracellular compartments compared to Mtb [57] . Pharmacological manipulation of intracellular cholesterol transport with U18666A confirmed that subcellular localization of cholesterol to lysosomes was essential to lysosomal dysfunction . Since foamy macrophages play an important role during progression of the TB granuloma [16 , 18] , our results suggest that increased levels of oxLDL could contribute to the enhanced TB susceptibility in DM patients . Our findings are in line with earlier studies that reported increased levels of oxLDL in DM patients [8–11] . Both hyperglycemia and dyslipidemia contribute to the generation of free radicals and oxidative stress during chronic DM [58 , 59] , which can lead to the pathological modification of proteins and lipids involved in foam cell formation and atherosclerosis , such as oxLDL . Additionally , DM and hyperglycemia are associated with increased expression of oxLDL scavenger receptors CD36 [60–62] , SR-A [62 , 63] and LOX-1 [62 , 64] , and macrophages from type 2 diabetics showed higher uptake of oxLDL [65] . Similar to DM , TB has been demonstrated to result in increased oxidative stress and a systemic decrease in antioxidant capacity , e . g . reduced levels of glutathione [66–69] . Mtb infection increased CD36 expression in vitro [19] and CD36-mediated uptake of surfactant lipids has been reported to support Mtb growth [20] . In contrast , a recent paper did not find a role for CD36-mediated macrophage lipid droplet formation in Mtb control [70] , which could indicate that not simply the presence of lipid droplets but rather the specific composition and/or localization of the intracellular lipids is most important for their effect on Mtb intracellular survival , similar to what we observed here when comparing acLDL and oxLDL . At the functional level , oxLDL treatment displayed potential to inhibit macrophage antigen presentation to CD4+ T cells , which could in principal lead to impaired activation of adaptive immune responses . While their phagocytic capacity was largely unaffected , oxLDL-treatment macrophages showed an overall decreased cytokine production in response to Mtb . These results were somewhat surprising , as oxLDL has been associated with increased inflammation during atherosclerosis [71] and non-alcoholic steatohepatitis ( NASH ) [72–74] , including activation of the NLRP3 inflammasome and subsequent secretion of IL-1β by macrophages [38 , 75] . However , in these studies oxLDL treatment was often accompanied by secondary factors which may be required for the observed pro-inflammatory responses , such as macrophage apoptosis , circulating anti-oxLDL immune complexes or the formation of intralysosomal cholesterol crystals . In agreement with our own observations , several studies reported diminished inflammatory responses of oxLDL-treated macrophages after stimulation with TLR ligands [76–78] . These divergent results could be related to study-specific differences in experimental setup , including variations in species , cell types , stimulations and degree of LDL oxidation . Additionally , oxLDL was reported to induce a long-lasting pro-inflammatory phenotype in monocytes through epigenetic changes , which possibly did not occur in our experiments due to their relatively short timeframe or lack of restimulation [79 , 80] . Hypercholesterolemia has been implicated in increasing the risk of developing TB [81–83] , and cholesterol catabolism is needed for mycobacterial persistence and growth [84 , 85] . For this reason , most studies on foamy macrophage induction by mycobacteria have focused on the relatively long-term nutritional benefits of intracellular lipid accumulation [20 , 86] . The results presented in this manuscript demonstrate that pathologically modified lipids also directly interfere with macrophage antimicrobial capacities , providing a novel perspective on the importance of foam cell formation during TB . These findings are corroborated by a study which demonstrated that M . smegmatis and M . bovis BCG blocked phagolysosomal fusion by inducing an NPC-like phenotype in infected macrophages [39] . Additionally , macrophage cholesterol depletion restored halted phagosome maturation during M . avium infection [87] . Drugs which target host cholesterol metabolism can therefore have potential for TB host directed treatment , and e . g . statins have shown promise as adjunctive anti-mycobacterial therapy both in vitro and in vivo [88–92] . Furthermore , our results suggest that oxLDL treatment supports mycobacterial survival through interference with phagolysosomal trafficking and/or fusion . Lysosomal lipid accumulation has been reported to influence these processes in several ways . Late endosomal transport is mediated by the lysosomal protein ORP1L , which modulates the interaction between Rab GTPases and their effectors , motor protein complexes and the ER through conformational changes induced by fluctuations in intraluminal cholesterol levels [93 , 94] . Furthermore , abnormal sphingolipid storage due to NPC1-deficiency or U18666A treatment was shown to disrupt lysosomal Ca2+ homeostasis , blocking vesicle transport and fusion [30 , 46] . Finally , several studies have reported that lysosomal storage disorders interfere with the autophagic system [31 , 32] , which might be reflected by the increased LC3-II levels detected in oxLDL- and U18666A-treated macrophages during Mtb infection . Although pharmacological modulation of these pathways did not ameliorate the oxLDL-induced effect on Mtb control , their involvement should not yet be excluded as the phenotype induced by oxLDL was practically irreversible in our experimental setup . Our study might have had a number of limitations . Firstly , the oxLDL used throughout this manuscript was generated by copper-induced oxidation of native LDL , which is sometimes referred to as extensively oxidized LDL in literature due to its high oxidation grade [6] . It is generally believed that naturally occurring oxLDL is composed of less extensively oxidized variants as abundantly oxidized LDL would be rapidly cleared from the circulation . Therefore , it is possible that the phenotypes observed in our experiments are more extreme than would have occurred using naturally oxidized LDL . However , the precise composition of physiological oxLDL is still uncertain as accurate characterization of isolated oxLDL is technically challenging . As LDL oxidation mostly occurs in the subendothelial space during atherosclerosis , locally generated oxidized species might be of greater importance for disease than circulating oxLDL . Regardless , it would be of interest to investigate the effects of minimally modified LDL ( mmLDL ) , a variant which is believed to be more similar to naturally occurring oxLDL [6] , on macrophage Mtb infection . Secondly , oxLDL was applied at a concentration of 25 μg/ml for the majority of the experiments , which is at the high end of what has been physiologically observed [95–97] . However , oxLDL treatment times were relatively short compared to what can be expected in vivo , and low levels of oxLDL ( 1 μg/ml ) were already sufficient to increase mycobacterial load during this period . Thirdly , oxLDL is a complex particle consisting of hundreds of phospholipids , triglycerides and cholesteryl esters , which vary in terms of composition and susceptibility to oxidation and therefore have different intracellular effects [12] . It would be of great interest to study whether specific oxidized lipids or proteins are required for the observed oxLDL phenotype . Finally , although not within the scope of this study and technically challenging , it would be important to validate these findings in a disease model for translation to in vivo settings , e . g . using monocytes isolated from DM patients . In conclusion , oxLDL treatment of human macrophages supports Mtb intracellular survival as a result of lysosomal dysfunction , providing a proof of concept for a contribution of increased levels of oxLDL as a potential risk factor for TB development during DM . While we previously demonstrated that hyperglycemia alone did not directly influence outcome of macrophage Mtb infection [98] , we postulate that elevated lipid levels , which are associated with DM , can be in involved in TB-DM pathogenesis [40] . These findings pave the way for further research , including the use of LDL-lowering drugs such as statins or antioxidant drugs as part of the DM-treatment regimen for the reduction of the risk of TB . The patient population was previously used in an extensive lipid profiling analysis using H+-NMR spectroscopy as part of an EU-funded collaborative project , TANDEM [99] , of which details regarding patient inclusion were reported earlier [40] . From this population plasma samples of 20 healthy endemic controls , 20 TB patients , 20 DM patients and 20 TB-DM patients were selected at random for oxLDL determination . Plasma oxLDL levels were measured by sandwich ELISA according to manufacturer’s instructions ( Mercodia AB , Uppsala , Sweden ) . One TB-DM patient was excluded post-hoc due to the presence of clinical evidence suggestive of type 1 diabetes , while all other DM patients suffered from type 2 diabetes . This study was approved by the Health Research Ethics Committee of the University of Stellenbosch , and conducted according to the Helsinki Declaration and International Conference of Harmonization guidelines . Written informed consent was obtained from all participants . Primary antibodies against LAMP1 , LAMP2 , Cathepsin D , Cathepsin L , p62 , galectin-3 and secondary goat anti-mouse IgG ( Alexa Fluor 647 ) were purchased from Abcam ( Cambridge , UK ) . LC3A/B was from Cell Signaling ( Bioke , Leiden , The Netherlands ) , actin-HRP from Santa Cruz Biotechnology ( Santa Cruz , CA , USA ) , CD86-Alexa700 and HLA-DR-PeCy5 from BD Biosciences ( Erembodegem , Belgium ) and CD80-BV650 , CD14-FITC and CD163-Alexa647 were bought from Biolegend ( ITK diagnostics , Uithoorn , The Netherlands ) . NDP52 ( CALCOCO2 ) , secondary goat anti-rabbit IgG ( Alexa Fluor 647 ) and HRP-conjugated antibodies reactive with mouse and rabbit were purchased from Thermo Fisher Scientific ( Merelbeke , Belgium ) . LDL was isolated from the serum of healthy volunteers by density gradient ultracentrifugation [100] . Blood was collected in clot activator tubes and clotted for 90 minutes at room temperature . Serum was obtained after 10 minutes of centrifugation at 1 , 500 g . EDTA was added to a final concentration of 1 mM , after which serum density was adjusted to 1 . 21 g/l by addition of solid potassium bromide and gentle stirring . The resulting serum solution was distributed over 13 . 7 ml UltraClear ultracentrifuge tubes ( Beckman Coulter , Woerden , The Netherlands ) and a density gradient was prepared by overlaying it with potassium bromide solutions of decreasing concentrations ( 1 . 063 g/l , 1 . 019 g/l , 1 . 0063 g/l ) in PBS supplemented with 0 . 3 mM EDTA ( pH 7 . 4 ) using a wide bore pipette tip . The serum was then centrifuged at 40 , 000 RPM for 20 h at 4°C in a SW41 Ti swinging bucket rotor ( Optima LE-80K , Beckman Coulter ) . After centrifugation the tubes were carefully removed from the rotor and the LDL fraction was aspirated using a glass Pasteur pipette . The LDL was dialyzed against PBS at 4°C for 16 h during which the buffer was refreshed three times . The protein concentration of LDL was determined using a BCA kit according to the manufacturer’s instructions ( Pierce , Thermo Fisher Scientific ) . OxLDL was generated by copper oxidation of native LDL . Copper sulfate was added to 200 μg/ml LDL in PBS at a final concentration of 5 μM and incubated for 20 h at 37°C in the dark . The reaction was stopped by addition of 0 . 2 mM EDTA and oxLDL was then dialyzed against PBS containing 1 mM EDTA at 4°C for 24 h during which the buffer was refreshed three times . To produce acLDL , LDL was acetylated according to the protocol by Fraenkel-Conrat et al . [101] . An equal volume of saturated sodium acetate was added to 1 mg/ml of LDL and stirred at 4°C until cold . During the following hour acetic anhydride was added in 2 μl aliquots until 1 . 5x the mass of LDL was added in total . The mixture was stirred for another 30 minutes after the last aliquot was added . The acLDL was then dialyzed against PBS containing 1 mM EDTA at 4°C for 24 h during which the buffer was refreshed three times . Finally , the modified lipoproteins were concentrated to 1 mg/ml using 100 kDa Amicon Ultracel centrifugal filter units ( Merck Millipore , Amsterdam , The Netherlands ) . CD14+ monocytes were isolated from buffy coats of healthy blood bank donors by positive selection using an autoMACS Pro Separator ( Miltenyi Biotec BV , Leiden , The Netherlands ) . Donors were not part of an already-existing collection . Monocytes were differentiated into macrophages by addition of 50 ng/ml macrophage-colony stimulating factor ( M-CSF ) ( Miltenyi Biotec ) during culture for 6 days at 37°C/5% CO2 [102] . Cells were cultured in RPMI-1640 medium with L-glutamine , without glucose and sodium bicarbonate ( Sigma-Aldrich Chemie BV , Zwijndrecht , the Netherlands ) , supplemented with 5 mM D-glucose , 2 g/l sodium bicarbonate , 10% fetal bovine serum , 100 units/ml penicillin and 100 μg/ml streptomycin . After differentiation macrophages were harvested by trypsinization and seeded in multi-well plates . As a quality control , macrophages were stained for surface expression of CD14 and CD163 and acquired on a BD LSRFortessa flow cytometer ( BD Biosciences ) ( S1A Fig ) . To generate foam cells , macrophages were treated with various concentrations of oxLDL overnight . PBS , native LDL and/or acLDL were used as controls . Foam cell formation was confirmed by Oil Red O staining . Macrophages were fixed for 30 minutes in 4% paraformaldehyde and subsequently stained with a filtered work solution of Oil Red O ( Sigma-Aldrich ) in isopropanol ( 0 . 3% Oil Red O in 60% isopropanol ) for 20 minutes . Afterwards , the red stain was dissolved in 4% NP-40 in isopropanol and quantified by measuring the optical density ( OD ) at 520 nm using a iMark Microplate Absorbance Reader ( Bio-Rad , Veenendaal , The Netherlands ) . Mtb H37Rv cultures were grown to mid-log phase in Middlebrook 7H9 liquid medium ( Difco , BD Biosciences ) supplemented with albumin/dextrose/catalase ( ADC ) ( BBL , BD Biosciences ) . Bacterial concentrations were determined by measuring culture optical density at 600 nm . Macrophages were infected with H37Rv at a multiplicity of infection ( MOI ) of 10:1 for 1 h at 37°C , after which the cells were washed twice with medium containing 30 μg/ml gentamicin and cultured overnight in fresh medium containing 5 μg/ml gentamicin . Infected cells were lysed either directly after infection or at 4 , 24 , 48 , 72 or 144 h post-infection using 0 . 05% Triton X-100 and a dilution series of the lysates was plated on 7H10 square agar plates ( Difco , BD Biosciences ) supplemented with oleate/albumin/dextrose/catalase ( OADC ) ( BBL , BD Biosciences ) . Colony-forming units ( CFU ) were determined after 2–3 weeks of incubation at 37°C . From some experiments supernatants were harvested and filtered for determination of IL-1β , IL-6 , TNF-α ( Invitrogen , Thermo Fisher Scientific ) and IL-10 ( Sanquin , Amsterdam , The Netherlands ) by ELISA or for testing using a Human Cytokine/Chemokine Immunology Multiplex Assay ( Merck Millipore , Amsterdam , the Netherlands ) according to their manufacturers’ instructions . To quantify phagocytic capacity , fluorescent polystyrene particles ( Fluoresbrite YG carboxylate microspheres ) ( Polysciences , Hirschberg an der Bergstrasse , Germany ) were used as described by Leclerc et al [103] . Macrophages were incubated with fluorescent beads in a ratio of 10 beads to 1 cell for 90 min at 37°C . Cells were subsequently harvested by gentle scraping and resuspended in a 1:1 mixture of culture medium and Trypan Blue , and internalization of the beads was quantified by acquisition on a BD Accuri C6 flow cytometer ( BD Biosciences ) . Non-internalized bead fluorescence was quenched by Trypan Blue and detected in the FL-3 channel ( red ) , whereas internalized beads were detected in the FL-1 channel ( green ) . Analysis was performed using Flowjo software ( version 10 . 1 , Tree Star Inc , Ashland , OR ) . HLA-DR2/HLA-DR3-postive macrophages were harvested , seeded in 96-well plates at 2 , 500 cells/wells and treated with PBS , 25 μg/ml oxLDL or native LDL . The following day the cells were washed once in assay medium ( IMDM with 10% human serum ) and HLA class II restricted CD4+ T cell clones were added at a ratio of 4:1 together with a dilution series of their specific cognate peptide ( R2F10 clone: HLA-DR2 restricted , reactive with Mycobacterium leprae ( Mlep ) hsp65; Rp15 1–1: HLA-DR3 restricted , reactive with Mtb and Mlep hsp65 ) or 1 . 25 μg/ml purified protein derivative ( PPD ) ( Staten Serum Institute , Copenhagen , Denmmark ) [104 , 105] . Medium was used as negative control . Macrophages and T cells were co-cultured for 3 days at 37°C/5% CO2 , and tritium-thymidine was added for the last 16 h of culture after which the cells were harvested and tritium-thymidine incorporation was measured using a Microbetaplate counter ( Wallac , Turku , Finland ) . Furthermore , macrophages were stained for surface expression of CD86 , CD80 and HLA-DR and analyzed on a BD LSRFortessa flow cytometer ( BD Biosciences ) . For analysis of lysosomal and autophagy-related proteins , ( Mtb-infected ) macrophages were lysed for 5 minutes using a buffer containing 3% SDS , 4 mm glycerol , 100 mM Tris-HCl ( pH 6 . 8 ) containing protease inhibitors ( Roche , Woerden , The Netherlands ) and the resulting lysates were boiled for 10 min at 95°C . Protein concentrations were determined by bicinchoninic acid assay ( Pierce , Thermo Fisher Scientific ) and equal amounts were mixed with 4x Laemmli buffer before loading on a 4–20% Mini-PROTEAN TGX precast protein gel ( Bio-Rad ) . After separation , proteins were transferred onto a polyvinylidene fluoride membrane and blocked for 1 h in Tris-buffered saline/2 . 5% Tween-20 containing 5% non-fat dry milk and subsequently probed with primary antibodies overnight at 4°C . Membranes were incubated with horseradish peroxidase-conjugated secondary antibodies ( reactive against mouse or rabbit ) for 2 h at room temperature before visualization by Amersham Enhanced Chemiluminescence Western Blotting Detection kit ( GE Healthcare , Hoevelaken , The Netherlands ) . Blots were quantified using Image J ( NIH , Bethesda , MD , USA ) and proteins were normalized versus actin . For confocal microscopy , macrophages were seeded in black poly-d-lysine coated glass 96-well plates ( MatTek Corporation , Ashland , MA , USA ) . To stain lysosomes , macrophages were incubated with 75 nM Lysotracker Red or Deep Red ( Thermo Fisher Scientific ) at 37°C/5%CO2 for 1 h before fixation . Cells were fixed for 1 h in 1% EM-grade formaldehyde , followed by quenching with PBS/1 . 5 mg/ml glycine for 10 min and blocking in 5% human serum for 45 min , all at room temperature . For immunostaining , cells were permeabilized for 10 minutes with 0 . 1% Triton X-100 before blocking and subsequently stained with primary and secondary antibodies for 30 minutes each in the dark at room temperature . Finally , cells were stained with phalloidin-Alexa488 ( Thermo Fisher Scientific ) and/or LipidTOX Green ( Thermo Fisher Scientific ) for 30 min according to the manufacturers’ instructions , and/or 50 μg/ml Filipin complex from Streptomyces filipinensis ( Sigma-Aldrich ) for 2 h at room temperature in the dark . Lysotracker and filipin pictures were taken using a SP8WLL confocal microscope ( Leica , Amsterdam , The Netherlands ) . Galectin-3 and NDP52 colocalization was visualized using a Dragonfly spinning-disk confocal microscope ( Andor Technologies , Belfast , UK ) equipped with 405 , 488 , 561 and 640nm lasers and a Zyla 4 . 2 sCMOS camera . Macrophages were infected for 4 h with a DsRed-expressing Mtb H37Rv strain at a MOI of 10:1 and stained with Lysotracker Deep Red or primary antibodies for galectin 3 and NDP52 as described above . Lysotracker channel background was subtracted by rolling ball algorithm ( 20 pixel radius ) . All images were analyzed using CellProlifer 3 . 0 . 0 [106] . First , pictures were corrected for non-homogenous illumination if necessary . DsRed-Mtb were segmented by manual global thresholding with intensity-based declumping , and stained objects were segmented by adaptive two-class Otsu thresholding with upper and lower bounds to correct for individual cell-specific differences in background signal with intensity-based declumping . Then , the percentage of staining object overlap with individual DsRed-Mtb was calculated for each image and the average colocalization was calculated for each treatment condition . To assess cell viability after treatment and infection with H37Rv Mtb , macrophages were stained with 2 μg/ml propidium iodide ( PI ) ( Sigma-Aldrich ) and 2 μg/ml Hoechst 33342 ( Sigma-Aldrich ) in RPMI without phenol red and FCS for 5 min in the dark . Cells were subsequently imaged on a AF6000 fluorescence microscope ( Leica ) and pictures were taken at a 20x magnification . Pictures were processed and analyzed in Image J . First , the background was subtracted by rolling ball algorithm ( 20 pixel radius ) . Then , Hoechst- or PI-positive nuclei were segmented by Otsu thresholding and counted , from which the percentages of viable macrophages were calculated . Staurosporin ( 5 μM ) ( Sigma-Aldrich ) was used as a positive control for cell death . Statistical significance was assessed by Kruskal-Wallis test with post-hoc Dunn’s test , or Wilcoxon signed rank test using GraphPad software ( version 7 . 02 , Prism , La Jolla , CA , USA ) with post-hoc false discovery rate ( FDR ) correction for multiple comparisons when necessary . Statistical analysis of patients characteristics was performed in SPSS 23 ( IBM , Armonk , NY , USA ) by one-way ANOVA ( reported p-values are the outcome of the F-test ) , independent samples t-test or chi-squared test .
Tuberculosis ( TB ) is an infectious disease of the lungs caused by a bacterium , Mycobacterium tuberculosis ( Mtb ) , and is responsible for over a million deaths per year worldwide . Population studies have demonstrated that type 2 diabetes mellitus ( DM ) is a risk factor for TB as it triples the risk of developing the disease . DM is a metabolic disorder which is generally associated with obesity , and is characterized by resistance to the pancreatic hormone insulin and high blood glucose and lipid levels . As the global incidence of DM is rising at an alarming rate , especially in regions where TB is common , it is important to understand precisely how DM increases the risk of developing TB . Both TB and DM are associated with the development of foamy macrophages , lipid-loaded white blood cells , which can be the result of a specific lipoprotein particle called oxidized low-density lipoprotein ( oxLDL ) . Here , we demonstrated that DM patients have high blood levels of oxLDL , and generating foamy macrophages with oxLDL supported Mtb survival after infection as a result of faulty intracellular cholesterol accumulation . Our results propose a proof of concept for oxLDL as a risk factor for TB development , encouraging future studies on lipid-lowering therapies for TB-DM .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]
[ "blood", "cells", "medicine", "and", "health", "sciences", "lysosomes", "immune", "cells", "bovine", "tuberculosis", "immunology", "tropical", "diseases", "diabetes", "mellitus", "bacterial", "diseases", "endocrine", "disorders", "cellular", "structures", "and", "organelles", "bacteria", "infectious", "diseases", "lipids", "white", "blood", "cells", "zoonoses", "animal", "cells", "tuberculosis", "endocrinology", "actinobacteria", "metabolic", "disorders", "cholesterol", "biochemistry", "cell", "biology", "mycobacterium", "tuberculosis", "biology", "and", "life", "sciences", "cellular", "types", "macrophages", "organisms" ]
2019
Oxidized low-density lipoprotein (oxLDL) supports Mycobacterium tuberculosis survival in macrophages by inducing lysosomal dysfunction
The Pacific Islands have environmental conditions highly favourable for transmission of leptospirosis , a neglected zoonosis with highest incidence in the tropics , and Oceania in particular . Recent reports confirm the emergence and outbreaks of leptospirosis in the Pacific Islands , but the epidemiology and drivers of transmission of human and animal leptospirosis are poorly documented , especially in the more isolated and less developed islands . We conducted a systematic review of human and animal leptospirosis within 25 Pacific Islands ( PIs ) in Polynesia , Melanesia , Micronesia , as well as Easter Island and Hawaii . We performed a literature search using four international databases for articles published between January 1947 and June 2017 . We further included grey literature available on the internet . We identified 148 studies describing leptospirosis epidemiology , but the number of studies varied significantly between PIs . No data were available from four PIs . Human leptospirosis has been reported from 13 PIs , with 63% of all studies conducted in Hawaii , French Polynesia and New Caledonia . Animal leptospirosis has been investigated in 19 PIs and from 14 host species , mainly pigs ( 18% of studies ) , cattle ( 16% ) and dogs ( 11% ) . Only 13 studies provided information on both human and animal leptospirosis from the same location . Serology results were highly diverse in the region , both in humans and animals . Our study suggests that , as in other tropical regions , leptospirosis is widespread in the PIs while showing some epidemiological heterogeneity . Data are scarce or absent from many PIs . Rodents , cattle , pigs and dogs are all likely to be important carriers , but the relative importance of each animal species in human infection needs to be clarified . Epidemiological surveys with appropriate sampling design , pathogen typing and data analysis are needed to improve our understanding of transmission patterns and to develop effective intervention strategies . Leptospira is a genus of bacteria belonging to the phylum of Spirochaetes causing leptospirosis in humans and other mammals [1] . Leptospirosis is the most widespread and potentially fatal bacterial zoonosis worldwide [2] , with an estimated 1 . 03 million human cases and 58 , 900 deaths worldwide each year [3] . The majority of the disease burden occurs in tropical regions where large epidemics can occur after heavy rainfall and flooding [4] . Leptospirosis is a neglected disease in most of the tropics , especially in the Pacific region [5] , and a recent systematic review found that Oceania was the region most impacted by leptospirosis in terms of morbidity ( 150 . 68 cases per 100 , 000 per year ) , mortality ( 9 . 61 deaths per 100 , 000 per year ) [3] , and disability adjusted life years ( DALY ) [6] . Incidence of up to 1 , 945 cases per 100 , 000 population has been reported in 2008 in Futuna ( a Polynesian island ) during a multi-year outbreak [7] . The health impacts of leptospirosis have been predominantly attributed to acute infections and early complications such as pulmonary haemorrhage and renal failure . However , leptospirosis can also cause subacute and chronic complications and long-term sequelae [8] . Two classification schemes are used for Leptospira , one based on serology with the serovar as the basic taxon , and another which uses molecular taxonomy to identify the species , sometimes referred to as genomospecies [2] . Leptospira have been classified serologically into over 300 serovars grouped in almost 30 serogroups ( both saprophytic and pathogenic ) using Microscopic Agglutination Test ( MAT ) and Cross Agglutination Absorption Test ( CAAT ) [9 , 10] . Phylogenetically , the genus Leptospira is divided into 23 species based on 16S rRNA phylogeny and DNA-DNA hybridization , and clustered into saprophytic , intermediate and pathogenic groups [11] . Laboratory diagnosis of leptospirosis may be accomplished by direct detection of the organism or its components in body fluid or tissues , by isolation of leptospires in cultures , or by detection of specific antibodies [12 , 13] . Molecular diagnosis is based on Leptospira DNA amplification from serum , urine , aqueous humour , cerebrospinal fluid ( CSF ) or post-mortem tissue samples [14] . Historically , most cases of leptospirosis have been diagnosed by serology , because capacity for culture and PCR were limited . IgM antibodies are detectable in the blood from 5–7 days after the onset of symptoms [2] . The use of agglutination tests was described soon after the first isolation of the organism , and the MAT remains the definitive serological investigation technique in both humans and animals [15] . Human infections range from , most commonly , a mild ‘flu-like illness , to severe complications including acute renal failure and pulmonary haemorrhagic syndrome associated with high fatality rates . Infection results from direct or indirect exposure to urine from infected reservoir host animals that carry the pathogen in their renal tubules and shed pathogenic leptospires which contaminate soils , surface waters , streams and rivers [2] . Humans are infected via mucous membranes , abrasions or cuts in the skin . Prolonged immersion in , or swallowing of , contaminated water can also result in infection . Numerous animal species , including rodents ( often considered as the main reservoir ) , domestic mammals ( including livestock and companion animals ) and wildlife , have been shown to be reservoirs for Leptospira [15] . In food-producing animals , cattle and pigs are relatively susceptible to clinical infection , resulting in production losses including reduced milk yield , reproductive failure , abortions , premature birth or stillbirth [16] . The Oceania region includes Australia , New Zealand and the Pacific Islands Countries and Territories ( PICTs ) , these latter all falling within the tropics . Poverty , remoteness and tropical climate all contribute to vulnerability to , and significant burden of , infectious diseases in the PICTs [17] . Global emergence of leptospirosis has been associated with environmental factors including rainfall , flooding , poverty , and urbanization [18–20] , all of which are important drivers of transmission in the Pacific Islands . Recent reports confirm the emergence of leptospirosis in the Pacific region , with increase in incidence and reports of unprecedented outbreaks [5 , 21] . However , the incidence of leptospirosis is unfortunately not well-documented from many Pacific Islands , mainly because of the unavailability of laboratory diagnosis [22 , 23] , poor medical awareness , and non-specific symptoms that overlap with many other tropical infectious diseases , especially arbovirus infections [24] . As a consequence , little is known about the ecological , epidemiological and clinical characteristics of leptospirosis in the region , and the burden of the disease might be even higher than recognized . To tackle these gaps in current knowledge and understanding of human and animal leptospirosis infection in the Pacific Islands , we conducted a systematic review of both peer-reviewed and grey literature following the Preferred Reporting Items for Systematic Reviews and Meta-analyses ( PRISMA ) guidelines [25] . Our aims were to summarise and compare human and animal leptospirosis within and between the Pacific Islands . The selection of the countries and territories to be included in this review was based on the official list of the United Nations Statistics Division for the geographical region #009 Oceania ( http://unstats . un . org/unsd/methods/m49/m49regin . htm#oceania ) . We excluded Australia and New Zealand ( #053 ) and focused on the small islands of the PICTs including five in Melanesia ( Fiji , New Caledonia , Papua New Guinea , Solomon Islands , Vanuatu ) , eight in Micronesia ( Guam , Kiribati , Marshall Islands , Federated States of Micronesia , Nauru , Mariana Islands , Palau , United States Minor Outlying Islands ) and ten in Polynesia ( American Samoa , Cook Islands , French Polynesia , Niue , Pitcairn Islands , Samoa , Tokelau , Tonga , Tuvalu , and Wallis and Futuna ) . The “United States Minor Outlying Islands” were restricted to Wake Island in our study . We further included Easter Island ( Chile ) as well as Hawaii ( U . S . A . ) ( Fig 1 ) . Except for Papua New Guinea and Hawaii , the included PIs had populations of less than one million inhabitants and represented significant diversity in economies , geography , culture and living conditions . For ease of reference , the 25 countries and territories included in this review will be referred to as ‘Pacific Islands’ ( PIs ) . Peer-reviewed studies were sought in June 2017 from four international databases ( PubMed , Web of Science , Scopus , and Embase ( Ovid ) ) for resources published between January 1947 and June 2017 inclusively , either in English or in French , and following the PRISMA guidelines [25] ( S1 Appendix ) . The search strategy is presented in detail in S2 Appendix . Secondly , additional peer-reviewed studies were retrieved by examining the references from the papers identified by the initial electronic search . Thirdly , grey literature ( i . e . print and electronic formats that have not been formally published by commercial publishers ) were reviewed by scrutinising the websites of government health departments and other relevant administrative bodies of the Pacific Islands ( see S2 Appendix for the list of websites browsed ) , and by bibliography hand searches of relevant articles from Google Scholar ( https://scholar . google . com ) . The data collection process was undertaken in two steps . First , abstracts and titles were compiled in EndNote ( Thomson Reuters , Philadelphia , PA , USA ) and reviewed by one researcher ( VG ) on the basis of the abstract and title . Second , the articles identified through the pre-selection process were retrieved in full text format and reviewed independently by two researchers for the full text ( VG and either CG , JB or CLL ) . A third researcher served as a tiebreaker for any discordant decisions . Following the inclusion/exclusion process , qualitative and quantitative data were extracted from each of the included articles . Papers were classified into human clinical studies , human community-based studies , and animal population studies ( no animal clinical studies were identified ) . For human studies , information was compiled on the country , year ( s ) of study , study design , target population , inclusion criteria , number enrolled , diagnostic tests used , confirmed leptospirosis cases and risk factors . For animal studies , information was compiled on country , year ( s ) of study , species investigated , methodology , number of samples , and confirmed positivity for Leptospira . When available , data on serology and genetic typing of isolates from humans and animals were also compiled and summarised by country and by animal species . MAT is a serogroup-specific assay and does not provide reliable information on the infecting serovar [26 , 27]; results were therefore summarised at the serogroup level even if studies reported MAT results at the serovar level . Although serogroups are no longer used in the taxonomic classification of serovars , they remain useful for laboratory purposes and epidemiological comparisons . As the definition of a “positive” serology result differed between studies , we standardised results across the studies by using the following case definitions: When MAT results were detailed , the serogroup with the highest titre was reported as putatively involved . If equally high titres were reported for more than one serogroup , MAT was considered positive but serogroup results were not reported . If data were not detailed enough to allow interpretation ( especially in the case of cross-reactions ) , serogroup results were not reported . Because of the great heterogeneity in the methodology and the quality of the data between studies , most of the data reported in our review are qualitative rather than quantitative . As detailed in the previous paragraph , methodological quality was assessed for serology studies by comparison to pre-determined case definition criteria to control for heterogeneity in study design and diagnostic methodology . Evidence of Leptospira infection was assessed for each study as ‘strong’ or ‘weak’ . The sample sizes ( number of animal tested , number of clinical cases reported ) are reported in our review when necessary , as an indication of the degree of confidence of the results . Exposure and risk factors are reported as per the included studies , and are considered mostly as untested hypotheses . Epidemiological studies providing risk factors based on robust analyses are highlighted . The initial search retrieved a total of 675 studies , including 386 on PubMed , 68 on Scopus , 68 on Web of Science , and 153 on Embase respectively . After removing 205 duplicates , 103 met the inclusion and exclusion criteria . A further 44 studies were identified , either from the reference list of already included papers ( n = 15 ) , or when searching internet websites for grey literature ( n = 29 ) . One publication from the last author of the present review ( CL ) that was accepted for publication after the search in June was also added , making a final list of 148 studies . For 11 studies , the full text document could not be retrieved but quantitative and/or qualitative data were available from the abstract , or from the full text of later published papers . The flow diagram of the search strategy is summarised in Fig 2 . The included leptospirosis studies were conducted in 21 out of the 25 PIs within the scope of our study . No information about leptospirosis was retrieved from Nauru , Pitcairn Islands , Tuvalu and Wake Island . Included studies were mostly either dedicated to animals ( n = 54 , 36 . 5% ) or humans ( n = 79 , 53 . 4% ) , while very few studies ( n = 13 , 8 . 7% ) provided information on both animals and humans . The remaining two studies ( 1 . 4% ) focused on Leptospira in the environment [29 , 30] . Detailed information on each study is provided in S1 Table . Human leptospirosis was investigated in 92 eligible studies from 14 PIs . We classified human studies into two categories: community-based studies ( investigating healthy people ) and clinical studies ( investigating people who were unwell ) ( see Fig 3 and S1 Table ) . Community-based studies consisted of 19 ( 21% ) seroprevalence studies , of which six were “mixed” studies that also included clinical case reports . The remaining 73 studies ( 79% ) were clinical , including surveillance data , case reports or leptospirosis investigation in sick and/or hospitalised patients ) . Clinical studies were reported as strong or weak evidence of leptospirosis following the criteria detailed in the methods section ( see S1 Table for detailed classification ) . French Polynesia was the most frequently represented PI with a total of 22 studies [5 , 24 , 31–48] , followed by 20 from New Caledonia [32 , 36 , 42 , 49–65] and 19 from Hawaii [66–84] . Of the 14 PIs investigated , confirmed cases were reported from 13 PIs only as four suspected leptospirosis cases investigated in Tonga were later reported as negative [5] . Past infection with , or renal carriage of , leptospires in animals was investigated in 66 eligible studies from 19 PIs , plus one paper that reported animal leptospirosis from 22 PIs [85] ( S1 Table ) . In total , 13 different animal species were tested: bandicoots ( Echymipera kalubu ) , cats ( Felis catus ) , cattle ( Bos spp . ) , deer ( Rusa timorensis ) , dogs ( Canis lupus ) , pigs ( Sus scrofa ) , goats ( Capra aegagrus ) , horses ( Equus ferus ) , mongooses ( Herpestes auropunctatus ) , mice ( Mus musculus ) , rats ( Rattus spp . ) , sheep ( Ovis aries ) , and in one case , Hawaiian monkey seals ( Monachus schauinslandi ) . Both feral and farmed populations of pigs and deer were investigated . Livestock represented the majority of the investigations , i . e . farmed pigs ( 29 studies from 18 PIs ) and cattle ( 27 studies from 14 PIs ) , followed by rats ( 28 studies from 8 PIs ) and domestic dogs ( 24 studies from 9 PIs ) . The majority of animal studies ( n = 54 , 82% ) were seroprevalence studies . Renal carriage was also investigated ( n = 14 , 21% ) especially in rats , either alone or in combination with serology . Older studies reported the presence of leptospires in kidneys from microscopic examination of tissues or by experimental infection of guinea pigs ( e . g . by injection of crushed rat kidneys ) , while more recent studies used real-time PCR for confirmation of infection . Evidence of past or present Leptospira infection was most commonly found in farmed pigs ( 27 studies from 15 PIs ) followed by cattle ( 24 studies from 13 PIs ) , rats ( 24 studies from 8 PIs ) and dogs ( 22 studies from 9 PIs ) ( Fig 3 ) . There was variation in the sample sizes between studies , ranging from one animal tested in some studies that reported on opportunistic sampling of animals to about 9 , 000 animals in a study of cattle in the Solomon Islands [86] . Studies reported a total of 21 putative Leptospira serogroups in humans or animals across the Pacific , the most common being Icterohaemorrhagiae ( 15 PIs ) , Pomona ( 15 PIs ) , Australis ( 14 PIs ) and Sejroe ( 13 PIs ) . Table 1 summarises the serogroups reported for humans and animal species for each PI . However , this finding should be interpreted with caution because of the many limitations of the MAT , including cross-reactions between serovars and serogroups , paradoxical reactions or anamnestic responses [2 , 87] . MAT results might also vary depending on the panel of serovars used . Human leptospirosis was recognised as early as 1936 in Hawaii; seroprevalence studies investigated the general population in Honolulu [66] and sugar cane workers [67] in 1936–1942 , and revealed seroprevalence rates of 3 . 8% ( 13/344 ) and 12 . 2% ( 105/860 ) respectively , using a cut-off titre of 1:100 . A seroprevalence study of US army blood bank donors conducted in 2002 in Oahu found a positivity rate of 1 . 4% ( 7/488 ) [79] . Sixteen publications reported leptospirosis cases from surveillance data and clinical studies between 1962 and 2008 . Information by island was not always available as surveillance data generally included cases from all islands , but a few studies were dedicated to Kauai and Oahu , reporting sporadic cases . One study investigated cases of leptospirosis during an outbreak of murine typhus on Kauai ( 1998 ) and reported two cases of co-infection with both diseases [76] . The complete list of human studies from Hawaii is reported in Table 2 . In Hawaii , the epidemiology and relative importance of risk factors for human leptospirosis have evolved over the past few decades . Leptospirosis in Hawaii has historically been considered an occupationally-acquired disease affecting primarily sugarcane labourers and farmers , but a shifting trend in exposure has been observed since the 1970s , with increasing importance of recreational exposure ( freshwater swimming , hunting , fishing , hiking ) [70 , 75 , 77] . During 1989–2008 , the frequency of recreational exposures plateaued while frequency of occupational exposures seemed to increase ( at least for the island of Hawaii ) . At the same time , a significant shift in the seasonal occurrence of leptospirosis from the drier summer months to the wetter winter months was observed [84] . An increase in “habitational risks” , especially gardening at home , was suspected to be linked to the resurgence of traditional taro farming . Lastly , exposure to feral pigs , although not investigated per se in the epidemiological surveys , was suspected to be responsible for the changing trend in the infecting serogroup in human cases , shifting from Icterohaemorrhagiae to Australis . Animals were first investigated for leptospirosis in Hawaii during the same 1936–1942 investigation targeting humans . Dogs , cats , rats and mongooses were serologically screened for leptospirosis , and revealed evidence of infection in all three species [66 , 88] . Between 1943 and 2009 , more animal studies followed , investigating six animal species . Seropositive rats and mongooses were reported from nine and seven more studies respectively . Evidence of leptospirosis infection was also found in cattle ( 2/2 studies ) , mice ( 6/6 ) , feral swine ( 1/1 ) and Hawaiian monkey seals ( 1/1 ) . Surprisingly , dogs and cats were never further investigated . The complete list of animal studies from Hawaii is reported in Table 3 . Hawaii is one of the two places ( with New Caledonia ) where leptospires were investigated in the environment . A molecular study from 2011 focused on environmental samples collected in 22 tropical streams of Oahu near the point at which they discharge to the coastal ocean , showing evidence of Leptospira in 87 of 88 samples . All of the sequenced amplicons ( n = 42 ) were characterised as pathogenic Leptospira wolffii . Human leptospirosis was first reported in 1954 and 1957 . A total of 20 articles from New Caledonia were identified . Two seroprevalence studies were conducted in 1985–86 [53 , 54] , and the majority of articles were clinical studies ( n = 18 ) including case reports , routine surveillance data , and case investigations . Clinical studies were conducted from 1973 to 2012 , with frequent reports except between 1990–1998 , suggesting endemic transmission . Three clinical studies focused on severe leptospirosis: severe icteric leptospirosis cases with cardiac manifestations [55] , the influence of age on the development of severe leptospirosis in children [63] , and risk factors and predictors of severe leptospirosis [64] . The complete list of human leptospirosis studies from New Caledonia is reported in Table 4 . The risk factors for human leptospirosis in New Caledonia varied little between the 1980s and today . The disease was more frequent among young men and during the wet season . The main risk factors identified were recreational exposure ( fishing and swimming in fresh water , hunting ) and contact with animals , with populations living in rural areas and local tribes being at highest risk . However , the probable source of infection was difficult to ascertain because of the multiplicity of potential infecting sources and exposure pathways , and the overlap between professional and recreational activities [56] . Also , identification of the animal source of leptospires in these ( human only ) studies was only presumptive as people were frequently exposed to multiple species , including cattle , pigs , horses , dogs , rats and , less commonly , deer [56 , 61] . Eleven studies were published on animal leptospirosis in New Caledonia between 1983 and 2004 . Leptospira spp . infection was demonstrated in a wide range of animal hosts , including cattle ( 6/6 studies ) , dogs ( 6/6 ) , deer ( 6/6 ) , horses ( 3/3 ) , pigs ( 4/5 , including one study of feral pigs ) , rats ( 3/3 ) and cats ( 2/3 ) . Results on goats largely lacked detail , however one study reported five seropositive animals . Results on sheep ( one study ) were inconclusive . The complete list of studies on animal leptospirosis in New Caledonia is reported in Table 5 . In one study from 2016 in New Caledonia , leptospires were investigated in the environment [29] . The study focused on environmental samples collected around human cases , showing that 58% of soil samples were contaminated with pathogenic leptospires . The first human leptospirosis cases were reported from Tahiti in the 1950s [31] . Two serological surveys in the general population were conducted in French Polynesia: one in Tahiti in 1970–71 [34] found a seroprevalence of 29 . 5% , and another in Marquesas in 1981 [39] where a lower seroprevalence ( 9 . 5% ) was found . In addition , 20 clinical studies from 1952 to 2015 provided evidence of human leptospirosis , suggesting endemic transmission throughout the archipelago . Available reports generally covered the whole of French Polynesia , a vast archipelago of 118 islands and atolls , because diagnostic laboratories received samples from clinically-suspected cases from all islands , but a few studies were dedicated to specific Islands , i . e . Marquesas [5 , 39 , 46] and Raiatea [5 , 46] . The complete list of human studies from French Polynesia is reported in Table 6 . Three clinical studies included case investigations ( through questionnaires ) that aimed to identify risk factors associated with human leptospirosis ( 2006–2010 ) , and important factors common between the studies included contact with rats or domestic animals , walking barefoot in water or mud , swimming in rivers , gardening , and occupational risks ( working in piggeries , farming ) [47 , 106 , 107] . In Marquesas , hunting and contact with dogs have been reported as possible sources of infection [5 , 46] . Risk factors in French Polynesia were mostly recreational rather than occupational , but potential infecting sources were numerous . Only three studies investigated animal leptospirosis in French Polynesia . The first study ( 1952–53 ) investigated renal carriage in rats from Tahiti by experimental infection of guinea pigs with crushed rat kidneys; four guinea pigs became icteric [31] . The second was a seroprevalence study ( 1981–86 ) targeting four host species from Tahiti , and cattle from both Tahiti and Marquesas [108] . Positive serology results were found in 15 . 5% ( 23/148 ) of cattle , 32 . 2% ( 37/115 ) of pigs , and all five horses tested . Serology results were not detailed by island , but only seven tested cattle originated from Marquesas . The two sheep and twelve dogs tested were found to be seronegative . The last study investigated animal leptospirosis in dogs , pigs and rats from Tahiti [48] . Renal carriage of pathogenic leptospires was demonstrated from 26 . 5% ( 48/181 ) of farmed pigs , 20 . 4% ( 23/113 ) of rats , and four sick dogs . The first study reporting human leptospirosis in Fiji was published in 1978 [109] , describing 240 cases between 1969 and 1977 . Since then , two community-based seroprevalence studies were published [110 , 111] , as well as five studies reporting clinical cases [5 , 112–115] . The first seroprevalence study in the 1980s identified 264 seropositive out of 300 healthy volunteers ( 88% ) from a rural area in the main island of Viti Levu [110] . A second seroprevalence study was conducted in 2013 and included 2 , 152 healthy individuals from 81 communities on the three main islands of Fiji , of whom 19 . 4% were seropositive [111] . Risk factors associated with infection included living in villages ( OR 1 . 64 ) , lack of treated water at home ( OR 1 . 52 ) , working outdoors ( 1 . 64 ) , living in rural areas ( OR 1 . 43 ) , high poverty rate ( OR 1 . 74 ) , living <100m from a major river ( OR 1 . 41 ) , pigs in the community ( OR 1 . 54 ) , high cattle density in the district ( OR 1 . 04 per head/km2 ) and high maximum rainfall in the wettest month . Using the data from the 2013 seroprevalence study ( which included questionnaire data about contact with animals as well as livestock data from the Fiji Ministry of Agriculture ) , a third study [116] showed significant heterogeneity in the relative importance of animal species in leptospirosis transmission in different ethnic groups and residential settings . Five studies have been published on animal leptospirosis in Fiji [113 , 117–120] , exploring a wide range of host species , including dogs , goats , mongooses , cattle , pigs , rats , sheep , mice , horses and cats . Except for cats ( only three animals tested ) , the studies identified seropositive animals in all species that were tested . Thus , Leptospira infection seems common in multiple animal species in Fiji . Studies on human leptospirosis in Papua New Guinea ( PNG ) were published between 1955 and 1968 [121–124] , including three seroprevalence studies targeting healthy volunteers , showing high seroprevalence of 53 . 0% to 57 . 5% , with Australis and Hebdomadis the most common serogroups . One study explored risk factors for human infections and found that leptospirosis in PNG was common in both males and females , and in both adults and children , reflecting high risk in the whole population from shared occupational , domestic , and other environmental exposures [123] . People have close contact with domestic animals , particularly dogs and pigs , as well as native fauna in gardens and uncleared areas surrounding the villages . Rats were suspected to be of less importance compared to other animal species because human studies in PNG found that serogroup Icterohaemorrhagiae ( commonly associated with rodents ) was relatively uncommon [122 , 125] . Leptospira infection in animals was explored in the 1960s and 1970s , with seven publications exploring infection in rats , cattle , dogs , pigs , goats and marsupial bandicoots [122 , 123 , 125–129] . Seropositive animals were identified from all tested species , but sample size was small for bandicoots ( n = 5 ) . After the 1970s , no further studies were published until a seroprevalence study investigated cattle ( n = 1 , 452 ) and pigs ( n = 326 ) in 2004 , and dogs and livestock in 2006 ( 111 cattle , 69 pigs , 22 dogs , 15 horses ) [130] . Seropositive animals were identified from all species , but seroprevalence was low in dogs ( 1/22 in 2006 ) and pigs ( 0/362 in 2004 , 1/69 in 2006 ) . The first study published in 1974 identified three seropositive stray dogs out of 180 tested ( 1 . 7% seroprevalence ) [131] . More recent studies investigated cattle ( n = 34 ) and pigs ( n = 52 ) in 1999 [132] , and feral pigs ( n = 46 ) in 2015 [133] . All livestock tested seronegative but the seroprevalence in feral pigs was 23 . 9% . The first human clinical study was published in 1998 , describing two human leptospirosis cases with pancreatitis [74] . Three human clinical studies in the 2000s reported sporadic cases only [5 , 74 , 134 , 135] . In addition , two leptospirosis cases reported in Hawaii for the period 1999–2008 were suspected to have been acquired in Guam [84] . One mixed study included both clinical data and a seroprevalence study conducted following an outbreak that occurred after an outdoor multisport athletic event: out of 46 participants surveyed , 21 reported being ill and three of them were confirmed with recent leptospirosis infections [136] . Reported exposures were often recreational activities involving swimming in fresh water or waterfalls , but water buffaloes were also reported as a suspected source of contamination . No seroprevalence study has been conducted in Wallis and Futuna . A multicentre survey of suspect clinical cases recruited by general practitioners conducted in 2003–2005 identified 3 cases out of 14 ( 21% ) and 31 cases out of 71 ( 44% ) in Wallis Island and Futuna Island respectively [5] . Another clinical study conducted in 2005 identified one case in Wallis and 21 cases in Futuna [137] , and reported some exposure information: one case swam in a river few days before the occurrence of the disease , and all the other cases were involved in breeding pigs ( at home for the Futuna cases , at work in a pig farm for the one Wallis case ) . A third study conducted over the period 2004–2014 reported 382 cases in Futuna , with a peak incidence in 2008 [7] . Serogroup Australis was predominant until 2007 , when Icterohaemorrhagiae became the most common . Despite similar cultural and socio-economical patterns between the islands , human leptospirosis was considered endemic in Futuna while it only occurred sporadically in Wallis . Three studies focused on animal leptospirosis in Wallis and Futuna . A first study focusing on livestock only reported the detection of specific Leptospira serogroups in pigs , with no prevalence provided [138] . A seroprevalence study was conducted in the 1990s on different animal species: 14% ( 12/88 ) of pigs were found to be seropositive in 1985 , and 33% ( 54/163 ) in 1997 but a wider range of serogroups was tested in 1997 . In 1998 , two dogs out of 10 and three horses out of six also tested seropositive , while all 11 goats tested were seronegative [138 , 139] . In 2008–2012 , Leptospira prevalence in three rat species was investigated [140] . Renal carriage of Leptospira interrogans was confirmed in 84 rats out of 286 in Futuna ( 36 . 4% Rattus rattus , 42 . 8% Rattus norvegicus and 18 . 8% Rattus exulans ) , while only one R . exulans out of 56 rats ( 1 . 8% ) was positive in Wallis; 15 rats were found negative in the uninhabited island of Alofi , 2 km from Futuna . Risk factors associated with human leptospirosis have not been formally investigated , but rodents in Futuna have a much higher Leptospira carriage rate than on Wallis . It has been hypothesised that this variation could be explained by differences in taro farming practices; people on Wallis irrigate their fields by ditches , while those on Futuna flood their fields . This practice in Futuna might facilitate the spread of Leptospira among rats and subsequently to humans [140] . Human leptospirosis was first investigated in American Samoa in 1948 [141]; ten patients hospitalised for jaundice were tested , but none were seropositive against four Leptospira serogroups . Thirty patients hospitalised for reasons other than jaundice were also tested , and three were found to be seropositive for serogroup Australis . Leptospirosis cases were later occasionally reported , including three cases of co-infection with dengue in 2008 [142] and one severe case in 2011 of a 15 year old boy who frequently slept in the rainforest and had recently waded through freshwater [143] . Four community-based seroprevalence studies were conducted in American Samoa . Two studies reported seroprevalence rates of 17% in 2004 ( n = 341 ) [144] and 15 . 5% in 2010 ( n = 807 ) [145] . Australis was the main serogroup in 2004 ( 71% ) while Hebdomadis , Australis and Pyrogenes were the most common serogroups in 2010 . The 2010 survey found that outdoor occupation ( OR = 3 . 25 ) , piggeries around the house ( OR = 2 . 63 ) and recreational exposure ( swimming at beach OR = 2 . 01 , fishing OR = 1 . 78 ) were significantly associated with seropositivity , but infections with each of the three main serogroups were associated with different behavioural and environmental exposures [145] . A third study compared the serogroups between the 2004 and 2010 surveys [146] and showed epidemiological evidence of serogroup emergence , possibly as a result of ecological and environmental change . The fourth study produced a predictive risk map using environmental variables from 2010 study and demonstrated the importance of environmental drivers of transmission [147] . Two studies focused on animal leptospirosis . Renal carriage of leptospires by rats was examined on Tutuila Island in 1948; out of 126 individuals from four rat species trapped , 24 ( 19% ) Rattus norvegicus kidney tissues stained by silver precipitation technique were found positive under microscopic examination [141] . One out of twelve dogs was also found seropositive by MAT . A more recent study published in 2005 reported data ‘from animal studies and literature’ ( with no date specified ) showing seropositive results from pigs , rats , dogs and cattle [135] . The authors suggested that , even though the animal studies were not conducted at the same time as the human studies ( 2004 ) , it appeared that Australis was the most common serogroup infecting people , and pigs were the most likely reservoir hosts . Seven clinical studies reported information about human leptospirosis in the Federated States of Micronesia ( FSM ) , which consists of the four states of Yap , Chuuk , Kosrae and Pohnpei in the Northwestern Pacific Ocean . In 1989–1997 , eight confirmed leptospirosis cases identified in Hawaii were acquired from Kosrae and Pohnpei [74] , and from 1999–2008 one reported case in Hawaii was acquired from FSM [84] . A multi-centre survey of patients with suspected leptospirosis conducted in 2003–2005 reported no seropositive case from Yap ( 0/1 suspected ) or Pohnpei ( 0/27 suspected ) [5] . In 2010 , an investigation of 10 febrile patients in Chuuk revealed two confirmed cases of leptospirosis [148] . In 2011 , a hospital-based survey conducted in Pohnpei on 54 patients presenting with undifferentiated fever found that 20 . 4% showed serologic evidence of acute infection by MAT [149] . In 2012 in Yap , 172 patients with suspected dengue were investigated by qPCR and five ( 2 . 9% ) were confirmed as acute leptospirosis infections [150] . Lastly , one study investigated the health risks associated with climate change in FSM using a time series distribution of monthly leptospirosis outpatient cases in Pohnpei . This study was based on hospital records and is assumed to represent close to all of the reported cases [151] . Taken together , those studies suggest that leptospirosis is endemic in FSM . Rats were the first animal species to be investigated in 1947 , but microscopic examination of kidney tissues stained by silver precipitation technique did not reveal any leptospires from 28 rats trapped in Chuuk and Pohnpei [152] . However , a seroprevalence study conducted in 1995–1996 on pigs ( in four states ) , dogs ( Pohnpei , Chuuk ) and rats of three species ( Pohnpei , Chuuk , Yap ) showed positive results from all species [153] . No papers on human leptospirosis were identified from the Solomon Islands , and only three papers on animal leptospirosis have been published . Between 1967 and 1977 , a large veterinary survey investigated cattle diseases from all the cattle herds listed in 1967 ( 165 herds with a total of 8 , 930 cattle ) plus some of the 650 herds established after 1967 [86] . For leptospirosis , only the female cattle over one year of age were tested , of which 62 were found seropositive . In 1985 , pigs were reported positive with serogroup Pomona , but detailed results were not available [154 , 155] . A seroprevalence study conducted in 1998 reported that 83% of 226 cattle , 12% of 298 pigs , 16% of 63 goats , and 71% of 31 horses tested were seropositive by MAT [155] . Only sporadic cases of human leptospirosis have been reported in Vanuatu in the 1990s [156 , 157] and the 2000s [5] . No human seroprevalence studies have been conducted . Two cases in travellers returning from Vanuatu were also reported in New Caledonia [51] and Australia [158] . The latter swam in a river in Vanuatu with an injured foot , and developed a fatal illness with acute renal failure , jaundice , respiratory failure , myocarditis and rhabdomyolysis . During an extensive survey of livestock diseases conducted between 1971 and 1981 throughout the Vanuatu archipelago , 6 , 719 cattle from 131 herds were investigated by MAT; 92 cattle ( 1 . 4% ) were found positive [159] . Following the presentation of three cases of Weil’s disease in 2000 , 171 patients presenting with a ‘viral syndrome’ were investigated in Palau , of whom seven were serologically confirmed as leptospirosis [160] . Over the period 2000–2006 , the disease surveillance system recorded 81 cases , all living in the most populated areas of the country [161] . In 2003–2005 , a multi-centre survey of patients with clinically suspected leptospirosis reported one seropositive out of eight tested [5] . In 2014 , two Japanese travellers developed leptospirosis after returning from Palau; the suspected exposure was swimming in Ngardmau falls [15] . Cattle ( n = 20 ) , goats ( n = 7 ) and pigs ( n = 55 ) were serologically investigated by MAT between 1993 and 1996 in Palau [162] . All three investigated species were found seropositive for Leptospira , i . e . 9 cattle ( 45% ) , 3 goats ( 43% ) and 22 pigs ( 40% ) . In Tonga , a multi-centre survey of clinically suspected cases was conducted in 2003–2005; four patients were tested by PCR or MAT but none were confirmed as leptospirosis [5] . Cattle ( n = 171 ) and pigs ( n = 244 ) from different Tongan islands ( Tongatapu , 'Eua , Ha'apai , Vava'u ) were serologically investigated by MAT between 1992 and 1994 [163] . Seroprevalence varied between the islands , from 19 . 6% to 45 . 0% in cattle , and from 5 . 0% to 16 . 7% in pigs . In the Commonwealth of the Northern Mariana Islands ( CNMI ) , leptospirosis has been reported as endemic , but our search retrieved only one study; in 2000–2001 , 10 cases of leptospirosis were reported in Saipan , of which eight were severe , and three were fatal [164] . In none of the cases was laboratory diagnosis available to the medical staff until weeks after patient was hospitalized or had died . Case reports available for four of the patients identified possible exposures as swimming in freshwater , cleaning out roadside sewers after a tropical storm , slaughtering pigs , and occupational gardening . In 1989–1997 , one confirmed leptospirosis case identified in Hawaii was acquired from the Marshall Islands [74] . For the remaining Pacific Islands , our search retrieved no studies on human leptospirosis , but few animal studies were available for Easter Island , Kiribati , Niue , Cook Islands , Samoa and Tokelau . The results are summarised in Table 7 . This systematic review is the first to synthesize and compile data on the epidemiology of human leptospirosis and pathogenic Leptospira spp . infection in animals in the PIs . Considering the limited population and economic resources in those mostly small and isolated islands , the number of studies retrieved was impressive ( n = 148 ) . Overall , the systematic review demonstrates that leptospirosis is an important cause of acute febrile illness across the PIs , with evidence of human disease demonstrated in 13 of 14 PIs where investigations have been conducted . Community-based seroprevalence studies were conducted in seven PIs and provided heterogeneous results , with prevalence ranging from 10% to 88% . Taken together , these findings reflect the public health importance of leptospirosis in the region , and corroborate recent estimates of very high disease burden in Oceania [3] . A wide range of domestic and wildlife species ( n = 13 when counting rats as a single species ) from across the PIs showed evidence of present and/or past infection with Leptospira . In some PIs , infected animals were mainly livestock ( cattle and pigs in particular ) , indicating the potentially important role of non-rodent reservoir species in human infections , especially in islands where backyard subsistence livestock are common . However , rats were investigated in fewer studies and less islands than livestock , even in islands where serogroup Icterohaemorrhagiae was identified from human cases ( e . g . Palau ) . Even though many studies on human leptospirosis in the PIs were retrieved , these studies probably under-estimate the true burden as leptospirosis is thought to be under-reported globally , particularly in developing countries such as the majority of PIs . Several studies included in our review were conducted during outbreaks of dengue [81 , 142 , 150] , chikungunya [24] or murine typhus [76] , and coincidentally identified leptospirosis cases that might otherwise have remained undetected . On the contrary , the co-circulation of different infectious diseases sharing similar clinical presentation in the PIs might lead to leptospirosis under-diagnosis , sometimes resulting in a fatal outcome [24] . In the reviewed human clinical studies , diagnosis for leptospirosis often occurred very late , in already hospitalised ( n = 21 studies where hospitalisation is specified ) or even deceased patients [164] . A case report from American Samoa highlighted some of the leptospirosis diagnostic challenges faced by clinicians in the PIs [143] . There is still a need to raise awareness and to reinforce diagnostic capabilities for leptospirosis in the PIs . These have to go hand in hand with better agreement about the use of diagnostic tools and interpretation of the results . The development of clinical guidelines for the management of febrile patients with suspected leptospirosis should be a priority . Serological studies suggested a wide variety of Leptospira serogroups ( n = 21 ) in humans and animals across the PIs , including common as well as rare serogroups . Reported serogroups varied between PIs , between humans and animals , and between animal species . Across the PIs , seropositive animal hosts were reported for all the serogroups detected in human cases . However , it was difficult to make serogroup links between animals and humans because data were reported from multiple studies with variable study designs . Also , MAT results were difficult to interpret because of the intrinsic limitations of the test , as well as the heterogeneity in data quality between the studies . MAT has limited sensitivity , especially with early acute-phase specimens , and with chronically infected animals ( reservoirs ) that may be serologically negative even while shedding the bacteria in their urine [173] . Also , MAT results may vary depending on subjective interpretation by laboratory personnel and the panel of serovars used [2] . Interpretation is further complicated by the high degree of cross-reactions that occurs between Leptospira of the same serogroup or even between serogroups [173] . Apart from those intrinsic limitations , many studies included in our review were classified as “weak evidence” because they did not comply with our case definition and/or provide the minimal information required to assess the quality of the results . Even in studies complying with our case definition , some MAT results were still overstated . For example , one study from New Caledonia [57] reported 12 different serogroups in severe cases identified in 1989 , while studies using Leptospira isolates or genotyping techniques have provided evidence that only six serogroups have been responsible for human cases in New Caledonia [60 , 98] . Therefore , tables of serology results provided in this review ( Table 1 and S2 Table ) should be taken as summaries of the reports from the studies , but interpreted with great caution . To ensure a proper quality assessment of the results and accurate interpretation , it is crucial that in the future MAT results are reported in detail , especially providing information regarding the panel of serovars used , MAT titres , and detailed and appropriate case definition criteria . In cases where serology is expected to be of poor sensitivity , the use of molecular methods might be more appropriate . In our review , a limited number of studies relied on a molecular diagnosis ( n = 14 in animals; n = 15 in humans ) . Studies including both animal and clinical data were uncommon ( n = 13 in 11 PIs ) in this review . Also , molecular characterization of Leptospira spp . diversity that allows human Leptospira infection to be traced back to the probable source of contamination [172] was attempted in very few studies . When Leptospira infection was investigated in separate studies for animals and humans in a same PI , it was generally not possible to link the results , for example because of a time gap between animal and human studies , and/or different study designs , and/or the use of different MAT panels . In some PIs , human leptospirosis was not investigated , even though Leptospira infection was demonstrated in animals , i . e . in the Solomon Islands , Cook Islands , Kiribati , Niue , Tokelau , Samoa , and to a lesser extent Tonga where only four suspect human cases were tested . Also , we retrieved livestock studies from 19 PIs while rodent studies were available from only eight of these 19 PIs , which might bias our understanding of leptospirosis infection in humans in the PIs . This review identified common behavioural risk factors and environmental drivers for leptospirosis infection across the region . Environmental drivers were mostly climate-related ( flooding , extreme events ) , while individual risk factors included backyard subsistence livestock , farming , lifestyle ( walking bare-foot , fishing , hunting ) , contact with animals and contact with fresh water sources ( recreational use , washing laundry ) . Our review only identified a few epidemiological surveys that conducted robust statistical analyses of risk factors associated with human leptospirosis . Many studies reported some “possible drivers” of infection , or some “possible exposure” but the hypotheses were rarely properly evaluated . Even where surveys on clinical cases were conducted with detailed questionnaires , the probable source of infection was difficult to ascertain because of the multiplicity of potential infecting exposures ( e . g . people who have been in contact with rats and pigs and dogs , and have been swimming in fresh water ) , the overlapping of professional and recreational activities and the lack of a comparator or control group . Leptospirosis as a human disease is the result of complex interactions between humans , animal reservoirs/carriers , and the environment where the bacteria can survive . Our findings suggest that the major animal reservoirs of human-infecting leptospires may vary across the PIs , but that livestock ( especially cattle and pigs ) , dogs and rodents may all play important roles in disease transmission to humans . However , intra-island heterogeneity has not been explored . In Fiji , the difference between habitats has been specifically investigated , showing that the relative importance of animal species in human infections varied between urban , peri-urban and rural settings [111] . We also showed a wide heterogeneity of leptospirosis serogroups and individual risk factors within and between islands . Combined with the already discussed limitations of the MAT and the limited number of studies combining animal , human and environmental investigations , overall the epidemiology of the disease is still unclear for most of the PIs . As already advocated in other studies [174 , 175] , eco-epidemiological studies following an integrated “One Health” approach are needed to understand the exposure pathways for leptospirosis in humans , including the specific role and relative importance of each animal species in different environmental settings . Future studies should further explore how disease transmission to humans is influenced by the complex interactions between humans , animals , and the environment , including interactions within as well as between ecological scales . Also , better integration of environmental studies ( only two in our search ) , inclusion of health economics evaluations and application of novel epidemiological methods such as mathematical modelling would be valuable next steps . This is crucial in a context where climate change , increased risk of flooding , population growth , urbanization , loss of biodiversity and agricultural intensification could individually , or possibly synergistically , lead to further increases in the burden of human leptospirosis in the Pacific . In the future , guidelines for a proper framework for leptospirosis research may help to improve our understanding of local epidemiology and complex transmission dynamics of leptospirosis worldwide .
Leptospirosis is an important bacterial zoonosis that affects people and animals worldwide . It is common in tropical areas , especially in island ecosystems . Because islands are relatively small , isolated , and have limited health and diagnostic facilities , the disease burden is often underestimated . In this systematic review , we aimed to describe the extent of leptospirosis in the Pacific Islands , including the diversity of pathogens and animal reservoirs . We identified 148 studies from 21 Pacific islands that described Leptospira infection in humans or animals . In hospitalized febrile patients , leptospirosis was a common cause of the acute febrile illness , but accurate diagnosis was challenging and often delayed because symptoms overlapped with many other infectious diseases , and access to laboratory diagnosis was limited . A wide variety of animal hosts of Leptospira were identified , with rodents , cattle , pigs and dogs reported as important hosts; however , their relative importance in human infection remains unclear . Our review demonstrates that the epidemiology of leptospirosis varies across the Pacific Islands , but information about risk factors and transmission routes is currently limited . We recommend more integrated studies , using an eco-epidemiological approach that includes human , veterinary and environmental factors , and interactions between factors at different ecological scales .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "geomorphology", "livestock", "medicine", "and", "health", "sciences", "leptospira", "pathology", "and", "laboratory", "medicine", "landforms", "ruminants", "pathogens", "topography", "tropical", "diseases", "microbiology", "vertebrates", "animals", "mammals", "dogs", "bacterial", "diseases", "neglected", "tropical", "diseases", "bacteria", "bacterial", "pathogens", "islands", "infectious", "diseases", "swine", "zoonoses", "medical", "microbiology", "microbial", "pathogens", "leptospirosis", "agriculture", "equines", "eukaryota", "horses", "earth", "sciences", "biology", "and", "life", "sciences", "cattle", "amniotes", "bovines", "organisms" ]
2018
A systematic review of human and animal leptospirosis in the Pacific Islands reveals pathogen and reservoir diversity
The delivery of blood-borne therapeutic agents to solid tumours depends on a broad range of biophysical factors . We present a novel multiscale , multiphysics , in-silico modelling framework that encompasses dynamic tumour growth , angiogenesis and drug delivery , and use this model to simulate the intravenous delivery of cytotoxic drugs . The model accounts for chemo- , hapto- and mechanotactic vessel sprouting , extracellular matrix remodelling , mechano-sensitive vascular remodelling and collapse , intra- and extravascular drug transport , and tumour regression as an effect of a cytotoxic cancer drug . The modelling framework is flexible , allowing the drug properties to be specified , which provides realistic predictions of in-vivo vascular development and structure at different tumour stages . The model also enables the effects of neoadjuvant vascular normalisation to be implicitly tested by decreasing vessel wall pore size . We use the model to test the interplay between time of treatment , drug affinity rate and the size of the vessels’ endothelium pores on the delivery and subsequent tumour regression and vessel remodelling . Model predictions confirm that small-molecule drug delivery is dominated by diffusive transport and further predict that the time of treatment is important for low affinity but not high affinity cytotoxic drugs , the size of the vessel wall pores plays an important role in the effect of low affinity but not high affinity drugs , that high affinity cytotoxic drugs remodel the tumour vasculature providing a large window for the normalisation of the vascular architecture , and that the combination of large pores and high affinity enhances cytotoxic drug delivery efficiency . These results have implications for treatment planning and methods to enhance drug delivery , and highlight the importance of in-silico modelling in investigating the optimisation of cancer therapy on a personalised setting . Inefficient delivery of drugs to solid tumours is one of the main reasons for chemotherapy failure . To reach cancer cells , blood borne therapeutic agents have to travel through the tumour vasculature to reach the tumour site , subsequently to extravasate into the tumour interstitial space and finally travel the remaining distance from the blood vessels to cancer cells . Abnormalities in the tumour microenvironment pose major physiological barriers to all three transport steps [1] . The tumour vasculature has an abnormal , chaotic structure with blood vessels being often hyper-permeable , hence , leaving large interendothelial openings , while being tortuous without any particular hierarchy [2] . Furthermore , the structure of the tumour vascular network continuously changes , new vessels are formed owing to hypoxia-induced angiogenesis , while existing vessels might collapse owing to mechanical compression by solid components of the tumour ( e . g . , cancer and stromal cells and extracellular matrix fibres ) [3] . The irregular structure of the tumour vasculature increases geometrical resistance to fluid flow through the vessels thus leading to sluggish blood flow , whereas vessel hyper-permeability might result in excessive plasma ( as well as nutrients ) loss from the vascular to the interstitial space of the tumour . As a result , these abnormalities can compromise blood perfusion downstream in the vascular network , while vessel collapse might exclude large intratumoural regions from blood supply and , hence , make these regions inaccessible to drugs [2 , 4–6] . Therefore , the structure and functionality of the tumour vasculature can determine the amount of drug delivered to the tumour and the efficacy of the therapy [7 , 8] . Apart from heterogeneous and low perfusion , other physiological barriers to the delivery of drugs to solid tumours is the uniform elevation of the interstitial fluid pressure owing to: ( i ) the hyper-permeability of the tumour blood vessels , ( ii ) the dysfunction of tumour lymphatics and in some ( desmoplastic ) tumour types , ( iii ) the dense interstitial space that resist to interstitial fluid flow [9 , 10] . All these parameters result in the accumulation of fluid in the tumour and interstitial hypertension , which in term eliminates pressure gradients across the tumour vessel wall and thus , convective transport of drugs [1] . As far as chemotherapy is concerned , cytotoxic drugs have a relatively low molecular weight so that they diffuse fast and do not require pressure gradients for their effective transport [11] . However , binding of the drug to cancer cells might change significantly the penetration and intratumoural distribution of the drug , depending on the binding affinity [12 , 13] . To date , mathematical and computational ( in-silico ) modelling of the tumour–host biophysics and fluid / drug transport phenomena has attracted fair attention in the scientific community . Amongst the early papers in modelling fluid flow in solid tumours—towards studying the interplay between highly permeable walls of neoplastic tumour vessels and interstitial fluid flow—is that of Netti and his colleagues [14] . Their model was capable of predicting the interstitial fluid pressure and velocity profiles , as well as the biomechanics of the tumour and host tissue matrix . Their numerical findings provided valuable insight to understanding the bio-fluid transport in biological tissues , and paved the way for the modelling works that follow . Along the same lines , Pozrikidis and Farrow [15] simulated bio-fluid flow at the interstitium ( modelled as an isotropic porous medium ) which was described using Darcy’s law , while intravascular flow was described by Poiseuille’s law , and the extravasation using Starling’s law . They investigated fluid flow on an idealised single vessel via a boundary integral formulation to obtain numerically interstitial fluid pressure . Adopting similar governing equations to [15] , Soltani and Chen [16] proposed an element-based finite volume methodology to simulate interstitial fluid at the tumour–host tissue . The tumour vascular network was assumed homogeneous , however , their results agreed well with corresponding experimental data of interstitial fluid pressure . Zao et al . [17] developed a computational fluid dynamics animal-specific computational model , informed using dynamic contrast-enhanced magnetic resonance imaging data . The authors investigated the distribution of the interstitial fluid transport within a murine sarcoma with regards to spatially varying properties of the vasculature at the tumour and the host tissue . In 2014 , Tang et al . [18] proposed a computational modelling framework of three-dimensional tumour growth and tumour-induced angiogenesis with the aim to evaluate chemical drugs transport . Despite that their model encompassed several bio-physical processes associated with tumour development , such as cell- and vascular-mediated interstitial pressure , angiogenesis , cell proliferation , cytotoxic chemotherapy , etc . , the model predictions ( tissue growth and vasculogenesis ) were not validated against experimental results . Following up [16] , Sefidgar et al . [19] investigated numerically , via a model parameter analysis , the effect of tumour shape and size on drug delivery to idealised in shape , solid tumours . The authors concluded that diffusion of cytotoxic drugs is dominant—for most tumour shapes and sizes—while when convection was considerable then drug concentration is larger when compared to similar size tumours . However , their model accounted for several simplifications , such as a homogeneous vascular network , regular tumour shape , and they did not account for the mechano-biology of the tumour–host tissue or the transient effects of the tumour vasculature . More recently , Dey and Sekhar [20] presented a bi-phase mathematical modelling framework—with the solid phase encompassing cell population , the extracellular matrix and the vasculature , while the fluid phase being the interstitial fluid—of solute ( macroscopic ) transport in soft biological tissues and solid tumours . The authors studied numerically , the impact of the interstitial space hydraulic conductivity , the rate of the solute supply or drainage from the vasculature and lymphs respectively , and the Thiele modulus ( see definition of term therein ) on the distribution of the interstitial fluid pressure , velocity and concentration in symmetrical tumours . Also , they investigated the role of the Thiele modulus with respect to the delivery of solutes—with emphasis in nutrients transport—and their impact in the tumour development and necrosis formation . We propose here a novel multiscale , multiphysics in-silico cancer and drug delivery modelling framework that circumvents the simplifications involved in previously-published relevant modelling efforts . We build on our previous in-silico framework that accounts for tumour growth , angiogenesis and vessel compression , oxygen supply , and solid and fluid stress evolution [21] . The model is extended to account for the delivery of drugs and study the effects of the time of chemotherapy administration on intratumoural drug concentration , vessel remodelling , tumour vessels’ permeability and perfusivity , and tumour growth . Particularly , to quantify the structure and hierarchy of the vessels , we employed two geometrical measures that we have previously shown to adequately characterize the tumour vasculature and can be related to drug delivery: ( i ) the maximum distance to the nearest vessels , δmax , which is a measure of the avascular spaces in the tumour , and ( ii ) a convexity index , λ , which serves as a measure of the three-dimensional structure of the vascular tree [22] . In principle , the parameter δmax quantifies the distance of adjacent blood vessels of the micro-vascular tree and it has been observed—by comparing healthy and tumour vascular networks in healthy and cancerous tissue using imaging—that the maximum distance is significantly increased in some solid tumours ( e . g . , see in-vivo scans in Fig 2 from [23] ) . However , to simplify the presentation of the in-silico results , we evaluate here the parameter in dimensionless form , δ ¯ max , i . e . as the fraction of δmax ( t ) over δmax ( 0 ) , with the latter calculated at the initial ‘healthy’ micro-vascular tree . Hence , at time t = 0 , the parameter is δ ¯ max = 1 ( the overbar is omitted in the remainder of the text and in the figures ) . Also , λ can be interpreted as a parameter that describes the three-dimensional distribution of the vessels and hierarchy . As such , for healthy vasculature it has been observed that λ takes positive values , while in cancerous tissue λ takes negative values [22] . Thus , for the adopted micro-vascular tree at time t = 0 , the corresponding parameter is calculated λ ( 0 ) ≈ 0 . 5 . In this contribution , we found that time of drug administration—with respect to the size ( or ‘stage’ in other words ) of the tumour is critical for the outcome of chemotherapy and that the drug can induce changes in the tumour vasculature bringing it to a more normalised state . Interestingly , in-silico model predictions also revealed a strong relation of intratumoural drug concentration to the permeability of the tumour vasculature and the binding properties of the chemotherapeutic agent . Detailed description of the dynamic angiogenesis model can be found in the recent paper of Vavourakis et al . [21] . In brief , the model is decomposed into to two primary components: ( a ) the model describing the extension of the tip blood vessels ( by following a snail-trail modelling approach ) , the sprouting of blood vessels and the formation of vascular anastomoses , and ( b ) a model that describes the capillary endothelial-wall remodelling and structural integrity with respect to tumour growth-induced solid and fluid mechanical forces . Regarding point ( a ) , vessel-tip and sprout elongation is described via a combination of the chemotactic contribution due to gradients of the angiogenic factors promoting vasculogenesis , the haptotactic contribution due to insoluble gradients of the ECM , and the mechanotactic contribution due to the mechanical forces elevation while the tumour develops in the host . Also , regarding point ( b ) , blood vessel lumen and wall is remodelled with respect to the capillaries haemodynamics . As such , wall shear stress works as a stimulus for vessel remodelling ( see Eqs ( 18 ) — ( 20 ) from [21] ) , while the balance of fluid pressures ( blood pressure and interstitial fluid pressure ) and mean solid stresses ( tissue hydrostatic pressure ) module the state of the vessel ( uncompressed , compressed or collapsed; see Eq ( 21 ) from [21] ) . However , in the present in-silico framework , the effect of the drug in vascular remodelling is implicitly accounted for . As explained in the Extracellular matrix structural model sub-section , the cytotoxic agent degrades the tumour following Eq ( 13 ) , which has a knock-on effect in the volumetric strain , ϑg , owing to the tumour development , as well as the tissue macroscopic elastic parameters of the stored-energy function , W ¯ , through the structural integrity / stiffness parameter of the ECM , m in Eq ( 15 ) . This in turn , as demonstrated in the Results and discussion section , is expected to dynamically impact the loading state of the vessels—for example by decompressing due to tumour regression existing mechanically-loaded capillaries . Evidently , vessel decompression improves blood perfusion in the vascular network and , thus , inherently promotes the remodelling of the blood vessel wall and lumen size . Finally , one modelling aspect the present in-silico framework does not account for is lymph-angiogenesis or the mechanics of lymphatic vessels in response to external mechanical stimuli . However , we leave this as a future modelling development to the present in-silico framework . The governing equations describing the biochemical model of the in-silico framework , i . e . the balance of the oxygen/nutrients , the tumour-angiogenic factors , and the matrix degrading enzymes are defined in detail in our recent paper ( see Eqs ( 12 ) — ( 14 ) in [21] ) . The adopted material parameters are listed in S3 Table therein , while some few parameters have been updated to reflect the impact of the cytotoxic drug in the extracellular matrix dynamics ( see Eq ( 12 ) ) . The latter are listed in S3 Table . The present multiscale , multiphysics , in-silico modelling framework consists of five interconnected core compartments that encompass different aspects of the tumour–host micro-environment mechano-biology . The compartments of the framework—called here modules—are the Vascular Network Module , the Biochemical Solver Module , the Solid Solver Module , the Fluid Solver Module and the Drug Delivery Solver Module . The corresponding modules and building blocks of the proposed in-silico framework are illustrated in Fig 1 , which depicts in a flow diagram the interaction among them . The numerical procedure of the coupled in-silico tumour-growth , hypoxia-induced angiogenesis and drug delivery solvers , in Fig 1 , employs four different time discretisation scales , with separate time-step for each of the four solver modules: It is important to highlight here that spatial discretisation of the weak-form of the governing equations has been carried out using the Finite Element ( FE ) method , while time-stepping for the five modules has been implemented in a staggered manner . As illustrated in S1 Fig , the equations involved in the Biochemical and Solid Solver Module and the extravascular model of the Drug Delivery Solver Module were discretised using three-dimensional FEs , while for the intravascular model of the Drug Delivery Solver Module and the Fluid Solver and the Vascular Network Module a one-dimensional FE discretisation has been adopted ( for further modelling details read [21] ) . Numerical solution of the balance equations has been accomplished sequentially and according to the following order: First , the Biochemical Solver Module PDEs are solved together , the solution of which is projected into the Solid Solver Module and equilibrium of solid forces is sought numerically ( owing to the tumour development ) . Subsequently , the solution of both above-mentioned modules is transferred into the Vascular Network Module and the tree gets updated ( i . e . , sprouting , branching , vessel compression , etc . ) . Then , the Fluid Solver Module is invoked to compute the interstitial and intra-/trans-vascular flow . The Fluid Solver Module operates at the end of every successful Solid Solver and Vascular Network Module , and before the Drug Delivery Solver Module , due to both blood/plasma and interstitial fluid flow being assumed viscous-dominated ( at extremely low Reynolds numbers ) and quasi-steady . This is necessary to account: ( a ) for the localised effects of solid stresses elevation or reduction , which is owed respectively to the natural tumour growth or the regression of the tumour in response to the cytotoxic drug , and ( b ) the dynamic changes of the vascular network growth ( sprouting , anastomosis ) which may directly affect blood flow and , thus , implicitly interstitial fluid flow . With regard to ( a ) , as elaborated in [21] , during the course of a simulation solid stresses may increase or decrease which in turn are expected to compress or decompress the capillaries adjacent to the tumour , respectively . This effect , in principle , is expected to modulate directly blood perfusion in the vascular tree and , thus , vascular remodelling in the Vascular Network Module . The solution of the Fluid Solver Module together with the solution of the Biochemical Solver Module is transferred to the Drug Delivery Solver Module to compute numerically the drug distribution in the vascular and extravascular spaces . Finally , the state of the vascular segments inside or proximal to the tumour region is re-assessed , and the list of collapsed blood vessels is revised while the microvascular pressure and interstitial fluid pressure distribution is updated by re-invoking the Fluid Solver Module . In summary , the coupled solver ( that encapsulates all five modules ) iterates until the simulation reaches the desired time point ( e . g . here is set to 40 days ) , where the frequency with which each module is invoked depending on the time-step/increment chosen . The above coupled multiscale numerical procedure is repeated until the termination of tumour growth/angiogenesis/drug transport simulations . Details about the FE implementation of the proposed tumour-induced angiogenesis and growth model are provided in S1 File . The C++ code of the in-silico cancer modelling platform can be accessed online via Bitbucket from: Finite Element Bioengineering in 3D ( FEB3 ) . For our drug of choice , the relative roles of diffusive and convective transport can be determined by estimating the Péclet number , defined as the ratio between the diffusive and convective flux ( see for example [39] ) . For a molecule of diameter sc = 1 nm approximately and the specific interstitium properties ( see material parameters in S1 Table ) , the diffusion coefficient of the drug , Dc , ( see Eq ( 9 ) ) is of the order of 10−4 mm2s−1 approximately . Fig 2 illustrates the averaged interstitial fluid velocity ( IFV ) magnitude—computed over the tumour and the surrounding host tissue 3D domain of analysis—with respect to time for four cases: low poresize and low affinity ( A ) , low poresize and high affinity ( B ) , high poresize and low affinity ( C ) , as well as high poresize and high affinity ( D ) . The largest IFV is predicted for high poresizes , which is expected—higher poresize means a larger transmural flux of biofluids—with a maximal value ranging between 0 . 9—1 . 2 μm s−1 approximately . The corresponding Péclet number we evaluate is less than 0 . 1 , which places the mode of transport firmly within the diffusion domain ( see also Box 1 in [39] ) . Therefore , supported by relevant experimental and theoretical observations [25 , 26 , 40 , 41] , we confirm that convection has a negligible role in the transport of cytotoxic drugs to the tumour and , hence , the main effect of increasing the poresize is that it also enhances transvascular solute transport ( i . e . Φvsc in Eq ( 6 ) ) and thus the total volume of free drug . This prediction is important when appraising the following results , particularly the effect of poresize and its dependency on drug affinity . Fig 2 shows that the temporal dynamics of the IFV magnitude ( averaged over the tumour and the peri-tumoural tissue area ) are also dependent on the injection time and drug affinity . Broadly speaking , this is due to the relationship between tumour volume and IFV . In agreement with [34] , as the tumour grows it deforms the surrounding tissue , which elevates the hydraulic conductivity of the tissue at the peri-tumoural stroma and , thus , resulting the IFV to increase ( see the control lines in each plot ) . Conversely , as the tumour is regressed by the drug , the surrounding tissue becomes less deformed and , hence , the IFV decreases . At high affinity ( second column in Fig 2 ) , this effect is consistent across all injection times . At low affinity , however , the relationship is more complex: later injections ( day 20 , day 30 ) are dependent on both poresize and affinity , with low poresizes causing the IFV to continue to increase or remain constant after injection , respectively . Comparing quantitatively the results between Fig 2B and 2D , IFV is scaled up by a factor of 2 whereas the hydraulic conductivity of the tumour vessels scales up by a factor of 200 approximately ( see Kvsc definition in Transvascular flow model ) . Also , in view of Fig 2C and 2D , we can project the efficacy of less diffusive drugs ( e . g . nanoparticles ) , post to administering a cytotoxic agent , be improved for hyperpermeable tumour vessels at early- to mid-stages of the tumour development if only the binding rate of the agent is very low . If the opposite occurs , then follow-up administration of drug-borne vesicles can potentially benefit from the enhanced convection transport for late-stages of the growing tumour and its increased vascular density . In summary , these predictions reflect the complex relationship between tumour growth and vessel poresize at low affinity , shown in Fig 3 , and discussed in detail in the following subsection . To investigate the relative effects of drug affinity ( binding rate ) , kon , and vessel poresize , rp , on tumour regression , Fig 3 shows the tumour volume over time for four cases: low poresize and low affinity ( A ) , low poresize and high affinity ( B ) , high poresize and low affinity ( C ) , and high poresize and high affinity ( D ) . Each plot shows the control ( i . e . no drug injected ) and the treated in-silico predictions with injections occurring at three different ‘stages’ with respect to the tumour growth baseline simulations , specifically at day 10 , day 20 or day 30 respectively . The control case shows that after 10 days the tumour grows approximately with constant rate ( see also document results of the tumour volume rate in S5 Table ) , as expected from the Gompertz growth function prescribing the growth . As seen in all plates of Fig 3 , the tumour size in the control simulations does not exceed 10 millimetres in diameter , which agrees with the experimentally measured tumour size in the murine models used for this study ( e . g . , see reported data in [42] ) . However , the present model can readily be used to simulate large tumour development ( e . g . by allowing the simulation to run over longer time periods ) , as well as other types of solid tumours ( e . g . brain gliomas , pancreative tumours , etc . ) . This could be achieved by adjusting the corresponding in-silico model parameters , as summarised in S1–S4 Tables . At high affinity ( Fig 3B and 3D ) the response of the tumour to the drug is consistent across all injection times: the tumour regresses to a minimal value , with volumes from all three injection times converging to approximately the same result by the end of the experiment . Almost no dependency on poresize is observed from the simulation results , which supports the argument about the competition between drug diffusion and its binding rate ( see for example Eq ( 1 ) in [13] . The above can result from the combination of two factors: ( i ) that diffusion is the dominant mode of transport for chemotherapeutic agents , and ( ii ) that for high-binding drugs the penetration length is small; while for high-binding drugs pertaining very fast diffusion rates , the drug clears out of the tumour quickly . Therefore , the limiting factor can be implicitly regarded as the size of the tumour , since it defines the limit on the mass of internalised drug . Interestingly , at low affinity a dependency on poresize is predicted: in Fig 3A at day 20 , the tumour is regressed slightly before relapsing shortly after injection , while in Fig 3C at day 20 the tumour is regressed more before relapsing near the end of the simulation . Similarly , different final values are predicted between low and high poresizes for injections at day 10 and day 30 . This suggests that at low affinity a higher volume of drug is necessary to overcome the additional limiting factor of the drug’s slow binding rate , i . e . to increase cb such that the tumour size—and hence the mass of drug that can be internalised—becomes the limiting factor . This effect can be predicted in the concentrations of bound/associated and internalised drug , cb and ci respectively , over time ( see S3 Fig ) : there is a limit beyond which increasing cb has little effect on ci level . Another interesting prediction from Fig 3 is that the final tumour volume is independent of the time of injection for high affinity but not low affinity drugs . This suggests that the time of drug administration might be of less importance for high affinity drugs; the tumour is regressed to the same final volume irrespective of its size when the drug is administered . Conversely , the time of injection is crucial for low affinity drugs , with early-stage ( day 10 ) injections only reducing the tumour’s growth rate as opposed to regressing it . Furthermore , we observe for the same drug properties that mid-stage injection are very likely to permit the tumour to relapse ( high probability for poorly permeable tumour vessels ) . On the contrary , late-stage injections ( day 30 ) lead to significant cancer volume reduction , with the rate varying depending proportionally on the permeability of the nascent vessels . S5 Table presents in tabular form the tumour volume rates ( in mm3 day-1 ) for the control versus the treated simulations ( injection times: D10 , D20 , D30 ) and for the four combinations of poresize and drug affinity values . In accordance with the results shown in Fig 3 , we note that tumour can be regressed significantly up to a ≈ 16 mm3 day-1 rate for late-stage injections of a high affinity drug; whereas for early-stage injections of the same drug can fairly regress a “premature” tumour at a rate 0 . 2—0 . 7 mm3 day-1 initially , but as depicted in Fig 3B and 3D , the tumour shows a trend towards gradual relapse ( after day 30 ) . Contrary to the latter case , early-stage injections of a low affinity drug has negligible impact to the tumour regression . In fact for both low and high poresize ( note that the drug size , sc , is comparably smaller to the poresize , rp , in both cases ) the average tumour development rate—within a 5-day time frame post injection at day 10—is 3 . 6 and 2 . 8 mm3 day-1 respectively , while the corresponding tumour growth rates of the control—at the same time frame—are 5 . 3 and 5 . 1 mm3 day-1 respectively . In summary , the above findings elucidate the implications for the outcome of low/high affinity cytotoxic drugs and the importance of the time of injection with respect to the tumour stage . In support to the above observations , Fig 4 illustrates the in-silico predictions of the proposed framework , where the control is compared against the treated ( for low poresize only ) with respect to the two extreme values of the drug affinity ratio . It is striking to note in this figure that ( a ) the concentration of the drug that has “hit” the cancer mass is approximately fifteen times higher for when the affinity is increased by four orders of magnitude ( see also S3 Fig ) . ( b ) Also , it is interesting to observe the direct effect of the tumour vascular tree non-hierarchical structure and pattern to the distribution of the drug in the cancer mass . For high affinity drug and early-stage drug administration , the concentration of the cytotoxic is enhanced at sparse locations of tumours—especially those where perfused newly-formed vessels are located adjacent to the tumour . On the contrary , for late-stage injection ( e . g . , day 30 ) , the concentration of the cytotoxic is rather evenly distributed at the tumour periphery . The effects of binding affinity and vessel poresize on the tumour’s physical environment were investigated by plotting the tissue hydrostatic pressure ( THP ) of the solid stresses and the interstitial fluid pressure ( IFP ) —both evaluated at the tumour and the peri-tumoural host tissue—as a function of time for the four combinations of low and high affinity and poresize ( Fig 5 ) . As before , each plot depicts the control case and the treated with injections at day 10 , day 20 or day 30 from baseline . Both THP and IFP show similar trends as the tumour volume ( Fig 5 ) , reflecting their interdependency . The THP increases approximately linearly in the control case , reflecting the increase in solid stress due to tumour growth ( Fig 5A ) . After injection , THP decreases with increasing affinity and poresize , with high affinity causing an almost complete alleviation of solid stress ( i . e . THP = 0 ) independently of poresize or injection time ( Fig 5B and 5D ) . Low affinity produces a more complex picture , which again mirrors the tumour volume: only late injection ( day 30 ) can reduce the THP monotonically , with the earlier injections showing either monotonically increasing THP ( day 10 ) , or decreasing THP followed by relapse ( Fig 5A and 5C ) . In the control case , the IFP increases logarithmically for high poresize , which produces a sharper initial gradient and maximum value than for low poresize due to the increased extravasation flux . After injection , the maximum value of the IFP decreases with increasing affinity , and at high affinity converges to approximately the same value , independently of poresize or injection time ( Fig 5F and 5H ) . At low affinity a similar trend to THP is predicted: only late injection ( day 30 ) can reduce the IFP monotonically , with the earlier injections showing either monotonically increasing IFP ( day 10 ) , or decreasing IFP followed by relapse ( Fig 5E and 5G ) . Taken together , these results propose two main points: ( a ) THP and IFP are implicitly reduced by cytotoxic drug delivery , and ( b ) as a result of the reduced IFP , drugs that are dependent on convective transport—such as liposomes or nanoparticles—should not be administered after treatment by cytotoxic drugs . This has implications for therapies that aim to alleviate solid stresses in order to decompress collapsed vasculature and , hence , enhance drug delivery and for staged treatments that aim to maximise delivery of nanoparticles [13 , 43] . To investigate the effect of cytotoxic drug delivery on tumour vessel architecture , Fig 6A–6D shows δmax while Fig 6E–6H λ as a function of time for the four combinations of low and high affinity and poresize . As before , each plot shows the control case and injections at day 10 , day 20 or day 30 from baseline . In the control case , δmax increases approximately linearly and in phase with the relative tumour volume increase , as can be seen by comparison of Figs 3A–3D and 6A–6D , respectively . This relationship with the tumour volume is also predicted after injection in all cases: δmax increases with tumour volume until treatment , when it either continues to increase but to a smaller maximum ( injection at day 10 ) , or decreases after treatment ( injections at day 20 or day 30 ) . This is due to the development of compressive solid stress that results in larger avascular regions within the tumour . As a result , δmax has a similar dependency on affinity and poresize: high affinity produces a more normalised vascular structure than low affinity , for all injection times ( comparing columns in Fig 6A ) , and poresize only influences earlier injections ( day 10 and day 20 ) at low affinity ( comparing rows in Fig 6A ) . The value of λ decreases smoothly from a positive to a negative value in the control case , which reflects the pathological change in distribution of the vasculature from a regular/uniform to an irregular/non-uniform pattern ( e . g . Fig 6B ) . After injection λ becomes less negative ( i . e . more physiological ) , with high affinity producing a more normal vasculature than low affinity ( comparing columns in Fig 6B ) and poresize only influencing the result at low affinity for an intermediate injection time at day 20 ( comparing rows in Fig 6B ) . Considered together , the results for δmax and λ indicate that ( a ) cytotoxic drugs can implicitly normalise the tumour-associated vasculature , and ( b ) this normalisation is only dependent on the size of the pores of the tumour vessels for low affinity drugs . The optimal time period ( ‘window’ ) for vascular architecture normalisation is explored in Fig 7 , which shows contour plots of ( plates A—D ) δmax , and ( plates E—H ) λ as a function of injection time and time from baseline for the four combinations of low and high affinity and poresize . The contours were calculated by cubic interpolation of the injection data ( D10 , D20 , D30 ) presented in Fig 6 using SciPy’s interpolation module ( scipy . interpolate ) . As in the previous section , Fig 7A , 7C , 7E and 7G indicate that at low affinity , hyperpermeable tumour vessels have a potential normalising the vascular network structure ( i . e . reduce δmax to 1 and increase λ to positive values ) . This is evident in Fig 7C with the formation of the blue-coloured valley ( bounded by the dashed white line ) which designates that for high poresize and low drug affinity extravascular space becomes more organised , δmax → 1; whereas in the corresponding Fig 7G for λ , the in-silico results do not suggest an improved vascular hierarchy—the parameter colour map is relatively insensitive to the time of injection . On the contrary , by comparison of columns 1 and 2 in Fig 7 , increasing the affinity of the chemotherapeutic agent both the extend of the vascular structure normalisation and the ‘window’ is significantly improved . The contours illustrate this effect graphically , i . e . cytotoxic drugs can normalise δmax across time post the time of injection , while λ can be normalised only at a broad range of injection—especially in early and intermediate times ( with respect to the tumour volume ) of injection . Interestingly , as indicated by the regions bounded by the dashed lines , in Fig 7D a breadth of a dark blue region—which indicates δmax approaching the physiological range , 1—1 . 4—orients the optimal window where the tumour vessels become relatively even spaced; while in Fig 7H the light green band—with λ converging towards physiological values , ≈ 0—indicates a strong tendency for the vascular re-organisation . This suggests that cytotoxic drugs can be used to implicitly normalise tumour-associated vasculature and are largely independent of the tumour’s stage . To explicitly link vascular architecture with cytotoxic delivery efficiency and follow-up drug treatment potential , contour plots of δmax versus λ with respect to the variable ch , which denotes the total ( bound and internalised ) cancer drug concentration , for the four combinations of low and high affinity and poresize are illustrated in Fig 8 . The contours were calculated via cubic interpolation of the three variables using the above-mentioned interpolation tools of the SciPy library . The range of δmax and λ , i . e . the area of the contour , depends on poresize only at low affinity ( compare rows in Fig 8 ) . Also , comparing the columns in Fig 8 , increased affinity expands the area of the contours irrespective of poresize . This supports the argument that a high-affinity drug has more potential normalising the architecture of the tumour vascular tree , which is reflected by the previous findings . Interestingly , the largest values of ch are obtained for the most pathological vascular structures , i . e . large δmax and negative λ as shown with dark red primarily in Fig 8D . Furthermore , the ‘window’ of treatment—referred here as the ‘hotspot’ region in the contours—can be increased substantially by increasing both affinity and poresize ( see Fig 8A and 8D ) . However , by comparison of Fig 8B and 8D , we note that less permeable tumour vessels decrease the potential of the cytotoxic drugs to target the tumour—especially for when the vascular tree is less hierarchical ( i . e . for negative convexity parameters ) and less structured ( i . e . increased vascular space; δmax > 1 . 4 ) . In summary , these results , and in conjunction with the extensive series of simulation results shown in S11 Fig , suggest that the combination of high poresize and high affinity allows the drug to target the tumour across a wide range of vessel architectures . Hence , we propose that blood vessel normalisation using chemical agents that could reinforce the endothelial wall barrier and reduce the size of intracellular fenestrations should be avoided prior to injecting cytotoxic drugs , as demonstrated in Fig 8A . We have presented a novel in-silico multiscale modelling framework of coupled tumour growth , angiogenesis and drug delivery . The model builds on our previous work [21] , allowing for realistic simulations of in-vivo conditions that include: ( i ) dynamic remodelling of the tumour-associated vascular network as a result of growth-induced solid stresses , ( ii ) biophysical vessel sprouting that explicitly accounts for chemo- , hapto- and mechanotaxis , and ( iii ) solid stress-dependent vascular remodelling and compression/collapse . Here we extended the model to include extra- and intravascular drug delivery , and specified the model to simulate the transport of cytotoxic drugs to murine mammary carcinomas . The complete model has been implemented in our in-house , open-source numerical platform FEB3 ( https://bitbucket . org/vasvav/feb3-finite-element-bioengineering-in-3d/wiki/Home ) . The proposed in-silico framework allowed us to study the dynamics of tumour growth and cytotoxic drug delivery , and hence comment on the following key topics identified by [39]: ( a ) the relative roles of convective and diffusive transport for cytotoxic drugs , ( b ) the effects of tumour microvascular structure and function , and ( c ) methods to enhance delivery by modification of IFP and reduction of tissue stress by induction of tumour cell apoptosis . Furthermore , our model is flexible: drugs of varying size and affinity can be simulated , while the poresize of the tumour vessels can be specified . We can thus implicitly simulate the effect of neoadjuvant blood vessel normalisation by setting a small initial vessel wall poresize . This allowed us to also comment on , firstly , the relative importance of affinity and poresize in targeting tumours , and subsequent normalisation of vasculature structure . Secondly , the in-silico model permitted us to investigate the potential implications for staged delivery ( viz . normalisation of THP and IFP ) . The main hypotheses proposed by our model are: ( i ) confirm that chemotherapeutic agents’ delivery is dominated by diffusive transport; ( ii ) the time of treatment is important for low affinity but not high affinity drugs; ( iii ) vessel poresize plays an important role in the effect of low affinity but not high affinity cytotoxic drugs; ( iv ) high affinity cytotoxic drugs provide a large window for vascular architecture normalisation; and ( v ) the combination of large poresize and high affinity enhances cytotoxic drug delivery efficiency . The model provides valuable insight into the complex system of biophysical factors that generate these hypotheses . In particular , it suggests that the combination of diffusive transport and pathological tumour-associated angiogenesis allows cytotoxic drugs to target the tumour across a broad range of vasculature architectures . Furthermore , our modelling framework allows for these hypotheses to be tested by comparing directly to experimental data from mouse models treated with vascular normalisation agents and drugs of varying affinity , when it becomes available . We note that it is difficult to directly validate our drug delivery model predictions , given the sparsity of literature available that is both suitable in terms of the application and that can completely specify the model . Here we have made every effort to specify the model where the data were available; this is evidenced in our previous work [21] , where we tested every component of the model , except the drug delivery module , against in vivo experimental data of murine tumours . We can , however , qualitatively compare our predictions to similar experimental work , such as [38 , 44] , who observed that structural vascular normalisation lowered interstitial fluid pressure in murine models and , hence , subsequent nanoparticle penetration from the vessels into the tumour tissue has been improved . This is in agreement with our prediction of smaller vascular pore sizes producing lower interstitial fluid pressure . Furthermore , it was found in Chauhan et al . [38] that chemotherapy delivery is optimised at pore sizes in the order of 150 nm and drops for smaller pore sizes , which also agrees with our model predictions that recommend that 150 nm , not smaller , pore sizes are more suitable for effective chemotherapeutic drug delivery . While we have strived to make the model representative of in-vivo conditions , it has limitations . The high complexity of the model requires a large number of model parameters to be specified and , hence , the results are dependent upon the choice of these parameters . Where possible we chose experimental data from the literature to best represent the growth of murine mammary carcinomas , detailed in S4 Table . Furthermore , as previously mentioned , we have already validated the coupled tumour angiogenesis and growth model against experimental data in previous work [21] . As such , while the choice of the drug delivery model parameters affect the result quantitatively , we expect the qualitative in-silico predictions to remain the same . In order to reduce the dependency of the model on material parameters , and in particular those where the literature is sparse , some simplifications were made . The tissue hydraulic conductivity was assumed isotropic and independent of solid deformations , which would affect fluid flow as the tumour grows [34 , 45] . Here however we have simulated a spherical tumour into a homogeneous matrix and , thus , can reasonably expect that this assumption would only change primarily the magnitude and not the direction of IFV . We did not model the deposition of new collagen by tumour cells , which in turn would affect the matrix composition and interstitial hydraulic conductivity . Again , due to the symmetric nature of the problem , we expect this to only affect the magnitude of the results . The dynamic viscosity of blood and interstitial fluid is assumed constant and rate-independent , which , given the heterogeneous nature of the developing vascular network , could be expected to affect both the magnitude and direction of the fluid flow . However , as we have focused on cytotoxic drugs—which are more dependent on diffusive than convective transport—it is reasonable to ignore this effect . Finally , the model does not account for lymph-angiogenesis and the lymphatic vessels are not modelled explicitly , i . e . no description of lymphatic biomechanics , such as compression and collapse . This would be more important for larger drugs than those studied here , where the convective component needs to be modelled realistically to account for drainage and retention effects [46] . The study of larger drugs , such as liposomes , micella or drug-borne nanoparticles , is the subject of future work .
One of the main challenges in optimising cancer therapy is understanding the in-vivo cancer environment and how it changes over time . The efficacy of chemotherapeutic drugs is known to be strongly dependent on blood vessel wall properties and the architecture of the developing tumour vasculature , which in turn are dependent on biochemical and mechanical interactions between cancer cells and their microenvironment . Here we present a novel in-silico modelling framework of dynamic tumour growth , angiogenesis and drug delivery , and we use it to explore biophysical factors governing the efficient delivery of cytotoxic drugs to solid tumours . We find that the time of treatment and vessel permeability are important factors for the efficacy of chemical agents with low binding affinity , that high affinity drugs can impact the tumour vasculature remodelling and bring vascular structure back to a more normalised state , and that the combination of large-sized vessel wall pores and high affinity enhances cytotoxic drug delivery and efficacy . These results have implications for treatment planning and optimisation , and show how in-silico models can be used to help understand and optimise cancer therapy .
[ "Abstract", "Introduction", "Materials", "and", "methods", "Results", "and", "discussion" ]
[ "medicine", "and", "health", "sciences", "body", "fluids", "cardiovascular", "physiology", "cardiovascular", "anatomy", "cancer", "treatment", "basic", "cancer", "research", "oncology", "angiogenesis", "developmental", "biology", "pharmaceutics", "tumor", "physiology", "drug", "administration", "tumor", "angiogenesis", "cellular", "structures", "and", "organelles", "blood", "vessels", "extracellular", "matrix", "drug", "delivery", "interstitial", "fluid", "anatomy", "cell", "biology", "physiology", "biology", "and", "life", "sciences", "drug", "therapy" ]
2018
In-silico dynamic analysis of cytotoxic drug administration to solid tumours: Effect of binding affinity and vessel permeability
Sugars are evolutionarily conserved signaling molecules that regulate the growth and development of both unicellular and multicellular organisms . As sugar-producing photosynthetic organisms , plants utilize glucose as one of their major signaling molecules . However , the details of other sugar signaling molecules and their regulatory factors have remained elusive , due to the complexity of the metabolite and hormone interactions that control physiological and developmental programs in plants . We combined information from a gain-of-function cell-based screen and a loss-of-function reverse-genetic analysis to demonstrate that fructose acts as a signaling molecule in Arabidopsis thaliana . Fructose signaling induced seedling developmental arrest and interacted with plant stress hormone signaling in a manner similar to that of glucose . For fructose signaling responses , the plant glucose sensor HEXOKINASE1 ( HXK1 ) was dispensable , while FRUCTOSE INSENSITIVE1 ( FINS1 ) , a putative FRUCTOSE-1 , 6-BISPHOSPHATASE , played a crucial role . Interestingly , FINS1 function in fructose signaling appeared to be independent of its catalytic activity in sugar metabolism . Genetic analysis further indicated that FINS1–dependent fructose signaling may act downstream of the abscisic acid pathway , in spite of the fact that HXK1–dependent glucose signaling works upstream of hormone synthesis . Our findings revealed that multiple layers of controls by fructose , glucose , and abscisic acid finely tune the plant autotrophic transition and modulate early seedling establishment after seed germination . Myriad metabolic pathways enable cells to sustain life with basic carbon and nitrogenous compounds . Thus , the integration of metabolite status , which reflects external and internal living conditions , into cellular activities ( e . g . , gene expression ) is a pivotal process that equips organisms with the ability to survive and proliferate . For example , cellular metabolites often serve regulatory roles in modulating organism growth and development , from unicellular bacteria and yeasts to multicellular animals and plants [1]–[6] . To sense and transduce such metabolite signals , organisms have developed sophisticated biochemical and cellular mechanisms . Glucose is an evolutionarily conserved regulatory sugar molecule in many different organisms [1]–[6] . It has multiple roles as an energy source , building block , and osmotic regulator , and also acts as a potent signaling molecule that regulates gene expression and controls organism growth and development . For example , in yeast , glucose is sensed by at least four different types of sensors , Hxk2 , Snf3 , Rgt2 and Gpr1 , and regulates gene expression and cell growth [4] . In mammalian pancreatic islet β cells , glucose signaling may be a function of the total amount of ATP generated via catabolism [6] . In plants , glucose [7]–[9] , sucrose [10]–[12] , trehalose-6-phosphate [13] , and low energy/high AMP concentrations [14] , [15] function as cellular signaling molecules in specific regulatory pathways that modulate plant growth and development . Of these signaling metabolites , glucose has been studied the most comprehensively in plants . Glucose signaling modulates the gene expression of enzymes in the glyoxylate cycle [16] and the photosynthesis pathway [17] , and is also involved in the developmental decision of whether to progress to normal seedling establishment after seed germination [18] . Glucose-mediated developmental repression is largely dependent on HEXOKINASE1 ( HXK1 ) [7]–[9] . HXK1's function in glucose-mediated developmental repression is mostly independent of its catalytic activity and integrates glucose signaling with other plant hormone such as auxin and cytokinin . HXK1-independent glucose signaling has also been reported in plants . For instance , expression of the genes encoding chalcone synthase , phenylalanine ammonia-lyase , and asparagine synthase responds to glucose signaling , but their regulation is independent of HXK1 activity [3] , [19] . A recent study further demonstrated that a refined low-glucose condition can uncouple HXK1-dependent and -independent glucose signaling responses during early A . thaliana seedling establishment [9] , [20] . In both animals and plants , the developmental roles and regulatory functions of hexoses other than glucose have remained largely unknown . However , within the last few years , dietary fructose was implicated in mammalian cell signaling perturbation and metabolic syndromes such as insulin resistance , obesity , type 2 diabetes , and high blood pressure [21] , [22] . Plant triose phosphates synthesized by photosynthetic activity are stored as transitory starch in chloroplasts or converted into sucrose in the cytoplasm through a series of enzymatic reactions carried out by fructose-1 , 6-bisphosphatase ( FBP ) , UDP-glucose pyrophosphorylase , sucrose phosphate synthase , and sucrose phosphatase [2] . Sucrose is then stored in vacuoles or cleaved into glucose and fructose by invertases or UDP-glucose and fructose by sucrose synthases [23] . Thus , following sucrose hydrolysis , fructose becomes one of the prevalent hexoses in plants and has long been proposed as a possible signaling molecule [24] . Nevertheless , fructose signaling in plants has remained largely unexplored . Recently , Kato-Naguchi et al . [25] showed that the fructose analog psicose induced root growth inhibition in lettuce . Fructokinase ( FRK ) , which performs the same catalytic function as HXK , but with fructose as the substrate rather than glucose , was the first fructose enzyme to be studied for a putative regulatory role in fructose signaling [24] , [26] , [27] . Although FRK is involved in modulation of plant growth , a regulatory role in fructose signaling was ruled out [28]; hence , little is known about fructose signaling and its regulatory pathways . In this study , we used a cell-based functional screen and a reverse genetics assay to investigate the signaling role of fructose in A . thaliana . We identified FRUCTOSE INSENSITIVE1 as an indispensable regulatory factor in the signaling pathway . Here , we report the molecular and genetic characterization of fins1 in a fructose signaling context , and its close interactions with ABA signaling during early seedling development . To evaluate the regulatory role of fructose signaling in plant developmental modulation , we examined A . thaliana seedling growth on 6% ( w/v ) fructose agar medium with full-strength Murashige and Skoog ( MS ) salts . Wild-type ( WT; Ler and Col accession ) seedlings grown on high-fructose medium exhibited a typical early developmental arrest , which was manifested by inhibition of hypocotyl and root growth and repression of cotyledon expansion and chlorophyll accumulation ( Figure 1A and 1B ) . Although the seedling development repression pattern caused by high fructose was similar to that caused by high glucose ( 6% ) [7] , [20] , fructose caused slightly more root growth inhibition than glucose ( Figure S1 ) . Mannitol , an osmotic control , did not induce the same seedling repression , suggesting that the observed phenotype was a developmental response to fructose signaling . Recently , we refined glucose assay conditions for growing A . thaliana seedlings and showed that the high glucose requirement is due to the high nitrogen content in MS media [9] , [20] . When MS salts were omitted , 2% glucose induced equivalent seedling growth repression to 6% glucose media including MS . However , decreasing the concentration of fructose in the absence of MS salts had little effect on seedling growth ( Figure S2 ) ; this suggested that nitrogen had a different effect on fructose and glucose signaling . Indeed , further experiments indicated that fructose and glucose signaling does rely on distinct sensors . The glucose-insensitive HXK1-null mutant gin2-1 ( gin2 ) exhibited normal fructose sensitivity , as did transgenic gin2-expressing WT HXK1 and its catalytically inactive mutants HXK1S177A or HXK1G104D ( Figure 1A ) . These data confirmed that the glucose sensor HXK1 was dispensable in fructose signaling . Although HXK1 carries out metabolic activities for both glucose and fructose , it does not appear to be involved in fructose signaling . This may reflect the fact that HXK1 has an approximately 100-fold higher affinity for glucose compared to fructose [28]–[30] . In a previous study , root growth inhibition in lettuce was reported in the presence of either the fructose analog psicose or the glucose analog mannose [25] . However , the HXK inhibitor mannoheptulose restored root growth in the presence of mannose , but not psicose . These results are further evidence that psicose/fructose signaling is independent of HXK function . Plant sugar signaling , mainly glucose and sucrose , interacts with stress and defense hormone signaling pathways and coordinates seedling growth and development [1]–[3] , [23] , [31] . For glucose signaling , gin1 , gin5 , and gin6 were respectively identified as alleles of aba-deficient2 ( aba2 ) , aba3 , and aba-insensitive4 ( abi4 ) in the ABA pathway , and gin4 was found to be a new allele of constitutive triple response1 ( ctr1 ) in the ethylene pathway [31]–[36] . These mutants have been selected repeatedly from various independent screens for sugar responses , further confirming that sugar signaling interacts with ABA and ethylene response pathways during early seedling development [37]–[40] . To test whether fructose signaling interacts with plant stress/defense hormones , we observed the early developmental response of ABA and ethylene mutants on a 6% fructose agar medium with MS salts . Unlike WT and gin2 , both gin1-3 ( gin1 ) and ctr1-1 ( ctr1 ) seedlings were not only insensitive to high glucose , but also overcame fructose repression and developed green cotyledons ( Figure 1B ) . GIN1/ABA2 encodes a short-chain dehydrogenase/reductase in ABA synthesis , and CTR1/GIN4 encodes a putative mitogen-activated protein kinase kinase kinase that functions as a negative regulator of ethylene signaling [31] , [33] . Therefore , fructose signaling appears to interact positively with ABA signaling via hormone biosynthesis , whereas it is likely antagonized by ethylene signaling . Interestingly , in ABA-deficient gin1 mutants , cotyledon repression was de-repressed by fructose , but root repression was not ( Figure 1B ) ; however , glucose relieved both cotyledon and root growth repression in gin1 mutants ( Figure S1 ) [20] . This indicated that fructose repression of root growth was independent of ABA biosynthesis , unlike cotyledon greening . This observation revealed differential seedling responses to fructose and glucose in an organ-specific manner . We further monitored marker gene expression using real-time PCR with cDNA templates generated from mRNA of five-d-old seedlings grown on MS agar medium containing 6% glucose , fructose , or mannitol . Expression of the photosynthesis-related CHLOROPHYLL A/B BINDING PROTEIN2 ( CAB2/AT1G29920 ) gene was markedly repressed in WT by both glucose and fructose ( Figure 1C and 1D ) . Gene expression was similarly repressed in gin2 seedlings by fructose , but not by glucose ( Figure 1C ) . However , CAB2 expression was de-repressed in both gin1 and ctr1 seedlings ( Figure 1D ) . The CAB2 gene expression patterns in the mutants reflected the fructose resistance revealed by their phenotypes ( Figure 1B ) . Taken together , these data indicated that fructose signaling was mediated through a unique/unknown sensor , but shared a downstream pathway with glucose signaling , which interacted with the plant stress and defense hormones ABA and ethylene to modulate early seedling development in A . thaliana . Although the application of high sugar to A . thaliana growth media has been criticized because it is not a normal physiological condition , it is unclear how much sugar is actually taken up by roots , how fast it is metabolized or fluxed , and in which suborganelles the sugar is partitioned . These factors could affect developmental responses to high sugar levels . Glucose and sucrose nanosensors , which detect cytoplasmic levels of sugar content , have demonstrated that plant roots take up sugars supplied in growth media rather efficiently [41] . To comprehensively understand sugar uptake and allocation in plants , apoplasmic sugar levels , sugar distribution in subcellular organelles , and fluxes for specific sugars need to be monitored more closely . Further development of sugar nanosensors will hopefully lead to a better understanding of sugar sensing and signaling [42] . To learn more about the specific regulatory components involved in fructose signaling , we took advantage of a cell-based functional screen using transient expression of the A . thaliana mesophyll protoplast system [43] . Because fructose caused deficient chlorophyll accumulation in A . thaliana ( Figure 1A and 1B ) , we reasoned that fructose signaling may affect photosynthetic gene expression in a manner similar to that of glucose signaling [7] , [17] . To monitor the fructose signaling response in leaf mesophyll protoplasts , we generated a reporter construct with an approximately 0 . 5 kb CAB2 promoter fused to the firefly luciferase gene ( CAB2-fLUC ) . In leaf mesophyll protoplasts , CAB2-fLUC activity was downregulated by fructose , but not by the osmotic control mannitol ( Figure S3 ) . We then screened several enzymes involved in fructose metabolism , including putative cytoplasmic FBP ( AT1G43670 ) , FRK1 ( AT5G51830 ) , and PFK1 ( AT4G29220 ) for their potential roles in fructose signaling ( Figure 2A ) . Of these enzymes , putative FBP ( we tested two independent constructs , FBP_3 and FBP_4 ) had the greatest suppressive effect on CAB2-fLUC activity ( Figure 2B ) . CAB2 promoter activity seemed to be suppressed even without high-fructose treatment , possibly because plant cells became hypersensitized to endogenous fructose when putative FBP was overexpressed . To test if putative FBP enzyme activity is required for CAB2 gene repression , we generated a catalytically inactive form , FBPS126AS127A ( SSM ) , based on domain conservation in plant and animal FBPs ( Figure S4 ) . The dual mutation of S126A and S127A in FBP caused a loss of FBP enzymatic activity in protoplasts ( Figure S5 ) . This mutation probably distorted the local structure and prevented FBP121D from associating with a divalent ion that is necessary for the enzyme activity [45] . Interestingly , the catalytically inactive form FBPS126AS127A suppressed CAB2-fLUC activity in the same manner as the wild-type FBP ( Figure 2B ) . This result indicates that the regulatory function of putative FBP in fructose signaling may be independent of its catalytic activity in sugar metabolism , similar to how HXK1 functions in glucose signaling [9] , [19] . Surprisingly , we observed putative FBP in both the cytoplasm and nucleus ( Figure 2C ) . We were not able to determine whether the nuclear localization of putative FBP depends on cellular fructose signaling ( Figure S6 ) , since it is almost impossible to generate zero-fructose conditions in plant cells . However , the nuclear localization of putative FBP certainly suggests that it could be directly involved in fructose-dependent gene regulation . Based on the initial functional screen and localization test in plant cells , we hypothesized that putative FBP was a regulatory factor in fructose signaling . To study the role of putative FBP in fructose signaling in whole plants , we first obtained a T-DNA insertion mutant that did not accumulate full-length FBP transcript and genetically characterized FBP's function in fructose signaling ( Figure 3A ) . The fins1 seedlings exhibited fructose-insensitive growth responses with progressive cotyledon greening with chlorophyll accumulation ( Figure 3B ) that was independent of osmotic effects ( Figure S7 ) , but displayed glucose-sensitive developmental arrest phenotypes . Since “fructose insensitivity” was the first phenotype that we encountered with this fbp mutant , we designated the allele fructose insensitive1 ( fins1 ) . In fins1 protoplasts , FINS1 expression ( using the two independent constructs FBP/FINS1_3 and FBP/FINS1_4 ) clearly suppressed CAB2-fLUC activity ( Figure 3C ) , which was similar to the effect of HXK1 [7] . To investigate FINS1 function in fructose-mediated gene regulation , we examined marker gene expression in WT and fins1 seedlings grown on 6% fructose agar media with MS salts . Consistent with their growth phenotypes ( Figure 3B ) , CAB2 expression was markedly repressed by fructose in WT , but not in fins1 seedlings ( Figure 3D ) . A key transcription factor in ABA signaling , ABI4 ( also known as GIN6/AT2G40220 ) [44] was induced by fructose in WT but not in fins1 seedlings . In contrast , ETHYLENE RESPONSE FACTOR1 ( ERF1/AT3G23240 ) , an ethylene response transcription factor [46] , was repressed by fructose in WT , but de-repressed in fins1 seedlings . However , the change in ERF1 expression levels was relatively weak compared to other marker gene responses . These data showed that FINS1 had a central role in fructose-inducible gene regulation . To verify that the fructose insensitivity exhibited by fins1 was due to the loss of FINS1 , we complemented the fins1 mutant with FINS1 cDNA using an Agrobacterium system . Transgenic lines with FINS1 expression levels similar to that of WT were selected by gene transcript and protein levels using reverse transcriptase–dependent PCR and protein blot analysis , respectively ( Figure 3E ) . The selected complementation lines had restored sensitivity to fructose and exhibited seedling developmental arrest similar to that of WT Col seedling ( Figure 3F ) ; this confirmed that loss of FINS1 function in fins1 seedling was responsible for fructose insensitivity . Furthermore , a fins1 mutant expressing catalytically inactive FBPS126AS127A also restored fructose sensitivity WT levels ( Figure 3G and 3H and Figure S8A ) . The seedling response was specific to fructose , and did not occur in the presence of mannitol ( Figure S8B ) . This response verified that the function of FINS1/FBP in fructose signaling was independent of its catalytic activity in sugar metabolism , as shown by the results of the cellular assay ( Figure 2B ) . As stated previously , unlike in the glucose assay , in which the high nitrogen levels of MS salts necessitated a high concentration of glucose , 2% fructose without MS salts did not cause the same phenotypic effect as 6% fructose with MS salts ( Figure S2 ) . Consequently , it was not clear whether fructose signaling was related to nitrogen signaling . To address this , we tested the effect of different concentrations of fructose on fructose-mediated seedling developmental responses without osmotic pressure , as well as the sugar-antagonistic effect of nitrate ( Figure S9 ) . At 3% fructose , fins1 , FINS1-complemented fins1 , gin2 , HXK1-complemented gin2 , and WT seedlings did not exhibit any obvious developmental phenotype ( Figure S9 ) , as was the case for 2% fructose ( Figure S2 ) . However , all of these seedlings exhibited severe growth repression at 5% fructose . Strikingly , at 4% fructose , fins1 showed a clear insensitivity , and FINS1-complemented fins1 restored seedling developmental arrest to a WT-like phenotype ( Figure S9 ) . The glucose-insensitive gin2 seedlings displayed consistent fructose-mediated developmental arrest phenotypes . Some of the extreme sensitivity of gin2 could have been due to its accession , because Ler was hypersensitive compared to Col at the same fructose concentration . These results confirmed that nitrogen affects fructose and glucose signaling in different ways [20] . Together with the initial cell-based functional screen , the reverse genetics analysis revealed the regulatory role of FINS1 in fructose signaling during early A . thaliana seedling establishment . FBP isozymes have multiple roles in plant sugar metabolic pathways at different subcellular locales [47] . Chloroplast-localized FBP ( AT3G54050 ) has 50% sequence homology to cytoplasmic FBP in A . thaliana and is mainly involved in starch biosynthesis [48] . Cytoplasmic FBP is involved in sucrose metabolism and is inactivated under dark conditions , mainly due to the increase in fructose-2 , 6-bisphosphate in some species [47] , [49] . Consistent with these previous findings , etiolated WT , fins1 , and FINS1-complemented fins1 seedlings did not show any striking phenotypic differences when they were grown on MS agar medium containing 6% glucose , fructose , or mannitol in completely dark conditions ( Figure 4A–4C ) . This result suggested that FINS1 mainly mediated fructose signaling under light conditions ( Figure 3B , 3F , and 3H ) . The genetic repression of FINS1 results in shifting sugar metabolism in favor of starch over sucrose synthesis , but does not affect A . thaliana growth [47] . To physiologically compensate for the decrease in sucrose content during the day , starch breakdown and sugar export are enhanced at night in A . thaliana [47] and tobacco [50] , but not in rice [49] . Because FBP/FINS1 plays a central role in sucrose synthesis , we tested whether low sucrose in fins1 was a direct cause of its fructose insensitivity [47] , [50] , [51] . When we observed seedling growth phenotypes on MS agar media containing 6% , 10% , or 12% sucrose in the presence of light , fins1 seedlings were resistant to developmental arrest at high concentrations of sucrose . However , gin2 was resistant only up to 10% sucrose ( Figure 4D–4F ) , indicating that sucrose levels were irrelevant to the fructose insensitivity of fins1 seedlings . Sucrose is converted to fructose and glucose or UDP-glucose and fructose in plant cells and then is likely integrated into FINS1-dependent or HXK1-dependent signaling . Thus , the strong sucrose resistance of fins1 seedlings ( Figure 4F ) indicated that fructose became a predominant hexose after sucrose hydrolysis [2] , [23] , [24] . This finding was supported by a previous observation using a fluorescence resonance energy transfer–based nanosensor , which showed that a measurable cytoplasmic glucose level was induced within 10–20 s of sucrose application to A . thaliana roots [41] . To obtain further molecular insights into the interconnected nature of sugar signaling , we have currently performing a comprehensive analysis of transcriptome changes . In summary , the fructose insensitivity of fins1 seedlings was most likely not caused by the loss of FBP catalytic activity or by lower sucrose in the mutant [47] , because ( 1 ) the fructose-responsive CAB2 promoter activity was modulated by FINS1/FBP , but not by FRK1 , which is also involved in the sucrose synthesis ( Figure 2B ) ; ( 2 ) the fructose signaling response was modulated similarly by catalytically active or inactive forms of FBP ( Figure 2B and Figure 3H ) ; and ( 3 ) high sucrose did not induce fins1 seedling developmental arrest ( Figure 4F ) . Upon fructose treatment , we noted a slightly more inhibition of root growth ( Figure 3B , Figure S1 ) and a marked ABA-dependent gene response ( Figure 3D ) . These results led us to examine the interaction between fructose and ABA signaling . To do so , we generated transgenic gin1 seedlings that overexpress FINS1 . We then analyzed the epistatic relationship between FINS1 in fructose signaling and GIN1 in the ABA pathway . FINS1-overexpressing gin1 seedlings exhibited a seedling developmental arrest phenotype like that observed in WT seedlings on 6% fructose agar medium with MS salts ( Figure 5A ) . The fructose-dependent seedling response was not due to high osmotic effects , because seedlings grew similarly on 6% mannitol agar medium with MS salts ( Figure S10 ) . Thus , fructose signaling appears to be integrated into FINS1 downstream of GIN1 , which is involved in ABA synthesis . Interestingly , gin1 seedlings that overexpress the plant glucose sensor AtHXK1 display glucose insensitivity , suggesting that glucose sensing by AtHXK1 occurs upstream of ABA synthesis [32] . Taken together , these findings indicate that although both fructose and glucose signaling crosstalk with ABA signaling during early seedling establishment , FINS1 and HXK1 function downstream and upstream of the ABA pathway , respectively . To further test whether FINS1 has a critical role in the ABA pathway , WT , gin1 , and FINS1-overexpressing gin1 seedlings were grown on MS agar media containing different concentrations of ABA ( Figure 5B–5D ) . All of the seedlings displayed characteristic developmental arrest phenotypes at a saturated level of 1 µM ABA ( Figure 5B ) . Notably , FINS1-overexpressing gin1 seedlings , but not WT or gin1 seedlings , displayed similar growth inhibition at a sub-potent level of 0 . 5 µM ABA ( Figure 5C ) . This result supports the notion that FINS1-dependent fructose signaling worked downstream of ABA synthesis ( Figure 5A ) . Because these transgenic lines did not show any growth inhibition in the absence ABA ( Figure 5D ) , it is unlikely that the growth response of the FINS1-overexpressing gin1 was caused by accelerated ABA synthesis rather than increased sensitivity to ABA . Based on the results shown in Figure 5 , we decided to investigate the definitive role of FINS1 in ABA signaling . When seedling growth was observed on MS agar media containing 1 µM ABA , fins1 and the constitutive ethylene signaling mutant ctr1 exhibited ABA insensitivity compared to WT , gin1 , and gin2 ( Figure 6A ) . Nevertheless , the fins1 phenotype clearly differed from that of ctr1 seedlings , suggesting that the ABA insensitivity of fins1 may not be directly related to an alteration in ethylene sensitivity . To elucidate the function of FINS1 in ABA-mediated gene regulation , we monitored the gene expression of ABI1 ( an ABA negative regulator ) and ABI3 , ABI4 , and ABI5 ( ABA positive regulators ) in fructose-insensitive fins1 , fructose/glucose-insensitive gin1 and ctr1 , and glucose-insensitive gin2 seedlings , and as well as in WT seedlings . ABI1 expression was higher in fins1 , ctr1 , and gin2 compared to its expression in WT and gin1 ( Figure 6B ) . In contrast , expression of the ABA positive regulators ( ABI3 , ABI4 , and ABI5 ) was suppressed in fins1 , and suppressed to an even greater extent in ctr1 ( Figure 6C–6E ) . The higher level of gene suppression in ctr1 correlated with its stronger ABA-insensitive response ( Figure 6A ) . The ABA-dependent seedling phenotypes and gene expression patterns of fins1 further supported the idea that fructose signaling closely interacted with ABA signaling through FINS1 . Unlike HXK1 in glucose signaling , FINS1 may not acts as a fructose sensor , because FINS1 binds more readily to fructose-1 , 6-bisphosphate than to fructose for its catalytic activity ( Figure S5 ) . However , it remains to be determined if fructose directly binds to putative FBP and acts as an allosteric regulator of the protein . Further elucidation of the biochemical and cellular processes underlying the interactions between GIN1 and FINS1 will provide a better mechanistic understanding of how fructose signaling controls early seedling establishment . We have identified fructose as a novel hexose signal that modulates early establishment of A . thaliana seedlings via a pathway that is distinct from glucose signaling ( Figure 1 ) . Genetic analyses revealed that fructose signaling interacted positively with ABA and negatively with ethylene , similar to high glucose signaling . Using a cell-based functional screen and reverse genetic analysis , we uncovered a regulatory role for FINS1/FBP in fructose signaling that is independent of its catalytic activity ( Figure 2 and Figure 3 ) . fins1 seedlings also showed sucrose insensitivity , indicating that alteration of sucrose content by loss of FINS1 is irrelevant to the fructose insensitivity of fins1 ( Figure 4 ) . The growth response of transgenic gin1 seedlings expressing FINS1 to fructose and ABA indicated that fructose signaling was acting downstream of ABA synthesis ( Figure 5 ) . The ABA response was consistently compromised in fins1 seedlings ( Figure 6 ) . Further explorations of the biochemical connections among GIN1/ABA2 , GIN2/HXK1 , and FINS1/FBP within a sugar-signaling context will provide a better mechanistic understanding of hexose signaling processes during early seedling establishment ( Figure 7 ) . However , it is apparent that multiple layers of interactions/cross-talk among glucose , fructose , and ABA signaling pathways tightly modulate plant growth promotion and inhibition , and provide developmental plasticity during the plant autotrophic transition following seed germination . Approximately 0 . 5 kb of the CAB2 promoter was amplified by PCR and fused to LUC to create the CAB2-fLUC reporter construct [38] . All of the effector constructs were generated by inserting the cDNA between the 35SC4PPDK promoter and the NOS terminator in a plant expression vector for protoplast transient assays and then verifying by DNA sequencing . Plants were grown in soil at 23°C for 20–22 d under 60 µmol/m2/s with a 13 h photoperiod . Protoplast isolation and transient expression assays were carried out as described previously [38] . All of the protoplasts transient assays were performed with UBQ10-renillaLUC ( UBQ10-rLUC ) as an internal control . The reporter activities were calculated based on the fLUC/rLUC ratio and normalized to the values obtained without treatment or effector expression . Plasmid constructs for transgenic plants were generated by inserting the cDNA of FINS1 between the 35SC4PPDK promoter and the NOS terminator in a mini-binary vector pCB302 [8] and expressing it in fins1 or gin1 mutant plants . The transgenic lines expressing transgenes at levels similar to those of WT were selected and used for further analyses . We analyzed the phenotypes of transgenic plants/seedlings from at least two independent lines at the T2 or T3 generation , except for catalytically inactive FINS1_ssm-complemented fins1 ( cSSM ) , which was used at the T1 generation . FINS1/FBP protein expression was analyzed using a cytoplasmic fructose-1 , 6-bisphosphatase–specific antibody ( Agrisera , #AS04043 ) or HA antibody ( Roche ) . For sugar repression assays , seedlings were grown on MS ( Caisson Laboratories ) agar medium containing 6% glucose ( Sigma ) , fructose ( Sigma ) , or mannitol ( Sigma ) for 5 d under constant light ( 60 µmol/m2/s ) . A germination test was performed to determine the ABA sensitivity of each genotype grown on half-strength MS agar medium containing 1% sucrose and a designated amount of ABA under a photoperiod of 16 h light/8 h dark . For the sucrose assay , seedlings were grown on 6 , 10 , or 12% sucrose MS agar medium with a photoperiod of 16 h light/8 h dark until they showed a clear phenotype . For each experiment , seeds were stratified at 4°C for 4 d before plating . The results were confirmed through several replications . For gene expression analysis , total RNA was isolated by the Trizol method ( Invitrogen ) and 1 µg of total RNA was used for cDNA synthesis [15] . We investigated glucose- and fructose-mediated gene regulation and their interactions with ABA and ethylene signaling by monitoring marker gene expression in WT and hormone mutants . Gene expression was quantitatively measured using real-time PCR with cDNA templates generated from the RNA of 5-d-old seedlings grown on MS media containing 6% glucose , fructose , or mannitol . Gene expression values in seedlings grown on mannitol served as osmotic controls . Real-time PCR was carried out with iQ SYBR Green dye-added PCR mix ( Bio-Rad ) . Tubulin4 ( AT1G04820 ) or elongation initiation factor4a ( ELF4a , AT3G13920 ) transcript was used as a real-time PCR control with gene-specific primers . Detailed primer sequences are listed in Table S1 . Each primer set was pretested by PCR for a single gene product . Experiments were repeated three times with consistent results .
Among the many plant sugar metabolites , glucose signaling has received the most attention . Although fructose is also an abundant hexose , its signaling role in plant growth and development has not been addressed clearly and systematically to date . We found that fructose functions as a regulatory sugar metabolite and interacts with signaling by the plant hormones abscisic acid ( ABA ) and ethylene in A . thaliana . The fructose-dependent growth response is mediated by FRUCTOSE INSENSITIVE1 ( FINS1 ) , which encodes an ancient metabolic enzyme , putative fructose-1 , 6-bisphosphatase . Interestingly , the catalytic function of FINS1 in sucrose biosynthesis is dispensable for its regulatory role in fructose signaling . FINS1 appears to act downstream of GLUCOSE-INSENSITIVE1 , which is involved in ABA synthesis . Overall , it is evident that although fructose and glucose have unique regulatory pathways , they also share some signaling interactions with plant stress and defense hormones and coordinate early seedling establishment of A . thaliana . Fructose affects cell signaling in mammals and causes various metabolic syndromes . However , a direct relationship between fructose and physiological diseases has not been established yet . Because FINS1 is evolutionarily conserved , our genetic evaluation of its signaling function may provide useful information about fructose signaling in animals as well as plants .
[ "Abstract", "Introduction", "Results/Discussion", "Materials", "and", "Methods" ]
[ "plant", "biology/plant", "growth", "and", "development", "plant", "biology/plant", "genetics", "and", "gene", "expression", "plant", "biology" ]
2011
Signaling Role of Fructose Mediated by FINS1/FBP in Arabidopsis thaliana
Dengue fever is an important arboviral disease . The clinical manifestations vary from a mild non-specific febrile syndrome to severe life-threatening illness . The virus can usually be detected in the blood during the early stages of the disease . Dengue virus has also been found in isolated cases in the cerebrospinal fluid , urine , nasopharyngeal sections and saliva . In this report , we describe the isolation of dengue virus from the upper respiratory tract of four confirmed cases of dengue . We reviewed all laboratory reports of the isolation of dengue virus from respiratory specimens at the clinical microbiology laboratory of the Kaohsiung Veterans General Hospital during 2007 to 2015 . We then examined the medical records of the cases from whom the virus was isolated to determine their demographic characteristics , family contacts , clinical signs and symptoms , course of illness and laboratory findings . Dengue virus was identified in four patients from a nasopharyngeal or throat culture . Two were classified as group A dengue ( dengue without warning signs ) , one as group B ( dengue with warning signs ) and one as group C ( severe dengue ) . All had respiratory symptoms . Half had family members with similar respiratory symptoms during the period of their illnesses . All of the patients recovered uneventfully . The isolation of dengue virus from respiratory specimens of patients with cough , rhinorrhea and nasal congestion , although rare , raises the possibility that the virus is capable of transmission by the aerosol route among close contacts . This concept is supported by studies that show that the virus can replicate in cultures of respiratory epithelium and can be transmitted through mucocutaneous exposure to blood from infected patients . However , current evidence is insufficient to prove the hypothesis of transmission through the respiratory route . Further studies will be needed to determine the frequency of respiratory colonization , viable virus titers in respiratory secretions and molecular genetic evidence of transmission among close contacts . Dengue fever is a mosquito-borne disease caused by dengue virus ( DENV ) . There are four serotypes–DENV-1 , DENV-2 , DENV-3 and DENV-4 . Dengue is widely distributed throughout tropical and sub-tropical areas around the world and imposes great economic burden . It is transmitted by Aedes aegypti and less commonly by Aedes albopictus . The World Health Organization ( WHO ) estimates that up to 100 million infections occur annually [1] . The incidence of dengue fever has increased 30-fold over the past 50 years , making it one of the most important arboviral disease around the world [1] . The course of dengue usually go through three phases: febrile phase , critical phase and recovery ( convalescence ) phase [1 , 2] . Febrile phase represents the beginning of high fever caused by dengue viremia , which lasts for 2–7 days . Critical phase is featured by signs of plasma leak , such as pleural effusion , ascites , petechiae or intracranial bleeding around the time of defervescence , which lasts for 24–48 hours . After patients survive the critical phase , they go through convalescent phase and recover spontaneously . Timely and efficient diagnosis is crucial to the management of dengue . Current diagnostic tests enable simple , rapid and sensitive detection of the virus . This is achieved by isolation of the virus or serologic or molecular methods depending on the phase of the disease [3] . Nonstructural 1 ( NS1 ) protein is a glycoprotein encoded by dengue virus and secreted from dengue-infected cells [4] . Detection of the NS1 antigen by enzyme-linked immunosorbent assay in the acute phase is an important tool for prompt diagnosis of dengue [5] . Confirmation is made by reverse-transcriptase polymerase chain reaction ( RT-PCR ) from viremic specimens [1] . Rapid diagnostic tests for dengue antibodies ( IgG , IgM ) are also commercially available . Although the early diagnosis of dengue fever is usually based on detection of viremia , there are several reports of DENV in cerebrospinal fluid [6] , saliva and urine [7] . We are aware of only one case report of the isolation of dengue virus from a respiratory specimen [8] . In this report , we describe four cases of dengue fever in which the virus was isolated from the upper respiratory tract . This study was approved by the institutional review board of the Kaohsiung Veterans General Hospital . Patient data were anonymized . The study was conducted at the Kaohsiung Veterans General Hospital , in southern Taiwan . We identified all isolations of dengue virus from respiratory specimens obtained from patients admitted to the emergency service , hospital or clinics setting during 2007 to 2015 . A respiratory specimen was defined as material obtained from the sputum , nasopharyngeal or pharyngeal secretions , or bronchoalveolar lavage fluid . The medical records of each patient were reviewed to provide demographic data , clinical signs and symptoms , contact history , laboratory findings , clinical course and outcome . Respiratory specimens were sent to the virology laboratory for identification . They were inoculated into six different cell lines: human lung fibroblast ( MRC-5 ) , human rhabdomyosarcoma ( RD ) , human lung adenocarcinoma ( A549 ) , African green monkey kidney cell ( Vero ) , Rhesus monkey kidney ( MK2 ) , and Madin Darby canine kidney ( MDCK ) . Fresh Eagle minimal essential medium was added and incubated at 35°C with 5% CO2 . Observations were made twice a week for 3 weeks at 4x magnification until a cytopathic effect ( CPE ) was evident . CPE positive cultures were identified using indirect immunofluorescence assay ( IFA ) screening kits for respiratory viruses ( Catalogue number 3360 , Chemicon International Inc . ) and enteroviruses ( Catalogue number 5007 , Chemicon International Inc . ) according to the manufacturer’s instructions . Respiratory IFA contains antibodies which recognize adenovirus , influenza A and B , parainfluenza 1 , 2 and 3 , and respiratory syncytial virus . Enterovirus IFA contains antibodies which recognize echoviruses , coxsackie A and B , polio 1 , 2 , and 3 , and enteroviruses 70 and 71 . Viral RNA was extracted from each culture supernatant using the QiAmp viral RNA kit ( Qiagen inc . ) according to the manufacturer’s instructions and analyzed using classic RT-PCR . We used primer DV1 and type-specific DSP1 , DSP2 , DSP3 and DSP4 according to a previous study [9] . One-step RT-PCR assays ( SuperScriptTM One-Step RT-PCR with Platinum Taq kit; Invitrogen Corporation ) were performed . The amplification reaction consisted of cDNA synthesis at 50°C for 30 minutes; predenaturation at 95°C for 1 minute , PCR amplification for 45 cycles at 95°C for 30 seconds , at 50°C for 30 seconds , and at 72°C for 7 minutes; followed by a final extension step at 72°C for 7 minutes . PCR products were analyzed by electrophoresis on a 1 . 5% agarose gel; the expected amplicons were 169 base pair ( bp ) for DENV-1 , 362 bp for DENV-2 , 265 bp for DENV-3 , and 426 bp for DENV-4 . Descriptive statistics were used to characterize the study population , clinical features and laboratory findings . A 64-year-old otherwise healthy woman presented to the emergency department with fever of 38 . 0°C for one day in November 2015 . This was associated with generalized myalgia , severe headache , rhinorrhea , nasal congestion , productive cough and gingival bleeding . She had no signs of respiratory distress when seen in the emergency department . Laboratory analysis revealed white blood cell count ( WBC ) of 3 . 18×109/L , hematocrit ( HCT ) of 35 . 9% and a platelet count of 166×109/L . She tested positive for serum dengue NS1 antigen and negative for dengue IgM and IgG ( SD BIOLINE Dengue Duo ) . A quick test ( BD VeritorTM System For Rapid Detection of Flu A+B ) was positive for influenza A . She was treated with oral oseltamivir and subsequently hospitalized . A nasopharyngeal culture obtained after admission was positive for DENV-2 . Her condition gradually improved and she was discharged home after 5 days of hospitalization . A 17-year-old young man presented to the pediatric emergency department with intermittent high-grade fever for two days in August 2015 . This was associated with myalgia , headache , rhinorrhea , sore throat , productive cough and abdominal pain . Physical examination showed a congested oropharynx and soft abdomen with hyperactive bowel sounds . Laboratory tests showed a WBC of 3 . 06×109/L , a HCT of 38 . 6% and a platelet count of 151×109/L . Throat swab tested negative for influenza quick test and negative for beta-hemolytic streptococcus and nasopharyngeal swab was positive for DENV-2 . His stool was negative for adenovirus ( Adenolex test , Orion Diagnostica , Finland , as text in S1 ) . He was treated for acute bacterial tonsillitis with oral amoxicillin and followed up in the pediatric clinic 2 days after , with marked improvement of symptoms . A 14-year-old girl presented to the emergency department with fever , chills and fatigue for 3 days in September 2010 . She had history of pneumonia and acute otitis media ( AOM ) when she was in elementary school . Her aunt , living with her , had similar signs and symptoms . She had no recollection of a mosquito bite . Her temperature was 38 . 9°C . Her conjunctivae and throat were congested . Laboratory investigations showed a WBC of 2 . 78×109/L , a platelet count of 139×109/L and HCT of 36 . 2% . A nasopharyngeal influenza quick test was negative . She was discharged with antipyretics , but returned the next day due to nausea , persistent fever and new-onset bilateral otalgia . Her eardrums were normal without discharge . There was no skin rash . She was treated with amoxicillin-clavulanate and antipyretics . A serologic test at day 4 was positive for dengue NS1 antigen . The throat swab collected at day 5 of fever showed a cytopathic effect . The virus culture was positive for DENV-2 by RT-PCR . She became afebrile on day 6 and gradually improved . A 12-year-old boy presented to the pediatric emergency room with a temperature of 40°C for one day in October 2008 . This was associated with generalized weakness , myalgia , severe headache and vomiting . His father had similar symptoms and was admitted in our hospital at the same time . The child’s blood pressure was 178/78 mmHg and heart rate was 133/min . He had a congested pharynx with bilateral coarse breath sounds . Laboratory test showed a WBC of 8 . 49×109/L , HCT of 41 . 5% and platelet count of 264×109/L . Chest X-ray was unremarkable . He was admitted to the pediatric service and treated with oseltamivir . His fever and other signs and symptoms persisted for 4 days and were accompanied by lethargy , severe bone pain , generalized skin rashes and one episode of epistaxis . Laboratory data revealed a WBC of 2 . 08×109/L , a platelet count of 70×109/L and hemoconcentration with HCT of 46 . 9% . The patient was transferred to the pediatric intensive care unit for monitoring because of signs of severe dengue . A nasopharyngeal influenza quick test was negative . Throat swab and blood virus cultures were obtained at day 4 of illness . DENV-1 was isolated from both specimens . Abdominal sonography and chest-x ray showed no signs of pleural effusion or ascites . The patient was treated supportively and was discharged at day 12 of illness . Their clinical findings are summarized in Table 1 and virology data in Tables 2 and 3 . Dengue virus is typically transmitted by means of its vector mosquitos . Humans are its primary amplifying host [1] . The incubation period after being bitten by a mosquito falls between 3–10 days in 95% of the patients [10] . After this period , DENV begins to replicate rapidly , causing viremia for an average of 5 . 1 days for primary infection and 4 . 4 days for secondary infection [11] . A mosquito can acquire DENV through ingestion of viremic blood that has high circulating virus [12] . Once the virus is acquired , the mosquito remains infected throughout its life and can pass it to its offspring through transovarial transmission [13] . Transovarial transmission of DENV 1–4 has been observed in both A . aegypti and A . albopictus [14] , making vector population control a crucial issue in the management of dengue outbreak . Dengue virus can also be transmitted from infected blood by transfusions [15] , organ transplantation [16–18] , vertical transmission from mother to fetus [19] and needle stick injury [20] . There has only been one prior report , to our knowledge , of the isolation of DENV from nasal and pharyngeal swabs of a patient residing in a dengue-endemic area [8] . We now add four more similar cases . Dengue fever is commonly associated with respiratory symptoms . A dengue surveillance system found that 35–38% of laboratory-positive dengue patients had sore throat , cough and nasal congestion [21] . This raises the possibility that dengue can be transmitted via respiratory aerosols or by direct contact from patients with respiratory infection . Half of the patients had family members who also had similar signs and symptoms during onset of their illness . The finding of dengue virus in their nasopharynx and throats and the close association of two of them with symptomatic relatives made us wonder whether respiratory aerosols might have transmitted dengue virus into the air or by close contact . Possible aerosol transmission has been reported with other flaviruses , including Zika virus [22] , tick-borne encephalitis virus [23] and Wesselsbron virus [8] . There is strong evidence from work conducted on dengue fever in World War II that dengue virus can be transmitted through nasopharyngeal exposure to infected serum [24] . Seventy-five percent of patients receiving intranasal instillation of infected serum developed febrile illness 11 days after inoculation . The median onset of leukopenia was slower compared to patients infected by mosquito-bites ( 10 . 5 days vs . 8 days ) . The duration of fever and leukopenia were also shorter in patients infected intranasally . Half of the intranasal-infected patients developed epistaxis compared to 6% of patients infected by mosquito bites or infected serum ( P<0 . 001 ) [25] . There are other reports of possible mucocutaneous transmission of dengue virus . A health care worker was diagnosed with dengue after she accidentally splashed the infected serum on her face , including her eye , nose and mouth . She had nosebleeds , anorexia and eye pain 10 days afterwards . Serological markers indicated acute dengue infection [26] . Another investigator was infected with the virus while performing a laboratory experiment involving primary infection of colony mosquitoes with dengue virus via an artificial membrane feeding apparatus . Nucleotide sequencing found 99 . 8% homology between the virus retrieved from patient and the laboratory strain . The patient was wearing gloves , gowns and eye protection during the procedure . It was postulated that the virus may have been transmitted mucocutaneously via virus-infected aerosolized blood droplets or through an unrecognized dermal abrasion [27] . Before the availability of polymerase chain reaction , typical viral isolation techniques for dengue virus involved inoculating specimens in C6/36 cells , larva tissue of A . Albopictus [28] . Our virology laboratory observed cytopathic effect in either Vero or MK2 cell lines . Aside from these cell lines , all four serotypes of DENV have been demonstrated to be able to infect and replicate in human primary lung epithelium and lung cancer cell lines [29] . These findings indicate that respiratory epithelium may be a possible target of dengue virus . The cells routinely used for viral culture of respiratory specimens in our laboratory were designed to detect common respiratory pathogen rather than dengue virus . Once inoculated cells show CPE , we would use screening IFA for respiratory virus and enteroviruses . Coinfections with respiratory virus and enterovirus IFA were excluded since all our specimens tested negative for these pathogens by IFA . Since this is a retrospective study , it has certain limitations . The respiratory secretions of dengue–infected patients were not routinely cultured for viral growth . A review of 281 cases of patients with dengue in our hospital in 2015 shows that only 2 patients had respiratory specimens sent for viral culture . The culture cells we used were not optimal for dengue virus growth and may underestimate dengue infection . Of 31893 specimens received , only 4 tested positive for dengue virus . Testing for dengue PCR from respiratory virus and enterovirus IFA-negative specimens were done only when dengue virus was suspected by the microbiologists according to their own experiences . This report of the isolation of dengue virus from the airway secretions of four patients with respiratory symptoms raises the possibility that it may spread by the aerosol route or by direct contact . This concept is supported by studies which show that the virus can replicate in cultures of respiratory epithelium and can be transmitted through mucocutaneous exposure to blood from infected patient . Current evidences are insufficient to prove the hypothesis of transmission through the respiratory route . Further studies are needed to determine the frequency of respiratory colonization in patients with dengue , viable virus titers in respiratory secretions and molecular genetic evidence of transmission of the same strain among close contacts .
Dengue virus is rarely identified in respiratory specimens . We retrospectively identified four patients with dengue fever who had the virus isolated from their nose or throat . All the patients had respiratory signs or symptoms . Half had family members who also had respiratory symptoms . Further studies are needed to evaluate the possibility of respiratory transmission of this virus .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "reverse", "transcriptase-polymerase", "chain", "reaction", "dengue", "virus", "medicine", "and", "health", "sciences", "body", "fluids", "pathology", "and", "laboratory", "medicine", "respiratory", "infections", "pathogens", "tropical", "diseases", "microbiology", "pulmonology", "viruses", "throat", "rna", "viruses", "signs", "and", "symptoms", "materials", "science", "neglected", "tropical", "diseases", "molecular", "biology", "techniques", "materials", "by", "structure", "research", "and", "analysis", "methods", "infectious", "diseases", "mixtures", "artificial", "gene", "amplification", "and", "extension", "medical", "microbiology", "dengue", "fever", "aerosols", "microbial", "pathogens", "molecular", "biology", "diagnostic", "medicine", "blood", "anatomy", "flaviviruses", "polymerase", "chain", "reaction", "fevers", "viral", "pathogens", "physiology", "biology", "and", "life", "sciences", "viral", "diseases", "physical", "sciences", "neck", "organisms" ]
2017
Isolation of dengue virus from the upper respiratory tract of four patients with dengue fever
The province of Bohol , located in the Visayas islands region in the Philippines has a human population of 1 . 13 million and was the 4th highest region for human rabies deaths in the country , averaging 10 per year , prior to the initiation of the Bohol Rabies Prevention and Elimination Project ( BRPEP ) . The BRPEP was initiated in 2007 with the goal of building a sustainable program that would prevent human rabies by eliminating rabies at its source , in dogs , by 2010 . This goal was in line with the Philippine National Rabies Program whose objective is to eliminate rabies by 2020 . The intersectoral BRPEP was launched in 2007 and integrated the expertise and resources from the sectors of agriculture , public health and safety , education , environment , legal affairs , interior and local government . The program included: increasing local community involvement; implementing dog population control; conducting mass dog vaccination; improving dog bite management; instituting veterinary quarantine; and improving diagnostic capability , surveillance and monitoring . Funding was secured from the national government , provincial , municipal and village units , dog owners , NGOs , the regional office of the WHO , the UBS Optimus Foundation , and the Global Alliance for Rabies Control . The BRPEP was managed by the Bohol Rabies Prevention and Eradication Council ( BRPEC ) under the jurisdiction of the Governor of Bohol . Parallel organizations were created at the municipal level and village level . Community volunteers facilitated the institution of the program . Dog population surveys were conducted to plan for sufficient resources to vaccinate the required 70% of the dogs living in the province . Two island-wide mass vaccination campaigns were conducted followed by “catch up” vaccination campaigns . Registration of dogs was implemented including a small fee that was rolled back into the program to maintain sustainability . Children were educated by introducing rabies prevention modules into all elementary schools in Bohol . Existing public health legislation at the national , provincial , and municipal level strengthened the enforcement of activities . A Knowledge , Attitude and Practices ( KAP ) survey was conducted in 2009 to evaluate the educational knowledge of the population . Increased surveillance was instituted to ensure that dogs traveling into and out of the province were vaccinated against rabies . Human and animal cases of rabies were reported to provincial and national authorities . Within the first 18 months of the BRPEP , human rabies deaths had decreased annually from 0 . 77 to 0 . 37 to zero per 100 , 000 population from 2007–2009 . Between October 2008 and November 2010 no human and animal cases were detected . Increased surveillance on the island detected one suspected human rabies case in November 2010 and one confirmed case of canine rabies in April 2011 . Two mass vaccination campaigns conducted in 2007 and 2008 successfully registered and vaccinated 44% and 70% of the dogs on the island . The additional surveillance activities enabled a mobilization of mop up vaccination activities in the region where the human and canine case was located . Due to the increased effective and continuous surveillance activities , rabies was stopped before it could spread to other areas on the island . The program costs totaled USD 450 , 000 . Registration fees collected to maintain the program amounted to USD 105 , 740 and were re-allocated back into the community to sustain the program . More than 99% of all global human rabies deaths occur as a result of being exposed to infected dogs . In the Philippines , as in most Asian countries , canine rabies is endemic and poses a particularly perilous risk for persons living in rural areas [1] . The number of human deaths attributed to rabies has not changed significantly in the past decade in the Philippines with an average of 250 reported annually between 1999–2009 [2] . As with other Asian countries , almost every human death in the Philippines occurs because the patient failed to seek post-exposure prophylaxis ( PEP ) after exposure due to a lack of educational awareness or , less often because the PEP received did not follow the WHO recommended protocol [3] . In 1991 , Fishbein et al reported that the elimination of canine rabies in the Philippines would prevent human rabies and result in an estimated cost savings of 25 million US dollars annually with the program costs being recuperated within 4 to 11 years [4] . This cost-benefit study provided evidence that the most effective strategy to prevent human rabies in a canine rabies endemic country would be to eliminate rabies at the source of infection , i . e . the dog population . Program sustainability is a critically important issue for all public health programs , but especially for resource-poor countries with limited budgets and many problems to resolve . Thus , a successful rabies prevention and control program must be built around the strengthening of intersectoral collaboration and cooperation between several public health components [5] . In 2007 , the provincial government in Bohol made a commitment to eliminate canine rabies throughout the province by developing a strategic plan that would involve all of the stakeholders required and would ultimately be sustainable once rabies was eliminated . The province of Bohol is located in central Visayas Region , Philippines and prior to 2007 , was among the top ten provinces in the country for human rabies deaths , averaging 10 deaths ( 0 . 77 per 100 , 000 population ) per year . Of the reported human rabies cases in 2007–2008 , 25% occurred in children less than 15 years old after being bitten by an infected dog [2] . In order to control canine rabies and eventually eliminate human rabies deaths in Bohol province , strategic plans for the Bohol Rabies Prevention and Elimination Program ( BRPEP ) were developed by integrating expertise and resources from the sectors of agriculture , human health , education , legal and finance . The BRPEP was initiated in 2007 by the Provincial Government of Bohol and was spearheaded by the Office of the Provincial Veterinarian ( OPV ) and the Provincial Health Office ( PHO ) . The ultimate goal of the BRPEP was to prevent human rabies by eliminating the disease at its source , in dogs , by 2010 and to ensure sustainability once rabies was eliminated , so that if rabies were re-introduced to the island , the ongoing surveillance would quickly detect the outbreak and ensure proper follow-up . The BRPEP was consistent with the Philippine National Rabies Program ( PNRA ) , enacted by the House of Senate in May 2007 that set a goal of eliminating human and dog rabies throughout the country by 2020 . The BRPEP utilized the PNRA's national policies and strategies for the control and elimination of human and animal rabies , to initiate and conduct the project . From the outset of the project , the BRPEP was developed and executed as an intersectoral program including: Local community involvement; communication management; dog population control; mass dog vaccination; dog bite management; veterinary quarantine; and improved diagnostic capability , surveillance and monitoring . Funding for the BRPEP program was secured through many avenues including: Cost-sharing activities from the national government , provincial , municipal and village ( or barangay ) local government units; dog owners; local non-government organizations; the regional World Health Organization ( WHO ) office , the UBS Optimus Foundation , and the Global Alliance for Rabies Control , a not-for-profit organization focused on rabies control and prevention that worked closely with the OPV to bring technical support and coordination to the project . Although securing external funding was a key component of eliminating canine rabies , it was also critical to build the program in a sustainable manner to ensure that after canine rabies elimination , the program would not fail due to dwindling resources as occurred in Bohol during a previous effort to eliminate canine rabies . In 2000 , seven years prior to the launch of the BRPEP , the first province-wide rabies mass dog vaccination program had been conducted on Bohol in conjunction with a regional initiative . This earlier program had been directly supported by the national government and had utilized only the existing veterinary service staff . The following year , in 2001 , the dog vaccination coverage on Bohol had dropped to 2% and no allocation had been made for the purchase of vaccine the succeeding years . This article focuses on how the BRPEP was developed and implemented over a three year period , from 2007–2010 , in Bohol , Philippines and discusses specific intersectoral strategies applicable to establishing sustainable rabies control and prevention programs in resource-poor countries . The island province of Bohol is the 10th largest island in the Philippines ( Figure 1 ) . The total human population is 1 , 139 , 130 living in 152 , 324 households [6] . The economic drivers are eco-cultural tourism and agro-industrialization . The annual average family income is 77 , 291 Philippine Pesos ( PHP ) ( 1 , 770 US$ ) and the annual average family expenditure is 66 , 907 PHP ( 1 , 534 US$ ) . Annual per capita income is 16 , 478 PHP ( 378 US$ ) and annual per capita expenditure is 14 , 364 PHP ( 329 US$ ) . At the provincial level , the BRPEP was managed by the Bohol Rabies Prevention and Eradication Council ( BRPEC ) under the jurisdiction of the Governor . Canine rabies prevention and elimination was coordinated by the Provincial Veterinarian while human rabies prevention and elimination was managed by the Provincial Health Officer . The BRPEC administered overall implementation , formulated proposals , measures and strategies that would ensure the implementation and sustainability of the BRPEP . This body also recommended the enactment of support legislation , policies and directives to strengthen the program and provided timely reports to program partners and the general public . Parallel organizations to the BRPEP were created at the municipal and barangay ( villages ) local government units ( LGU ) namely the Municipal Rabies Prevention and Elimination Council ( MRPEC ) and the Bantay Rabies sa Barangay ( BRB ) or the “Rabies Watchers” . The MRPEC assumed the same roles and functions at their areas of jurisdiction in accordance with the BRPEP . The BRB ensured implementation of the program at the community level , arranged mass vaccination campaigns in their areas and compiled a master list of dogs and dog owners . The legal framework for implementing a rabies prevention and control program in the Philippines was already in place at the inauguration of the BRPEP and included several national regulations: Republic Act No . 8485 known as the Animal Welfare Act was enacted in 1998; Philippine Republic Act No . 9482 was enacted on 25 May 2007 and is identified as the Anti-Rabies Act of 2007 [7] . The Philippine Republic Act was approved by the national government to be implemented at the LGUs . At the local level , the provincial Governor promulgated the Provincial Ordinance No . 2007-012 “Strengthening the Bohol Rabies Prevention and Eradication Program” that was approved on 10 July 2007 by the Sangguniang Panlalawigan or the local legislative body . This ordinance stipulated the establishment of the implementing bodies ( BRPEC , MRPEC and BRB ) at the provincial , municipal and barangay level . Membership of the municipal councils was designated by the Mayors and brought together representatives of the national offices ( Departments of Agriculture , Health , Education , Interior and Local Government , Philippine National Police , Agricultural Training Institute and the Philippine Information Agency ) , the provincial offices ( provincial veterinarian , health , tourism , legal , social welfare and agriculture ) , as well as representatives of several NGOs , league of elected and appointed officials and the local media group . BRB membership was designated by the barangay captains . The local legislation defined the roles and responsibilities of the councils including: The organization of dog and dog owner registration; collection of registration fees; elimination of stray dogs ( defined in the national law as any dog leaving its owner's place or premise and that is no longer in the effective control of the owner ) ; dog vaccination; surveillance of human and animal rabies and dog bite incidents; settlement of disputes/agreements between bite victims and dog owners; and promotion of responsible dog ownership . To ensure dissemination of information and understanding of the supporting legislation and to promote program advocacy throughout the entire province , orientation and paralegal training sessions were conducted for 7 , 763 BRB volunteers . All communications regarding the BRPEP were managed in a coordinated manner to ensure integration of all components of the program as it progressed through the development , initiation , implementation , maintenance and sustainability phases . It was deemed critically important from the beginning of the BRPEP to facilitate understanding , cooperation , and support among stakeholders including the citizens living on Bohol , medical professionals , volunteers and paid employees implementing the program as well as government officials and funding agencies . Information was therefore delivered to the beneficiaries at the grass roots level as well as to government agencies overseeing the project and funding agencies supporting the project . The Provincial as well as the Municipal/City Rabies Task Forces designated key spokespersons authorized to dispense information and answer questions relevant to their areas of jurisdiction . As part of the communications strategy , a BRPEP handbook was developed , published and distributed to every municipal rabies council to serve as a reference for all field units working within the program [8] . The handbook was designed to provide a logical web of operational activities among inter-agency stakeholders and a common approach in controlling rabies within the province . Moreover this document provided clear and distinct roles and responsibilities at various management levels from the national level down to the barangays and individual households . It also included vital information regarding the disease and its epidemiology . It outlined the goals and objectives of the BRPEP , strategies of implementation , program management , and information on legislation and issuances as well as contact phone numbers in case of questions about the program . Additionally , the BRPEP handbook included an example of an annual operational plan including the report forms and the monitoring format . A Barangay Handbook with simplified standard operating procedures written in the local dialect was also distributed to each of the 1 , 109 BRBs . A two-pronged social mobilization plan , including a ‘Community-focused program’ and a ‘School-based education program’ , was launched as a first step to increase awareness and enhance community participation and support . Components of the IEC included discussions on rabies as a disease , its epidemiology , and its prevention and control , the Bohol Program in general and related national and municipal rabies ordinances as they supported the program implementation and responsible pet ownership . The community program concentrated on campaigns using tri-media ( television , radio , newspapers ) , display of posters and banners in strategic areas , distribution of flyers and other materials , public hearings of local ordinances and hosting of municipal and barangay symposia , meetings and seminars . Educational campaigns were also conducted at various government offices and in churches . Philippine National Rabies Awareness Month in March , and World Rabies Day , held annually on September 28 were both observed to remind people of the continual threat of rabies and the importance of the program to eliminate rabies on Bohol . The School-based educational program , designed to improve awareness about rabies prevention , was developed and implemented in close supervision with the Department of Education and in coordination with the Department of Health and other member agencies of the BRPEC . The integration of rabies education into the school curriculum was initially developed by the Department of Health's National Rabies Control Program in 2006 . In 2008 , this program was piloted in Bohol beginning with round table discussions with teachers , followed by intensive planning , a workshop to develop lesson plans , orientation/training of teachers , and testing of the developed lesson plans for 6 months in the municipality of Corella during the 2008–2009 school year . In 2009 , they were integrated into the curriculum of all 962 public elementary schools in Bohol . Educational activities for the children included: Incorporation of rabies modules into various subjects in the public elementary school curriculum; creation of “Rabies Scouters” ( boy and girl scouts who have successfully completed a rabies and responsible pet ownership training program ) ; creation of a campaign slogan to encourage responsible pet ownership; conducting fun educational events to celebrate the bond between children and pets . Other forms of campaigns promoting responsible pet ownership were also adopted in the city/municipalities . Dog population data on Bohol was first estimated from the 2006 census published by the Bureau of Agriculture Statistics ( BAS ) . To secure data on the number and location of dogs throughout Bohol , the BRB initially conducted a house-to-house inquiry using a master list of households . The collected data reflected dog owner's name , number of dogs owned , whether they were confined , leashed or free-roaming , sex of each dog and total number of households . Dog population data was updated annually . To further regulate the possession of dogs , establish dog ownership , and facilitate the traceability of dogs involved in bite cases , the mandatory registration of dogs , with a corresponding fee collection , was implemented at the barangay level in accordance with the Provincial Ordinance . Dogs from households that were not able to afford the fees were also registered and the dog owners were given a promissory note and allowed a staggered payment . The collected registration fees were shared in a manner specified in the Provincial Ordinance as follows: 50% was retained in the barangay and 50% divided between the municipal and provincial treasury to support the sustainability of the entire rabies control program . A parenteral mass dog vaccination program was initiated in August of 2007 with the Governor of Bohol proclaiming August 2007 as the synchronized rabies vaccination month . Vaccination teams were organized at the provincial and municipal levels . Provincial teams were assigned to oversee the vaccination activities and to ensure the presence and usage of cold chain equipment in every municipality . The Municipal Agricultural Officers ( MAO ) led the municipal vaccination teams composed of livestock technicians , Barangay Livestock Aides and other personnel duly designated by the MRPEC trained as dog vaccinators . Vaccination activities were supervised by the provincial and district veterinarians . The majority of the members of the vaccination team , ( including those who administered the vaccine , or assisted in handling and tagging animals ) , received pre-exposure rabies vaccination . Team members who handled registration and collection of fees , or prepared reports did not come in contact with animals , and were not given pre-exposure rabies vaccination . All team members were briefed on proper rabies vaccination activities , provided vaccination supplies and paraphernalia , registration/health certificates and dog tags , recording forms and education campaign materials . Mop-up dog vaccination campaigns targeting low coverage areas were conducted within six months following the initial mass vaccination campaign . Dogs not vaccinated during the scheduled synchronized mass rabies vaccination campaigns were accommodated upon special arrangement with the municipal vaccination teams . A uniform dog tag , indicating vaccination , was securely fastened on a dog collar with the help of the assistant dog vaccinator . The tag was valid for one year from the date of vaccination and was replaced annually upon renewal of registration at which time a booster rabies vaccination was administered . Standardized dog vaccination report forms were consolidated and submitted to the BRB , the MAO and the BRPEP . The community was also encouraged to bring their cats for vaccination during the mass vaccination campaign . Dog population and movement control was implemented as part of the BRPEP and in compliance with the PNRA and the Animal Welfare Act . The dog population was managed by selective elimination of captured stray dogs , impounded dogs unclaimed within 3 days , and unmanageable dogs voluntarily submitted by owners [7] . Municipal rabies ordinances in the Philippines include a section on dog population management and designated a task force to perform this function . At the barangay level , socially acceptable procedures were discussed and widely disseminated throughout the community . Euthanasia procedures were initially conducted in accordance with Administrative Order No . 21 of the Department of Agriculture on the Code of Conduct in the Euthanasia for Pets/Companion Animals . During the third year of the project ( 2010 ) , the purchase of a mobile veterinary clinic was funded by one of the partners and provided the opportunity to improve neutering , spaying , and euthanasia procedures . Additionally , partnerships were established with animal welfare organizations to improve dog population management practices to comply with recommended international standards . In 2007 , prior to the launch of the BRPEP , two government-operated Animal Bite Treatment Centers ( ABTC ) were located on Bohol . As part of the BRPEP , four additional ABTCs , all privately operated , were established on the island to increase accessibility of timely post-exposure prophylaxis ( PEP ) . Medical personnel were trained to administer rabies vaccines intradermally ( ID ) in order to provide effective PEP as economically as possible . Rabies biologicals were acquired either through the support of the DOH National Rabies Program , the investment of the private ABTCs , or direct procurement by the provincial government . Additionally , the government subsidized the purchase of PEP for indigents without sufficient financial resources , and for persons bitten by the dogs of responsible pet owners ( those that had kept their animals registered and vaccinated ) and responsible pet owners exposed to suspect rabid dogs . All exposed patients received PEP according to the WHO standard ID regimen and all adverse events were reported and managed accordingly [3] . Training on bite management and PEP for all district hospital doctors & nurses of the primary health care units was conducted in collaboration with the Research Institute for Tropical Medicine ( RITM ) , the national rabies referral center of the Department of Health . When possible , animals involved in biting incidents were observed for 14 days . If the animal demonstrated clinical signs of rabies during the observation period , including behavioral change or illness , it was euthanized and submitted for testing at the Regional Animal Disease Diagnostic Laboratory ( RADDL ) located in the city of Cebu on the adjacent island province . Submitted specimens were analyzed using the direct fluorescent antibody test . Laboratory results usually available within 1–2 days were relayed to the OPV and the MRPEC who releases this to the victim or the immediate relatives . The sample testing was free of charge but the cost of transporting specimens was assumed by the BRPEP . Surveillance systems for both human and canine rabies cases were established in order to ensure immediate and reliable transfer of information and follow-up in the case of human or animal exposure to a confirmed rabid animal . The protocol for investigation and follow-up of all bite cases was established , and continues presently as follows: The reporting of bite cases is initiated at the BRB level when the bite victim presents at the Rural Health Center for consultation , first aid treatment and assessment; if referred , the patient attends the closest ABTC for PEP . In cases where multiple biting incidents have occurred or when the involved animal is suspected to be rabid , the MAO and the OPV are informed in order to monitor the status of the biting animal ( s ) . In highly suspicious cases involving the death of a bite victim , the attending physician immediately reports the case to the Provincial Rabies Coordinator who in turn contacts the Quick Response Team ( QRT ) and the Provincial Surveillance Unit to initiate a field investigation . Contact tracing is conducted in coordination with the MRPEC , all human and animal contacts of the biting animal are assessed and , if deemed necessary , immediately given PEP ( human patients ) or a booster vaccination ( animals ) . Movement of all animals involved in biting incidents is strictly monitored , the dog vaccination record at the barangay level is reviewed and mop-up vaccination is conducted . For planning purposes , estimating dog vaccination coverage , and evaluating the outcome of the project , a household survey was conducted to collect data on the owned dog population , knowledge , attitudes and practices ( KAP ) , and data regarding the dog-human relationship . The survey was designed using questionnaires and cluster sampling procedures and collected data among 300 households [9] , [10] . Sustainability was ensured through the BRPEP activities on advocacy , general public awareness , child education , legislation , dog registration with fees , 100% of which was re-invested back into the BRPEP to establish a self-sustaining funding stream for the program . Sustainability was strategically built into the program through an integrated program interface with LGU officials , community leaders and other stakeholders to ensure community participation; establishment of a rabies diagnostic laboratory on the island for increasing further disease surveillance; creation of volunteer quarantine aides in coastal barangays to continually monitor the entry of new dogs from other provinces; and provision of annual award system to motivate excellence in program execution . Approximately 25% of all reported human deaths reported prior to the launch of the BRPEP occurred in children less than 15 years of age . After the initiation of the campaign , the number of human deaths dropped dramatically ( Figure 2 ) . The number of animal deaths also dropped with the last case of dog rabies being reported in April of 2011 ( Figure 2 ) . The BPREP was strongly supported by the National Rabies Law and the local ordinances at the provincial and municipal level . There were 4 , 379 BRB members and 96 members of the MRPECs that received instruction through the paralegal training courses associated with the BRPEP . Local legal procedures and the utilization of paralegal forms reduced violations of the law and improved community compliance , resulting in fewer free roaming dogs to be eliminated . Guidelines at the provincial , municipal and barangay council levels ensured uniformity in the understanding of the program and resulted in synchronized efforts across the 47 municipalities , one city and 1 , 109 barangays of the province . The successful engagement of the local communities resulted in a community-focused program that increased the total workforce of the rabies elimination program from the initial 124 government paid staff prior to 2007 , to about 15 , 021 in 2009 . This included the participation of community volunteers and other local officials to augment the government employees . The KAP survey suggested that effective communications had promoted a sense of program ownership since 89% of the households surveyed believed that the BRPEP was good for their community and 73% of households no longer allowed their dogs to roam free . As a result of the extensive IEC , there was a dramatic increase in the number of residents that acknowledged ownership of and registered their dogs . The community focused program also directly increased the number of bite victims that sought medical treatment after exposure . Increased dog bite reporting is a function of awareness , and the KAP survey revealed a high level of rabies knowledge among the general public: 94% claimed they had heard about rabies disease , 82% had knowledge about the local rabies program and 85% were aware of the rabies ordinances . The school-based education program provided rabies lessons and modules that were integrated into the curriculum of all elementary schools throughout the province by year two of the program . This ensured early and continuous education for children considered most at risk . Rabies prevention education was expanded to all elementary schools , reaching over 182 , 000 children or 16% of the total provincial population . Additionally , 128 Rabies Scouters from three municipalities were mobilized as peer advocates for rabies prevention . Province-wide simultaneous World Rabies Day activities were observed in all elementary schools through the issuance of a directive from the Governor to the Education Department-Bohol and were highlighted by candle lighting ceremonies and offering of prayers for children that had died of rabies . The 2006 BAS data , reported prior to the initiation of the BRPEP , indicated that the dog population was 168 , 161 of which 2 . 8% were estimated to be vaccinated . The total dog population on Bohol in 2007 , as more accurately reported by the house to house survey , was 100 , 752 dogs , a significantly lower number than the 2006 data . For 2007 and 2008 , dogs registered and paid for by owners totaled 44 , 516 and 53 , 692 respectively . Vaccination coverage of the dog population increased from 2 . 6% in 2007 to 44% in 2008 . In 2009 , after a second BRB survey , the dog population was recorded to be 76 , 407 , of which 70% were registered and vaccinated . This coverage was obtained despite the introduction of mandatory registration fees . Reasons why the remaining 30% of dogs were not vaccinated included: owner absent during the vaccination campaign ( 20% ) ; owner unable to restrain dogs ( 18% ) ; dogs were thought to be too young ( 18% ) ; and unable to afford fees ( 5% ) . In 2008 , the LGU task forces eliminated 2 , 677 free-roaming dogs and 705 in 2009 . The same household survey revealed that unowned dogs were always present in the neighborhood , the median was 3 . 0 with a range of 1–18 [10] . Backed by continuous public advocacy , LGUs began adopting the paralegal measures where the focus was to penalize dog owners violating the Rabies law . The KAP survey revealed that 34 . 5% of the male dogs , accounting for 66 . 3% of the total dog population , were castrated; and 73% of the households no longer allowed their dogs to roam freely day and night . Through the BRPEP , a specific protocol was established to ensure that all persons exposed to potentially rabid animals received appropriate and prompt PEP . As part of the program , the number of animal bite treatment centers/clinics ( ABTCs ) was increased from two to six . Between 2007 and 2009 , 8 , 158 patients received PEP through the provincial and private ABTCs ( Fig . 2 ) . Of these patients , 52 . 5% had WHO category III wounds . The number of patients that received PEP increased annually between 2007 and 2009 . Animal bite reporting forms indicated that the number of potentially exposed children less than 15 years of age comprised 47 . 27% of all patients reporting to an ABTC between 2007–2009 . Of the recorded animal bites during this time period , 95 . 78% ( 7 , 812 ) of all potential exposures were reported to have been caused by dog bites . A total of 148 dog head samples were tested by FAT between 2007 and 2010 . Of the samples submitted , 4% were confirmed positive for rabies . All of the 134 samples submitted for testing in 2009 and 2010 were confirmed negative for rabies . In April 2011 , one dog sample was confirmed positive in Ubay Municipality , the same area as where a suspected human case had been reported in November 2010 . The dog was <1 yr old and unvaccinated and had bitten two people , including a 7 year old . All persons exposed to the dog received PEP and a mop-up vaccination campaign and a house-to-house educational program were both initiated within the week . One suspect human rabies case was reported in November 2010 . The young girl had been bitten on the upper left thigh on 18 October 2010 by a nursing dog after she had provoked it with a stick . The wound had not been cleaned and the patient was taken to a traditional healer the same afternoon as the bite occurred . On 23 November , the patient experienced intermittent low-grade fever and was given paracetamol , on 25 November , the patient manifested hydrophobia . On 26 November the patient was brought to a hospital in Ubay and was prescribed paracetamol and sent home . The patient died at home the next day and was buried before samples could be secured for testing . Following the provincial program SOP , a joint investigation by the provincial health , veterinary and local government staff was conducted within 72 hours of the time of death . Contact tracing and interview of key informants including the family and neighbors was immediately conducted by the investigating team but revealed no possible related human and animal cases in the locality and neighboring villages . There was immediate initiation of a mop up dog vaccination , intensified public awareness campaign and dog catching and testing for rabies . In 2010 , the Provincial Government allocated funds for the establishment of a Provincial Rabies Diagnostic Laboratory . In preparation for its actual operation , a training course for rabies diagnoses using the direct rapid Immunohistochemistry test ( dRIT ) was conducted for the veterinary staff by the US Centers for Disease Control and Prevention ( CDC ) and RITM . A total of 12 local government veterinarians were trained . The CDC subsequently signed a Material Transfer Agreement with the provincial government for the equipment , supplies and specimen sharing . Thereafter , testing tissue samples using the dRIT began with validation by RITM . Reporting of suspected dog rabies was not routinely conducted prior to the initiation and launch of the BRPEP . Between 2002 and 2006 , sample submission and confirmation of rabies infection was minimal ( Table 1 ) . During the program implementation years , surveillance for rabies in animals was enhanced through the introduced system of observation of biting animals that were not immediately tested for rabies . Two rounds of compulsory registration and the collection of yearly registration fee of a total of 95 , 167 dogs between 2007–2009 established dog ownership and traceability of biting incidents , and enabled the program to generate revolving funds in the amount of 105 , 740 US$ . Annual data from the inter-provincial records of dog movement indicated that the number of dog entries recorded increased each year ( Table 1 ) . Recorded data for the same time span indicated that the number of dogs leaving Bohol each year also increased ( Table 1 ) . Dogs newly introduced into the community from new births or importation were targeted during routine vaccination campaigns . The three-year BRPEP , initially launched in 2007 , successfully achieved the goal of establishing a sustainable program with the aim of eliminating human and canine rabies throughout the province of Bohol contrary to the program that was initiated in 2000 . The lack of a sustainable program in 2000 inevitably allowed canine rabies to spread throughout Bohol and the number of human rabies cases began to steadily increase . As 25% of the rabies deaths were in children , there was a need to especially focus on increasing education in this age group . Support from several stakeholders , including the Governor of Bohol , resulted in an agreement on a new action plan to tackle the increasing rabies problem . From the early planning stages of the BRPEP , all stakeholders and partners committed to building an intersectoral rabies control program on Bohol that would be self-sustaining when outside funding channels were no longer available . The BRPEP was strongly supported by existing national and local laws and ordinances including the PNRA . However , actual implementation of the program required an understanding of the need to implement the existing laws . This was achieved by increasing educational awareness across all levels of society , particularly focusing on how enforcement of the laws would improve the daily lives and the public health of the community . From the beginning of the program it was understood that only acceptance and ownership of the program at the community level would achieve sustainable and effective field operation . Thus , the massive social mobilization component of the BRPEP , including increasing awareness and improving citizen involvement in rabies prevention activities , were major factors in creating a successful community-based rabies control and prevention program on Bohol . The support and involvement of the Department of Education was critical for the successful implementation of the early childhood education about rabies prevention . The full integration of lessons on rabies and responsible pet ownership into the curriculum of all elementary schools throughout the province was the first program of its kind in the country . It demonstrated that working with the education sector was an effective strategy to improve awareness in the young population who are at most risk to rabies exposure . The surveillance of animal rabies prior to the initiation of the BRPEP was very poor . There were no diagnostic facilities on the island and accurate data regarding the number and species of animals infected with rabies throughout the province was unavailable . The importance of submitting suspected dogs for laboratory examinations was not appreciated and sending these samples for testing was costly resulting in few samples being submitted . The program improved reporting of suspected canine rabies cases through the active participation of the BRBs , rural health centers and the ABTCs . Prior to the program , dogs were commonly raised without confinement and stray dogs were a part of daily life . Dogs were responsible for 98% of the human rabies cases . Therefore , controlling the dog population and dog movement to achieve a feasible regular vaccination program and prevent the spread of rabies were both deemed necessary . Although stipulated in the Anti-Rabies Act of 2007 , that “LGU should enforce dog impounding activities and field control to eliminate stray dogs” , strong political will and sufficient financial resources required to comply with the law were lacking [7] . In order to generate more community support and overcome cultural barriers regarding responsible pet ownership , the BRPEC increased community advocacy about rabies as a disease , its public health importance , legal implications and how everyone in the community could help to eliminate the disease throughout the province . For planning and evaluation of control and vaccination activities and acceptable methods , accurate information on dog population density and dog-human relationships was extremely valuable . Data collected by the relatively simple field methods employed in this project sufficed for planning cost-effective dog rabies control campaigns throughout the province . Dog population and ecology data collected will also be useful in planned epidemiological analyses of canine rabies in Bohol . The dog population on Bohol was decreased over four years by various methods including: strengthening of the national and provincial rabies laws that supported the community task forces; owners voluntarily reducing the number of dogs they retained; tighter controls on animal movement; increased clinics for spaying and neutering; operation of dog pounds/cages; and strengthening of veterinary quarantine services that ensured only vaccinated dogs with registration cards entered and left the province . Province-wide paralegal trainings also reinforced responsible dog ownership and reduced the stray dog population . The component on dog population management and movement control was effectively implemented only during the second year as pressure mounted from the community itself , in response to the BRPEP . In any large rabies control program high operational costs become significant challenges not only for the provincial government but also for each partner community . To help defray the cost of the program and improve community involvement , the BRPEP enlisted volunteers throughout the province . Volunteers are well-known and respected by their neighbors , have a thorough knowledge of the local settings , particularly in remote areas , and are motivated to serve their own communities . Through volunteerism , the number of people involved in implementing the rabies control program was increased from 124 to over 15 , 000 . The Bohol Rabies program implementation between 2007 and 2010 , required a budget allocation of approximately USD 450 , 000 and therefore resource mobilization was essential . Funding was supplied by several different program stakeholders including local and national governments and partner NGOs . At the community level , funding was generated through the collection of dog registration fees . This was in compliance with the National Rabies Law and the Provincial Local Ordinance . Although the main purpose of the registration fee was to regulate the keeping of dogs at the household level , fees collected have also served as a resource for operational funds since registered dogs are entitled to a free rabies vaccination , a dog tag with collar and a registration card . The average program cost per vaccinated dog was USD 1 . 62 . The total community funds generated since the initiation of the program was enough for the next annual program budget . This article reports the overall results from the model rabies eradication program in an island province in the Philippines . Human deaths and canine cases have been dramatically decreased and surveillance improved . The success of the program was achieved through empowerment of the local communities and the use of the one health intersectoral approach to rabies control . The program was achieved through the joint efforts and shared resources of local and national government , various sectors of public health , animal health and agriculture , environment , legislation and policy , as well as non-government partners .
The Province of Bohol , Philippines has eliminated dog and human rabies in less than three years by empowering the community and implementing an intersectoral strategy . In 2006 , Bohol ranked 4th highest in the Philippines for human rabies , averaging 10 deaths per year . Launched in 2007 , the program utilized a social awareness campaign , dog population control , mass dog vaccination campaigns , improved dog bite management and veterinary quarantine , a new diagnostic laboratory , expanded surveillance , and the inclusion of education modules into the school curriculum . Improving community compliance to existing national and provincial rabies laws and engaging volunteers to help conduct the project was a key to success . The program , led by the Governor of Bohol , was administered through a group of departments working together at a provincial and local level , and supervised through the Office of the Provincial Veterinarian . Financial support came through the Governor and several NGOs including the Global Alliance for Rabies Control . The program is self-sustaining , through a small dog registration fee fed back into the program , through the continuing education of children in their classrooms , and through the dedicated efforts of over 15 , 000 staff and volunteers throughout the island .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "medicine", "public", "health", "and", "epidemiology", "rabies", "veterinary", "diseases", "zoonotic", "diseases", "animal", "management", "public", "health", "veterinary", "science", "agriculture" ]
2012
Implementation of an Intersectoral Program to Eliminate Human and Canine Rabies: The Bohol Rabies Prevention and Elimination Project
This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 μm meso-scale . The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by section rigid motions and unknown diffeomorphic transformation of a three-dimensional atlas . To regularize over the high-dimensionality of our parameter space ( which is a product space of the rigid motion dimensions and the diffeomorphism dimensions ) , the histology stacks are modelled as arising from a first order Sobolev space smoothness prior . We show that the joint maximum a-posteriori , penalized-likelihood estimator of our high dimensional parameter space emerges as a joint optimization interleaving rigid motion estimation for histology restacking and large deformation diffeomorphic metric mapping to atlas coordinates . We show that joint optimization in this parameter space solves the classical curvature non-identifiability of the histology stacking problem . The algorithms are demonstrated on a collection of whole-brain histological image stacks from the Mouse Brain Architecture Project . Recent advances in brain imaging [1 , 2] , methods to label neurons [3] , and computational methods have brought about a new era of neuroanatomical research , with a focus on comprehensively mapping brain circuits [4] . Mapping whole-brain circuitry is important for three distinct reasons: scientific understanding of how the brain works , mechanistic understanding of neurological and neuropsychiatric disorders , and as a comparison point for artificial neural networks used in machine learning [5 , 6] . Circuit mapping is technique limited , and falls into three broad scales corresponding to distinct imaging modalities—indirect mapping at a macroscopic scale corresponding to MRI-based methods [7] , and direct mapping at light ( LM ) and electron microscopic ( EM ) scales . For MRI and LM data , atlas mapping is an important step in the analysis . Several approaches exist for gathering LM data at the whole brain level [8–10] . For some of these approaches ( two-photon serial block-face imaging , knife edge scanning microscopy and light sheet microscopy for cleared brains ) two-dimensional ( 2D ) optical sections are acquired in three-dimensional ( 3D ) registry with each other , so that the only computational step required is 3D volumetric registration of the individual brain data set to a canonical atlas . However , for classical neurohistological approaches using tissue sectioning followed by histochemical processing , the 2D sections are gathered independently and each section can undergo an arbitrary rotation and translation compared to the block face . This may be considered a disadvantage of the classical neuroanatomical workflow , however the physical sectioning method followed by conventional histochemical analysis has certain important advantages . This allows for the full spectrum of histochemical stains , acquisition of physical sections for downstream molecular analyses , and processing for larger brains ( upto and including whole human brains ) . Therefore it is necessary to perform an intermediate 2D to 3D registration step , where the individually acquired 2D sections are mutually co-registered into a 3D volume . This paper develops a joint stack reconstruction and atlas mapping procedure that simultaneously restacks the 2D histology sections , applying a sequence of rigid motions to the sections , and estimates the diffeomorphic correspondence between the registered histology stack and the 3D atlas . We apply these algorithms to data sets from the Mouse Brain Architecture Project ( MBAP ) , for which the experimental workflow generating the data utilizes a tape transfer technique [11] , allowing for the sections to maintain geometrical rigidity within section and also allowing for physically disjoint components to maintain their spatial relations . The tape method ensures that the number of missing sections is minimal , with serial sections cut at a thickness of 20 μm and alternate sections subjected to Nissl staining alongside staining with histochemical or fluorescent label . These Nissl stained sections form the basis of alignment to a Nissl whole-brain reference atlas . The histological reconstruction problem has been explored by several groups previously . Malandain first described the ill-posedness of reconstructing 3D sections and object curvature without prior knowledge of the shape of the object [12] . Rigid transformations for stack reconstruction have been estimated via block-matching of histological sections in [13] , with point information based on landmarks introduced to guide volume reconstruction [14] . Dense external reference information such as MRI has been applied to guide reconstruction via registration of corresponding block-face photographs and for histology to MRI mapping [15 , 16] . The principal contribution of this work is to rigorously solve the problem when an external resource of identical geometry ( such as an MRI of the same mouse ) is not available , while accommodating for the innate anatomical variation from atlas to subject . The lack of a same-subject reference volume is often the standard in mouse brain histology and other large scale histology studies . This places us into the computational anatomy ( CA ) orbit problem for which constraints are inherited from an atlas that is diffeomorphic but not geometrically identical . With the availability of dense brain atlases at many resolution scales [17–20] , methods to map atlas labels onto target coordinate systems are being ubiquitously deployed across neuroscience applications . Since Christensen’s early work [21] , diffeomorphic transformation has become the de-facto standard as diffeomorphisms generate one-to-one and onto correspondences between coordinate systems . Herein we focus on the diffeomorphometry orbit model [22 , 23] of computational anatomy [24] , where the space of dense volume imagery is modelled as a Riemannian orbit of an atlas under the diffeomorphism group . We use the large deformation diffeomorphic metric mapping ( LDDMM ) algorithm first derived for dense imagery by Beg [25] to retrieve the unknown high-dimensional reparameterization of the template coordinates . Of course , for the histological stacking problem solved here , the interesting twist is the augmentation of the random orbit model with 3 rigid motion dimensions for each target section . At 20 μm , this implies as many as 500 sections augmenting the high-dimensionality of the diffeomorphism space to include as many as 1500 extra dimensions for planar rigid motions for restacking . Here lies the crux of the challenge . To accommodate the high-dimensionality of the unknown rigid motions , the space of stacked targets is modelled to have finite-squared energy Sobolev norm , which enters the problem as a prior distribution restricting the roughness of the allowed restacked volumes . The variational method jointly optimizes over the high-dimensional diffeomorphism associated to the atlas reparameterization and the high-dimensional concatenation of rigid motions associated to the target . Fig 1 shows the components of the model for the histology stacking problem . We define the mouse brain to be sectioned as a dense three-dimensional ( 3D ) object I ( x , y , z ) , ( x , y , z ) ∈ R 3 , modelled to be a smooth deformation of a known , given template I0 so that I = I0 ∘ φ−1 for some invertible diffeomorphic transformation φ . The Allen Institute’s mouse brain atlas [26] ( CCF 2017 ) is taken as the template . Distinct from volumetric imaging such as MRI which delivers a dense 3D metric of the brain , the histology procedure ( bottom row , Fig 1 ) consisting of sectioning , staining , and imaging generates a jitter process which randomly translates and rotates the stack sections . Denote the rigid motions acting on the 2D sectioning planes R i : R 2 → R 2 , R i ( x , y ) = ( cos θ i x + sin θ i y + t i x , − sin θ i x + cos θ i y + t i y ) , ( x , y ) ∈ R 2 , ( 1 ) with θi the rotation angle and ( t i x , t i y ) ∈ R 2 the translation vector in section i . The histology stack J i ( x , y ) , ( x , y ) ∈ R 2 , i = 1 , … , n , is a sequence of 2D image sections with jitter under smooth deformation of the atlas in noise: J i ∘ R i ( x , y ) = I 0 ∘ φ − 1 ( x , y , z i ) + noise ( x , y ) , ( x , y ) ∈ R 2 . ( 2 ) Modeling the photographic noise as Gaussian and conditioning on the sequences of jitters Ri , i = 1 , … , n and atlas deformation I = I0 ∘ φ−1 , φ ∈ Diff , the photographic sections Ji are a sequence of conditionally Gaussian random fields with log-likelihood ℓ ( v , R ; J ) = ∑ i ( − α i ∫ R 2 | J i ∘ R i ( x , y ) − I 0 ∘ φ v , − 1 ( x , y , z i ) | 2 d x d y ) . ( 3 ) Here αi is a weighting factor dependent on the noise of each section such that damaged sections can be weighted; v denotes the vector field which indexes the deformation as a diffeomorphic flow ( see below ) . The parameterization of the histology pipeline augments the standard random orbit model of computational anatomy with the rigid-motion dimensions of the random jitter sectioning process . The unknowns to be estimated become ( R 1 , … , R n , φ ) ∈ R 3 n × Diff for n−sections . At 20 μm then n = 500 implying the nuisance rigid motions are of high dimension O ( 1500 ) . The solution space must be constrained . We use priors on the deformations and on the rigid motion stacking of the images . Model the random sectioning with section-independent jitter as a product density π ( R ) = ∏ i π ( θ i , t i x , t i y ) , the priors centered at identity , with the priors on θ circular Gaussian with standard-deviation σθ and translation with means μ c x , μ c y at the center of the sections with σ c x = σ c y: π ( θ , t x , t y ) = 1 2 π σ θ e − θ 2 2 σ θ 2 1 2 π σ c x e − ( t x − μ c x ) 2 2 σ c 2 1 2 π σ c y e − ( t y − μ c y ) 2 2 σ c 2 . ( 9 ) We choose our standard-deviations so that they are small relative to the center of the image , and a small rotation , roughly 5 percent of the total range of each . Generating MAP estimates of the rigid motions generates the MAP estimator of the histology restacking problem denoted as I R ( x , y , z i ) = J i ∘ R i ( x , y ) , ( x , y ) ∈ R 2 , i = 1 , … , n . Since the diffeomorphisms are infinite dimensional , the maximization of the log-likelihood function with respect to a function with the deformation penalty is termed the “penalized-likelihood estimator” . Conditioned on the known atlas , the augmented random variables to be estimated are ( R 1 , … , R n , φ ) ∈ ( R 3 n × Diff ) . Problem 1 ( MAP , Penalized-Likelihood Estimator ) . Given histology stack Ji ( x , y ) , ( x , y ) ∈ℝ2 , i=1 , … and reconstructed stack IR ( ⋅ , zi ) = Ji ∘ Ri ( ⋅ ) , i = 1 , … , n modelled as conditionally Gaussian random fields conditioned on jitter and smooth dormation of the template . The joint MAP , Penalized-Likelihood estimators arg maxR , v log π ( R , v|J ) given by argmaxR , v−12∫01‖vt‖V2dt−12∑i‖DhIR ( · , zi ) ‖22+∑i ( logπ ( Ri ) −αi‖IR ( · , zi ) −I0∘φv , −1 ( · , zi ) ‖22 ) . ( 10 ) The MAP , Penalized-Likelihood estimators satisfy { R*=argmaxRi , i=1 , …∑i ( logπ ( Ri ) −12‖DhIR ( · , zi ) ‖22−αi‖IR ( · , zi ) −I0∘φv* , −1 ( · , zi ) ‖22 ) , v*=argmaxv−12∫01‖vt‖V2dt−∑iαi‖IR* ( · , zi ) −I0∘φv , −1 ( · , zi ) ‖22 with ‖ · ‖ 2 2 denoting the norm per z-axis section: ‖ f ( · , z i ) ‖ 2 2 = ∫ R 2 f ( x , y , z i ) 2 d x d y . ( 11 ) We call this the atlas-informed model . The first two prior terms of ( 10 ) control the smoothness of template deformation and the realigned target image stack , with the third keeping the rigid motions close to the identity . The last term is the “log-likelihood” conditioned on the other variables . The optimization for the R* rigid-motions is not decoupled across sections because of the smooth diffeomorphism of the LDDMM update and the Sobolev metric represented through the difference operator across the z− sections . Clearly , the smooth diffeomorphism is able to interpolate through the measured target sectioning data when the restacking solution gives a relatively smooth target , as diffeomorphisms are spatially smooth with at least one derivative . The optimization of the vector field v* corresponds to the LDDMM solution of Beg [25] . The principal algorithm used for solving this joint MAP-penalized likelihood problem alternates between fixing the rigid motions and solving LDDMM and fixing the diffeomorphism and solving for the rigid motions . This is described below in the following section . When there is no atlas available this is equivalent to setting αi small and becomes a MAP rigid motion restacking of the sections: argmax R i , i = 1 , … ∑ i ( log π ( R i ) − 1 2 ‖ D h I R ( · , z i ) ‖ 2 2 ) . We term this the atlas-free model . The gradient of the rigid motions with respect to the components of translations tx , ty and rotation θ is defined in S3 Text . The registration is not independent across sections due to coupling through the Sobolev metric . Here we describe the details of the algorithm used for solving for the MAP/penalized–likelihood problem described above . The algorithm alternately fixes the set of rigid motions while updating LDDMM and fixes the diffeomorphism while updating the rigid motions . Algorithm 1 . 0 . Initialize φnew , Rnew ← φinit , Rinit , Iold ← J ∘ Rinit: 1 . Update φold←φnew , Riold←Rinew , Iold ( ⋅ , zi ) ← Inew ( ⋅ , zi ) , i = 1 , … . 2 . Update LDDMM for diffeomorphic transformation of atlas coordinates: v n e w = argmax v − 1 2 ∫ 0 1 ‖ v t ‖ V 2 d t − ∑ i α i ‖ I R − o l d ( · , z i ) − I 0 ∘ φ 1 v − 1 ( · , z i ) ‖ 2 , φ n e w = ∫ 0 1 v t n e w ∘ φ t n e w d t + id . ( 12 ) 3 . Deform atlas I0 ∘ φnew−1 and generate new histology image stack: R n e w = arg max R i , i = 1 , … ∑ i ( log π ( R i ) − 1 2 ‖ D h I R ( · , z i ) ‖ 2 2 − α i ‖ I R ( · , z i ) − I 0 ∘ φ n e w − 1 ( · , z i ) ‖ 2 2 ) ; I R − n e w ( · , z i ) = J i ∘ R i n e w ( · ) , i = 1 … ( 13 ) 4 . Return to Step 1 until convergence criterion met . The form of the gradients for the rigid motions is given in S4 Text . The LDDMM update solutions are given by Beg [25] . The algorithm described above is applied to Nissl histological stacks using the Allen Institute’s mouse brain atlas as a template . The Allen Mouse Brain Atlas is a micron-scale atlas that includes annotated Nissl-stained images at 10 , 25 , 50 , and 100 μm voxel resolution , with 738 labeled compartments in the annotation . Atlas mapping is computed on the Nissl-stained histological image stack showing the clear definition of anatomical boundaries . The associated fluorescent tracer images are transformed to the Nissl stack so that the atlas subvolume labels can be cast onto the new modality . The fluorescent and Nissl images are registered within animals by applying rigid registration based on a mutual information cost function . A software pipeline which performs start-to-finish registration operations was implemented on a high performance computing cluster for atlas-mapping and histology restacking on the Mouse Brain Architecture data . To date , the pipeline has been successfully run on over 1000 MBAP brains . The general pipeline workflow is illustrated in Fig 2 . In our application , we apply a two channel LDDMM [32] algorithm for the optimization with respect to φ , where the first channel is the Nissl-stained grayscale image , and the second channel is a mask of the brain tissue with ventricles and background set to a pixel value of zero . The brain mask for each brain stack is automatically generated by thresholding at an estimated background intensity value and applying morphological opening and closing for denoising . The threshold value is estimated by a RANSAC-like procedure over the image histogram , assuming a normal distribution of intensity values in the image foreground . A first-order Sobolev-norm ( see below ) is used for the smoothness constraint regularization of the histology stack . In order to accommodate for sections damaged by the histology process or structures excluded from imaging , the objective functions in all parts of the algorithm are optimized with respect to only the image data that exists . Essentially , this is a masking procedure on the cost function that allows matching between a whole atlas brain and some target which is a partial , or subset of a whole brain . After registration of the structural Nissl image , the fluorescence volume is registered to its corresponding Nissl volume . The registration is restricted to rigid motions on each individual section . The optimization bears a similar form to Eq ( 13 ) with the squared error matching term replaced with mutual information in order to account for the different modalities of the template and target histology stack . Once fluoro-to-Nissl registration is complete , the Nissl segmentation can be applied to the fluorescence image . The LDDMM algorithm that maps the atlas image to an aligned stack of sections is implemented in C++ . Images and other data are stored as basic arrays , and there are no dependencies other than for FFTs ( we use FFTW or Intel MKL depending on availability ) . The remainder of code is written in Matlab ( Natick , MA ) . The run-time/complexity for the volume LDDMM algorithm has complexity order nT Nvoxlog ( Nvox ) , where nT is the number of steps for integrating the time varying velocity field , and Nvox is the total number of voxels . The slice based portion of the code is order Nvox . While the FFTs are order NlogN , in practice most computation time is spent during linear interpolation ( order N ) . The end-to-end running time from initial stack alignment to completed atlas registration is approximately 6-8 hours using 8 cores on an Intel Xeon E5-2665 processor for target and template image volumes of approximately 200 × 300 × 300 voxels . Jobs are performed in parallel on a high performance cluster at CSHL . The fluoro-to-nissl cross registration running time is approximately 1 hour on the same environment and volume size . The following hyper-parameters are required by our model , with sample values provided for the MBAP dataset: the weights between the matching term ( 1 . 0 ) , the regularizing prior ( 0 . 001 ) , and the Sobolev norm ( 1 . 0 ) on the rigid objective function the variances of the priors on rotation ( π 9 ) and translation ( 7 . 0 ) in each stacking plane the weight between the matching term ( 0 . 4 ) and the regularizing term in LDDMM ( 1 . 0 ) the LDDMM kernel size ( a cascade of 0 . 05 , 0 . 02 , and 0 . 01 ) the initial gradient descent step size ( 0 . 000025 for rigid parameters and 5e-13 for LDDMM parameters ) The hyper-parameters were selected by grid search on a predefined range of parameter values , testing the rigid stack alignment and LDDMM parameters separately . A final experiment was conducted on brain data sampled from the MBAP database , using the Allen mouse brain as the atlas . We selected specific targets which were prone to poor registration results due to image intensity local minima . In particular , structures like the cerebellum tend to be difficult to register accurately due to their folded nature; one fold can easily be mistaken for the adjacent fold , and if the target and atlas are not well initialized , the deformation required to flow one fold onto another can have a high metric cost . We are also interested in inspecting lower-contrast structures like the corpus callossum , which may be poorly registered due to local minima in other nearby bright structures . We also evaluate our mapping quality in the hippocampal region , which is one of the most relevant regions for the study of neurodegenerative diseases . The reconstructed histological target stack in the atlas-informed model shown in Fig 8a takes on the shape of the atlas but is prone to reconstruction artifacts . The deformation grids produced by the atlas-informed mapping is much smoother and has many fewer wrinkles than the atlas-free mapping . This is seen clearly in Fig 9 . Fig 10 shows examples of improved segmentations in selected regions of the brain . The atlas-informed model generates more accurate segmentation results and produces smoother mappings as exhibited by the less wrinkled and distorted grids ( bottom row b ) , showing more consistent results throughout the MBAP dataset . This paper examines the CA random orbit model at the mesoscale for the stacking of sectioned whole brains coupled with mapping to annotated atlases . The standard CA model has been expanded to include the O ( 3 × n ) extra rigid motion dimensions representing the planar histology sections . The estimation procedure solved here simultaneously estimates the diffeomorphic change of coordinates between atlas and target histological stack , as well as the “nuisance” rigid motion parameters for each section in stack space . This requires the introduction of a smoothness constraint on the target jitter simultaneous with LDDMM , which is enforced via a Sobolev metric , encouraging the reconstructed stack to be smooth by controlling the derivative along the cutting axis . Results are shown demonstrating that the introduction of an atlas into the estimation scheme and simultaneously accommodating for the nonlinear atlas-to-target shape difference via diffeomorphism solves several of the classic problems associated with volume reconstruction , including the recovery of the curvature of extended structures . Since the atlas gives a priori indication of the global shape , the tendency to remove distortions along the section axis is balanced against the desire to minimize the amount of deformation of the atlas onto the reconstruction . The algorithm is shown to mediate this tension well . The clear limitation of this method is that we model sections that are out of order , folded upon themselves , or damaged by censoring from the mapping solution using the weighting coefficient αi and removing their impact from the overall deformation . This is a global censoring , but we do not apply shearing deformations within plane and we do not include in the algorithm an automatic solution to detecting the folding problem . Although we do not currently include correction beyond rigid motion within the plane of each section , one could imagine attempting to add such shearing distortions to the model , which would remain stable as the number of new dimensions would remain low . The global censoring solution requires human quality control within the pipeline for detection of globally deformed or damaged sections . The use of dense large deformation diffeomorphic image matching is being used extensively for magnetic resonance imaging in the brain at 1 millimeter scale for both T1 and DTI [23 , 25 , 32 , 33] as well as for human anatomy [22] including for transferring the geometries of Cardiac fibers in dense Cardiac imaging [34 , 35] and for radiation treatment planning [36] . These technologies form the basis of many implementations such as Ashburner’s important SPM [37 , 38] . The aforementioned applications have not included complex prior distributions to encode distortions such as the Sobolev derivative prior introduced here that may have be required due to the distortions introduced in the imaging and stacking process .
New developments in neural tracing techniques have motivated the widespread use of histology as a modality for exploring the circuitry of the brain . Automated mapping of pre-labeled atlases onto modern large datasets of histological imagery is a critical step for elucidating the brain’s neural circuitry and shape . This task is challenging as histological sections are imaged independently and the reconstruction of the unsectioned volume is nontrivial . Typically , neuroanatomists use reference volumes of the same subject ( e . g . MRI ) to guide reconstruction . However , obtaining reference imagery is often non-standard , as in high-throughput animal models like mouse histology . Others have proposed using anatomical atlases as guides , but have not accounted for the intrinsic nonlinear shape difference from atlas to subject . Our method addresses these limitations by jointly optimizing reconstruction informed by an atlas simultaneously with the nonlinear change of coordinates that encapsulates anatomical variation . This accounts for intrinsic shape differences and enables rigorous , direct comparisons of atlas and subject coordinates . Using simulations , we demonstrate that our method recovers the reconstruction parameters more accurately than atlas-free models and innately produces accurate segmentations from simultaneous atlas mapping . We also demonstrate our method on the Mouse Brain Architecture dataset , successfully mapping and reconstructing over 1000 brains .
[ "Abstract", "Introduction", "Materials", "and", "methods", "Results", "and", "discussion" ]
[ "fluorescence", "imaging", "medicine", "and", "health", "sciences", "classical", "mechanics", "diagnostic", "radiology", "neuroscience", "magnetic", "resonance", "imaging", "optimization", "histology", "mathematics", "brain", "mapping", "damage", "mechanics", "neuroimaging", "research", "and", "analysis", "methods", "specimen", "preparation", "and", "treatment", "imaging", "techniques", "specimen", "sectioning", "deformation", "physics", "radiology", "and", "imaging", "diagnostic", "medicine", "anatomy", "biology", "and", "life", "sciences", "physical", "sciences" ]
2018
On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model
Aerobic glycolysis is essential for supporting the fast growth of a variety of cancers . However , its role in the survival of cancer cells under stress conditions is unclear . We have previously reported an efficient model of gammaherpesvirus Kaposi’s sarcoma-associated herpesvirus ( KSHV ) -induced cellular transformation of rat primary mesenchymal stem cells . KSHV-transformed cells efficiently induce tumors in nude mice with pathological features reminiscent of Kaposi’s sarcoma tumors . Here , we report that KSHV promotes cell survival and cellular transformation by suppressing aerobic glycolysis and oxidative phosphorylation under nutrient stress . Specifically , KSHV microRNAs and vFLIP suppress glycolysis by activating the NF-κB pathway to downregulate glucose transporters GLUT1 and GLUT3 . While overexpression of the transporters rescues the glycolytic activity , it induces apoptosis and reduces colony formation efficiency in softagar under glucose deprivation . Mechanistically , GLUT1 and GLUT3 inhibit constitutive activation of the AKT and NF-κB pro-survival pathways . Strikingly , GLUT1 and GLUT3 are significantly downregulated in KSHV-infected cells in human KS tumors . Furthermore , we have detected reduced levels of aerobic glycolysis in several KSHV-infected primary effusion lymphoma cell lines compared to a Burkitt’s lymphoma cell line BJAB , and KSHV infection of BJAB cells reduced aerobic glycolysis . These results reveal a novel mechanism by which an oncogenic virus regulates a key metabolic pathway to adapt to stress in tumor microenvironment , and illustrate the importance of fine-tuning the metabolic pathways for sustaining the proliferation and survival of cancer cells , particularly under stress conditions . It has been recognized that metabolic reprogramming is a core hallmark of cancer[1] . The Warburg effect describes the dependence of cancer cells on aerobic glycolysis for their growth and proliferation[2] . Increased glucose uptake and aerobic glycolysis are widely observed in cancer and clinically exploited for diagnosis[3] . Aerobic glycolysis provides a fast supply of ATP to support the rapid growth and proliferation of cancer cells[3] . Recent works have shown that besides energy , cancer cells have special needs for macromolecular building blocks and maintenance of redox balance[4 , 5] . Accordingly , metabolic adaptation in cancer cells has been extended beyond the Warburg effect[5] . Several types of cancers depend on glutamine or one carbon amino acids for growth and proliferation[4 , 5] . Cancer cells often encounter a variety of stress conditions including low nutrients , low oxygen and excess byproducts in the microenvironment[4 , 6] . To optimize the growth , proliferation and survival under diverse conditions , cancer cells must fine-tune the metabolic pathways . Hyperactivation of metabolic pathways can generate toxic products that are detrimental to the cancer cells[6] . For examples , overflow of oxidative phosphorylation produces reactive oxidative species while excess of aerobic glycolysis leads to the buildup of lactate and low pH in the microenvironment[6] . How cancer cells regulate metabolic pathways to adapt to different stress conditions is not entirely clear . Kaposi’s sarcoma-associated herpesvirus ( KSHV ) is an oncogenic virus associated with several cancers including Kaposi’s sarcoma ( KS ) and primary effusion lymphoma ( PEL ) [7] . Infection by KSHV has become an excellent model for understanding the mechanism of oncogenesis . Experimentally , KSHV can efficiently infect and transform primary rat mesenchymal precursor cells ( MM ) and human mesenchymal stem cells[8 , 9] . KSHV-transformed MM cells ( KMM ) efficiently induce tumors with features closely resembling KS[8] . In KS tumors , PEL and KMM tumors , most of tumor cells are latently infected by KSHV . These cells have restricted expression of viral genes including vFLIP ( ORF71 ) , vCyclin ( ORF72 ) , LANA ( ORF73 ) and 12 precursor microRNAs ( pre-miRNAs ) [8 , 10 , 11] . Genetic analyses have revealed that viral miRNAs and vCyclin are critical for KSHV-induced oncogenesis by regulating cell cycle and apoptosis[10] , and overriding cell contact inhibition[12] , respectively . KSHV infection induces Warburg effect in human endothelial cells ( ECs ) and lipogenesis in ECs and PEL cells , and these altered metabolic processes are required for maintaining KSHV latency[13–15] . Among the KSHV-encoded products , the miRNA cluster decreases mitochondria biogenesis and induces aerobic glycolysis in ECs[16] . KSHV also induces glutamate secretion in ECs[17] . Nevertheless , in these studies , KSHV infection did not lead to cellular transformation . Thus , whether metabolic reprogramming is essential for KSHV-induced cellular transformation remains unknown . In this study , we have discovered that KSHV suppresses aerobic glycolysis and oxidative phosphorylation in KSHV-transformed cells and this reprogramed metabolic pathway is essential for adaptation to glucose deprivation . These findings indicate that fine-tuning of metabolic pathways is essential for the proliferation and survival of cancer cells , particularly under stress conditions . KSHV-transformed cells ( KMM ) proliferated significantly faster than their uninfected/untransformed counterparts ( MM ) , and KMM but not MM cells lost contact-inhibition and formed colonies in softagar ( Fig 1A and 1B ) [8] . To determine the metabolic state of KSHV-transformed cells , we examined the consumption of glucose , the main carbon source for most normal and cancer cells . In normal cells , glucose flows through glycolysis and tricarboxylic acid ( TCA ) cycle to generate ATP and NADH with the latter further serving as a substrate for oxidative phosphorylation to produce additional ATP , a process that consumes oxygen[4] . However , many cancer cells have fast ATP production through a higher rate of aerobic glycolysis , resulting in higher rates of glucose consumption and lactate production despite the presence of oxygen[3] . To our surprise , KMM cells consumed significantly less glucose than MM cells did ( Fig 1C ) . This effect was even more dramatic after taking into account of cell proliferation rate ( Fig 1D ) . KMM cells also produced less lactate , and had lower levels of intracellular ATP and oxygen consumption compared to MM cells ( Fig 1E–1H ) . Thus , despite their rapid proliferation , KMM cells consume less glucose , and have lower activities of aerobic glycolysis and oxidative phosphorylation . Because KMM cells consumed less glucose , we postulated that they might not require glucose to support their proliferation . Indeed , glucose deprivation affected neither the proliferation nor colony formation of KMM cells in softagar while it caused proliferation arrest of MM cells ( Fig 2A and 2B ) . Glucose deprivation caused G1 arrest , reduced BrdU incorporation , increased apoptotic cells and decreased the intracellular ATP level in MM but not KMM cells ( Fig 2C–2F ) . Collectively , these results indicate that KSHV has reprogramed the cellular metabolic pathways following cellular transformation . KMM cells are latently infected by KSHV and mostly express only viral latent genes/products including vFLIP , vCyclin , LANA and miRNAs[8] . To identify KSHV genes/products that mediate metabolic reprogramming , we generated MM cells latently infected by KSHV mutants containing individual deletion of vFLIP , vCyclin or 10 of the 12 pre-miRNAs ( miR-K1-9 and 11 ) [10 , 12 , 18] . We were unable to obtain cells stably infected by a mutant of LANA because of its essential role in persistent infection[19 , 20] . Under normal culture condition , deletion of vFLIP or the miRNA cluster reduced cell proliferation rates to levels similar to those of MM cells ( Fig 3A ) . Deletion of vCyclin had no effect on cell proliferation though a slower rate was observed at contact-inhibited high cell density[12] . We further examined the metabolic states of these cells . Deletion of vFLIP or the miRNA cluster but not vCyclin increased glucose consumption , lactate production , intracellular ATP and oxygen consumption to levels close to or even higher than those of MM cells ( Fig 3B–3E ) . Furthermore , deletion of vFLIP or the miRNA cluster sensitized the cells to glucose deprivation , causing cell proliferation arrest similar to MM cells ( Fig 3F ) . While vCyclin mutant cells continued to proliferate upon glucose deprivation , they did so at a rate slower than that of KMM cells ( Fig 3F ) . Consistently , glucose deprivation caused G1 arrest , reduced BrdU incorporation and increased apoptotic cells in cells of vFLIP and miRNA cluster mutants ( Fig 3G–3I ) . Interestingly , the basal level of dead cells in the vFLIP mutant cells were higher than those of MM and KMM cells ( 25% vs 8% and 3% , respectively ) , and were further increased upon glucose deprivation , reaching as high as 95% ( Fig 3I ) , which could be attributed to the oncogenic stress in KSHV-transformed cells[10] and vFLIP activation of the NF-κB[21 , 22] . In contrast , glucose deprivation had minimal effect on cell cycle progression and BrdU incorporation of vCyclin mutant cells ( Fig 3G and 3H ) ; however , it increased apoptotic cells to a level similar to that of MM cells ( Fig 3I ) , which might explain the slower proliferation rate of vCyclin mutant cells than KMM cells ( Fig 3F ) . We further correlated the metabolic states of these cells with cellular transformation . Cells of both vFLIP and miRNA cluster mutants failed to form any colonies in softagar , a phenotype resembling that of MM cells ( Fig 3J ) . vCyclin mutant cells formed significantly less and smaller colonies than KMM cells did in normal culture condition as previously reported[12] but continued to form colonies upon glucose deprivation albeit at a reduced efficiency ( Fig 3J ) . Together , these results indicate that both vFLIP and the miRNA cluster mediate KSHV reprograming of metabolic pathways , contributing to KSHV-induced glucose-independent cell proliferation , survival and cellular transformation . While vCyclin can override contact inhibition to promote cellular transformation[12] , it does not contribute to KSHV reprogramming of metabolic pathways . To identify the mechanism of KSHV inhibition of aerobic glycolysis and oxidative phosphorylation , we examined changes of gene expression of key enzymes in the glycolysis pathway following KSHV transformation . All glycolysis enzymes either had minimal change or were upregulated ( S1 Fig ) ; hence , they were unlikely the candidates that mediated KSHV suppression of glycolysis . GLUT1 and GLUT3 directly mediate glucose uptake , which is the first step in the glycolysis pathway[4] . Downregulation of GLUT1 and GLUT3 was observed at mRNA and protein levels ( Fig 4A–4C ) . Importantly , deletion of vFLIP or the miRNA cluster was sufficient to restore the GLUT1 and GLUT3 expression levels ( Fig 4D and 4E ) . Interestingly , the mRNA levels of both GLUT1 and GLUT3 detected by reverse transcription quantitative real time PCR ( RT-qPCR ) and the protein level of GLUT1 detected by Western-blot were even higher in vFLIP mutant cells than in MM cells . The results of flow cytometry were inconsistent , which were probably due to the fact that the antibodies detected surface expression while RT-qPCR and Western-blot detected the total levels of mRNA and protein in cells , respectively . Deletion of vCyclin did not affect GLUT1 and GLUT3 mRNA expression levels but marginally increased their protein levels ( Fig 4D–4F ) . These results indicate that vFLIP and the miRNA cluster mediate KSHV downregulation of GLUT1 and GLUT3 . To investigate the mechanism of KSHV downregulation of GLUT1 and GLUT3 , we searched for a common pathway regulated by vFLIP and the miRNA cluster . Both vFLIP and the miRNA cluster activate the NF-κB pathway[21–23] , and both are required for the maximal activation of the NF-κB pathway in KSHV-transformed cells[10] . Because knock down of RelA , a key component of the NF-κB complexes , is sufficient to inhibit the NF-κB pathway in KMM cells[10] , we examined the effect of knock down of RelA on the expression of GLUT1 and GLUT3 ( Fig 5A and 5B ) . Knock down of RelA significantly increased the protein and mRNA expression levels of both GLUT1 and GLUT3 ( Fig 5A–5D ) . As previously reported[10] , knock down of RelA slightly decreased cell proliferation of KMM cells but had no effect on MM cells ( Fig 5E ) . Importantly , knock down of RelA increased glucose consumption and lactate production in KMM cells ( Fig 5F and 5G ) . These effects were even more obvious when adjusted for cell numbers . In MM cells , knock down of RelA slightly increased the glucose consumption but had no detectable effect on lactate production . To confirm the above results , we carried out pharmacological inhibition of the NF-κB pathway with two specific inhibitors , JSH-23 and BAY11-7082 . Both inhibitors significantly induced the expression of GLUT1 and GLUT3 at mRNA and protein levels ( Fig 6A and 6B ) . Interestingly , neither knockdown of RelA nor the NF-κB inhibitors fully rescued the expression of GLUT3 in KMM , suggesting that another pathway , besides the NF-κB pathway , might be involved in the inhibition of GLUT3 expression in KMM cells . Inhibition of the NF-κB pathway increased glucose consumption and lactate production in both MM and KMM cells ( Fig 6C and 6D ) . Importantly , the increased glucose consumption and lactate production rates were correlated with reduced cell proliferation rates in both MM and KMM cells and a reduced efficiency of colony formation of KMM cells in softagar ( Fig 6E and 6F ) . Consistent with these results , inhibition of the NF-κB pathway sensitized KMM cells to apoptosis and inhibited BrdU incorporation ( Fig 6G and 6H ) . Thus , the NF-κB pathway promotes cell proliferation and cellular transformation at least in part by inhibiting the expression of GLUT1 and GLUT3 to limit the glucose consumption in KMM cells . To confirm if downregulation of GLUT1 and GLUT3 mediated KSHV inhibition of glucose consumption and lactate production , we overexpressed GLUT1 and GLUT3 in MM and KMM cells ( Fig 7A and 7B ) . Overexpression of GLUT1 or GLUT3 was sufficient to increase glucose consumption and lactate production in KMM cells but the results were inconsistent with MM cells , which might reflect their cell surface expression levels ( Fig 7C and 7D ) . While overexpression of GLUT1 or GLUT3 neither significantly affected cell proliferation of both MM and KMM cells under normal culture condition nor altered the sensitivity of MM cells to glucose deprivation , it reduced cell proliferation of KMM cells upon glucose deprivation ( Fig 7E ) . Consistently , glucose deprivation increased the number of apoptotic cells in KMM cells with overexpression of GLUT1 or GLUT3 ( Fig 7F ) . As expected , MM cells were sensitive to glucose deprivation . Overexpression of GLUT1 or GLUT3 increased the basal number of apoptotic cells in MM cells , which was further increased upon glucose deprivation ( Fig 7F ) . Interestingly , overexpression of GLUT1 or GLUT3 had no effect on cell cycle progression in KMM cells in either normal culture condition or in medium deprived of glucose ( Fig 7G ) . Thus , the glucose transporters regulate cell survival rather than cell cycle progression under nutritional stress conditions in KSHV-transformed cells . Finally , overexpression of GLUT1 or GLUT3 in KMM cells slightly increased the sizes of some colonies but significantly reduced the number of colonies in softagar in normal culture medium , which was further reduced upon glucose deprivation ( Fig 7H ) . Taken together , these results indicate that suppression of GLUT1 and GLUT3 expression confers KMM cells lower levels of glucose consumption and lactate production , and endow them the potential for glucose-independent cell proliferation , survival and cellular transformation . To determine the mechanism mediating GLUT1 and GLUT3 regulation of the survival of KMM cells , we examined two main cell survival pathways AKT and NF-κB . Overexpression of GLUT1 or GLUT3 in KMM cells reduced the phospho-AKT level ( Fig 8A ) . The AKT downstream targets phospho-NF-κB p65 and phospho-4EBP1 were also reduced ( Fig 8A ) . Accordingly , we observed increased levels of autophagy , which is regulated by the AKT pathway , in these cells . Specifically , there were increased LC3-II/LC3-I ratio , more cells with the typical LC3 punctate staining and increased number of punctates per cell in KMM cells with overexpression of GLUT1 or GLUT3 ( Fig 8A–8D ) . These results indicate that GLUT1 and GLUT3 impair the AKT and NF-κB survival pathways in KMM cells . To determine if AKT pathway mediated the activation of NF-κB pathway in KMM cells , we treated cells with an inhibitor of the AKT upstream activator PI3K . Interestingly , the PI3K inhibitor and glucose deprivation reduced the total and phosphorylated p65 levels ( Fig 8E ) . The PI3K inhibitor and glucose deprivation synergized with each other to further reduce the total and phosphorylated p65 levels . As shown in Fig 7F , KMM cells with overexpression of GLUT1 or GLUT3 were sensitive to glucose deprivation with increased numbers of apoptotic cells ( Fig 8F ) . Treatment with the PI3K inhibitor alone was sufficient to increase the numbers of apoptotic cells in these cells , and further sensitized them to glucose deprivation . While KMM cells were resistant to glucose deprivation ( Fig 2E ) , treatment with the PI3K inhibitor alone was sufficient to increase the number of apoptotic cells in KMM cells overexpressing the vector control , or GLUT1 and GLUT3 , and further sensitized them to apoptosis upon glucose deprivation ( Fig 8F ) . Collectively , these results indicate that the resistance of KMM cells to glucose deprivation is likely due to their reduced GLUT1 and GLUT3 levels , resulting in the enhanced persistent activation of the AKT-NF-κB pathway . We have so far demonstrated that KSHV promotes cell survival under nutrient deprivation by downregulating GLUT1 and GLUT3 to suppress aerobic glycolysis . To demonstrate the pathological relevance of these observations , we examined the expression of GLUT1 and GLUT3 proteins in human KS tumors on a tissue array by dual-color immunofluorescence staining ( Fig 9A and 9B ) . The expression of GLUT1 and GLUT3 was evaluated using a modified Histo-score ( H-score ) as described in the Materials and Methods . We observed significant downregulation of GLUT1 and GLUT3 in LANA-positive cells compared to LANA-negative cells in the KS tumors as well as adjacent uninvolved tissues ( Fig 9C and 9D ) . A total of 27 specimens were retained following GLUT1 staining ( S2 Fig ) . Of the 22 specimens that had robust LANA signal ( detection of > 10 LANA-positive cells ) , 20 ( 90% ) had significantly downregulation of GLUT1 in the LANA-positive cells compared to LANA-negative cells . Three specimens had weak LANA signal ( detection of < 10 LANA-positive cells ) . Of the 2 specimens that had no detectable LANA signal ( normal skin tissues ) , we detected strong GLUT1 signal . Among the specimens that had LANA-positive cells , the average GLUT1 signal was already negatively correlated with the numbers of LANA-positive cells ( r = -0 . 5351 , P = 0 . 0233 in Fig 9E ) . A total of 22 specimens were retained following GLUT3 staining ( S3 Fig ) . Of the 17 specimens that had strong LANA signal ( detection of >10 LANA-positive cells ) , 13 ( 76% ) had significantly downregulation of GLUT3 in the LANA-positive cells compared to LANA-negative cells . Two specimens had weak LANA signal ( detection of 10 or < 10 LANA-positive cells ) . Of the 3 specimens that had no detectable LANA signal ( normal skin tissues ) , we detected strong GLUT3 signal . Among the specimens that had LANA-positive cells , there was already a tend of negative correlation between the average GLUT3 signal with the numbers of LANA-positive cells albeit it had not reached statistical significance ( r = -0 . 3932 , P = 0 . 0573 in Fig 9F ) . Together , these results suggest that KSHV suppression of aerobic glycolysis is present in the KS tumors . PEL is another malignancy associated with KSHV infection . Since primary PEL specimens are rare , we examined the expression of GLUT1 and GLUT3 in three PEL lines that are only infected by KSHV including BCBL1 , BC3 and BCP1 cells ( Fig 10A ) . Compared to BJAB , a KSHV-negative and EBV-negative Burkitt's lymphoma cell line , the expression of GLUT1 was downregulated in all PEL lines . However , the expression of GLUT3 had no obvious difference among the cell lines examined . As there is no appropriate control for the PEL cell lines , we examined BJAB cells infected by KSHV ( BJAB-KSHV ) . KSHV infection downregulated the expression of both GLUT1 and GLUT3 in BJAB cells . BCBL1 and BC3 cells had slightly slower proliferation rates compared to other cell lines . However , by day 1 post-seeding , we observed slower glucose consumption rates in all KSHV-infected lines compared to BJAB ( Fig 10C ) . By day 2 post-seeding , BJAB and BCP1 cells no longer had detectable glucose in the culture medium . We detected a higher level of lactate production by BJAB cells than those of all the KSHV-infected cell lines at day 3 post-seeding ( Fig 10D ) . These results indicate that aerobic glycolysis is likely suppressed in PEL cells though further investigations are required to understand the metabolic reprogramming in the PEL cells , as well as how it might affect cell proliferation and survival . We have shown that KSHV downregulates the expression of GLUT1 and GLUT3 to inhibit glucose uptake resulting in the suppression of aerobic glycolysis and oxidative phosphorylation . Under glucose deprivation condition , downregulation of GLUT1 and GLUT3 is required for optimal cell survival and efficient colony formation of KSHV-transformed cells in softagar . Significantly , we have detected downregulation of GLUT1 and GLUT3 in KSHV-infected cells in KS tumors , suggesting that suppression of aerobic glycolysis is likely important in these tumors . Mechanistically , KSHV inhibits the expression of GLUT1 and GLUT3 through activation of the NF-κB pathway by vFLIP and the miRNA cluster . Downregulation of GLUT1 and GLUT3 further maximizes KSHV activation of the AKT-NF-κB survival pathway resulting in enhanced cell survival and cellular transformation . These results have also revealed a negative feedback loop of the AKT-NF-κB pathway imposed by the glucose transporters , which is disrupted by vFLIP and the miRNA cluster ( Fig 11 ) . To adapt to diverse conditions for growth , proliferation and survival , cancer cells must undergo reprograming of the metabolic pathways[4] . For fast growing cancer cells , glucose is diverted to aerobic glycolysis from the TCA cycle and oxidative phosphorylation to provide rapid supply of the energy and substrates for synthesis of macromolecules[3] . Surprisingly , we have found that both aerobic glycolysis and oxidative phosphorylation are suppressed in KSHV-transformed cells . It has been recognized that metabolic pathways must be tightly regulated to ensure the homeostasis of cells , particularly under stress conditions as overflow of the metabolic pathways could generate cytotoxic products[4] . Mesenchymal stem cells have an intrinsic high level of aerobic glycolysis compared to differentiated cells[24] . Thus , suppression of the glycolytic and oxidative phosphorylation activities by KSHV might avoid the overflow the pathways . Interestingly , it has been reported that aerobic glycolysis is upregulated in untransformed KSHV-infected ECs compared to the uninfected control cells[13 , 16] . AKT hyperactivation by KSHV is responsible for GLUT1 membrane exposure in KSHV latent infection of a human monocytic cell line[25] . Whether these contradictory observations are due to different cell types or the states of cellular transformation remains to be determined . The origin of KS tumor cells remains unclear . Our previous studies indicate that KS tumor cells could be derived from mesenchymal stem cells[9 , 10] . In the KMM model , KSHV-induced cellular transformation is immediate upon KSHV infection and is dependent on the KSHV genome[8] . If this scenario exists in human KS tumors , downregulation of GLUT1 and GLUT3 , and suppression of aerobic glycolysis should be readily present regardless of the status of acute or persistent infection in the tumors . In contrast , if KS tumor cells are derived from endothelial cells , enhanced aerobic glycolysis without suppression of GLUT1 and GLUT3 should be expected . However , our results so far indicate that GLUT1 and GLUT3 are downregulated in KS tumors ( Fig 9 ) . It remains possible that KS tumor cells are derived from endothelial cells and are transformed by KSHV in KS tumors but the cellular transformation phenotype has not been genuinely recapitulated in any of cell culture systems , which could explain the discrepancies between the in vivo and in vitro phenotypes . Further studies are required to clarify these contradictions . The Warburg effect in a tumor is often measured by the avidity of fluorodeoxyglucose ( FDG ) , a glucose analog . Low FDG avidity was detected in pulmonary and lymph node KS but not in skin KS[26 , 27] . However , occult KS lesions were detected by FDG-positron emission tomography and computed tomography ( FDG-PET/CT ) in advanced KS[28 , 29] . It was also reported that 55% ( 5 in 9 ) of KSHV-associated MCD patients who had cutaneous KS showed mildly hypermetabolic cutaneous abnormalities in FDG-PET[30] . Therefore , whether KS tumors , particularly early stage KS tumors , have increased glucose uptake remains unclear . It should be noted that KS lesions are highly heterogeneous , consisting of LANA-positive spindle tumor cells , and various LANA-negative cell types including vascular and lymphatic endothelial cells , macrophages , lymphocytes , plasma cells and red blood cells[31] . Recent studies have shown that some cancer cells have low levels of aerobic glycolysis but they induce aerobic glycolysis in neighboring stromal cells , which in turn provide fuels for the cancer cells and contribute to the overall Warburg effect in the tumors[32–34] . This model , termed “reverse Warburg effect” , explains some challenges of the Warburg effect and reveals the complex metabolic interactions of tumor and tumor microenvironments . The detection of Warburg effect in a fraction of the KS tumors could also be a reflection of the aerobic glycolytic activities of the stromal cells rather than the LANA-positive tumor cells . In fact , our results have clearly shown that the LANA-positive tumor cells express lower levels of GLUT1 and GLUT3 than the LANA-negative cells . Furthermore , advanced KS tumors are often composed of diverse genetic alterations , some of which could result in metabolic reprograming in these cells . Further research is warranted to delineate the molecular basis underlying the metabolic heterogeneity in KS tumors . The findings that KSHV inhibits aerobic glycolysis are analogous to results of several studies on PKM2 . As a tumor-specific glycolytic enzyme , PKM2 promotes the proliferation of cancer cells by inhibiting ATP generation and antagonizing the Warburg effect in some cancers[35–38] . This observation was initially regarded as counterintuitive but it has become clear that the shift of glucose to the TCA cycle and oxidative phosphorylation can generate metabolic intermediates for the synthesis of lipids , nucleotides and amino acids in addition to ATP[4 , 39] . On the other hand , compared to cancer cells that have upregulated levels of PKM2 , KSHV-transformed cells are distinct in that they have lower levels of intracellular ATP and oxygen consumption , reflecting the general lower activities of the TCA cycle and oxidative phosphorylation . Since hyperactivation of oxidative phosphorylation can generate reactive oxygen species[6] , minimizing ATP production and oxygen consumption might allow KSHV-transformed cells to maintain a balanced cellular redox status . The fact that KSHV-transformed cells consume less glucose than the untransformed cells despite their faster proliferation rates implies that they might utilize other carbon sources to support their proliferation . Recent studies have shown that in addition to glucose , cancer cells also utilize glutamine , one carbon amino acids or fatty acids to support their growth[4 , 5] . Indeed , PEL cells and KSHV-infected ECs utilize fatty acids to support their proliferation and survival[14 , 15] . Whether KSHV-transformed cells also depend on these carbon sources for proliferation remains to be investigated . Regardless the alternative carbon sources , such metabolic reprogramming enables KSHV-transformed cells to adapt to glucose-deprived condition . We have shown that under this condition , KSHV-transformed cells maintain normal proliferation and cellular transformation while the untransformed cells undergo arrest and apoptosis . Cancer cells , particularly those in solid tumors , often encounter stress conditions including nutrient deprivation[40 , 41] . Glucose concentrations are frequently 3- to 10-fold lower in tumors than in normal tissues[40 , 41] . Thus , the observed metabolic reprograming provides the advantage for KSHV-transformed cells to survive in a stress tumor microenvironment . These findings are consistent with results of another study showing that deficiency in PKCξ promotes the plasticity necessary for cancer cells to survive and proliferate in the absence of glucose by reprograming their metabolism[42] . In fact , up to 30% of cancers are FDG-PET-negative , indicating the lack of excessive glucose consumption in these cancers[43] . A number of cancers can survive therapies aimed at curtailing the supply or utilization of glucose by reprogramming their metabolic needs[44 , 45] . As a result , such treatment often leads to increasing cancer aggressiveness[44 , 45] . It is important to note that , under normal culture condition , KSHV-transformed cells are capable of consuming glucose , and maintaining aerobic glycolytic and oxidative phosphorylation activities albeit at lower levels than the untransformed cells . Thus , KSHV-transformed cells likely have optimized their metabolic pathways to adapt to different proliferation conditions . Our results show that both KSHV vFLIP and the miRNA cluster are required for suppressing GLUT1 and GLUT3 expression by activating the NF-κB pathway . While overexpression of vFLIP or the miRNA cluster is sufficient to activate the NF-κB pathway[21–23] , both are required for the maximal activation of the pathway in KSHV-transformed cells[10] . The mechanism by which vFLIP and the miRNA cluster synergize with each other to maximize the activation of the NF-κB pathway remains unclear . The NF-κB pathway transduces crucial survival signals and is frequently activated in cancer . GLUT3 is a NF-κB transcriptional target[46–48] and RelA inactivation can lead to upregulation of GLUT1 and GLUT3[48] . Silencing of RelA in murine tumors that heavily rely on NF-κB activation resulted in increased activity of aerobic glycolysis , rendering these tumors especially sensitive to metabolic challenges including glucose deprivation[48] . Indeed , silencing RelA or inhibition of the NF-κB pathway leads to upregulation of GLUT1 and GLUT3 , and increase of aerobic glycolysis in KSHV-transformed cells ( Figs 5 and 6 ) . These results illustrate NF-κB as a central regulator of energy homeostasis and metabolic adaptation in addition to its pro-survival function . Importantly , NF-κB activation by KSHV miRNAs is essential for the survival , proliferation and cellular transformation[10] . Similarly , vFLIP is also required for KSHV-induced cellular transformation ( Fig 3J ) . Thus , by activating the NF-κB pathway , both KSHV vFLIP and the miRNA cluster play critical roles in KSHV-induced cellular transformation by regulating energy homeostasis and metabolic adaptation in addition to providing survival signal . Interestingly , overexpression of GLUT1 and GLUT3 suppresses NF-κB activation ( Fig 8A ) . Thus , higher levels of GLUT1 and GLUT3 might suppress the NF-κB pathway in primary cells . By activating the NF-κB pathway , KSHV vFLIP and the miRNA cluster inhibit the expression of GLUT1 and GLUT3 in KSHV-transformed cells , which further enhance the AKT and NF-κB signaling . These results have established a NF-κB signaling loop negatively regulated by the glucose transporters , which is disrupted by KSHV vFLIP and the miRNA cluster ( Fig 11 ) . The PI3K/AKT pathway is often hyperactivated in malignant cells and is known to promote the survival and proliferation of cancer cells[49] . We have shown that KSHV-transformed cells have hyperactivated AKT . Both KSHV GPCR ( ORF74 ) and ORF-K1 can activate the AKT pathway[50–52]; however , KSHV-transformed cells are predominantly latent with minimal expression of these two viral proteins[8] . Both cellular and viral IL-6 can also activate the AKT pathway through the gp130 receptor[53 , 54] . We have shown that stable overexpression of GLUT1 or GLUT3 suppresses AKT activation . Thus , KSHV downregulation of GLUT1 and GLUT3 can maximize AKT activation . While increased glucose uptake is known to enhance AKT activation[55 , 56] , the roles of glucose transporters in AKT activation are unclear . Our results indicate that glucose metabolism and glucose transporters might regulate AKT signaling by distinct mechanisms . AKT is an important driver of the tumor glycolytic phenotype and stimulates ATP generation through multiple mechanisms[57 , 58] . In particular , AKT1 simulates aerobic glycolysis by promoting the transcription and incorporation of GLUT1 into the plasma membrane[59–61] . Thus , suppression of aerobic glycolysis in KSHV-transformed cells where there is hyperactivation of AKT appears to contradict with these observations; however , this might be due to the intrinsic high level of aerobic glycolysis in the mesenchymal stem cells[24] . Similarly , hypoxia and HIF1α also enhance aerobic glycolysis[4 , 5] , and HIF1α is upregulated in KSHV-infected cells[62] . It is possible that NF-κB is the dominant pathway that regulates the expression of GLUT1 and GLUT3 , and aerobic glycolysis in KSHV-transformed cells though further investigations are required to clarify these issues . AKT is a known upstream regulator of NF-κB[49] . Indeed , chemical inhibition of the AKT pathway reduces the level of activated NF-κB , and sensitizes KSHV-transformed cells to glucose deprivation ( Fig 8E and 8F ) . However , the activated AKT only partially accounts for the NF-κB activities as activation of the NF-κB pathway by both vFLIP and miRNAs is independent of the AKT pathway[21–23] . Nevertheless , AKT hyperactivation is essential for the survival and proliferation of KSHV-transformed cells , particularly under stress conditions such as glucose deprivation , and KSHV downregulation of GLUT1 and GLUT3 can maximize the AKT activation . In summary , KSHV suppression of aerobic glycolysis and oxidative phosphorylation through inhibition of glucose uptake enables the adaption of KSHV-transformed cells to different proliferation and survival conditions . Our findings illustrate the importance of fine-tuning of the metabolic pathways in cancer cells , which could be explored for therapeutic application . Rat primary embryonic metanephric mesenchymal precursor cells ( MM ) , KSHV-transformed MM cells ( KMM ) [8] , MM cells infected by KSHV mutants with a cluster of 10 precursor miRNAs deleted ( ΔmiRs ) [10] , vFLIP deleted ( ΔvFLIP ) [18] , vCyclin deleted ( ΔvCyclin ) [12] and 293T cells were maintained in Dulbecco’s modified Eagle’s medium ( DMEM; with 25 mM glucose , 4 mM L-glutamine and 2 mM sodium pyruvate ) supplemented with 10% fetal bovine serum ( FBS; Sigma-Aldrich , St . Louis , Mo ) and antibiotics ( 100 μg/mL penicillin and 100 μg/mL streptomycin ) . Only MM and KMM cells at early passage ( <15 ) were used for the experiments . For glucose starvation , cells were cultured in DMEM without glucose ( with 4 mM L-glutamine and 2 mM sodium pyruvate ) , supplemented with 10% FBS ( Sigma-Aldrich ) . PEL cell lines BCBL1 , BC3 and BCP1 , and EBV-negative Burkitt’s lymphoma cell line BJAB and KSHV-infected BJAB ( BJAB-KSHV ) were cultured in RPMI-1640 medium with 10% FBS . JSH-23 ( inhibitor of NF-κB nuclear translocation ) , BAY 11–7082 ( an inhibitor of IκBα phosphorylation ) and LY294002 ( PI3K inhibitor ) were purchased from Sigma-Aldrich . Softagar assay was performed as previously described[12] . Briefly , a total of 2x104 cells suspended in 1 ml of 0 . 3% top agar ( Cat . A5431 , Sigma-Aldrich ) were plated onto one well of 0 . 5% base agar in 6 well-plates and maintained for 2–3 weeks . Colonies with a diameter of >50 μm were counted and photographed at 40× magnification using a microscope . Cell cycle and BrdU incorporation were performed at the indicated time points as previously described[12] . Cell cycle was analyzed by propidium iodide ( PI ) staining . BrdU incorporation was performed by pulsing cells with 10 μM BrdU for 1 h and then stained with a Pacific Blue monoclonal antibody to BrdU ( Cat . B35129 , Life Technologies , Grand Island , NY ) . Apoptotic cells were detected by Fixable Viability Dye eFluor 660 staining ( Cat . 650864 , eBioscience , San Diego , CA ) and with a PE-Cy7 Annexin V Apoptosis Detection Set ( Cat . 88810374 , eBioscience ) following the instructions of the manufacturer . Flow cytometry was performed in a FACSCanto System ( BD Biosciences , San Jose , CA ) and analysis was performed with FlowJo ( FlowJo , LLC , Ashland , OR ) . Cells were seeded in 24-well plates , media were changed 24 h later and assays were carried out at the indicated time points in normal medium or in glucose-free medium ( glucose starvation ) . Glucose and lactate concentrations were measured in the culture media using the Glucose Colorimetric/Fluorometric Assay Kit ( Cat . K606-100 , BioVision , Milpitas , CA ) and the Lactate Assay Kit ( Cat . MAK064 , Sigma-Aldrich ) , respectively , according to the manufacturer’s instructions . Intracellular ATP levels were determined in cell lysates using the ATP Bioluminescent Somatic Cell Assay Kit ( Cat . FLASC-1KT , Sigma-Aldrich ) according to the manufacturer’s instructions . Oxygen uptake was measured in 24-well plates using a Seahorse XF24 Extracellular Flux Analyzer ( Seahorse Bioscience , Billerica , MA ) . Cells were seeded at 2x104 cells per well and incubated overnight in normal growth medium . Next day , medium was changed to 8 . 3 g/L DMEM base medium at pH 7 . 4 supplemented with 200 mM GlutaMax-1 , 100 mM sodium pyruvate and 32 mM NaCl in the presence of 25 mM glucose , and oxygen consumption was continuously measured . The coding sequences of rat GLUT1 ( GenBank accession no . NM_138827 . 1 ) and GLUT3 ( NM_017102 . 2 ) were cloned into the FseI/PacI sites of pSMPUW-IRES-Bsd ( Cat . VPK-219 , Cell Biolabs , San Diego , CA ) by PCR amplification to generate expression vectors named pSMPUW-IRES-Bsd-GLUT1 and pSMPUW-IRES-Bsd-GLUT3 . The primers were 5’-AGTGGCCGGCCATGGAGCCCAGCAGCAAGA-3’ ( forward ) and 5’-AGTTTAATTAATCACACTTGGGAGTCAGCC-3’ ( reverse ) for GLUT1 , and 5’-AGTGGCCGGCCATGGGGACAGCGAAGGTGA-3’ ( forward ) and 5’-AGTTTAATTAATCAGGCATTGCCAGGGGTCT-3’ ( reverse ) for GLUT3 with all the restriction enzyme sites underlined . All constructs were confirmed by direct DNA sequencing . The mCherry coding sequence from the pmCherry-C1 vector ( Addgene , Cambridge , MA ) and the rat LC3 coding sequence from the pEGFP-LC3 vector ( Addgene ) were cloned into the XbaI/BamHI and BamHI/NotI sites of the pCDH-hygro vector to generate a fusion mCherry-LC3 expression vector named pCDH-mCherry- ( rat ) LC3 . To obtain the recombinant lentivirus , pSMPUW-IRES-Bsd overexpression plasmids were cotransfected with pMDLg/pRRE , pRSV-Rev and pMD2 . G packaging plasmids into actively growing HEK293T cells by using Lipofectamine 2000 transfection reagent . Virus-containing supernatants were collected 72 hr after transfection and filtered to remove cells , and target cells were infected in the presence of 8 μg/mL polybrene . MM-Vector , KMM-Vector , MM-GLUT1 , KMM-GLUT1 , MM-GLUT3 and KMM-GLUT3 cells were selected with 10 μg/mL Blasticidin after transduction . siRNA knock down of RelA was performed as previously described[10] . Briefly , the small interfering ( si ) RNA targeting Rat RelA ( GenBank Access . No . NM_199267 . 2 ) transcript was designated siRelA ( sense: GUGACAAAGUGCAGAAAGAUU; antisense: UCUUUCUGCACUUUGUCACUU ) . A scrambled oligonucleotide containing a random sequence was obtained from the manufacturer ( Ambion , Thermo Fisher Scientific , Waltham , MA ) and used as a control . Reverse transfection of siRNA duplex was performed using Lipofectamine-RNAiMAX ( Invitrogen , Carlsbad , CA ) . Transfection was performed at a final concentration of 50 nM . Total RNA was isolated with TRI Reagent ( Cat . T9424 , Sigma ) according to the instructions of the manufacturer . Reverse transcription was performed with total RNA using Maxima H Minus First Strand cDNA Synthesis Kit ( Cat . K1652 , Thermo Fisher Scientific ) . qPCR analysis was performed on Eppendorf Real Plex using KAPA SYBR FAST qPCR Kits ( Cat . KK4602 , Kapa Biosystems , Wilmington , MA ) . The relative expression levels of target genes were normalized to the expression of internal control genes , which yielded a 2-ΔΔCt value . All reactions were run in triplicates . The cycle threshold ( Ct ) values should not differ more than 0 . 5 among triplicates . Rat β-actin was used as an internal control . The primers were 5’-GCGAGCTCTTTGAATGTGTG-3’ ( forward ) and 5’-GGCTCAGGTCCTTCACGTAG-3’ ( reverse ) for GLUT1 , 5’-ATGTTGGCCAGTCAAGTTCC-3’ ( forward ) and 5’-CTGTCACCTCTGGGAGCAG-3’ ( reverse ) for GLUT3 , and 5’-GCAGGAGTACGATGAGTCCG-3’ ( forward ) and 5’-ACGCAGCTCAGTAACAGTCC-3’ ( reverse ) for β-actin . Total cell lysates were separated in SDS-polyacrylamide gels , electrophoretically transferred to nitrocellulose membranes ( GE Healthcare , Piscataway , NJ ) . The membranes were incubated sequentially with primary and secondary antibodies . The signal was developed using Luminiata Crescendo Western HRP substrate ( cat . WBLUR0500 , EMD Millipore , Billerica , MA ) . The antibodies used for Western blot include rabbit monoclonal antibodies ( mAbs ) for GLUT1 ( cat . ab115730 , Abcam , Cambridge , MA ) , phospho-AKT ( Thr308 ) ( cat . 2965 , Cell Signaling Technology , Danvers , MA ) and NF-κB p65 ( cat . 8242 , Cell Signaling Technology ) ; rabbit polyclonal antibodies against GLUT3 ( cat . ab41525 , Abcam ) , phospho-4E-BP1 ( Ser65 ) ( cat . 9451 , Cell Signaling Technology ) and AKT ( cat . 9272 , Cell Signaling Technology ) ; and mouse mAbs for LC3 ( cat . CTB-LC3-1-50 , COSMO BIO CO . , Tokyo , Japan ) , phospho-NF-κB p65 ( Ser 536 ) ( cat . 3036 , Cell Signaling Technology ) and β-tubulin ( 7B9 , Sigma ) . Cells fixed with 80% methanol ( 5 min ) were permeabilized with 0 . 1% PBS-Tween for 20 min . The cells were then incubated in PBS containing 10% normal goat serum and 0 . 3 M glycine to block non-specific protein-protein interactions followed by GLUT1 or GLUT3 antibody ( ab115730 or ab41525 , respectively ) at 1/500 dilution for 30 min at room temperature . The secondary antibody used was Alexa conjugated at 1/2000 dilution for 30 min at room temperature . Flow cytometry was performed with a FACS Canto II flow cytometer and analyzed with FlowJo . All runs included a control without the primary antibody . KMM , KMM-GLUT1 and KMM-GLUT3 cells were seeded on 24-well culture plate that contained 12 mm diameter round glass coverslips ( 2x104 cells per well ) . After infection 48 h , cells were fixed with 4% paraformaldehyde in PBS for 15 min at room temperature and washed with PBS . Samples were incubated with 0 . 5 μg/ mL 4- , 6-diamidino-2-phenylindole ( DAPI ) in PBS for 1min , then were mounted in FluorSave Reagent ( Calbiochem , San Diego , CA ) . Samples were imaged with laser-scanning confocal microscopy ( Nikon Eclipse C1 ) . Formalin-fixed , paraffin-embedded tissue microarray consisting of tissue specimens from healthy subjects and patients with KS were obtained from the AIDS and Cancer Specimen Resource ( ACSR ) . Sections were de-paraffinized in xylene , rehydrated through graded ethanol , quenched for endogenous peroxidase activity in 3% hydrogen peroxide in methanol for 10 min and processed for antigen retrieval by microwave heating in 1 mM EDTA at pH 8 . 0 . Immunostaining was performed using an anti-LANA antibody LN35 ( cat . ab4103 , Abcam ) and an anti-GLUT1 antibody ( ab115730 , Abcam ) or an anti-GLUT3 antibody ( cat . sc-30107 , Santa Cruz Biotechnology , Santa Cruz , CA ) antibodies , followed by Alexa-488 and Alexa-568 conjugated secondary antibodies ( Thermo Fisher Scientific ) . Nuclei were stained with 4’ , 6-diamidino-2-phenylindole ( DAPI ) . The stained cells were viewed under a confocal fluorescence microscope with a 60x objective . Tissue sections without incubating with primary antibody were used as negative controls . For each specimen , three images of representative areas were acquired and a total of 200 to 500 cells were counted unless stated otherwise . The scoring of the expression of GLUT1 and GLUT3 was performed using a modified Histo-score ( H-score ) , which included a semi-quantitative assessment of both fraction of positive cells and intensity of staining . The intensity score was defined as no staining ( 0 ) , and weak ( 1 ) , moderate ( 2 ) , or strong ( 3 ) staining . The fraction score was based on the proportion of positively stained cells ( 0%-100% ) . The intensity and fraction scores were then multiplied to obtain H-score , which ranged from 0 to 3 and represented the levels of GLUT1 and GLUT3 expression . Data were expressed as the mean ± standard error of the mean ( s . e . m . ) from at least three independent experiments , each with three repeats unless stated otherwise . The differences between groups were analyzed using Student’s t-test when two groups were compared and using one-way ANOVA when more than two groups were compared unless otherwise noted . Correlation was determined using Spearman’s correlation coefficient . Statistical tests were two-sided . A P < 0 . 05 was considered statistically significant . Statistical symbols “*” , “**” and “***” represent P-values < 0 . 05 , < 0 . 01 and < 0 . 001 , respectively , while “NS” indicates “not significant” . All analyses were performed using the GraphPad Prism program ( GraphPad Software Inc . , San Diego , CA ) .
KSHV is causally associated with the development of Kaposi’s sarcoma and primary effusion lymphoma; however , the mechanism underlying KSHV-induced malignant transformation remains unclear . The recent development of an efficient KSHV-induced cellular transformation model of primary rat mesenchymal stem cells should facilitate the delineation of KSHV-induced oncogenesis . In this report , we have used this model to investigate the metabolic pathways mediating the proliferation and survival of KSHV-transformed cells . In contrast to most other cancers that depend on aerobic glycolysis for their fast growth , we demonstrate that KSHV suppresses aerobic glycolysis and oxidative phosphorylation in the transformed cells . Significantly , suppression of aerobic glycolysis enhances the survival of the KSHV-transformed cells under nutrient deprivation . Mechanistically , KSHV-encoded microRNAs and vFLIP suppress aerobic glycolysis by activating the NF-κB pathway to downregulate glucose transporters GLUT1 and GLUT3 . We have further shown that GLUT1 and GLUT3 inhibit constitutive activation of the AKT and NF-κB pro-survival pathways . Strikingly , GLUT1 and GLUT3 are significantly downregulated in KSHV-infected cells in human KS tumors . Furthermore , we have detected reduced levels of aerobic glycolysis in several KSHV-infected primary effusion lymphoma cell lines and a KSHV-infected Burkitt’s lymphoma cell line BJAB . Our results reveal a novel mechanism by which an oncogenic virus regulates a key metabolic pathway to adapt to stress in tumor microenvironment , and illustrate the importance of fine-tuning the metabolic pathways for sustaining the proliferation and survival of cancer cells , particularly under nutrient stress microenvironment .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
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2016
An Oncogenic Virus Promotes Cell Survival and Cellular Transformation by Suppressing Glycolysis
Acute kidney injury ( AKI ) from leptospirosis is frequently nonoliguric with hypo- or normokalemia . Higher serum potassium levels are observed in non-survivor patients and may have been caused by more severe AKI , metabolic disarrangement , or rhabdomyolysis . An association between the creatine phosphokinase ( CPK ) level and maximum serum creatinine level has been observed in these patients , which suggests that rhabdomyolysis contributes to severe AKI and hyperkalemia . LipL32 and Lp25 are conserved proteins in pathogenic strains of Leptospira spp . , but these proteins have no known function . This study evaluated the effect of these proteins on renal function in guinea pigs . Lp25 is an outer membrane protein that appears responsible for the development of oliguric AKI associated with hyperkalemia induced by rhabdomyolysis ( e . g . , elevated CPK , uric acid and serum phosphate ) . This study is the first characterization of a leptospiral outer membrane protein that is associated with severe manifestations of leptospirosis . Therapeutic methods to attenuate this protein and inhibit rhabdomyolysis-induced AKI could protect animals and patients from severe forms of this disease and decrease mortality . Leptospirosis is an emerging zoonosis that is caused by pathogenic spirochetes of the genus Leptospira . Approximately 1 . 03 million cases of the disease occur in humans worldwide , with approximately 60 , 000 deaths annually [1] . Many species of wild and domestic animals are leptospirosis reservoir hosts and eliminate leptospires to the environment via urinary shedding . Infection may result from direct contact with carrier animals or indirect contact with contaminated soil and water [2 , 3] . Human leptospirosis ranges from an asymptomatic or self-limited febrile illness ( 80 to 90% of cases ) to a life-threatening illness ( 5 to 10% of cases ) . The life-threatening manifestation is characterized by Weil´s syndrome ( renal failure , hemorrhage and jaundice ) or severe pulmonary hemorrhagic syndrome [2–5] . Leptospirosis-induced acute kidney injury ( AKI ) is typically nonoliguric at the beginning of renal failure evolution or during mild forms with high frequency of hypokalemia [4] . In a prior study , higher serum potassium levels were observed in patients with more severe renal dysfunction concomitant with rhabdomyolysis . In addition , an association between creatine phosphokinase levels ( CPK ) ( a marker of muscle injury ) and maximum serum creatinine levels has been reported . This suggests that rhabdomyolysis is associated with severe AKI in leptospirosis [6] . Various bacterial , viral , fungal and protozoal infections lead to rhabdomyolysis [7–10] , but the mechanism of muscle damage has not been established for many infections , including leptospirosis . Muscle injury may result from a direct pathogen invasion of skeletal muscle , tissue hypoxia , high lysosomal enzymatic activity or the release of toxins [11 , 12] . The identification of proteins that act as toxins in the host during leptospiral infection is essential to understanding the pathophysiological mechanisms of rhabdomyolysis and the mechanisms that contribute to severity of AKI . The subsurface lipoprotein LipL32 is present in pathogenic leptospires , and it is the most abundantly expressed protein ( 40 , 000 copies per cell ) [13 , 14] . However , the role of this protein in the pathogenesis of leptospirosis remains unknown [15] . Lp25 is a putative outer membrane lipoprotein of pathogenic Leptospira spp . , but its function is not known . No sequences similar to this protein were identified in saprophytic Leptospira spp . [16–18] . The present study investigated whether the LipL32 and Lp25 proteins expressed by pathogenic Leptospira were associated with rhabdomyolysis and oliguric AKI in guinea pigs . To our knowledge , this study is the first characterization of a leptospiral protein associated with renal and muscular manifestations of leptospirosis . Leptospira biflexa serovar Patoc strain Patoc I , Leptospira noguchii serovar Panama strain CZ214K , Leptospira borgpetersenii serovar Javanica strain Veldrat Batavia 46 , Leptospira borgpetersenii serovar Tarassovi strain 17 , Leptospira kirschneri serovar Cynopteri strain 3522C , Leptospira interrogans serovar Hardjo strain Hardjoprajitno , Leptospira interrogans serovar Pomona strain 13A , and Leptospira interrogans serovar Copenhageni strain Fiocruz L1-130 were obtained from the Laboratory of Bacterial Zoonosis , School of Veterinary Medicine and Animal Science , University of São Paulo , Brazil . Leptospira strains were cultured at 29°C under aerobic conditions in liquid EMJH medium ( Difco , Thermo Fisher Scientific , Boston , MA , USA . ) with 10% rabbit serum , enriched with L-asparagine ( 0 . 015% ) , sodium pyruvate ( 0 . 001% [wt/vol] ) , calcium chloride ( 0 . 001% [wt/vol] ) , magnesium chloride ( 0 . 001% [wt/vol] ) , peptone ( 0 . 03% [wt/vol] ) , and meat extract ( 0 . 02% [wt/vol] ) [17] . LipL32 and Lp25 proteins were chosen for this study because no research has been performed to investigate their effects on renal function experimentally in animals . Lp25 was identified by bioinformatics analyses using the L . interrogans serovar Copenhageni strain Fiocruz L1-130 genome sequence previously described in studies published by our group [17] . The selection was based on the prediction of protein localization in the outer membrane . We gave priority to Lp25 because its function is not known . Leptospiral immunoglobulin-like protein A ( LigA ) [19] and LpL31 [20] were used as controls in the immunoblot analysis . LigA is a known outer membrane protein , and LipL31 is an inner membrane-associated protein [19 , 20] . Open reading frames LIC10009 ( encoding a protein designated Lp25 , for leptospiral protein 25 , based on its molecular mass ) [21] and LIC11352 ( LipL32 ) were cloned into pAE [17 , 22] and pDEST-17 ( Invitrogen , Carlsbad , CA , USA -or- Paisley , Scotland , UK . ) vectors , respectively , as previously described [23] . The coding sequence of the carboxy-terminal portion of LigA ( LigAC ) , corresponding to nucleotides 1891–3675 ( LIC10465 ) , was cloned into a pAE vector as previously described [21] . The coding sequence of the LipL31 ( LIC11456 ) was amplified using PCR from genomic DNA of L . interrogans serovar Copenhageni strain Fiocruz L1-130 using the following primers: F: CTCGAGGGAGATAATTCCG and R: CTGCAGTTACTGCCCAGTAG . Sequences were digested using XhoI and HindIII restriction enzymes , and fragments were cloned into the pAE vector [22] . Competent cells of the E . coli BL21 ( DE3 ) strain were transformed with pAE-Lp25 , pAE-LigAC , pDEST-LipL32 , and pAE-LipL31 constructs and cultivated until the optical density at 600 nm reached 0 . 6 . The expression of recombinant proteins was induced with 1 mM isopropyl-1-thio-β-D-galactopyranoside ( IPTG ) at 37°C for 3 h . The His-tagged Lp25 , LipL32 , LigAC , and LipL31 proteins were purified using metal affinity chromatography , as previously described [21] . A New Zealand White rabbit was immunized for each protein via subcutaneous injection of 2 mg of purified recombinant protein absorbed in aluminum hydroxide as an adjuvant . The rabbits were immunized more two times with the same antigen preparation with fifteen day of interval . The rabbits were bled two weeks after last immunization . The IgG fraction from sera was precipitated using caprylic acid , as previously described [24] . OMPs from L . interrogans serovar Copenhageni strain Fiocruz L1-130 were extracted with Triton X-114 according to a previously described method [25] . Three distinct fractions were recovered: ( P ) the detergent-insoluble pellet that corresponds to inner membranes , cytoplasmic components , and non-lysed cells; ( A ) the aqueous phase that contains periplasmic content , and , ( D ) the Triton X-114 detergent phase with outer membranes . The whole cell lysate ( W ) and the obtained fractions were analyzed using immunoblots with LipL32 , Lp25 , LigA , and LipL31 antisera . L . interrogans serovar Copenhageni strain Fiocruz L1-130 ( 5×108 cells/ml ) were harvested via centrifugation at 2000 × g for 7 min , gently washed with PBS containing 5 mM MgCl2 , and collected via centrifugation at 2000 × g for 10 min . Washed spirochetes were gently resuspended in PBS-5mM MgCl2 , and the evaluation of surface protein localization on intact leptospires was performed by treatment of proteinase K ( PK—Sigma-Aldrich , St . Louis , MO , USA ) as previously described [13] . Immunoblot analyses were performed using antibodies against LipL32 and Lp25 . Proteins were transferred to a nitrocellulose membrane and probed with LipL32 , Lp25 , LigA , or LipL31 rabbit polyclonal antisera ( diluted 1:500 ) . The membrane was incubated with a secondary peroxidase-conjugated anti-rabbit antibody at a 1:5 , 000 dilution . The positive signals were detected using enhanced chemiluminescence ( Thermo Fisher Scientific , Boston , MA , USA ) . The guinea pigs were housed one per metabolic cages with food and drinking water freely available . The animals were acclimated to the housing conditions for 1 day prior to experimental procedures . Three groups of animals were studied: ( 1 ) control , n = 10 ( 1 ml of PBS ) ; ( 2 ) LipL32 , n = 13 ( 1 ml of PBS plus 400 μg of LipL32 ) and ( 3 ) Lp25 , n = 14 ( 1 ml of PBS plus 400 μg of Lp25 ) . The amount of protein used per dose ( 400 μg ) was the greatest amount maintained in solution without precipitation . The solutions were injected intraperitoneally for 4 days . The animals were placed in metabolic cages for 12 h for urine collection on day 5 . The volume of each 12-h urine sample was measured gravimetrically ( UV ml/12 h ) . Urine samples were centrifuged in aliquots to remove suspended material , and urinary creatinine and sodium in the supernatants were measured . After urine collection all animals were anesthetized with a dose of sodium pentobarbital , and whole blood was collected by cardiac puncture . The animals were then killed with an overdose of anesthesia . Serum potassium and sodium were measured using flame photometry . The enzymatic colorimetric method ( Labtest , Lagoa Santa , Brazil ) was used to measure urinary and serum levels of creatinine , CPK and uric acid . The molybdate method was used to measure serum phosphate . The creatinine clearance ( CrCl ) was used to estimate the glomerular filtration rate ( GFR ) by formula: CrCl = Ucr ( mg/dL ) x UV ( ml/min ) / Pcr ( mg/dL ) , corrected by 100 g of body weight ( ml/min/100 g body weight ) [26] . Fractional excretion of sodium was calculated using the formula: FENa = UNa x PCr / PNa x UCr x 100% . AKI was defined as a decrease in the GFR of more than 50% from the mean value obtained in the control group ( PBS ) , and oliguria was defined as a urinary output of less than 50% of mean value of control group ( PBS ) . Rhabdomyolysis was defined as an elevation in serum CPK of at least 3 times the mean value obtained in the control group . The occurrence of hyperkalemia , hyperphosphatemia , and hyperuricemia was defined as an increase of phosphate , potassium , and acid uric levels of more than the mean value of control group ( PBS ) . Muscle fragments from legs and paravertebral regions were collected at the time of euthanasia , routinely fixed in 10% buffered formalin ( pH 7 . 2 ) , embedded in paraffin and sectioned at 3 μm . Fragments from kidney were also collected and submitted to routine histological procedures . Sections were analyzed using EnVision ( Dako , Glostrup , Denmark ) -based immunohistochemistry methods , as previously described ( 5 ) . The antigen retrieval step was performed by pressure cooking in 10 mM sodium citrate pH 6 . Following the overnight incubation with primary rabbit polyclonal antisera ( diluted 1:3 , 000–1:4 , 000 ) at 4°C and with secondary antibody ( Envision peroxidase Dako K4002 ) for 30 min at room temperature . The presence of nonspecific staining was assessed using preimmune sera . Tissue sections for morphological analyses were stained with hematoxylin and eosin ( H&E ) and Gomori trichrome stain in selected sections . Muscular lesions were graded on a scale from 0 to 2: 0 as normal ( without lesions ) , 1 as mild ( chiefly the presence of individual hyaline contraction change and focal inflammatory interstitial reactivity ) , and 2 as severe ( presence of necrosis , multiple lesions of individual myocytes and interstitial inflammatory infiltrated ) . Kidney sections were also fixed in 10% buffered formalin and stained with H&E for morphological analyses . Images were captured on an Axiophot Zeiss Axio microscope and analyzed using AxionVision 4 . 6 software . The Ethic Committee on Animal Use of the Butantan Institute ( CEUAIB ) , São Paulo , Brazil , previously approved the experimental protocols under the license numbers 55708 for the rabbit procedure and 99112 for guinea pig procedure . All animal procedures were conducted following the rules issued by the Brazilian National Council for Control of Animal Experimentation ( CONCEA ) . All quantitative data are expressed as the means ± SEM . Differences between the means of multiple parameters were analyzed using ANOVA followed by Student-Newman-Keuls test . Histological scores were compared using Student’s t-test . Values of p < 0 . 05 were considered statistically significant . All analyses were performed using GraphPad Prism 5 ( Graphpad , La Jolla , CA ) . A total of 37 guinea pigs were assigned into one of three treatment groups: control ( n = 10 ) , LipL32 ( n = 13 ) or Lp25 ( n = 14 ) . Initial body weights were similar between the 3 groups: 194±4 g ( control ) , 195±4 g ( LipL32 ) and 191±3 g ( Lp25 ) . Weight gain was lower in the LipL32 group ( 18±3 g , p<0 , 05 ) and Lp25 group ( 13±3 g , p<0 , 01 ) than the control group ( 28±2 g ) on day 5 . Fig 1 shows that the GFR , evaluated as creatinine clearance ( CrCl ) , was significantly lower in the Lp25 group ( 0 . 47±0 . 03 mL/min/100 gBW ) than the control group ( 1 . 05±0 . 13 mL/min/100 gBW ) and LipL32 ( 0 . 87±0 . 10 mL/min/100 gBW ) . The urinary volume was lower in the Lp25 group ( 12 . 0±1 . 3 UV mL/12h ) than the control group ( 23 . 0±3 . 8 UV mL/12h ) and the LipL32 group ( 17 . 3±3 . 6 UV mL/12h ) . Notably , the serum potassium level in the Lp25 group ( 6 . 7±0 . 5 mEq/L ) was elevated , compared to the control group ( 4 . 7±0 . 2 mEq/L ) and the LipL32 group ( 5 . 7±0 . 3 mEq/L ) . The fractional excretion of sodium was similar in the three groups ( control 0 . 82±0 . 18%; LipL32 0 . 60±0 . 09%; Lp25 0 . 83± 0 . 15% ) . The Lp25 group had significantly higher levels of serum CPK , phosphate and uric acid ( 2060±338 CPK U/L; 8 . 36±0 . 32 P mg/dL and 2 . 75±0 . 56 acid uric mg/dL ) ( Fig 2 ) . These parameters in the LipL32 group ( 726±216 CPK U/L; 6 . 70±0 . 41 P mg/dL and 1 . 06±0 . 28 acid uric mg/dL ) were similar to the control group ( 763±197 CPK U/L; 7 . 06±0 . 30 P mg/dL and 1 . 07±0 . 20 acid uric mg/dL ) . AKI was observed in all animals of Lp25 group ( 100% ) and 7/14 ( 50% ) of these also had oliguria . In the LipL32 group , 2/13 animals ( 15 . 38% ) had AKI oliguric and 1/13 ( 7 . 69% ) presented AKI nonoliguric . Hyperkalemia was seen in 13/14 ( 92 . 85% ) guinea pigs in the Lp25 group vs 8/13 ( 61 . 53% ) in the LpL32 group . In the Lp25 group , rhabdomyolysis , hyperphosphatemia , and hyperuricemia were encountered in 12/14 ( 85 . 71% ) , 10/14 ( 71 . 42% ) and 9/14 ( 64 . 28% ) animals , respectively . Seven animals of this group ( 50% ) showed the three outcomes concomitantly . In the LipL32 group , 2/13 ( 15 . 38% ) had rhabdomyolysis , 3/13 ( 23 . 07% ) had hyperphosphatemia and 2/13 ( 15 . 38% ) had hyperuricemia . We performed immunohistological and morphological analyses to assess the effect of the LipL32 and Lp25 proteins on muscle tissues . The anti-rabbit Lp25 antibodies labeled isolated or small groups of muscular fibers with granular , faintly brownish antigen deposits that may partially delineate the sarcolemma and spread to the cytoplasm below ( Fig 3A ) . The inflammatory infiltrate was generally discrete and primarily composed of small groups of monocytes that were present as focal isolated interstitial groups or small groups of isolated muscular fibers , frequently with antigenic linear deposits that partially circumscribed muscular fibers or inside their cytoplasm . Cytoplasmic antigenic granules were also detected occasionally in mononuclear phagocytic cells when the inflammatory infiltrate is more conspicuous . Immunohistochemistry with the anti-rabbit LipL32 antibodies was also positive on the sarcolemma and in the cytoplasm of isolated muscular fibers . Scarce mononuclear inflammatory interstitial reactions near the damaged muscle were also present ( Fig 3B ) . Histological analyses of the muscle fragments revealed essentially similar muscular lesions were present in animals inoculated with both proteins , but with different degrees of severity ( Fig 3C–3H ) . No wide range of muscular fiber sizes ( small or large groups of atrophic or hypertrophic fibers ) were observed in either protein group . Internal sarcolemmal nuclei were not detected in isolated muscle fibers . However , isolated nonspecific muscular lesions were present and ranged from hyaline contraction cytoplasmatic changes ( Fig 3E and 3F ) to small vacuoles , which may progress to hypercontracted isolated fibers to necrosis prior to phagocytosis ( Fig 3G ) or to intermediate damage , which was characterized by staining changes in myofibrils ( Fig 3F ) , including the appearance of pale necrotic cells in H&E and Gomori trichrome stains ( “ghost cells “ ) ( Fig 3F and 3H ) . Irregular areas of muscle necrosis were the end result of these muscular disturbances ( Fig 3C and 3D ) . Mild inflammatory infiltrate composed of macrophages were also observed around necrotic areas . Fragments from the LipL32 group revealed essentially similar findings to the Lp25 group , but with less frequent focal muscular damage and areas of necrosis ( Fig 3D ) . Muscular lesions severity scores were significantly lower ( p<0 . 05 ) in the LipL32 group ( 0 . 66 ± 0 . 21 ) than the Lp25 group ( 1 . 43 ± 0 . 20 ) , and no muscular lesions were observed in the control group . The difference between LipL32 and Lp25-inoculated animals was statistically significant ( p<0 . 05 , LipL32 vs . Lp25 ) ( Fig 4 ) . Specifically , 53 . 85% of animals treated with LipL32 shown no muscular lesions , whereas the lesions of severity stage 1 and 2 were observed in 30 . 77% and 15 . 38% , respectively , of guinea pigs inoculated with this protein . Otherwise , all Lp25-inoculated animals presented muscular lesions , of these 42 . 86% had mild manifestations ( stage 1 ) and 57 . 14% had severe signs ( stage 2 ) . Histological examination of the kidneys revealed no lesions ( S1 Fig ) . Immunoblots of whole cell lysates revealed that the Lp25 protein was expressed by all strains of pathogenic leptospires tested , and it was not detected in the non-pathogenic strain ( Fig 5A ) . Equal results were obtained in immunoblot assays using LipL32 antiserum as a positive control and demonstrated that the Lp25 protein , like the LipL32 [25] , was conserved and only found in pathogenic species of Leptospira . All control proteins were detected in whole cell extracts . LigA was completely solubilized by the detergent and fractionated into the aqueous and detergent phases . LipL31 was detected in the insoluble pellet fraction and aqueous phase , and it was completely absent from the detergent phase ( Fig 5B ) . We performed an additional experiment using the LipL32 antiserum because a previous work reported that LipL32 was solubilized by Triton X-114 and mostly detected in the detergent fraction [25] . Fig 5B shows that the presence of LipL32 in the Triton X-114 fraction was confirmed [25] . These results demonstrated the correct functioning of the Triton X-114 fractionation method . We also investigated the surface localization of Lp25 using proteolysis of intact cells of the L . interrogans serovar Copenhageni strain L1-130 using proteinase K . Fig 5C shows that Lp25 was susceptible to protease treatment in a dose-dependent manner , and the subsurface LipL32 was not susceptible , which suggests that Lp25 is exposed on the surface . These results are consistent with a previously published study that demonstrated that LipL32 was not exposed on the leptospiral surface , despite its localization in the outer membrane [13] . The renal manifestations of leptospirosis are variable and range from mild symptoms , such as low urinary protein excretion and sediment changes , to fatal AKI [4 , 27 , 28] . Severe cases of AKI are generally oliguric and hyperkalemic with a prolonged course and high mortality rate . Nonoliguric and normo- or hypokalemic AKI-forms are associated with a better prognosis [4 , 27–29] . The renal pathophysiology that is consequent to leptospirosis infection is not clearly known despite advances in the knowledge of AKI epidemiology [4 , 5 , 28–32] . Different factors may be involved , such as inflammatory processes , rhabdomyolysis , hemodynamic alterations , immune responses , and direct effects of leptospires and their products [30 , 33 , 31] . This study evaluated the effect of the LipL32 and Lp25 proteins on renal function in normal guinea pigs . We found that only Lp25 was associated with the development of oliguric AKI and rhabdomyolysis-induced hyperkalemia ( elevated CPK , uric acid and serum phosphate ) . Lp25 decreased the GFR compared LipL32 and control experiments ( Fig 1 ) . These results demonstrate , for the first time , that a specific protein from pathogenic Leptospira spp . plays an important role in the establishment of the AKI that is observed in Weil’s syndrome . In contrast , LipL32 protein did not produce a decrease in the GFR , despite a report that LipL32 induced interstitial nephritis-mediated gene expression in cultured mouse proximal tubule cells [34] and acute tubular injury in proximal pronephric ducts from zebrafish larvae kidneys [35] . These experiments were performed using in vitro preparations and another animal species . The decrease in GFR may not be directly dependent on the tubular damage because the FENa was not different between the three groups . Previous clinical studies also demonstrated that rhabdomyolysis-induced acute renal injuries did not modify sodium excretion [36–38] . Our results from cellular localization assays agree with previous studies that demonstrated that LipL32 was a subsurface protein that was not accessible on the leptospiral surface [13] . Notably , the role of LipL32 in Leptospira biology is not defined despite its abundant expression [14] in all pathogenic serovars . Murray and colleagues ( 2009 ) demonstrated that LipL32 was not required in acute ( hamster ) or chronic ( rat ) infection models for leptospirosis [39] . The results in Figs 1 and 2 , such as the increase in potassium , creatine phosphokinase , uric acid and phosphate serum levels , are characteristics of the presence of rhabdomyolysis . The classification of the muscular lesions observed in histological preparations using a score ( Fig 4 ) revealed that Lp25 induced the same type of muscular lesions as LipL32 , but the Lp25 lesions were much more severe . Fig 3 shows areas of necrosis and moderate inflammatory infiltrate in the Lp25 group , and the LipL32 group exhibited small areas of necrosis and few inflammatory areas . Immunohistochemistry with anti-rabbit Lp25 and LipL32 antisera were positive for the two proteins and both proteins exhibited the same histological patterns . The deposition of the antigens was more intense in the Lp25 group . These results suggested that the LipL32 and Lp25 proteins reached the muscle tissue and induced lesions . However , lesions in the Lp25 group were more diffuse and apparently more accentuated than the LipL32 group . We also demonstrated that Lp25 was a surface-exposed and conserved protein in pathogenic species of Leptospira . Previously published immunoblot studies using sera from leptospirosis patients and infected hamsters showed that Lp25 protein was expressed during the course of leptospiral infection [40 , 21] . This protein was recently included in the Leptospira endostatin-like ( Len ) family by the automatic NCBI prokaryotic genome re-annotation pipeline [41] . The members of the Len family bind plasminogen , laminin and human complement regulator factors [42–44 , 18] . However , we previously demonstrated that Lp25 did not exhibit extracellular matrix-binding properties or play a role in immune evasion via interacting with the human complement regulator C4BP [17 , 45] . Comparative proteomic analyses of leptospira outer membrane proteins also demonstrated that the Lp25 protein , encoded by the LA0009 gene in L . interrogans serovar Lai , was up-regulated ( 1 . 3-fold ) after an overnight upshift to 37°C [46] . These features are also compatible with one of the potential roles of the Lp25 protein , which is causing muscular damage that consequently is associated with oliguric AKI and hyperkalemia . These data demonstrated , for the first time , that Lp25 is associated with rhabdomyolysis , which is an important sign in leptospirosis and may underlie the muscular pain , which is a pathognomonic symptom of this disease .
Rhabdomyolysis is a syndrome that results from the disruption of skeletal muscle integrity , leading to a massive release of the intracellular contents into the blood stream , including myoglobin , creatine phosphokinase , aspartate transaminase , lactate dehydrogenase , aldolase , and electrolytes . Complications of rhabdomyolysis include acute kidney injury ( AKI ) , hyperkalemia , hyperphosphatemia , and hypovolemia , which may result in death without early treatment . The most frequent causes of this syndrome are trauma , excessive muscle activity , drugs , toxins , electrolyte imbalance , muscle ischemia , metabolic disorders , and infectious diseases . Among leptospirosis cases , the AKI induced by rhabdomyolysis has been described almost exclusively in patients with severe form of leptospirosis . However , the role of rhabdomyolysis in the pathogenesis of AKI due to leptospiral infection is not understood .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "medicine", "and", "health", "sciences", "leptospira", "pathology", "and", "laboratory", "medicine", "pathogens", "immunology", "tropical", "diseases", "microbiology", "vertebrates", "animals", "mammals", "membrane", "proteins", "animal", "models", "bacterial", "diseases", "signs", "and", "symptoms", "outer", "membrane", "proteins", "experimental", "organism", "systems", "neglected", "tropical", "diseases", "rhabdomyolysis", "cellular", "structures", "and", "organelles", "bacteria", "bacterial", "pathogens", "research", "and", "analysis", "methods", "infectious", "diseases", "zoonoses", "inflammation", "medical", "microbiology", "microbial", "pathogens", "cell", "membranes", "leptospirosis", "immune", "response", "guinea", "pigs", "rodents", "diagnostic", "medicine", "cell", "biology", "biology", "and", "life", "sciences", "leptospira", "interrogans", "amniotes", "organisms" ]
2017
Lp25 membrane protein from pathogenic Leptospira spp. is associated with rhabdomyolysis and oliguric acute kidney injury in a guinea pig model of leptospirosis
Although the protective functions by T helper 17 ( Th17 ) cytokines against extracellular bacterial and fungal infection have been well documented , their importance against intracellular bacterial infection remains unclear . Here , we investigated the contribution of Th17 responses to host defense against intracellular bacteria Listeria monocytogenes and found that Th17 cell generation was suppressed in this model . Unexpectedly , mice lacking both p35 and EBI3 cleared L . monocytogenes as efficiently as wild-type mice , whereas p35-deficient mice failed to do so . Furthermore , both innate cells and pathogen-specific T cells from double-deficient mice produced significantly higher IL-17 and IL-22 compared to wild-type mice . The bacterial burden in the liver of double-deficient mice treated with anti-IL-17 was significantly increased compared to those receiving a control Ab . Transfer of Th17 cells specific for listeriolysin O as well as administration of IL-17 and IL-22 significantly suppressed bacterial growth in p35-deficient mice , indicating the critical contribution of Th17 responses to host defense against the intracellular pathogen in the absence of IL-12 and proper Th1 responses . Our findings unveil a novel immune evasion mechanism whereby the intracellular bacteria exploit IL-27EBI3 to suppress Th17-mediated protective immunity . The generation of pathogen-specific T cell responses is essential for the clearance of infectious agents . This involves the differentiation of naïve T cells into distinct pathogen-specific helper T cell lineages in a process that largely depends on the cytokine milieu created by innate immune cells upon their activation . Among these innate cytokines , the IL-12 family plays a pivotal role during the differentiation of helper T cells by promoting or inhibiting the lineage program of Th1 or Th17 cells . IL-12 and Th1 responses mediate protective immunity against intracellular pathogens such as Mycobacterium tuberculosis , Francisella tularemia , and Listeria monocytogenes [1] , [2] . Conversely , the production of IL-23 and the generation of Th17 responses are thought to mediate host defense against extracellular bacteria such as Staphylococcus aureus , Klebsiella pneumoniae , and Citrobacter rodentum [3] , [4] , [5] , [6] , as well as fungi such as Candida albicans and Pneumocystis carnii [7] , [8] . The function of Th17 cells following intracellular bacterial infection is less clear . The IL-12 gene family consists of p35 , p40 , p19 , p28 and Epstein-Barr virus-induced 3 ( EBI3 ) . Different combination of two gene-products from this family results in the production of four cytokines: IL-12 ( p35/p40 ) , IL-23 ( p19/p40 ) , IL-27 ( p28/EBI3 ) and IL-35 ( p35/EBI3 ) [9] , [10] . IL-12 , IL-23 and IL-27 are produced by antigen-presenting cells such as dendritic cells ( DC ) and macrophages , whereas IL-35 is primarily produced by regulatory T cells [9] , [10] . IL-12 is essential for promoting IFNγ production by innate cells such as NK and NKT cells following viral and bacterial infections . The IL-12 family also impacts adaptive T cell responses where IL-12 promotes Th1 generation and IL-23 promotes Th17 cells . IL-27 is thought to mediate the early phase of Th1 responses [11] . For instance , mice deficient in IL-27Rα exhibit reduced Th1 responses following infection with intracellular pathogens such as Listeria monocytogenes and Leishmania major [12] , [13] . In contrast , others have shown that the IL-27 receptor signal is not required for Th1 polarization but rather inhibits IFNγ production by CD4+ T cells in an animal model of Toxoplasma gondii infection [14] . IL-27 has also been shown to suppress Th17 differentiation and Th17-mediated tissue inflammation [15] , [16] , probably by inducing the expression of PD-L1 on T cells [17] . More recently , it has been demonstrated that IL-27 drives the differentiation of IL-10 producing CD4+ T cells [18] , [19] , [20] , suggesting anti-inflammatory function of this cytokine . Thus , IL-12 family of cytokines are involved in complex and often opposing roles in the development of helper T cell responses during infection and inflammation . Listeria monocytogenes ( Lm ) is a Gram-positive , intracellular bacterium that can cause meningitis and encephalitis in immune-compromised individuals as well as reproductive issue in pregnant women [21] . The host defense against Lm involves a complex network of innate and adaptive immune cells . Following infection , Lm promptly triggers a series of innate immune cell activation where IFNγ produced mainly by natural killer ( NK ) cells contributes to initial resistance then triggers the induction of TNF-α and iNOS-producing dendritic cells ( Tip-DC ) that can control bacterial growth in vivo . In addition , neutrophils and macrophages are recruited and mediate killing of the intracellular pathogen . Finally , pathogen specific CD4+ T cells and CD8+ T cells are generated and mediate efficient bacterial clearance and recall responses to the pathogen [21] . γδ T cells may also be involved in an innate capacity as mice deficient in γδ T cells are more susceptible to the Lm infection [22] . In this regard , a recent study showed that IL-23 mediated activation of IL-17-producing γδ T cells can contribute the resistance against Lm infection [23] , [24] . The importance of Th17 responses in the host defense against extracellular pathogens has been well described , however , whether Th17 cells and Th17 cytokines play a role against intracellular pathogen remains unclear . In addition , no study to date has fully addressed the relative contribution of IL-12 family cytokines following intracellular bacterial infection . To address these issues , we investigated anti-Listeria immunity in mice deficient in IL-12p35 , IL-27EBI3 , or both . Unexpectedly , our findings uncovered a dominant negative regulatory role of IL-27EBI3 in the protective immunity to Lm , especially in the absence of IL-12p35 . The function of EBI3 was , at least in part , mediated by inhibiting the production of Th17 cytokines . Systemic infection with Lm is known to induce pathogen-specific Th1 cells . To examine if pathogen-specific Th17 cells are also generated during infection , we intravenously infected C57BL/6 mice with Lm expressing ovalbumin ( Lm-Ova ) [25] , and examined the expression of IL-17 and IFNγ by splenic CD4+ T cells after restimulation with an Lm-specific , MHC II-restricted peptide ( listeriolysin O ( LLO ) 190–201 ) . As expected , intravenous infection with live Lm-Ova induced a high percentage of IFNγ-producing CD4+ T cells ( Figure 1A ) . By contrast , very few CD4+ T cells expressed IL-17 in the spleens of the infected mice . Among the IL-12 family cytokines , IL-23 mediates Th17 immunity while IL-12 and IL-27 induce Th1 and suppress Th17 responses . To determine if the Lm dominant Th1 responses were due to a preferential induction of IL-12 and IL-27 , we examined the induction of IL-12 family genes in dendritic cells and macrophages stimulated with lethally irradiated Lm . Importantly , irradiation induces the inactivation of Lm without affecting adjuvanticity and immunogenicity [26] . Stimulation of bone marrow-derived dendritic cells or macrophages with irradiated Lm induced the expression of Il12a ( encoding IL-12p35 ) , Il12b ( encoding IL-12/IL23p40 ) , Il23a ( encoding IL-23p19 ) , Ebi3 ( encoding IL-27EBI3 ) and Il27 ( encoding IL-27p28 ) as efficiently as LPS stimulation ( Figure 1B & C ) . Together , these data demonstrate that while all genes in the IL-12 family were induced upon Lm encounter , only Th1 immunity was induced after systemic infection with Lm-Ova in vivo . We next sought to address whether the lack of pathogen-specific Th17 immunity in wild-type mice after Lm-Ova infection was due to IL-12 and IL-27 . To analyze the relative contribution of IL-12p35 and IL-27EBI3 , we first crossed p35−/− mice with EBI3−/− to generate p35−/− EBI3−/− mice . Wild-type , p35−/− , EBI3−/− , or p35−/−EBI3−/− mice were then systemically infected with Lm-Ova via the intravenous route . Seven days later , we restimulated splenocytes from the infected mice with LLO190–201 to measure pathogen-specific CD4+ T cell responses . As expected , we observed high percentages of IFNγ-producing CD4+ T cells ( ∼20% ) , while few CD4+ T cells produced IL-17 in the wild-type mice ( <0 . 5% ) ( Figure 2A & B ) . Compared with wild-type mice , the production of IFNγ by LLO-specific CD4+ T cells was greatly diminished in p35−/− mice . Notably , although the IL-27 may be an inducer of Th1 responses [12] , [13] , we did not observe any defect in the percentage of IFNγ-producing CD4+ T cells in EBI3−/− mice ( Figure 2A & B ) . Instead , we observed that the frequency of IL-17-producing CD4+ T cells in the EBI3-deficient mice was significantly higher than those of wild-type mice , likely due to the increased population producing both IFNγ and IL-17 among CD4+ T cells ( Figure 2A & B ) . Notably , compared with p35−/− and EBI3−/− mice , p35−/−EBI3−/− mice exhibited a significantly increased frequency of IL-17+IFNγ− CD4+ T cells ( Figure 2A & B ) . Consequently , the production of IL-17 and IL-22 by Lm-specific CD4+ T cells was far higher in the p35−/−EBI3−/− mice compared to wild-type mice ( Figure 2C ) . p35−/− and EBI3−/− mice both showed a slight increase in the frequency of IL-17+ CD4+ T cells , however , the amounts of IL-17 produced after antigen restimulation were far less than that of p35−/−EBI3−/− mice . Thus , p35−/−EBI3−/− mice exhibited diminished Th1 and enhanced Th17 responses to Lm-Ova infection , indicating that IL-27EBI3 and IL-12p35 cooperatively suppress the generation of pathogen-specific Th17 cells after infection . To measure the pathogen-specific CD8+ T cell responses to Lm-Ova , we restimulated splenocytes from infected mice with SIINFEKL peptide . CD8+ T cells derived from p35−/− mice and EBI3−/− mice exhibited similar or higher percentages of IFNγ compared to wild-type T cells ( Figure 3A ) . Moreover , the percentages of Ova-specific MHC I tetramer-positive CD8+ T cells were significantly higher in p35−/− , EBI3−/− , and p35−/−EBI3−/− mice compared to wild-type mice ( Figure 3B ) . The frequencies of CD8+ T cells expressing granzyme B were comparable among wild-type , p35−/− , and p35−/−EBI3−/− mice while decreased in EBI3−/− mice ( Figure 3A & B ) . Hence , the generation of pathogen-specific CD8+ T cells is largely independent of p35 and EBI3 . These results are consistent , in part , with a previous study showing that IL-12 is not required for IFNγ production but rather inhibits the generation of memory CD8+ T cells [27] . By contrast , we observed that the amounts of IL-17 and IL-22 produced by CD8+ T cells were remarkably higher in p35−/−EBI3−/− mice than those in the wild-type ( Figure 3C ) . Hence , in the absence of IL-12p35 and IL-27EBI3 , systemic Lm-Ova infection triggers increased production of IL-17 and IL-22 by pathogen-specific CD8+ T cells . To further examine the regulation of host defensive immunity by the cytokines of IL-12 family , we analyzed the activation of innate immune cells during the early phase of Lm infection . IL-12 triggers IFNγ production in NK cells and NKT cells which is critical for the activation of innate cells and the prevention of Lm propagation [28] . Consistent with this notion , we observed a significant reduction of IFNγ-producing NKT cells and NK cells in p35−/− mice as well as in p35−/−EBI3−/− mice infected with Lm-Ova ( Figure 4A ) . The percentages of IFNγ-producing NKT cells and NK cells in EBI3−/− mice were comparable to those from wild-type mice , indicating that there is no significant role of EBI3 in the induction of IFNγ from NK and NKT cells after Lm-Ova infection . Ly6C+CD11bhi dendritic cells , also known as Tip-DC , suppress the dissemination of Lm [28] , [29] . We observed comparable percentages of the Ly6C+CD11bhi DC in p35−/− , EBI3−/− as well as p35−/− EBI3−/− mice with that of wild-type mice ( Figure 4B ) . Therefore , the induction of Tip-DC was likely normal in mice lacking p35 , EBI3 , or both in this experimental setting . NK , NKT , and γδ T cells represent additional sources of innate Th1 and Th17 cytokines that could be potentially released following Lm infection . To investigate the contributions of the cellular subsets , we measured the production of IFNγ , IL-17 , and IL-22 from splenocytes obtained three days after Lm-Ova infection . As depicted in Figure 4C , p35−/− mice as well as p35−/− EBI3−/− mice showed significantly diminished IFNγ while EBI3−/− mice showed comparable IFNγ production . In contrast , the amounts of IL-17 in the supernatant were higher in EBI3−/− and p35−/− EBI3−/− mice compared with those of wild-type mice . The IL-22 production was higher in p35−/− and p35−/− EBI3−/− mice . Collectively , these data suggest that the regulation of Th1 and Th17 cytokines by innate immune cells is also under the control of multiple IL-12 family cytokines . We next addressed the differential roles of the IL-12 family cytokines in host defense against Lm infection . Wild-type , p35−/− , EBI3−/− , or p35−/−EBI3−/− mice were intravenously infected with Lm-Ova and bacterial burden in the livers and spleens were measured three days later . As expected , p35−/− mice showed higher bacterial burden in the livers compared to wild-type controls ( Figure 5A ) . We observed significantly less bacterial burden in the livers of EBI3−/− mice compared with those from wild-type , indicating that EBI3 is not required for the host defense against the infection . To our surprise , the bacterial burden in the livers of p35−/−EBI3−/− mice was significantly lower than that of p35−/− mice , to levels comparable to EBI3−/− mice ( Figure 5A ) . Within the spleens , p35−/−EBI3−/− mice exhibited significantly lower bacterial burden compared to p35−/− mice; however , there was no evident difference in bacterial burden between wild-type and EBI3−/− or p35−/−EBI3−/− mice ( Figure S1A ) . We also measured bacterial burden 7 days after infection and found that p35−/− mice failed to control bacterial growth with significantly higher levels of bacteria in the livers compared to wild-type animals ( Figure 5B ) . However , EBI3−/− as well as p35−/− EBI3−/− mice showed comparable levels of bacteria in the livers compared to wild-type mice ( Figure 5B ) . We also observed similar pattern of bacterial burdens in the spleens of these mice ( Figure S1B ) . Therefore , the bacterial resistance observed at day 3 largely remained intact by day 7 post infection . Collectively , these findings demonstrate that EBI3-deficiency conferred resistance to Lm-Ova infection in the absence of IL12p35 , indicative of possible antagonistic function of IL-12p35 and IL-27EBI3 in host defense to the intracellular bacterial infection . Furthermore , in the absence of IL-12p35 , IL-27EBI3 likely exerts strong immunosuppressive activity and thus mediates immune evasion of the Lm in vivo . The enhanced production of IL-17 and IL-22 we observed in p35−/−EBI3−/− mice by both innate and adaptive immune compartments led us to hypothesize that the induction of the Th17 cytokines might be responsible for the observed resistance of p35−/−EBI3−/− mice against Lm-Ova infection . To test this hypothesis , we infected p35−/−EBI3−/− mice with Lm-Ova and then injected anti-IL-17 or control Ab . Notably , the bacterial burden in the livers of the mice receiving anti-IL-17 showed a modest but significant increase ( 8 times higher ) compared with that of the control Ab group; however the burden was still substantially lower than that observed in p35−/− mice ( Figure 6A ) . This result demonstrates that the upregulated production of IL-17 , at least in part , contributed to the observed resistance of p35−/−EBI3−/− mice to Lm-Ova infection . Based on our findings , we hypothesized that IL-17-producing cells suppress the growth of Lm , especially in the absence of IL-12p35 . To address this point , we investigated if Lm-specific Th17 cells are sufficient to limit the growth of Lm in the absence of IL-12-mediated innate and adaptive immunity . To obtain Lm-specific Th17 cells , we first isolated lymphoid cells from IL-17Frfp mice [30] after immunization with LLO190–201 emulsified in CFA and then restimulated them with peptide in the presence of IL-23 , IL-1β and anti-IFNγ to specifically expand the Th17 population [31] . After 5 days culture , we sorted RFP+ CD4+ cells ( Figure 6B; >80% IL-17+ and ∼15% IFNγ+ ) , and transferred them i . v . into p35−/− mice . Wild-type and p35−/− mice receiving no cells were used as controls . All mice were then infected with Lm-Ova , and the bacterial burden in the liver was measured 7 days post infection . As shown in Figure 6C , the p35−/− mice receiving the RFP+ CD4+ T cells showed significantly less bacterial load in the liver compared to p35−/− mice receiving no cells ( 26 . 8 times lower ) , although the bacterial burden in the former group was still higher than that of the wild-type mice . These results demonstrated that Lm-specific Th17 cells are protective against Lm-Ova in the absence of IL-12p35; however , it is possible that small population of IFNγ-producers among the RFP+ donor T cells ( ∼15% ) mediated this protection . To rule out this possibility and to further determine the protective immunity mediated by IL-17 and IL-22 , we next examined if administration of recombinant IL-17 or IL-22 mediates host defense against Lm-Ova in the absence of IL-12p35 . As depicted in Figure 7 , p35−/− mice treated with IL-17 or IL-22 alone showed a slightly lower , but not statistically significant , bacterial load in the liver than saline-treated mice . Notably , administration of both cytokines induced a significantly lower bacterial burden in the liver than saline- , IL-17- or IL-22-treated p35−/− mice ( 29 . 5 times less than saline-treated mice ) . Administration of IL-17 and IL-22 , however , did not fully restore the resistance of p35−/− mice , since the bacterial load was still higher than that of wild-type mice ( Figure 7 and Figure S2 ) . The inhibition of bacterial growth by exogenous IL-17 or IL-22 was more evident in the bacterial load in the spleens ( Figure S2 ) . Taken together , these results indicate that the Th17 cytokines IL-17 and IL-22 act synergistically to induce protective anti-Listeria immunity in the absence of IL-12p35 . In this study , we comparatively analyzed the contribution of IL-12p35 and IL-27EBI3 to the host defense against the intracellular pathogen Lm . We demonstrate that , although p35−/− mice failed to control bacterial growth , mice deficient in both p35 and EBI3 had no such defect in controlling bacterial growth . Our study also revealed that IL-17 is involved in the protective immunity in p35−/−EBI3−/− mice . Furthermore , administration of Th17 cells as well as recombinant IL-17 and IL-22 significantly suppressed bacterial growth in p35−/− mice . These findings strongly suggest that Lm utilizes IL-27EBI3 to escape Th17-mediated immune surveillance in IL-12p35-deficient mice . Thus , the present study unveils a previously unappreciated immune escape mechanism of intracellular bacteria through IL-27EBI3 , and that Th17 responses play an important role in intracellular bacterial infection , especially in the absence of IL-12 and Th1-mediated immunity . NK cells , NKT cells and Tip-DC are well known innate effector cells that suppress bacterial growth during the early phase of Lm infection [28] , [29] . IL-12 is required for the induction of IFNγ from NK and NKT cells which then mediates the recruitment of Tip-DC . Comparative analysis between p35−/− and p35−/− EBI3−/− mice showed no apparent difference in the activation of NK and NKT cells and the frequency of Tip-DC . In addition , the percentages of effector CD8+ T cells expressing granzyme B were similar between p35−/− and p35−/−EBI3−/− mice . Moreover , although IL-27 has been reported to drive the differentiation of IL-10 producing CD4+ T cells [18] , [19] , [20] , we observed comparable expression of the Il10 transcript between wild-type and EBI3−/− mice after Lm-Ova infection ( data not shown ) . Therefore , we conclude that the increased resistance to Lm in p35−/−EBI3−/− mice is not due to the enhanced activity of these innate immune cells nor CD8+ T cells . Accumulating evidence suggests that some of the Th1 cells recruited to inflamed tissues are actually derived from Th17 cells [32] , [33] . However , we observed that very few LLO-specific IFNγ-producing CD4+ T cells in wild-type mice after Lm infection co-expressed IL-17 . In addition , LLO-specific , IFNγ-producing CD4+ T cells in IL-17FCre×Rosa26eYFP mice after Lm infection were >99% YFP-negative ( data not shown ) , indicating that Th1 cells do not originate from Th17 cells in this model . Notably , we observed increased production of IL-17 and IL-22 by innate immune cells , presumably γδ T cells [24] , [34] , as well as Lm-Ova-specific CD4+ T and CD8+ T cells in p35−/−EBI3−/− mice . IL-22 is a Th17 cytokine that induces a series of anti-microbial peptides upon infection [4] , [5] , [35] , [36] . The mechanism of protection by these Th17 cytokines , however , significantly differs from that of IFNγ due to the distribution of receptors and differential downstream targets . IFNγ mediates protective immunity by multiple mechanisms including the induction of iNOS and autophagy [21] , [37] , [38] , whereas IL-17 does so possibly through neutrophil recruitment and by enhancing cross-presentation of bacterial antigens [24] , [34] . In the present study , the amounts of IL-17 and IL-22 produced by innate cells and the pathogen-specific T cells were significantly increased in p35−/−EBI3−/− mice . Furthermore , exogenous IL-17 and IL-22 synergistically induced protective immunity in p35-deficient mice , while each cytokine individually could only invoke marginal protection . Supporting this notion , it has been documented that IL-17 and IL-22 synergistically induce the expression of antimicrobial peptides [36] . Conversely , IL-22 has been shown to be dispensable for the clearance of Lm in p35-sufficient mice [39] . Our present work combined with other reports then suggests that , in the absence of IL-12-mediated protective immunity , Th17 cytokines IL-17 and IL-22 cooperatively inhibit the growth of Lm and are negatively regulated by EBI3 . Importantly , since the bacterial burden in p35−/− mice treated with exogenous IL-17 and IL-22 was still higher than that of wild-type mice , undefined alternative protective mechanism may still exist . One can assume that the difference between p35−/− mice and p35−/−EBI3−/− mice in anti-Lm immunity could be due to the effect of IL-35 , which is composed of p35 and EBI3 [40] . Given that p35−/− mice cannot produce IL-12 and IL-35 , and that p35−/−EBI3−/− mice cannot produce IL-12 , IL-35 and IL-27 , the only cytokine that is lacking in the latter mice compared with the former mice is IL-27 . Recent studies have shown that the other subunit of IL-27 , IL-27p28 , can be secreted in the absence of EBI3 to act as an antagonist of gp130 [15] , [41] , [42] or alternatively form a heterodimer with Cytokine-Like Factor 1 ( p28/CLF ) to promote NK and T cell activity [43] . Hence , EBI3-deficiency may lead to the production of p28 and p28/CLF , which may exert biological activities independently of IL-27 . The role of p28 subunit of IL-27 during host defense in the present study is not clear . Future studies with p28-deficient mice will be important for a complete understanding on the mechanism by which EBI3 regulates protective immunity to intracellular pathogens . IL-27 has been shown to trigger preliminary Th1 responses , where mice deficient in the IL-27 receptor ( WSX-1−/−; TCCR−/− ) are more susceptible to Leishmania major [12] and Lm infection [13] due to decreased Th1 responses . On the contrary , WSX-1−/− mice generate more IFNγ-producing CD4+ T cells than wild-type mice after infection with Toxoplasma gondii [14] , indicating that IL-27 signal is not necessary for the generation of Th1 immunity to the infection . Therefore the effect of IL-27 on pathogen-specific Th1 response is likely dependent on the infectious agents . It is not clear why IL-27EBI3−/− mice in the present study did not recapitulate the phenotype of IL-27R−/− mice in a previous study [13] . It is possible that the route of infection ( intravenous versus subcutaneous ) results in distinct immune responses to Lm . Alternatively , it is possible that the phenotype of EBI3−/− mice described in this study may in fact be IL-27 independent and instead mediated through IL-27p28 [42] , [44] , [45] . Interestingly , fundamental differences have also been reported between WSX-1−/− and EBI3−/− mice . For instance , WSX-1−/− mice exhibited enhanced liver inflammation , whereas EBI3−/− mice showed reduced liver inflammation in the same Con A-induced hepatitis animal model [46] , [47] . Moreover , while T cells from WSX-1−/− mice produce less IFNγ , T cells from EBI3−/− mice produce higher IFNγ and less IL-4 than wild-type T cells [12] , [13] , [48] . Further study is needed to demonstrate the mechanism of these differences in the regulation of infectious and inflammatory diseases between the EBI3 and IL-27 receptor signaling pathways . Collectively our findings demonstrate that the immune system produces IL-12 to suppress bacterial growth upon infection while Lm utilizes another host immune component , EBI3 , to escape immune surveillance . Increased susceptibility to intracellular pathogens in patients with deficiency in IL-12 or its receptor has been demonstrated [49] , [50] . Based on our findings , blockade of EBI3 may provide a new therapeutic approach for the treatment of infectious diseases , particularly in patients with defective IL-12 immunity . All the animal experiments were performed in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and with the permission of the American Association for the Assessment and Accreditation of Laboratory Animal Care . The protocol was reviewed and approved by the Institutional Animal Care and Use Committee of MD Anderson Cancer Center ( identification number: 10-04-09833 ) and University of Texas Health Science Center at Houston ( identification number: HSC-AWC-12-008 ) . C57BL/6 and IL-12p35−/− mice were purchased from the Jackson Laboratory . IL-27EBI3−/− mice were generated as described previously [48] . Double-deficient mice ( p35−/−EBI3−/− ) were obtained by crossing IL-12p35−/− and IL-27EBI3−/− mice . IL-17Frfp-reporter mice were generated as described previously [30] . All mice were kept under specific pathogens-free condition . The animal experiments were performed at the age of 6–12 weeks . Bone marrow cells from femurs and tibia of C57BL/6 mice were cultured with 10% FBS supplemented RPMI containing GM-CSF or M-CSF for 6 days . For irradiation , log-phase cultured Lm-Ova were exposed to 300 K rad of γ-irradiation . After extensive washing , BM-DC and BM-M cells were incubated with the irradiated Lm-Ova at the ratio of 1∶10 . As controls , LPS ( 100 ng/ml ) and Pam3CysSK4 ( Pam; 1 µg/ml ) were added in the culture . Four hours after the stimulation , cells were harvested and resuspended in Trizol for mRNA expression analysis . An erythromycin resistant strain of Lm-Ova was grown in brain heart infusion media supplemented with 5 µg/ml erythromycin [51] . The bacteria were harvested at mid-log growth phase and were intravenously injected into animals ( 2 . 5×104 CFU/mouse ) . In some experiments , mice were intraperitoneally administered recombinant murine IL-17 , IL-22 ( Peprotech ) , or both ( 1 µg/injection ) on day 0 , 2 , 4 after infection . Three or seven days after infection , spleens and livers of the infected mice were harvested . Bacterial burdens were determined by measuring colony forming unit , as described previously [52] . Splenocytes were stimulated with SIINFEKL peptide or LLO190–201 peptide overnight for intracellular cytokine staining , or 3 days for ELISA analysis [52] . In some experiments , splenocytes were resuspended in Trizol for mRNA expression analysis . The following antibodies were used for cell surface and intracellular staining; PerCPCy5-5- or FICT-labeled anti-TCRβ ( H57-597 ) , PerCPCy5-5-labeled anti-CD4 ( GK1 . 5 ) , Alexa 488-labeled anti-CD8 ( 5H10-1 ) , APC-labeled anti-CD11b ( M1/70 ) from Biolegnd; PE- or Alexa 488-labeled anti-IFNγ ( XMG1 . 2 ) , PE-labeled anti-IL-17 ( clone TC11-18H10 ) , Alexa 647-labeled anti-GranzymeB ( GB11 ) FITC- or PerCPCy5 . 5-labeled anti-NK1 . 1 ( PK136 ) , PerCPCy5-5-labeled anti-Ly6C ( AL21 ) from BD Biosciences . For intracellular staining , cells were incubated with permeabilization buffer ( BD Biosciences ) , and then further stained with intracellular staining Abs described above . These cells were analyzed by using LSRII flow cytometer ( BD Bioscience ) and Flowjo software . Total RNA was prepared from splenocytes with TriZol reagent ( Invitrogen ) . Complementary DNA ( cDNA ) was synthesized with Superscript reverse transcriptase and oligo ( dT ) primers ( Invitrogen ) , and gene expression was examined with a Bio-Rad iCycler Optical System with iQ SYBR green real-time PCR kit ( Bio-Rad Laboratories ) . The data were normalized to Actb reference . The following primer pairs were used: ActB: F-GAC GGC CAG GTC ATC ACT ATT G and R-AGG AAG GCT GGA AAA GAG CC; Ifng: F-GAT GCA TTC ATG AGT ATT GCC AAG T and R-GTG GAC CAC TCG GAT GAG CTC; Il17: F-CTG GAG GAT AAC ACT GTG AGA GT and R-TGC TGA ATG GCG ACG GAG TTC; Il17f: F-CTG GAG GAT AAC ACT GTG AGA GT-3′ and R-TGC TGA ATG GCG ACG GAG TTC; Il22: F-CAT GCA GGA GGT GGT ACC TT and R-CAG ACG CAA GCA TTT CTC AG; Il10: F-ATA ACT GCA CCC ACT TCC CAG TC and R-CCC AAG TAA CCC TTA AAG TCC TGC; Ebi3: F-TCC CCG AGG TGC AAC TGT TCT CC and R-GGT CCT GAG CTG ACA CCT GG . Primers for p35 , p40 , p19 were described previously [53] . To obtain IL-17-producing CD4+ T cells specific for Lm-Ova , we s . c . immunized IL-17Frfp-reporter mice with LLO peptide in CFA . A week later , lymphoid cells from the draining lymph nodes and spleen were pooled and restimulated with the same peptide in the presence of IL-23 ( 50 ng/ml ) and IL-1β ( 10 ng/ml ) plus anti-IFNγ ( 5 µg/ml; XMG1 . 2 ) for five days . The cells were stained with APC-labeled anti-CD4 , and APC-positive and RFP-positive cells were sorted by using FACS-Influx ( BD Biosciences ) . 2 . 5×105 sorted cells/mouse were intravenously injected into IL-12p35−/− mice followed by Lm-Ova inoculation and analysis of bacterial burden , as described above . The Student t test was used to assess the statistical values . P values were determined , and error bars represent standard error of the mean ( SEM ) or standard deviation ( SD ) .
There is a considerable gap in our understanding of how pathogenic intracellular bacteria escape innate and adaptive host immunity . Production of IL-12 , and subsequently IFNγ , upon infection triggers host immunity that prevents early dissemination of pathogenic intracellular pathogens . This is evident in observing the increased susceptibility of patients with deficiencies in IL-12 , IFNγ , or their receptors to pathogenic intracellular bacteria such as Mycobacterium tuberculosis and Listeria monocytogenes ( Lm ) . Paradoxically , the regulation of host defense by other members of the IL-12 family is poorly understood . Through the use of an animal model of Lm infection , we show that mice lacking IL-27EBI3 were resistant to Lm infection , even in the absence of IL-12 . Neutralization and adoptive transfer studies showed that this protection was mediated through IL-17 , IL-22 and Th17 responses . Thus our results identify IL-27EBI3 as a critical mechanism for immune escape by Lm in the absence of IL-12-mediated protective immunity . Furthermore , our work suggests that targeting IL-27EBI3 may represent a novel strategy for the treatment of bacterial infection in individuals lacking proper IL-12 responses .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[]
2013
Epstein Barr Virus-Induced 3 (EBI3) Together with IL-12 Negatively Regulates T Helper 17-Mediated Immunity to Listeria monocytogenes Infection
MPV17 is a mitochondrial inner membrane protein whose dysfunction causes mitochondrial DNA abnormalities and disease by an unknown mechanism . Perturbations of deoxynucleoside triphosphate ( dNTP ) pools are a recognized cause of mitochondrial genomic instability; therefore , we determined DNA copy number and dNTP levels in mitochondria of two models of MPV17 deficiency . In Mpv17 ablated mice , liver mitochondria showed substantial decreases in the levels of dGTP and dTTP and severe mitochondrial DNA depletion , whereas the dNTP pool was not significantly altered in kidney and brain mitochondria that had near normal levels of DNA . The shortage of mitochondrial dNTPs in Mpv17-/- liver slows the DNA replication in the organelle , as evidenced by the elevated level of replication intermediates . Quiescent fibroblasts of MPV17-mutant patients recapitulate key features of the primary affected tissue of the Mpv17-/- mice , displaying virtual absence of the protein , decreased dNTP levels and mitochondrial DNA depletion . Notably , the mitochondrial DNA loss in the patients’ quiescent fibroblasts was prevented and rescued by deoxynucleoside supplementation . Thus , our study establishes dNTP insufficiency in the mitochondria as the cause of mitochondrial DNA depletion in MPV17 deficiency , and identifies deoxynucleoside supplementation as a potential therapeutic strategy for MPV17-related disease . Moreover , changes in the expression of factors involved in mitochondrial deoxynucleotide homeostasis indicate a remodeling of nucleotide metabolism in MPV17 disease models , which suggests mitochondria lacking functional MPV17 have a restricted purine mitochondrial salvage pathway . Mitochondria contain their own DNA ( mtDNA ) , which encodes thirteen subunits of the oxidative phosphorylation ( OXPHOS ) complexes essential for cellular respiration and ATP production . The faithful synthesis and maintenance of mtDNA depends on nuclear-encoded genes which , when mutated , can cause quantitative ( depletion ) and qualitative ( multiple deletions and point mutations ) mtDNA abnormalities , resulting in human diseases [1] . MtDNA depletion syndromes ( MDS ) manifest as severe , tissue-specific diseases of early infancy [2–5] while multiple deletions typically accumulate much later in life , leading to adult-onset phenotypes in skeletal muscle and possibly in brain [6–11] . Although these disorders are genetically heterogeneous , MDS and multiple deletions can result from mutations in the same gene [2–9 , 11–13] . One such gene is MPV17 , in which loss-of-function causes a fatal infantile hepatocerebral syndrome with mtDNA depletion [14] , or an adult-onset multisystemic disorder with multiple deletions of mtDNA [15 , 16] . However , neither the function of MPV17 protein nor the mechanism leading to mtDNA perturbation is yet known . DNA replication in mitochondria , as in the nucleus , depends on a balanced supply of deoxynucleoside triphosphates ( dNTPs ) , the building blocks of DNA . The provision of dNTPs for the mitochondrial replisome is maintained either by in organello recycling of the deoxynucleosides or by the import from the cytosol of dNTPs either synthesized de novo by ribonucleotide reduction or from the cytosolic deoxynucleoside salvage pathway . In non-cycling cells , DNA replication in the nucleus is suspended , and the production of dNTPs in the cytosol is downregulated; nevertheless , mtDNA replication persists , and a distinct form of cytosolic ribonucleotide reductase ( RNR ) , containing the p53R2 subunit in place of R2 , remains active [17–19] . Thus , in non-cycling cells p53R2 plays a critical role in sustaining DNA replication in the mitochondria [17 , 20] , aided by the organelle’s salvage pathway , especially the rate-limiting enzymes , thymidine kinase 2 ( TK2 ) and deoxyguanosine kinase ( DGUOK ) . The importance of dNTP availability for mtDNA integrity is underscored by the number of human diseases caused by mutations in genes encoding factors involved in nucleotide metabolism [2–4 , 7 , 21 , 22] . The first such factor , thymidine phosphorylase ( TP ) , was causally linked to mtDNA abnormalities and a specific mitochondrial disease , MNGIE ( mitochondrial neurogastrointestinal encephalomyopathy ) in 1999 [7 , 22] . Deficiency in this catabolic enzyme greatly increases the level of dTTP leading to mtDNA nucleotide substitutions [23] , depression of mitochondrial dCTP levels , and mtDNA deletions and depletion [24 , 25] . Later , other mitochondrial diseases were attributed to genetic inactivation of enzymes involved in the synthesis of dNTPs , namely , TK2 [3] , DGUOK [2 , 3] , and p53R2 [4] and recently the GABA transaminase [21] , indicating that either a surplus or a deficiency of nucleotide precursors can be detrimental to mtDNA integrity , and thereby cause disease . Although in vivo and in vitro models of these disorders are providing invaluable information on the pathways contributing to the cellular dNTP pool , we lack a comprehensive understanding of the factors that serve to maintain the mitochondrial dNTP pool . For example , while mtDNA apparently relies on cytosolic enzymes for dNTP biosynthesis , it has been proposed that these activities and the known salvage pathways in mitochondria are insufficient to support mtDNA replication [26] . Hence , the organelles are inferred to possess the capacity to generate dNTPs de novo , and a ribonucleotide reductase activity has been described in mitochondria [27 , 28] as well as a mitochondrial isoform of dihydrofolate reductase [29] . Transport of dNTPs into the mitochondrion is another area where our knowledge is currently limited . What is clear is that nucleotide pool perturbation is a generic cause of mitochondrial genomic instability , and so we sought to determine the consequences of MPV17 deficiency on dNTP metabolism in murine and human models of the disease . Both models display decreases in the mitochondrial dNTP pool , accompanied by depletion of mtDNA , and highly abundant replication intermediates in liver mitochondria lacking Mpv17 . The adverse effects of MPV17 deficiency on mtDNA can be prevented and rescued in cultured cells by deoxynucleoside supplementation , establishing that MPV17 deficiency causes deoxynucleotide insufficiency in mitochondria , which slows replication in the organelle leading to DNA loss . A comparison of the expression of proteins involved in nucleotide metabolism suggests that mitochondrial dNTPs are scarce in MPV17 deficiency owing to repression of the mitochondrial salvage pathway ( MSP ) . The Mpv17 ablated CFW mouse was used for the study and the genotype was confirmed by PCR analysis ( see Methods ) . Comparison of Mpv17 -/- mice and wild-type littermates demonstrated the absence of the protein in the liver , brain , and kidney ( Fig 1A ) ; however , significant reduction in the level of mtDNA was restricted to the liver ( Fig 1B ) , in mice aged 8–10 weeks . Specifically , mitochondrial DNA copy number in Mpv17 -/- liver was less than 10% of wild-type mice whereas it was 75% in the kidney and 90% in the brain . The severe depletion of mtDNA in liver was accompanied by decreased steady-state levels of multiple subunits of the OXPHOS complexes ( Fig 1C ) , which were reproduced at the level of the holoenzymes and their supercomplexes ( Fig 1D ) . Furthermore , Mpv17 ablation was associated with the appearance of sub-complexes of ATP synthase ( Fig 1D ) , another characteristic feature of mtDNA maintenance defects [30] . In contrast , in the kidney and brain of Mpv17-/- mice , tissues that had no significant decrease in mtDNA copy number , all OXPHOS components analyzed were maintained at levels comparable to wild-type mice ( Fig 1C ) . Another strain of mouse lacking Mpv17 had similar levels of mtDNA in liver , kidney and brain to those reported here [31] . However , the OXPHOS abnormalities of liver mitochondria appear more marked in this study , suggesting that the loss of Mpv17 induces a more severe phenotype on a CFW/MF1 , than a C57BL/6 genetic background , as previously reported [31 , 32] . As the limited availability of precursors for DNA synthesis is a cause of mitochondrial genomic instability , we determined the levels of mitochondrial dNTPs in the Mpv17 deficient mouse . Liver mitochondria of Mpv17-/- mice displayed reduced levels of dGTP ( 30% relative to the wild-type ) and dTTP ( 35% of wild-type ) ( Fig 2A ) , whereas there was no decrease in mitochondrial dNTP levels in kidney or brain ( Fig 2B and 2C ) . Hence a strict correlation exists between mtDNA copy number and dNTP levels in the organelles ( Figs 1B and 2 ) , strongly suggesting that mitochondrial nucleotide insufficiency is responsible for the depletion of mtDNA in the liver of the Mpv17-/- mice . Although nucleotide perturbation was first linked to mitochondrial genomic instability 15 years ago [7 , 22] , the process of mtDNA replication has been little studied in this context [33] . To assess the effect of the nucleotide insufficiency on DNA replication , we analyzed the intermediates of mitochondrial DNA replication of Mpv17-/- and control liver , using neutral two-dimensional agarose gel electrophoresis ( 2D-AGE ) [34 , 35] . The striking feature of material isolated from the Mpv17-/- liver is the high abundance of the replication intermediates ( Fig 3 , and further interpreted in S1 Fig ) . This indicates that many more mtDNA molecules are in the process of being replicated in liver mitochondria lacking Mpv17 . The fact that the increase in mitochondrial replication intermediates ( Fig 3 ) is associated with mtDNA loss and nucleotide insufficiency ( Figs 1B and 2A ) strongly suggests that the rate of mtDNA replication is much slower than normal in the liver of the Mpv17 ablated mouse . In proliferating cells mtDNA depends primarily on the dNTP pools derived from cytosolic de novo synthesis , and to a minor extent on both the cytosolic and mitochondrial recycling salvage pathways . In non-dividing cells , the cytosolic processes are repressed [36] , as evidenced by the marked decreases in expression of the R2 subunit of RNR and thymidine kinase 1 ( TK1 ) ( S2A Fig ) , restricting the dNTPs available for the import into the mitochondria . Therefore , the provision of dNTPs for mtDNA replication relies on the mitochondrial salvage pathway enzymes [37] , and on the alternative form of RNR containing the p53R2 subunit [20] , which are upregulated in response to quiescence ( S2B Fig ) . Accordingly , proliferating cells from patients with MDS often have normal mtDNA levels , and the gene defects adversely impact mtDNA only when the cells stop dividing [38–40] . Thus , we analyzed the expression of MPV17 protein in proliferating and non-dividing cells and found that it is upregulated in quiescent cells ( Figs 4A and S2C ) , and so closely follows the behavior of other factors linked to MDS that influence mitochondrial dNTP pools ( S2B Fig ) . In five fibroblast cell lines derived from patients with autosomal recessive MPV17 mutations , all of which displayed decreased expression of the protein ( S2D Fig ) , we found that dividing cells had similar levels of mtDNA to control fibroblasts ( Fig 4B ) , whereas 10–14 days of quiescence led to decreases in mtDNA copy number of 34–80% ( Fig 4C ) . On average the mtDNA copy number in the quiescent MPV17-deficient fibroblasts was 62% lower than the controls ( p < 0 . 001 ) ( Fig 4D ) . Notably , the smallest decrease in mtDNA copy number among the pediatric cases ( patient 5 , 34% in fibroblasts , 60% in liver [41] ) occurred in the longest surviving child ( 8 . 5 years of age at last follow-up ) . As in the Mpv17-/- mouse , the loss of mtDNA in human fibroblasts was associated with low dNTP levels , although it appeared to be generalized in the cultured cells , with marked decreases in all three dNTPs that could be quantified ( dTTP , dGTP and dCTP ) ( averaging 80% , relative to controls ) ( Fig 4E ) . The low dNTP levels accompanying the depletion of mtDNA ( Fig 4C–4E ) implied the underlying problem in MPV17 deficiency is a shortage of precursors for DNA synthesis . To further test this hypothesis , we attempted to rescue the mitochondrial dNTP deficiency in MPV17-mutant fibroblasts by exploiting the dNTP salvage pathway [20 , 38 , 40] . Supplementation of the culture medium with three deoxynucleosides , GdR , AdR and CdR prevented mtDNA depletion in three quiescent MPV17-deficient fibroblast lines ( Fig 4F and S3A Fig ) . Individually , none of the four deoxynucleosides was able to prevent a decrease in mtDNA copy number ( Fig 4F ) , and the same was true of several pairs of deoxynucleosides ( A + C , T + G , and G + A ) ; an exception was GdR plus CdR , which was sufficient to prevent significant mtDNA depletion in all three MPV17-deficient fibroblast lines tested ( Fig 4F ) . The experiments in quiescent cells identified nucleoside supplementation as a potential prophylactic treatment for mtDNA depletion in MPV17 deficiency , but did not indicate whether it was beneficial after mtDNA loss has occurred . To investigate whether nucleoside supplementation could rescue , as well as prevent mtDNA depletion in MPV17 deficiency , we performed mtDNA depletion-recovery ( repletion ) experiments in quiescent cells ( see Methods and [20 , 42] . Repletion was compromised in quiescent MPV17-deficient cell lines: after fourteen days of recovery , the mtDNA level of control cells was close ( 87% ) to the original , whereas in four MPV17-deficient cell lines it was 25% ( Fig 5A ) . This figure increased to 102% in MPV17-deficient cells when the culture medium was supplemented with deoxynucleosides GdR , AdR and CdR , or GdR plus CdR ( Fig 5B and 5C ) . Together these data provide strong evidence that limited precursor availability for DNA synthesis is the underlying cause of the mtDNA depletion in MPV17 deficiency . MPV17 deficiency has been associated with two types of tissue-specific mtDNA abnormalities–a quantitative loss of mtDNA copy number ( mtDNA depletion ) and multiple deletions- in humans and in mice . Perturbation of the dNTP pools could also affect the fidelity of mtDNA replication and therefore the quality of the RNA and protein products of the mtDNA . To determine the effect of the reduced dNTP pools on mtDNA fidelity , we performed deep sequencing of purified mtDNA from the livers of two pairs of WT and Mpv17-/- mice . The sequencing coverage was comprehensive for all the samples , with a small trough in the vicinity of the large non-coding region ( S4A Fig ) . The error rates for the wild-type and knockout mice were similar; for one pair , the knockout mouse had a slightly lower error rate than the wild-type littermate ( 0 . 033% v 0 . 043% ) , and in the other pair a 1 . 7 fold higher error rate was observed in the knockout mouse ( Table 1 , run 1 ) . The read depth was lowest in the second knockout animal; however , a replica experiment produced greater depth and confirmed the error rate as higher than the paired control ( Table 1 , run 2 ) . The error rates for the four individual bases differed to similar extents in all four mtDNA samples ( P > 0 . 05 using one-way ANOVA ) , with dGTP consistently the lowest ( 0 . 007% ) and dATP the highest ( 0 . 014% ) . Therefore , the dNTP insufficiency in the Mpv17-/- mouse appears to have little or no effect on the fidelity of mitochondrial DNA replication . Imbalances of mitochondrial dNTP pools affect replication fidelity [23] and it has been shown that a greater asymmetry of dNTP levels leads to a higher rate of mutation by the mitochondrial DNA polymerase γ in vitro [43 , 44] . However , rather than being asymmetric , the mitochondrial dNTP pools were close to equimolar in both Mpv17-/- mouse liver and MPV17-deficient human cells , albeit at reduced abundance relative to controls ( Figs 2A and 4E and S5 ) . This fits with the hypothesis that the dNTP levels may be reconfigured to the lowest common denominator , and thus are equalized , in order to make the best use of the low dNTP pools and minimize replication infidelity . From this perspective , the two MPV17 disease models are very similar , displaying little or no protein , a dNTP insufficiency adjusted to equimolarity and mtDNA depletion . The dNTP insufficiency resulting from the loss of function of MPV17 could result from an impairment of de novo synthesis , the mitochondrial salvage pathway or dNTP ( precursor ) transport into the organelles . Therefore , we examined the impact of MPV17 loss-of-function on several factors involved in these processes , with the emphasis on the last two pathways in light of MPV17’s inner mitochondrial membrane localization . The equilibrative nucleoside transporter ENT1 is located in the mitochondrial inner membrane [45 , 46] as well as the plasma membrane , where it supplies the mitochondria with purine and pyrimidine deoxynucleosides for the MSP . Similar to MPV17 and a number of MSP enzymes , its expression is upregulated in control quiescent cells ( Fig 6A ) and [47]; however , ENT1 protein levels were not affected by MPV17 deficiency in patient-derived fibroblasts ( Fig 6A ) , or tissues of Mpv17-/- mice ( Figs 6B and S6A ) . In contrast to ENT1 , the mitochondrial deoxynucleotide ( di- and tri- phosphate ) transporter PNC2 [48] is repressed in non-dividing cells compared with proliferating cells ( Fig 6C ) . This supports the previously proposed role of PNC2 in mtDNA replication [48]; in cycling cells the cytosolic dNTP pool is high and the mitochondria access most of the precursors of mtDNA synthesis from this pool , with PNC2 acting as a key nucleotide transporter . In quiescent cells , the cytosolic pool shrinks considerably and PNC2’s role diminishes accordingly , which is reflected in its expression ( Fig 6C ) . In the livers of mice lacking Mpv17 , Pnc2 expression was elevated ( Fig 6D ) , and it was high in two of three MPV17-deficient cell lines ( Fig 6C ) . Pnc1 levels were also elevated in the liver of Mpv17 knockout mice ( Fig 6D ) , although no change in expression was evident in MPV17 deficient cells ( Fig 6C ) . The tissue-specific increases in the expression of two dNTP transporters in Mpv17 deficiency ( Figs 6C and S6A ) suggest an attempt by the mitochondria to access more dNTPs from the cytosol in the disease state . This could have two explanations , either MPV17 is a functional substitute for PNC2 ( and to a lesser extent PNC1 ) in non-proliferating cell , promoting cytosolic nucleotide uptake , or it supports the MSP . In the former case the MSP would be expected to be unaffected or elevated , whereas in the latter case deficiencies of the MSP should be evident . The expression of TK2 , a key kinase of the pyrimidine branch of the MSP was not affected by the absence of MPV17 either in mutant fibroblasts or mouse tissues ( Figs 7A and 7B and S6B ) . In contrast , analysis of kinases involved in the mitochondrial purine salvage pathway revealed liver-specific decreases of approximately 50% in the amounts of adenylate kinase 2 and 3 ( Ak2 and Ak3 ) , in the Mpv17 knockout mouse , and a marked tissue-specific decrease in the expression of an isoform of Dguok ( Figs 7C and S6B ) . These changes in expression suggest that the purine branch of the MSP is repressed in the liver of Mpv17 ablated mice . Furthermore , MPV17 deficiency alters the mitochondrial purine salvage pathway in human fibroblasts that have exited the cell cycle . AK3 protein level was lower than controls in two of three quiescent MPV17 deficient cell lines , and DGUOK was low in all three patient-derived fibroblasts ( Fig 7D ) . The fact that the mutant cells with the highest AK3 expression had the lowest level of DGUOK ( Fig 7D , lane 8 ) suggests the mitochondrial purine salvage pathway is down-regulated in response to MPV17 deficiency by one means or another . Succinate- CoA Ligase ( SUCL ) is integral to the citric acid cycle , but is also linked to the MSP via the mitochondrial diphosphate kinase ( NDPK ) [21 , 49] and mutations in SUCL are an established cause of MDS [50 , 51] . Therefore , we also screened Sucla2 , Suclg1 and Suclg2 in the three mouse tissues but found no appreciable change in the protein levels between control and Mpv17 knockout mice ( S6C Fig ) . Although MPV17 deficiency was known to cause mtDNA abnormalities in human , mice and yeast [14 , 31 , 52] the basis of the association was unclear . This report shows that loss of function of MPV17 causes nucleotide insufficiency in the mitochondria , thereby establishing the underlying cause of mitochondrial DNA abnormalities in this disease . Hitherto the two main categories of disease gene linked to depletion and multiple deletions of mtDNA encode either components of the replication machinery or enzymes involved in nucleotide metabolism [36] . The clear correlation between MPV17 loss-of-function and nucleotide insufficiency , reported here , places MPV17-related disease firmly in the category of mtDNA disorders caused by deoxynucleotide perturbation , together with mutant forms of TYMP , TK2 , DGUOK , RRM2B and ABAT [2–4 , 7 , 21] . Moreover , this report suggests that the pathophysiological consequence of Mpv17 deficiency is a dearth of deoxynucleotides that slows the rate of mtDNA replication , and it is the latter that ultimately causes mtDNA depletion . Thus , it is expected that other cases of nucleotide disturbance will also result in slow mtDNA replication , as previously inferred from the reduced rates of DNA synthesis in an in organello model [24] The marked mitochondrial deoxynucleotide insufficiency resulting from loss-of-function of MPV17 can explain the mtDNA depletion seen in mouse tissues , patient-derived cells , and by inference the patients’ themselves . More specifically , several findings point to perturbed guanosine metabolism as a key feature of MPV17 deficiency . Previously , it was shown that zebrafish deficient in Mpv17 lack stripes owing to an inability to produce guanine crystals [53] , the loss of function of deoxyguanosine kinase and MPV17 produce a similar hepatocerebral phenotype in humans [2 , 14] and dGTP was the dNTP most depleted in Mpv17-/- mouse liver ( Fig 2A ) . The additional findings that MPV17 expression increases in non-dividing cells ( Figs 4A and S2C ) and enzymes of mitochondrial purine salvage pathway are repressed in MPV17 deficiency ( Fig 7C and 7D ) support the hypothesis that the purine MSP is dependent on MPV17 . Given MPV17’s location in the inner mitochondrial membrane [14] , the recent reports of its channel activity in mammals [54] and in yeasts [55] , and the mitochondrial dNTP insufficiency associated with its loss of function ( this report ) , MPV17 might be involved directly or indirectly in the uptake of nucleotides by the mitochondria . Although it would be premature to fully discount this possibility , the pattern in expression of mitochondrial proteins involved in nucleotide metabolism does not support this idea . Thus , while MPV17 might in theory facilitate the action of ENT1 in mitochondrial nucleoside uptake , this role would not account for the repression of mitochondrial salvage pathway enzymes observed in the absence of MPV17 . Moreover , ENT1’s expression , which appears to be demand driven in normal [45] and disease states [47] , is unaffected by the loss of MPV17 . If instead MPV17 were a mitochondrial dNDP/dNTP transporter , we would expect its expression to match that of other proteins involved in the maintenance of mitochondrial dNTP pools in cycling cells , similar to PNC2 , whereas MPV17 expression correlates with MSP enzymes and allied proteins . While it could be argued that MPV17 is the replacement for PNC1 and 2 in non-dividing cells , analogous to the p53R2 subunit of RNR , and that PNC1 and PNC2 expression increase in the absence of MPV17 for this reason , this is again difficult to reconcile with a decrease in MSP enzymes . Hence , it is unlikely MPV17 is a deoxynucleotide carrier . The human PNC1 ortholog in yeasts ( rim2 ) and flies ( drim2 ) makes a critical contribution to the transport of dNTPs into the mitochondria , as evidenced by the loss of mtDNA in both organisms lacking the gene [56 , 57] . The yeast mtDNA abnormality can be rescued with either PNC1 or PNC2; and human PNC1 and PNC2 , and yeast rim2 , function as nucleotide transporters in reconstituted liposomes [48 , 58] . The combination of increases in Pnc2 and Pnc1 expression and decreases in MSP enzymes in Mpv17 deficiency is consistent with the affected mitochondria attempting to increase the import of dNTPs from the cytosol , in response to the dNTP insufficiencies resulting from the restricted MSP . However any increase in flux of deoxynucleotide triphosphates into the mitochondrial matrix is expected to be modest as cytosolic de novo synthesis is low in non-dividing cells , and the mtDNA depletion observed indicates the mitochondrial dNTP pools are inadequate for mtDNA replication in liver and non-dividing cultured cells . Finally , there was no evidence of repression of the MSP or increases in PNCs in unaffected tissues , supporting the view that these are critical elements of the MPV17-related MDS . The changes in the expression of the deoxynucleotide transporters and MSP enzymes also suggest the route by which NdR supplementation rescues and prevents mtDNA depletion in MPV17 deficiency . It is most likely that the exogenous deoxynucleosides are converted to dNTPs in the cytosol and imported into the mitochondria via PNC1 and PNC2 , rather than being transported directly into the mitochondria to serve as substrates of the restricted MSP . PNC2 in particular could play a critical role in preventing mtDNA depletion: it is the deoxynucleotide transporter upregulated in two of three MPV17 deficient cell lines , it has a higher substrate specificity for dGTP and dCTP than PNC1 [48] , and dG and dC proved sufficient to prevent and rescue the mtDNA depletion ( Figs 4F and 5C ) . The mitochondrial genome has a higher mutation rate than the nuclear genome [59] , with explanations ranging from oxidative stress [60] and a limited repair system [61] to dNTP pool asymmetries [43] . The observation that in two models of MPV17 deficiency the pool asymmetry narrows suggests cells and tissues operate to find a balance between replication accuracy and velocity , and that when precursors are scarce , replication fidelity is prioritized . This fits with the model proposed by Mathews and colleagues that equimolar concentrations of the dNTP pools minimize the error rate [43] . Accordingly , while the low dNTPs levels in liver of the Mpv17 KO mouse can sustain mtDNA at only 10% of the normal level , we propose that dNTP equimolarity can maintain mtDNA at this level indefinitely , thereby explaining the early rapid depletion that stabilizes for the reminder of the mouse’s lifespan [31] . The questions as to why mitochondrial dNTP pools are maintained in asymmetry in normal cells , and of how these pools are equalized in the case of MPV17 deficiency , remain to be answered . Despite the links between MPV17 and guanosine metabolism , GdR was not sufficient to prevent mtDNA depletion in the absence of MPV17 , a pyrimidine was also required ( Fig 4F ) . The ‘pool symmetry hypothesis’ offers a possible explanation of this apparent discrepancy: adjustments made in response to the chronic dNTP pool insufficiency cannot rapidly be reversed by an exogenous supply of deoxyguanosine alone . That said , TdR with GdR was no better than GdR alone in preventing mtDNA depletion in the MPV17 mutant cells , presumably owing to the known adverse effects on mtDNA copy number of an excess of thymidine [7 , 22] . Thymidine supplementation induces mtDNA depletion in cultured cells [62] , unless accompanied by deoxycytidine supplementation [24] , and here , 50 μM thymidine for 14 days depressed mtDNA copy number by 40% in the control fibroblasts ( S3B Fig ) . Moreover , in isolated organelles , an excess of dTTP impairs mitochondrial DNA synthesis owing to decreased dCTP , irrespective of dGTP abundance [24] . In contrast to TdR , CdR , a precursor of both pyrimidines via the conversion of dCMP to dTMP [20] in addition to GdR prevented and rescued the mtDNA depletion . Thus , the combination CdR plus GdR ( with AdR ) appears to be the best therapeutic strategy for MPV17 related MDS . The similarities between the two models suggest the benefits of deoxynucleoside supplementation seen in cell culture ( Figs 4F and 5B and 5C ) might well translate to living organisms , including human patients , as proposed for other MDS caused by dNTP perturbations [63] [40] . Hence , the Mpv17 ablated mouse represents an important model to evaluate the uptake , therapeutic potential , and possible side-effects of deoxynucleoside administration alone , or in combination with an inhibitor of their catabolism , before contemplating the application of this approach to patients . Male and female Mpv17-/- CFW embryos were purchased from The Jackson Laboratory ( stock number 002208 ) and bred with MF1 purchased from Charles River , UK . Littermate controls where used for all studies . Animals were genotyped via the polymerase chain reaction using suitable primers ( S1 Table ) . All animal protocols used in this study were approved by the UK Home Office and the University of Cambridge and conducted in collaboration with the Wellcome Trust-MRC Institute of Metabolic Science Disease Model Core Primary skin fibroblast cultures were obtained from 4 healthy controls and from patients with mutations in MPV17 ( n = 5 ) ( S2 Table ) , All cells were negative for mycoplasma based on regular screening using LookOut Mycoplasma PCR Detection Kit ( Sigma ) . Primary fibroblasts were cultured in Dulbecco’s Modified Eagle’s Medium ( DMEM , Life Technologies ) supplemented with 10% fetal bovine serum ( FBS , Hyclone ) , 1% penicillin and streptomycin ( PS , Life Technologies ) at 37°C in a 5% CO2 atmosphere . Mitochondria were isolated for mouse tissues ( liver , kidney , brain ) by differential centrifugation as previously described [14 , 24] . For dNTP isolation , the mitochondrial pellets were resuspended in MAITE buffer ( 25mM sucrose , 75mM sorbitol , 100mM KCl , 10 mM H3PO4 , 0 . 05mM EDTA , 5mM MgCl2 , and 10 mM Tris-HCl , pH 7 . 4 ) , and protein concentration was determined . An aliquot of protein from each mitochondrial preparation ( 500 μg ) was precipitated with 0 . 5 M trichloroacetic acid ( final concentration ) by centrifugation at 20 , 000 x g for 5 min at 4°C , the supernatant was neutralized with 1 . 5 volumes of 0 . 5 M trioctylamine in Freon ( 1 , 1 , 2-trichlorotrifluoroethane ) and re-centrifuged for 10 min at 10 , 000 x g at 4°C . The second supernatant was vacuum dried , redissolved in 125 μL of 40 mM Tris-HCl pH 7 . 4 , and stored at -80°C until analysis . Mitochondrial dNTPs were extracted from primary cultured fibroblasts as previously described [62] . dNTP concentrations were determined by a polymerase-based method using mitochondrial extract , as described in [40] . Isolated mitochondria were lysed in 20 mM HEPES , 5 mM EDTA , 75 mM NaCl , 2 mM DTT , 0 . 4% n-Dodecyl β-D-maltoside ( DDM ) and 1X protease and phosphatase inhibitor cocktail ( Cell signaling and Roche , respectively ) . Whole cells were lysed in PBS , 1% SDS , 1X protease and phosphatase inhibitor cocktail , and 50 Units benzonase ( Millipore ) . Protein concentration was determined by DC protein assay ( Biorad ) . Western blotting and immunodetection were performed as described in [64] . The primary antibodies and relative dilutions used are described in S3 Table . Isolated mitochondria from mouse liver were resuspended in 1M 6-aminohexanoic acid , 50 mM Bis-Tris-HCl ( pH 7 . 0 ) at 10 mg/mL final concentration . Mitochondrial membranes were solubilized by the addition of n-Dodecyl β-D-maltoside ( DDM ) at 1 . 6 g/g or digitonin ( DIG ) at 4 g/g . Samples were incubated on ice for 5 min and centrifuged at 16 , 000 x g for 30 min . Supernatants were collected and combined with an equal volume of native sample buffer ( Biorad ) . Mitochondrial membrane complexes ( 25 μg ) were separated on a NativePAGE 3–12% Bis-Tris gel ( Life Technologies ) and transferred to PVDF membrane . After blocking , membranes were incubated overnight with the indicated primary antibodies ( S3 Table ) . Total DNA was isolated from human fibroblasts and mouse tissues using DNeasy Blood and tissues Kit ( QIAGEN ) , according to the manufacturer’s protocol , and quantified by spectrophotometry ( Nanodrop , Thermoscientific ) . Real-time quantitative PCR was performed in triplicates on 96-Well Reaction Plates ( Applied Biosystems ) . Each PCR reaction ( final volume 25 μl ) contained 25 ng DNA , 12 . 5 μl of Power SYBR-Green PCR Master Mix ( Applied Biosystems ) and 0 . 5 μM of a forward and a reverse primer . MtDNA was amplified using primers specific to a region of murine or human COXII gene and APP1 was amplified as a nuclear gene standard reference . The sequences of the primers used are listed in S1 Table . Changes in mtDNA amount were calculated using the 2-ΔΔCt method [65] and represented as fold changes relative to the indicated control . For the analysis of mtRIs , mitochondria were isolated from mouse liver as described above , with an additional sucrose-gradient step to preserve the integrity of the replication intermediates [66] . Nucleic acids were extracted from mouse liver by detergent lysis , protease digestion and successive phenol and chloroform extractions , as described previously [67] . BclI ( New England Biolabs ) digestions of mouse liver mtDNA were performed under conditions recommended by the manufacturer . 2D-AGE was performed according to the standard method [68] . After electrophoresis , the DNA was Southern blotted to solid support ( Magnaprobe , Osmonics Inc ) and the membranes probed with radiolabeled strand-specific RNA probes , generated using T7-maxiscript kit ( Ambion ) as per the manufacturer’s instructions . The template for the synthesis of an H-strand specific riboprobe corresponding to nt 15 , 196–16 , 006 of mouse mtDNA was generated via PCR , using forward and reverse primers , 5´-TAATACGACTCACTATAGG GCCAACTAGCCTCCATCTCATAC-3´ ( 15 , 196–15 , 218 , T7 sequence underlined ) , and 5´-AATGATTCTTCACCGTAGGTGCG-3´ ( 15 , 984–16 , 006 ) , respectively . Hybridizations were overnight at 55°C in 2 x SSPE , 2% Sodium dodecyl sulfate , 5 x Dernhardt’s Reagent , 5% Dextran sulfate buffer . After overnight incubation , membranes were washed 4–6 times with 0 . 1 x SSPE , 0 . 5% SDS , at 55°C . Membranes were exposed to phosphorscreens ( GE Healthcare ) for 12–120 h and imaged on a Typhoon scanner ( GE Healthcare ) . To obtain quiescent fibroblasts , 2 . 0 x 105 cells were seeded in 60 mm dishes and grown in 10% FCS until confluent ( 5–7 days ) , when the serum was changed to 0 . 1% dialyzed FBS ( Pan Biotech ) . Where indicated , the medium was supplemented with 50 or 100 μM AdR , CdR , GdR , TdR ( Sigma ) or different combinations of the four deoxynucleosides . During the treatment , media were replaced every 3–4 days . To obtain quiescent mtDNA-depleted cultures , proliferating fibroblasts were first incubated in DMEM , 10% FCS containing 50 ng/mL ethidium bromide ( EB ) and 50 μg/mL uridine until confluent ( 7 days ) . After a further 7 days of EB treatment in low serum ( 0 . 1% dialyzed FCS ) , EB was removed by washing the cells 5 times , and fresh DMEM with 0 . 1% dialyzed FCS , 50 μg/mL uridine was added . Quiescent fibroblasts were cultured for a further 14 days , with DNA sampling at intervals . Where indicated , the medium was supplemented with , deoxynucleoside ( 50 or 100 μM each ) . Data are expressed as the mean ± standard error of the mean ( SEM ) . Group means were compared using parametric t-test or non-parametric Mann-Whitney test . One-way ANOVA was used to compare more than two independent groups . A P-value of <0 . 05 was considered to be statistically significant .
Mitochondrial DNA depletion syndrome ( MDS ) is a genetically heterogeneous condition characterized by a decrease of mitochondrial DNA ( mtDNA ) copy number and decreased activities of respiratory chain enzymes . Depletion of mtDNA has been associated with mutations in several genes , which encode either proteins directly involved in mtDNA replication or factors regulating the homeostasis of the mitochondrial deoxynucleotide pool . However , for some genes the mechanism linking mutations and mtDNA depletion is not known . One such gene is MPV17 , whose loss-of-function causes mtDNA abnormalities in human , mouse and yeast . Here we show that MPV17 dysfunction leads to a shortage of the precursors for DNA synthesis in the mitochondria , slowing DNA replication in the organelle . Not only does mtDNA copy number correlate with dNTP pool size in both mouse tissues and human cells , deoxynucleoside supplementation of the growth medium prevents depletion and restores mtDNA copy number in quiescent MPV17-deficient cells . Hence , our study links MPV17 deficiency , insufficiency of mitochondrial dNTPs , and slow replication in mitochondria to depletion of mtDNA manifesting in the human disease , and places MPV17-related disease firmly in the category of mtDNA disorders caused by deoxynucleotide perturbation . The prevention and reversal of mtDNA loss in MPV17 patient-derived cells identifies potential therapeutic strategy for a currently untreatable disease .
[ "Abstract", "Introduction", "Results", "Discussion", "Methods" ]
[]
2016
MPV17 Loss Causes Deoxynucleotide Insufficiency and Slow DNA Replication in Mitochondria
The clinical signs of active trachoma are often present in the absence of ocular Chlamydia trachomatis infection in low prevalence and mass treated settings . Treatment decisions are currently based on the prevalence of clinical signs , and this may result in the unnecessary distribution of mass antibiotic treatment . We aimed to evaluate the diagnostic accuracy of a prototype point-of-care ( POC ) test , developed for field diagnosis of ocular C . trachomatis , in low prevalence settings of The Gambia and Senegal . Three studies were conducted , two in The Gambia and one in Senegal . Children under the age of 10 years were screened for the clinical signs of trachoma . Two ocular swabs were taken from the right eye . The first swab was tested by the POC test in the field and the result independently graded by two readers . The second swab was tested for the presence of C . trachomatis by Amplicor Polymerase Chain Reaction . In Senegal , measurements of humidity and temperature in the field were taken . A total of 3734 children were screened , 950 in the first and 1171 in the second Gambian study , and 1613 in Senegal . The sensitivity of the prototype POC test ranged between 33 . 3–67 . 9% , the specificity between 92 . 4–99 . 0% , the positive predictive value between 4 . 3–21 . 0% , and the negative predictive value between 98 . 0–99 . 8% . The rate of false-positives increased markedly at temperatures above 31 . 4°C and relative humidities below 11 . 4% . In its present format , this prototype POC test is not suitable for field diagnosis of ocular C . trachomatis as its specificity decreases in hot and dry conditions: the environment in which trachoma is predominantly found . In the absence of a suitable test for infection , trachoma diagnosis remains dependent on clinical signs . Under current WHO recommendations , this is likely resulting in the continued mass treatment of non-infected communities . Trachoma is caused by ocular infection with the bacterium Chlamydia trachomatis and is the leading infectious cause of blindness worldwide [1] . The World Health Organization ( WHO ) simplified grading system , designed for the simple and reliable grading of trachoma clinical signs by non-specialist staff , is predominantly used for trachoma diagnosis in the field [2] . This system classifies the clinical signs into five categories: trachomatous inflammation-follicular ( TF ) , trachomatous inflammation-intense ( TI ) , trachomatous scarring ( TS ) , trachomatous trichiasis ( TT ) , and corneal opacity ( CO ) . Clinical signs are however poorly correlated with detection of ocular C . trachomatis infection , since they may persist for months or years after infection has cleared [3] , [4] , [5] , [6] , [7] . The WHO recommends that any district or community where the prevalence of TF in children aged 1–9 years is at least 10% should receive mass antibiotic treatment annually for three years , before the prevalence is re-assessed [8] . Since antibiotics are given to treat C . trachomatis infection , and the prevalence of clinical signs is a poor predictor of infection especially in low prevalence and mass treated settings , treatment may be unnecessarily commenced and continued , thus wasting scarce resources . A point-of-care ( POC ) test capable of detecting infection in the field would enable treatment to be directed to those communities in need . Since a POC test would be used to make treatment decisions at the community , rather than the individual , level , it is important that it has high specificity ( >98% ) , otherwise it has no advantage over the use of clinical signs . A prototype POC test for trachoma , developed by the Diagnostics Development Unit ( University of Cambridge , UK ) , and currently not commercially available , has previously been evaluated on a small scale in a medium prevalence Tanzanian setting ( 12 . 5–37 . 9% TF in children aged 1–9 years ) , with encouraging results [9] . This assay is a modified version of a test for genital C . trachomatis infection [10] , [11] , optimised for use with conjunctival swabs . The assay detects the chlamydial lipopolysaccharide ( LPS ) , using lateral flow technology . The dipstick is made up of a nitrocellulose membrane affixed to a backing sheet , and connected to an absorbent pad , with two immobilised monoclonal antibodies ( mAbs ) lined on the dipstick membrane . The mAb at the capture line is against chlamydial LPS , and that at the procedural control line is an antibiotin antibody . This assay was designed specifically for use in resource-limited settings , and therefore has no electricity , water or laboratory equipment requirements [9] . We aimed to conduct a larger scale evaluation of this prototype POC test's diagnostic accuracy in children aged under 10 years in the low prevalence settings of The Gambia and Senegal . The functional temperature and humidity range of the prototype test was unknown before this study's field testing . Research was done in accordance with the declaration of Helsinki . Ethical approval was obtained from the London School of Hygiene & Tropical Medicine ( LSHTM ) ethics committee ( No . 2067 ) , the Gambia Government/Medical Research Council Joint Ethics Committee ( SCC 979 ) , and the Comité d'éthique du CNRS , Dakar , Senegal . Written ( thumbprint or signature ) informed consent was obtained from the guardians of all children . Three studies were conducted , two in The Gambia and one in Senegal . An overview of the study methods is depicted in Figure 1 . Study 1 was part of a survey of the Lower River ( LRR ) and North Bank ( NBR ) Regions of The Gambia . The sample selection has been described in detail elsewhere [13] . Briefly , 19 census Enumeration Areas ( EAs ) , which are designed to be of approximately the same population size , were randomly selected in LRR . A random selection of households was made so that 50 children aged under 10 years would be included . In Studies 2 and 3 , all children aged under 10 years were included . Study 2 took place in 6 Gambian communities and Study 3 in 12 Senegalese communities . The Gambian communities were selected on the basis of having a TF prevalence of at least 10% in the Gambian survey [13] , increasing the likelihood of finding infection . Study 3 was based in the health post of Keur Samba Kane in Bambey District , which had been identified by the National Eye Care Programme as fulfilling the WHO criteria for mass treatment . Study 1 was conducted in January–March 2006 , Study 2 in March–May 2006 , and Study 3 in January–February 2007 . The village head ( alkalo ) and villagers were sensitised to the study's aims and methods . Household head lists were made and the de facto population was enumerated , recording their name , alias names , age and sex . Date-of-birth was noted when possible using ID cards and infant vaccination cards . The census team identified eligible children and informed household heads of the day and place of examination to ensure optimum participation . Experienced Gambian and Senegalese graders were used . Their grading was verified and standardised using WHO grading slides , and a chance corrected agreement ( Cohen's kappa [14] ) score of at least 0 . 8 was required for the scoring of each sign ( TF , TI , TS , TT ) . NBR villages with the highest active trachoma prevalence in the Gambian survey were re-visited and children diagnosed with active disease were re-screened by a senior grader to verify clinical diagnoses . The examination team located itself in a central point in the village . Eligible children were called and written informed consent ( signature or thumbprint ) from the participants' guardians was obtained . The validated grader examined each consenting participant's eyes using a 2 . 5× magnifying loupe and torchlight . In order to avoid cross-contamination , the examiner wore and changed gloves between each participant . The clinical diagnosis was made according to the WHO simplified grading system [2] . Two swabs were taken from the tarsal conjunctiva of each participant's right eye using a standardised technique [15] , whereby the swab was held horizontally and drawn lengthways across the everted upper tarsal conjunctiva four times , rotating the head of the swab a quarter turn with each pass . Cross-contamination of samples was limited by using a field worker to pass the swab to the examiner . The field worker then held the tube into which the swab would be stored dry , so that the examiner never touched the tube , and the swab's head only ever contacted the participant's conjunctiva . In The Gambia , both samples were collected with the POC test's sterile polyurethane swab ( Becton , Dickinson and Company , Franklin Lakes , USA ) . In Senegal , the first sample was collected with the POC test's swab , and the second sample was with a dry Dacron polyester-tipped swab ( Quelab Laboratories , Montreal , Canada ) . This swab change was because inhibition in Studies 1 and 2 was believed to be due to the polyurethane swab , as a cloudy lysate was observed in the Amplicor extract . The first swab was processed immediately in the field by the POC test . The second-collected swabs , to later be tested for the detection of ocular C . trachomatis with the qualitative PCR Amplicor Chlamydia trachomatis/Neisseria gonorrhoeae ( CT/NG ) Test ( Roche Molecular Systems , Indianapolis , IN , USA ) , were stored in a cool box in the field and archived frozen at −20°C within ten hours of collection . POC testing was carried out according to the POC test's protocol [9] . Briefly , the eye swab was placed in a sample preparation tube to which three reagents were added for the release of Chlamydia LPS . Five drops of the sample extract were transferred to a detection tube , rehydrating two lyophilised signal amplification reagents . A dipstick was then placed inside the tube and the mixture was left to wick up for 25 minutes before the results were read . The same person performed all POC testing and was masked to the clinical diagnosis . Results were read at 25 minutes by two different readers each masked to the other's grading . The first reader was trained by the Diagnostics Development Unit , and the second reader was trained by the first reader . Grading was practised on non-clinical samples prior to participant sample collection . The signal strength was graded from 0 ( negative ) to 5 ( strongly positive ) using a signal grading card with increments of 0 . 5 . A positive sample is defined as any signal with a signal strength of 0 . 5 or more noted by the reader . In Senegal , a pocket size temperature/humidity handheld datalogger ( RH32 Series , Omega , Manchester , UK ) was used with values measured every 30 minutes . Amplicor , which detects the multi-copy cryptic plasmid , was performed on the second-collected swab . Amplicor was chosen as the reference test due to its good diagnostic performance on ocular samples [16] , [17] , [18] , its history of use for detection of ocular C . trachomatis detection [15] , [19] , [20] , [21] , and its use as the reference test in the previous evaluation of this prototype POC test [9] . Study 1 samples were tested within 42 days of collection at the Medical Research Council ( MRC ) Laboratories , Fajara , The Gambia . About half of Study 2 samples were processed within 1 month at the MRC , and the remainder within 4 months at the London School of Hygiene & Tropical Medicine ( LSHTM ) . All Senegalese samples were processed at the LSHTM between 2 and 6 months of collection . A previously published [15] sample preparation protocol was used instead of that in the Amplicor package insert . Positive and negative controls provided with the assay were included to validate the runs . When clusters of positives were observed on the detection plate , the positive samples were retested on-site . Those confirmed positive on the retest were considered Amplicor positives , and the others were considered negatives . Amplification of both the plasmid DNA and the master-mix internal control sequence was tested , allowing for inhibition to be detected . Inhibited samples were diluted from 1/5 up to 1/100 with a 50∶50 lysis∶diluent mix , until inhibition was resolved . The bacterial load of Amplicor positive samples was estimated by processing the samples with a real-time quantitative PCR assay targeting the single-copy ompA gene [15] . The reverse primer , common to all ocular serovars , was 5′-TTT AGG TTT AGA TTG AGC ATA TTG GA-3′ . The serovar A and B forward primers were 5′-GCT GTG GTT GAG CTT TAT ACA GAC AC-3′ and 5′-TCT GTT GTT GAG TTG TAT ACA GAT AC-3′ ( Sigma-Genosys , Gillingham , UK ) , respectively . Quantitation was done on two 4 µL replicate samples for both serovar A and serovar B . The Gambian samples were processed in a LightCycler ( Roche Diagnostics , Indianapolis , USA ) . The Senegalese samples were processed on a Rotor-Gene RG3000 ( Qiagen , Crawley , UK ) . Protocol changes were introduced as the study progressed to help ensure data quality . These changes involved the introduction of a POC test panel to be performed in the field , mock swabs inserted between patient samples in the field , environmental controls ( air , loupe and glove swabs ) , and testing for laboratory contamination . Results were double-entered by different entry clerks and verified in Microsoft Access ( MS Access v2000/2003XP ) . Any discrepancies after verification were checked against the original paper forms . Data cleaning was performed in Stata ( v9 . 2 , STATA Corp . , College Station , TX , USA ) . Data analysis was performed in Stata , except for the humidity and temperature analyses which were performed in R ( v 2 . 9 . 0 , R Foundation for Statistical Computing , Vienna , Austria ) . As a result of the change in Amplicor swab type between Studies 2 and 3 , and that the graders in The Gambia and Senegal were different , results from the 3 studies have not been combined . The kappa statistic was used to assess between-grader agreement for the POC test and to assess Amplicor reproducibility . The performance ( sensitivity , specificity , Positive Predictive Value ( PPV ) and Negative Predictive Value ( NPV ) ) of the POC test was compared against Amplicor as the gold standard . Binomial exact 95% confidence intervals ( CI ) were calculated to quantify uncertainty . Proportions were compared using Pearson's chi-squared statistic . Cuzick's trend test was used to look at the relationship between quantitative load , clinical sign status and POC test result . The effect of temperature and humidity on the POC test's performance was measured using logistic regression . A scatter plot of false-positives ( FPs ) and true-negatives ( TNs ) by temperature and humidity was made , with contours of the relative risk of FPs relative to TNs . For each TN , a bivariate Normal density function was centred on the corresponding point . At any point on the graph , a density for TNs was calculated by summing these individual densities . A similar procedure was applied to the FP results . At any point , the relative risk is the ratio of these two densities . Contours of this relative risk were then added to the scatter plot ( Figure 2 ) . During Amplicor processing of the Senegalese samples , 12 lab controls were taken to check for lab contamination ( 3 for the hood , 2 of the glove box , 2 of the cabinet , and 5 of the gloves ) . All were Amplicor-negative . In Senegal , 14 negative , 15 low load , and 11 high load mock swabs were introduced in between patient samples . Amplicor correctly detected all results . For the POC test , the number of correctly identified negative , low and high load positive cases differed significantly for reader 1 ( p = 0 . 007 ) and reader 2 ( p = 0 . 004 ) . For both readers , the POC test correctly detected the high load positives in 100% of cases . These had a signal strength ranging from 1 . 0 to 2 . 0 for reader 1 , and from 1 . 0 to 2 . 5 for reader 2 . For low load positives ( that tested POC negative under standard laboratory conditions ) , 5 samples were graded as positive by both the first and second readers . An additional sample was graded positive by reader 1 , and 3 other samples as positive by reader 2 . Thus , a total of 9/15 low load positives were detected by the POC test in the field . The signal strength of these false-positives ranged from 0 . 5 to 1 . 5 for both readers . For the negative controls , the readers both graded 5 samples as positive , and reader 2 additionally graded 4 samples as positive . These false-positives had signal strengths of 0 . 5 or 1 . 0 for both readers . In Senegal , there were 16 air controls , 16 glove controls , and 17 loupe controls . All were Amplicor negative . Less than half the POC test results were negative for both reader 1 ( 42 . 9% ) and reader 2 ( 46 . 9% ) . In total , there were 101 panel positive and negative controls aliquoted in the field and tested by Amplicor ( 52 in Study 2 and 49 in Study 3 ) . All 63 positive controls were correctly detected by Amplicor . Of the 38 negative controls , one from The Gambia tested positive repeatedly and two initially tested equivocal but were negative when repeat tested in duplicate . There was an additional equivocal negative control result by Amplicor , but the sample was erroneously labelled only as “negative control” on the template , without specifying which negative control this was , meaning it could not be retested . A total of 56 panels were tested by the POC test ( 14 in Study 2 and 42 in Study 3 ) . All positive panels , regardless of concentration , were positive by the POC test for both readers . The proportion of all negative panels correctly recorded as negative by the POC test was 60 . 7% for reader 1 ( 85 . 7% for Study 2 , 52 . 4% for Study 3 ) , and 66 . 1% for reader 2 ( 92 . 9% for Study 2 and 57 . 1% for Study 3 ) . Of 942 Amplicor-negative samples for which sample was available in Study 1 , positive results for human-specific hypervariable D-loop region mtDNA were obtained in 937 ( 99 . 5% ) samples . The five mtDNA-negative samples and five samples that could not be tested for mtDNA because of insufficient material from Study 1 have been removed from analyses . Three field air controls were randomly selected and also tested for C . trachomatis and human mtDNA , and provided negative results . Inhibition in Studies 1 and 2 which used the POC test polyurethane swab was 23 . 4% ( 220/940 ) and 22 . 8% ( 2671171 ) , respectively . The proportion of inhibited samples in Study 3 was 18 . 2% ( 293/1613 ) , so the change of swab did not make a noticeable difference to the level of inhibition . Only one inhibited sample , from Study 2 , retested as Amplicor positive . Of the 35 Amplicor-positive samples from Study 2 retested at the University of Cambridge , 27 were confirmed positive ( 23 as positive and 4 as equivocal ) , 3 were negative but failed the Internal Control ( IC ) , and 5 were negative and passed the IC . All 21 Amplicor-negatives retested as negative . These retests resulted in a kappa score between the initial and retest results of 0 . 73 , demonstrating substantial agreement . All 35 samples originally tested as positive were considered true positives for the analyses presented . Of the 5 negatives , three were positive by quantitative PCR with estimated loads of 5 , 7 and 25 ompA copies/swab . The remaining two positives were isolated among a string of negatives in the field , and were not near positive samples on the Amplicor detection plate . If a true positive was considered to be one that was positive at both LSHTM and the University of Cambridge ( 27 samples retested positive or equivocal ) , the specificity and PPV estimates remain the same . The NPV increases slightly to 98 . 7% ( 97 . 9–99 . 3 ) for reader 1 but decreases to 98 . 8% ( 98 . 0–99 . 4 ) for reader 2 . The sensitivity increases for both readers , but insignificantly: 48 . 1% ( 28 . 7–68 . 1 , p = 0 . 384 ) for reader 1 and 51 . 9% ( 31 . 9–71 . 3 , p = 0 . 352 ) for reader 2 . For Study 1 , none of the 3 Amplicor positives were retested . For Study 2 , 10/39 Amplicor positives were retested , and 6 retested negative . For Study 3 , 13/51 Amplicor positives were retested and all retested positive . The prevalence of active trachoma and Amplicor positives was , respectively , 11 . 9% and 0 . 3% in Study 1 , 23 . 9% and 3 . 0% in Study 2 , and 14 . 9% and 1 . 8% in Study 3 . During field processing of the POC test , mistakes were made for 4 samples in Study 1 , and 3 samples in Study 3 . These samples have been removed from analyses involving the POC test . The POC test's sensitivity , specificity , PPV and NPV against Amplicor showed similar point estimates and 95% CI for the two readers ( Table 1 ) . Overall , sensitivity and PPV were low , with respective estimates ranging from 33 . 3%–67 . 9% , and 4 . 3%–21 . 0% . The specificity met the minimum target of 98% in Study 1 , but not in Studies 2 or 3 . There is no evidence of a significant difference between the point estimates and corresponding 95% CI for NPV , PPV , or sensitivity between the three studies . Precision for the sensitivity estimates was low due to small numbers ( Table 1 ) . Compared with Study 1 , the specificity of the POC test was significantly lower in both Study 2 ( p<0 . 001 ) and Study 3 ( p = 0 . 001 ) . In Studies 2 and 3 , the specificity upper confidence bounds did not exceed 96 . 8% . Temperature and relative humidity data were collected for all samples from Study 3 ( 1584 Amplicor-negatives and 29 Amplicor-positives ) . Figure 2 shows contours of the relative risk ( RR ) for FPs relative to TNs , with shading from green to red as the RR increases ( see under Statistical Analyses in the Methods section ) . It is apparent that the false-positive RR began to increase at temperatures above 30°C and at relative humidities below 10% . The RR of a FP is approximately three times that of a true-negative at a temperature of about 36°C and at a relative humidity of 10% , and increases more rapidly as temperature rises and humidity falls . Plots of FP rates against temperature and relative humidity indicated an increase at temperatures above 31 . 4°C , and a relative humidity below 11 . 4% . Estimates of diagnostic accuracy calculated for samples processed above and below the 31 . 4°C temperature threshold showed that the specificity was significantly lower in samples processed above the threshold than below , for both the first and second POC test readers ( p<0 . 001 ) ( Table 2 ) . For humidity , the specificity was significantly lower in samples processed below a threshold of 11 . 4% compared with those above the threshold , for both readers ( p<0 . 001 ) ( Table 2 ) . Of the 67 Amplicor-positives , positive ompA results were obtained in 58 ( 86 . 6% ) samples by quantitative PCR . The estimated number of ompA copies/swab ranged from 5 to 3 , 008 , 063 , with a median of 670 . Although a few low load PCR positives were POC test positive , the POC test consistently detected positives from 1000 ompA copies/swab . Although the POC test is a qualitative assay , the signal strength was scored on a scale from 0 . 5 ( weak ) to 5 . 0 ( strong ) in the field . There was a significant association between increased organism load and increased POC test signal strength ( p<0 . 001 ) . The kappa score for inter-grader variability between the two POC test readers was lowest for Study 1 and highest for Study 3 . For exact signal strength the kappa score ranged from 0 . 41 to 0 . 59 , showing moderate agreement . When the results were categorised as positive ( signal strength ≥0 . 5 ) or negative ( signal strength <0 . 5 ) , the scores ranged from 0 . 26 to 0 . 68 , demonstrating fair to substantial agreement . In this study we conducted an evaluation of a prototype POC test for the detection of ocular C . trachomatis in children aged under 10 years in The Gambia and Senegal . After following standardised field and laboratory protocols , ensuring quality assurance and data validity , the results demonstrated that in its present format , this POC test is not suitable for use in the field . Under laboratory conditions , the negative and low positive mock swabs resulted in negative POC tests . In the field , the POC test gave false-positive results for approximately half of these mock swabs . This demonstrates that the POC test does not pass quality control procedures when tested in the field . When tested on children's ocular swabs , specificity in Study 1 was excellent ( 99 . 0% and 97 . 6% for readers 1 and 2 , respectively ) . This is consistent with the specificity reported from the previous evaluation of this test performed in Tanzania , where the overall specificity was 99 . 4% ( 95%CI 98 . 8–100 ) [9] . However , in Studies 2 and 3 , the specificity ranged from 92 . 4% to 95 . 7% , falling short of the 98% minimum specificity required for this test . The temperature and relative humidity data provide the most likely explanation for the lower POC test specificity in Studies 2 and 3 . Study 1 was conducted in January and February , when The Gambia is experiencing its cool season . Study 2 took place just before the rainy season , when temperatures rise . In Study 3 , high temperatures and low relative humidities were recorded whilst performing the test , and these conditions were shown to significantly affect the false positive rate of the POC test . These data indicate that the prototype POC test's format is not appropriate for these environmental conditions . Evaluations of rapid POC tests for other infectious diseases have observed a detrimental effect of high temperature and humidity during test storage on performance [22] , [23] , [24] . However , we observed an effect on the test's performance in the field during processing . A review of malaria rapid diagnostic tests which also use lateral flow technology , notes that humidity and wind rapidly degrade nitrocellulose capillary flow action . This effect on reagent flow could result in false-positives . In addition , temperature and time could be detrimental to the test's sensitivity , as they have been reported to deconjugate the signal line antibody-indicator complex , detach the capture antibody from the nitrocellulose strip , and unfold the binding sites of antibodies [25] . Since the dipstick of the ocular C . trachomatis POC test under evaluation is made up of a nitrocellulose membrane transversely lined with mAb against chlamydial LPS and antibiotin , these are plausible explanations for the observed deleterious effect of high temperature and low relative humidity on the test's performance . These results suggest that the environmental conditions during Studies 2 and 3 were harsher than those experienced in Study 1 and Tanzania , and emphasise the importance of conducting POC test evaluations in different settings . A change in the format of the prototype POC test that prevents its performance from being affected by the dry , hot , and dusty environments in which trachoma is predominantly found [26] , would no doubt improve the usefulness of this test for trachoma control . False-positives may also have appeared as a result of the POC test's target being the genus-specific chlamydial LPS . We do not believe , however , that cross-reaction with non-C . trachomatis bacteria was the cause of the POC test false-positives observed in this study . As noted by Michel et al . , the POC test's specificity has been established against a panel of microorganisms commonly associated with the human eye and skin ( such as Staphylococcus , Pseudomonas , Streptococcus , Escherichia , Proteus , and Candida , obtained from ATCC ) [9] . In addition , if cross-reaction were taking place , it would not explain the observed association between FPs with temperature and humidity . The advantage of testing the prototype POC test in low prevalence settings was the ability to gain a good estimate of specificity . The disadvantage is that we have been unable to determine an accurate estimate of the test's sensitivity . In addition , the active disease found in this study was mild with only 6 . 5% of clinically active children having TI . Infection load is correlated with disease severity [4] , [15] , [27] . The consequence of lower infection loads is a lower test sensitivity , especially in an assay that detects a surface antigen as opposed to one using PCR technology . Indeed , the Tanzanian evaluation observed a lower ( albeit non-significant ) sensitivity ( 76 . 9% ) of the POC test in the lower prevalence site ( TF prevalence 12 . 5% ) compared with a sensitivity of 85 . 5% where the TF prevalence was 31 . 5% [9] . Michel et al . ( 2006 ) noted that the assay has an analytical sensitivity of 2500 chlamydial EBs per test [9] . Our quantification demonstrated consistent detection from approximately 1000 ompA copies/swab . In terms of the limitations of this study , there was a delay of up to 6 months between sample collection and sample processing , which could have resulted in low load positives testing negative . This may have contributed to the number of POC test false-positives observed . However , since samples were stored at −20°C , we do not believe that the DNA would have degraded and that waiting would have led to a decrease in the number of true positives . The POC test was performed on the first-collected swab whereas the “gold standard” Amplicor testing was on the second-swab . There may be differences between the two swabs , for example , in cases where there are few EBs in the conjunctiva the first swab may not leave any for the second swab . One of the swabs may also collect more PCR-inhibiting material , such as mucous , resulting in inhibition in one of the assays . Furthermore , one swab may be passed more forcefully over the conjunctiva , collecting more DNA or inhibiting material . Michel et al . ( 2006 ) demonstrated that first-collected swabs had higher loads than second-collected swabs by comparing organism load in the first- and second-collected swabs from 13 Amplicor positive individuals . The first swab's mean EB count was 643 , 424 compared with 181 , 310 for the second swab . This should not affect the Amplicor prevalence as its detection level is in the range of 1–10 EBs [28] , [29] . Furthermore , Amplicor result concordance between first- and second-collected swabs has been shown to be excellent [5] , [30] . There was a change in swab type between Study 2 and Study 3 because it was believed that the polyurethane swab led to inhibition . However , the swab change did not make a noticeable difference to the level of inhibition . The disadvantage of inhibition is the need to dilute the sample , which would reduce the copy number in any sample tested , resulting in Amplicor false-negatives . Since load of infection in the study sites was often low ( with 37 . 3% of all Amplicor positives having a load of <10 ompA copies/swab or being negative ) , this is a distinct possibility , and could have contributed to the low specificity of the POC test . Another possible limitation is our choice of gold standard . In the absence of a universally accepted gold standard for C . trachomatis , we chose Amplicor as it was used in the previous evaluation of this POC test [9] , and it has been used in multiple studies of ocular C . trachomatis infection . Controls included to assure the quality of our gold standard produced excellent results . Air , loupe , glove and spiked mock swab field controls were all correctly identified . The Amplicor results for aliquots from the POC test control panel were correct except for one negative panel from Study 2 , which was repeatedly positive , and one equivocal which could not be repeat tested because the sample name was not correctly written on the Amplicor plate template . This suggests contamination of the negative panel from the positives when aliquoting in the field , which is possible as stringent laboratory conditions cannot be maintained in such an environment . Furthermore , it was a requisite for a successful run that the Amplicor-provided positive and negative controls processed for each plate produce the correct result , indicating that contamination in the lab is unlikely . This is supported by the Amplicor negative results of swabs taken of lab surfaces to check for lab contamination . When positives clustered on the detection plate were repeat tested , 6/10 retested samples from Study 2 retested negative . This could indicate that there was contamination between the wells on the detection plate , and for this reason they were considered negative in analyses . Alternatively , these samples could have been low load positives that did not repeat test positive . Of the 35 Amplicor positives retested by Amplicor at the University of Cambridge , five tested negative . . The failure to retest these five samples as positive was not unexpected as reproducibility when retesting the original sample with the same test is known to be poor for low load samples [31] , [32] , [33] , [34] , [35] , [36] . However , when samples that were not repeated positive at the University of Cambridge were removed from the analyses , there was no significant effect on the prototype POC test's performance . The development of effective diagnostic tools is considered a priority for Neglected Tropical Diseases ( NTDs ) [37] , and it is therefore important to be aware of the impact the environment can have on the operational performance of POC tests . A lateral flow platform in an open system appears not to be suitable for the environments in which NTDs , such as trachoma , are often found . A rapid , accurate , simple , and affordable POC test which can be performed in the field could be a great asset to trachoma control , particularly in low prevalence settings . The specificity of the test must be high ( >98% ) to prevent communities from being unnecessarily mass treated . The specificity of the prototype POC test evaluated in this study decreased as the temperature increased and relative humidity decreased , indicating the importance of field testing POC tests in the different environments in which the target disease is found , in addition to being evaluated in different prevalence settings . Until a suitable test is made available , trachoma control decisions in the field remain reliant on clinical diagnosis , potentially wasting scarce resources .
Trachoma , caused by infection of the eye with the bacterium Chlamydia trachomatis , is the leading infectious cause of blindness and is associated with poverty . Antibiotic treatment of all community members is one of the recommended control strategies for trachoma . However , in places where the prevalence of clinical signs is low , C . trachomatis eye infection is often absent . Laboratory testing for C . trachomatis infection by polymerase chain reaction ( PCR ) is highly sensitive but expensive and requires well-trained staff . A simple point-of-care ( POC ) test that can be used in trachoma-affected communities could help trachoma control efforts . We evaluated a POC test for C . trachomatis eye infection . Children under 10 years of age were screened for clinical signs of trachoma and C . trachomatis eye infection . The POC test result was compared with laboratory PCR test results . The POC test detected just over half of PCR test positives correctly . However , the POC test tended to give false-positive results in hot and dry conditions , which is the typical environment of trachoma . The POC test requires high specificity since it would be used to make treatment decisions at the community level . Therefore , its present format requires improvement before it can be utilized in trachoma control .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "medicine", "infectious", "diseases", "public", "health", "and", "epidemiology", "diagnostic", "medicine", "epidemiology", "global", "health", "ophthalmology", "public", "health" ]
2011
Diagnostic Accuracy of a Prototype Point-of-Care Test for Ocular Chlamydia trachomatis under Field Conditions in The Gambia and Senegal
Over 1 . 5 billion people lack the skeletal muscle fast-twitch fibre protein α-actinin-3 due to homozygosity for a common null polymorphism ( R577X ) in the ACTN3 gene . α-Actinin-3 deficiency is detrimental to sprint performance in elite athletes and beneficial to endurance activities . In the human genome , it is very difficult to find single-gene loss-of-function variants that bear signatures of positive selection , yet intriguingly , the ACTN3 null variant has undergone strong positive selection during recent evolution , appearing to provide a survival advantage where food resources are scarce and climate is cold . We have previously demonstrated that α-actinin-3 deficiency in the Actn3 KO mouse results in a shift in fast-twitch fibres towards oxidative metabolism , which would be more “energy efficient” in famine , and beneficial to endurance performance . Prolonged exposure to cold can also induce changes in skeletal muscle similar to those observed with endurance training , and changes in Ca2+ handling by the sarcoplasmic reticulum ( SR ) are a key factor underlying these adaptations . On this basis , we explored the effects of α-actinin-3 deficiency on Ca2+ kinetics in single flexor digitorum brevis muscle fibres from Actn3 KO mice , using the Ca2+-sensitive dye fura-2 . Compared to wild-type , fibres of Actn3 KO mice showed: ( i ) an increased rate of decay of the twitch transient; ( ii ) a fourfold increase in the rate of SR Ca2+ leak; ( iii ) a threefold increase in the rate of SR Ca2+ pumping; and ( iv ) enhanced maintenance of tetanic Ca2+ during fatigue . The SR Ca2+ pump , SERCA1 , and the Ca2+-binding proteins , calsequestrin and sarcalumenin , showed markedly increased expression in muscles of KO mice . Together , these changes in Ca2+ handling in the absence of α-actinin-3 are consistent with cold acclimatisation and thermogenesis , and offer an additional explanation for the positive selection of the ACTN3 577X null allele in populations living in cold environments during recent evolution . The sarcomeric α-actinins , α-actinin-2 and -3 , are highly homologous actin-binding proteins localised to the Z-discs of skeletal muscle fibres , where they cross-link the actin filaments of adjoining sarcomeres and interact with a host of metabolic and signalling proteins . α-Actinin-2 is present in all muscle fibre types , while α-actinin-3 is found only in fast glycolytic fibres . An estimated 18% of individuals worldwide completely lack α-actinin-3 , due to homozygosity for a common nonsense polymorphism ( R577X ) in the ACTN3 gene [1] . The ACTN3 577XX null genotype ( α-actinin-3 deficiency ) is not associated with disease , possibly because there is compensatory up-regulation of α-actinin-2 . However , it does appear to have subtle effects on athletic performance . Compared to the general population , the frequency of this genotype is markedly reduced in elite sprint and power athletes [2–6] , and increased in elite endurance athletes [6–8] . Hence the ACTN3 gene has become known as the “gene for speed” . The ACTN3 577XX null genotype is also associated with reduced muscle strength and sprint performance in non-athletes [9–12] . We have generated an Actn3 knockout ( KO ) mouse to investigate the mechanisms by which α-actinin-3 deficiency affects muscle function . The muscles of the KO mouse show striking changes in metabolic properties , with an increased activity of mitochondrial enzymes involved in aerobic metabolism and a reduced activity of enzymes involved in anaerobic metabolism [1 , 13 , 14] . This suggests that , in the absence of α-actinin-3 , the fast glycolytic fibres have shifted their metabolism from the anaerobic pathway towards the oxidative pathway . There is , however , no change in the myosin heavy chain ( MyHC ) isoform expression [13] . The metabolic changes in the Actn3 KO mouse are similar to those seen in the muscles of wild-type mice following endurance training , suggesting that Actn3 KO muscle is “pre-trained” for endurance performance [15] . One intriguing question is why the ACTN3 577XX null genotype is so common in humans , and why there is such geographic variation in the frequency of the ACTN3 577X null allele , being less than 10% in African populations and more than 50% in European and Asian populations [1] . Our linkage disequilibrium analysis suggests that the 577X null allele has undergone strong , recent positive selection as modern humans migrated out of Africa into the Northern Hemisphere 40 , 000–60 , 000 years ago [1] . This is one of the very rare examples in the human genome of a single-gene loss-of-function variant being positively selected during recent evolution [16] . Friedlander et al . [17] have found that the ACTN3 577XX null genotype has evolved along a global latitudinal gradient , with the null genotype being more common in places with lower mean annual temperature and lower species diversity . Hence the question is why α-actinin-3 deficiency should provide a survival advantage where food resources are scarce and climate is cold . The altered metabolic profile of Actn3 KO mice provides part of the answer , as a shift from anaerobic to oxidative metabolism would enable more efficient use of the scarce food resources . It would also explain the benefits of α-actinin-3 deficiency to elite athletic endurance performance . This raises the question: if α-actinin-3 deficiency “pre-trains” muscles for endurance performance , could it also “pre-acclimatise” muscles to cold environments ? Bruton et al . [18] have shown that muscles of wild-type mice exposed to prolonged cold undergo changes similar to those observed with endurance training , with increased Ca2+ leak from the sarcoplasmic reticulum ( SR ) , increased resting [Ca2+]i ( free myoplasmic Ca2+ concentration ) and increased fatigue resistance . Mechanistically , changes in Ca2+ handling by the SR are a key factor underlying these adaptations . Our aim in this study , therefore , is to investigate the Ca2+-handling characteristics of single fibres from Actn3 KO mouse muscle , to see if there are any features consistent with cold acclimatisation . We examine the Ca2+ transients in fast glycolytic fibres from the flexor digitorum brevis ( FDB ) muscle of untrained , non-cold-exposed Actn3 KO mice , and provide the first evidence of a heat-generating mechanism that could enhance survival in cold environments and promote the positive selection of the 577X null allele in certain populations . As an overall indicator of potential alterations in Ca2+ handling by α-actinin-3-deficient muscle fibres , we examined Ca2+ kinetics of individual twitches in single FDB fibres from WT and Actn3 KO mice . Fig . 1 summarises the kinetics of Ca2+ transients elicited by a single action potential . Fig . 1A shows sample transients recorded during a single twitch in a WT fibre and a KO fibre . The superimposed recordings show a clear difference in the shape of the transients . Across our whole sample , there was no difference between WT and Actn3 KO fibres in the time taken to rise from 20% to 80% of maximum [Ca2+]i ( Fig . 1B ) . However , the rate constant of decay was significantly higher in Actn3 KO fibres than in WT fibres , both during the fast phase ( Fig . 1C ) and slow phase ( Fig . 1D ) of [Ca2+]i decay . In some cases the resting [Ca2+]i was measured and was not significantly different between WT and Actn3 KO fibres ( 46 ± 5 nM for WT , n = 5; 47 ± 5 nM for KO , n = 10 ) . Ca2+ re-uptake by the sarcoplasmic reticulum ( SR ) is one of the main contributors to the decline of [Ca2+]i following fibre stimulation [19] . Hence , altered SR Ca2+ re-uptake could underlie the faster [Ca2+]i decay rates of Actn3 KO fibres observed in Fig . 1 . To examine SR Ca2+ re-uptake , we derived SR pump function curves from the slow phases of [Ca2+]i decay in the twitch transients from Fig . 1 . SR pump function curves are a standard methodology for examining the function of the SR Ca2+ pump under steady-state conditions [18 , 20–22] . The derivation of the SR pump function curves is explained more fully in the Methods . Fig . 2A shows the SR pump function curves for FDB fibres from WT and Actn3 KO mice . Each curve shows the relationship between [Ca2+]i and the rate of [Ca2+]i decline during the slow phase of [Ca2+]i decay . It is clear that for any level of [Ca2+]i , the rate of [Ca2+]i decline is higher in KO than in WT . The rate of [Ca2+]i decline is a balance between the rate of SR Ca2+ pumping and the rate of Ca2+ leak from the SR [21] . To distinguish between these two factors , we used the SR pump equation ( Eqn 3 ) shown in the Methods . The value of A , which reflects the rate of pumping , was significantly higher in fibres of Actn3 KO mice ( Fig . 2B ) . The value of L , which reflects the rate of leak , was also significantly higher in fibres of Actn3 KO mice ( Fig . 2C ) . Hence in Actn3 KO fibres , the faster rate of Ca2+ pumping by the SR is counteracted by a faster rate of Ca2+ leak from the SR , but overall , [Ca2+]i still declines more quickly during a twitch than in WT fibres . As improved fatigue resistance is one of the changes found in muscle fibres of mice exposed to prolonged cold [18] , we examined the effect of fatigue on Ca2+ transients in muscle fibres of WT and Actn3 KO mice . In muscle fibres fatigued by repeated tetanic stimulation , there is an impairment of SR Ca2+ release , manifested as a progressive fall in tetanic [Ca2+]i , and an impairment of SR Ca2+ re-uptake , manifested as a progressive fall in the [Ca2+]i decay rate of each tetanic transient [23] . We therefore measured these two factors during a fatigue protocol in which fibres were stimulated at 50 Hz , 500 ms on , 500 ms off until [Ca2+]i had fallen to 30–40% of original . Fig . 3A shows sample recordings of the progress of [Ca2+]i during the whole fatigue run in a WT fibre and a KO fibre . Individual transients from selected time points are shown on an expanded time scale in Fig . 3B . The recordings show the characteristic pattern of [Ca2+]i changes during repeated stimulation [23] , with tetanic [Ca2+]i initially rising , then progressively falling . It is clear that the KO fibre was able to maintain tetanic [Ca2+]i longer into the fatigue run than the WT fibre . Across the whole sample , the time taken for tetanic [Ca2+]i to fall to pre-determined percentages of original was significantly longer in KO fibres than in WT fibres ( Fig . 3C ) . The rate constant of [Ca2+]i decay of each tetanic transient fell throughout the fatigue run , and the fall was significantly less pronounced in KO than in WT fibres ( Fig . 3D ) . Hence impairment of SR Ca2+ release and re-uptake is less pronounced in Actn3 KO fibres than in WT fibres , and thus Actn3 KO fibres are more resistant to fatigue . The speed of shortening of a muscle fibre depends largely on the myosin heavy chain ( MyHC ) isoform present , but also on the Ca2+-sensitivity of the contractile proteins , and on the Ca2+ release properties of the SR [24] . In previous studies we have already determined that: ( i ) MyHC expression is unaltered at baseline in Actn3 KO fibres [25]; and ( ii ) there is no difference between Actn3 KO and WT fibres in the Ca2+-sensitivity of the contractile proteins [26] . Hence a difference in speed of shortening could indicate a change in the Ca2+ release properties of the SR . We therefore examined the speed of shortening in WT and Actn3 KO fibres by means of a recently developed high-speed imaging technique [27] . Fig . 4A shows image processing results in a representative WT fibre recorded during a single twitch . Fig . 4B shows biomechanical results from WT and Actn3 KO fibres shortening during a single twitch . Maximum shortening distance was about 12% of initial fibre length and not different between WT and KO fibres . Maximum shortening velocities ( Fig . 4B and C ) were not different between WT and Actn3 KO fibres , and were in agreement with wild-type fibres in our previous studies [27] . The lack of difference in shortening velocities suggests that the Ca2+ release properties of the SR are similar in Actn3 KO and WT fibres , and confirms the lack of difference in the rise times of the twitch transient ( Fig . 1B ) . As we have demonstrated that [Ca2+]i kinetics are altered in Actn3 KO fibres , it was important to examine the expression of the major proteins involved in Ca2+ release and re-uptake . Fig . 5 shows results of Western blots performed on extensor digitorum longus ( EDL ) , FDB and quadriceps muscles from WT and Actn3 KO mice . The major proteins involved in the rise of the Ca2+ transient are the dihydropyridine-receptor voltage sensor ( DHPR ) and the ryanodine-receptor Ca2+-release channel ( RyR1 ) [28] . There was no difference between WT and KO in the expression of either of these proteins ( Fig . 5A ) . The decay of the Ca2+ transient in fast-twitch fibres involves the binding of Ca2+ to myoplasmic buffers , the main one being parvalbumin , and the re-uptake of Ca2+ by the SR [28] . In fast-twitch fibres the SR Ca2+ pump is SERCA1 , while calsequestrin and sarcalumenin are Ca2+-binding proteins within the SR lumen [29 , 30] . There was no difference between WT and KO in parvalbumin expression ( Fig . 5A and B ) . However , muscles from Actn3 KO mice showed significantly increased expression of SERCA1 , calsequestrin and sarcalumenin ( Fig . 5A , B and C ) . The FDB fibres from Actn3 KO mice show increased Ca2+ leak from the SR ( Fig . 2C ) and improved fatigue resistance ( Fig . 3C and D ) . Together with an increased activity of mitochondrial enzymes , as reported in our previous publications [1 , 13 , 14] , these three changes are also hallmarks of FDB muscle fibres from wild-type mice exposed to prolonged cold [18] . Hence α-actinin-3-deficient muscles can be said to be “pre-acclimatised” to cold . In addition to demonstrating that muscles from Actn3 KO mice are “pre-acclimatised” to cold , we have provided evidence of a heat-generating mechanism in fast glycolytic fibres lacking α-actinin-3 . Compared to WT fibres containing α-actinin-3 , Actn3 KO fibres have an approximately fourfold higher rate of Ca2+ leak from the SR ( Fig . 2C ) . This leaked Ca2+ must be pumped back into the SR; accordingly , there is an approximate threefold increase in the rate of Ca2+ pumping ( Fig . 2B ) . In fact , the increase in pump rate is so effective that in Actn3 KO fibres the SR is able to reduce [Ca2+]i even more quickly during twitch relaxation than in WT fibres ( Fig . 2A ) , even though more Ca2+ is leaking back out . This represents a significant increase in the amount of ATP consumed by the pump , and the heat generated by ATP hydrolysis would be especially advantageous in cold environments . The increase in SERCA1 expression ( Fig . 5A , B and C ) is the most likely source of the increased pump rate , as well as providing the pathway for increased Ca2+ leakage . However , an increase in the number of Ca2+ pumps would not in itself guarantee such a large increase in the rate at which the SR resequesters Ca2+ from the myoplasm . As Ca2+ re-enters the SR lumen , the increase in intraluminal free Ca2+ concentration would reduce the gradient for Ca2+ pumping and limit the rate of pumping . This problem is overcome by the presence of Ca2+ buffers within the SR lumen that bind Ca2+ and keep the intraluminal free Ca2+ concentration at low levels [28] . The major buffering protein is calsequestrin [29] , while sarcalumenin also plays a role [30] . We detected increased expression of both these proteins in the muscles of Actn3 KO mice ( Fig . 5A , B and C ) . Hence it is likely that increased expression of SERCA1 , calsequestrin and sarcalumenin all work in concert to markedly raise the rate of SR Ca2+ pumping in α-actinin-3-deficient muscle fibres . This cycle of continuous Ca2+ leakage and re-pumping must be sustained by a large increase in ATP production , and one might speculate that the shift towards oxidative metabolism so consistently observed in Actn3 KO muscle [1 , 13 , 14] is a response to the metabolic demands of this thermogenic process . The activation of TCA cycle enzymes by mitochondrial uptake of leaked Ca2+ represents a direct pathway by which this response might be effected . Hence Ca2+ leakage from the SR in α-actinin-3-deficient muscle fibres not only provides the stimulus for thermogenesis , but also provides the stimulus for producing the energy to sustain this process . In summary , we propose that α-actinin-3 deficiency adapts skeletal muscle to cold environments through the mechanisms depicted in Fig . 6 . In this scheme , the primary event is a genetic deficiency in α-actinin-3 ( 1 ) , which through as yet unidentified mechanisms results in an increase in the number of SERCA1 channels ( 2 ) . These channels provide the pathway for an increased Ca2+ leak ( 3 ) . The uptake of leaked Ca2+ into mitochondria causes an increase in mitochondrial enzyme activity ( 4 ) . Mitochondrial enzyme activity can also be stimulated through increased activity of calcineurin ( 3a ) , which has been released from calsarcin-2 inhibition by the up-regulation of α-actinin-2 ( 2a ) . The increased oxidative capacity for ATP generation leads to increased fatigue resistance ( 5 ) . The three characteristics of increased Ca2+ leak ( 3 ) , increased mitochondrial enzyme activity ( 4 ) and increased fatigue resistance ( 5 ) are also found in the muscles of mice exposed to prolonged cold , and hence α-actinin-3-deficient muscle can be said to be “pre-acclimatised” to cold . In addition , these muscles contain a thermogenic mechanism . The increased Ca2+ leak is matched by an increased rate of pumping by the SERCA1 pumps ( 6 ) , and the pumping is facilitated by the increased expression of the Ca2+-binding proteins , calsequestrin and sarcalumenin , within the SR lumen ( 7 ) . The increased ATP hydrolysis ( 8 ) by the SERCA1 pumps generates heat ( 9 ) . This cold-acclimatisation and thermogenesis in α-actinin-3-deficient muscle provides one possible explanation for the selective favouring of the ACTN3 577X null polymorphism in populations living in cold environments during recent evolution , one of the very rare cases in the human genome of positive selection for a single-gene null allele . Despite the similarities between the Actn3 KO muscle fibres in our study and the muscle fibres from the cold-acclimatised mice studied by Bruton et al . [18] , we acknowledge that further studies are required to truly confirm the cold-acclimatisation effects of α-actinin-3 deficiency . One important study would be to subject our WT mice to prolonged cold-exposure , and compare the Ca2+-handling characteristics of fast glycolytic fibres from these mice with those from non-cold-exposed Actn3 KO mice . It would be important also to measure differences in temperature within muscle fibres from WT , cold-exposed WT and Actn3 KO mice in order to quantify the possible thermogenic effects of α-actinin-3 deficiency . It is also important to confirm that differences in genetic background are not contributing to the differences in Ca2+ handling between WT and Actn3 KO mice . Such problems have been minimised in our present study by using Actn3 KO and WT littermates generated on the same genetic background [1] . WT and Actn3 KO mice on a C57BL6 background were sacrificed with an overdose of halothane ( UNSW animal ethics approval 11/140B ) . A separate cohort of WT and Actn3 KO mice was sacrificed at the Children’s Hospital Westmead ( CHW animal ethics approval K190/11 ) . WT and Actn3 KO mice on a C57BL6 background were sacrificed with an overdose of halothane ( UNSW animal ethics approval 11/140B ) . The FDB muscle was dissected from the hindlimb and incubated in a muscle digest solution for 30 min at 37°C . The digest solution was a Krebs solution ( 4 . 75 mM KCl , 118 NaCl , 1 . 18 KH2PO4 , 1 . 18 MgSO4 , 24 . 8 NaHCO3 , 2 . 5 CaCl2 and 10 glucose ) to which was added 3 mg/mL collagenase I ( Sigma Chemical Co . , St Louis , MO , USA ) and 1 mg/mL trypsin inhibitor ( Sigma ) , aerated with 95% O2-5% CO2 to maintain pH at 7 . 4 . Following incubation , the muscle mass was washed twice in Krebs-only solution . Individual fibres were then obtained by gently pipetting the muscle mass [42] . A separate cohort of WT and Actn3 KO mice were sacrificed at the Children’s Hospital Westmead ( CHW animal ethics approval K190/11 ) . The EDL , FDB and QUAD muscles were dissected and cryopreserved using tissue Tek imbedding medium ( O . C . T ) and frozen in pre-chilled isopentane for immunohistochemistry ( IHC ) and western blot analysis . The fibres were placed onto glass coverslips for fluorescence microscopy and became firmly attached . Individual muscle fibres were viewed with a 40 UV-F objective on a Nikon TE300 inverted microscope equipped a xenon light source . Fibres with diameter >40 μm were selected; these larger fibres were the MT-II fast [Ca2+]i transient type [43] . An intracellular electrode was used to fill the muscle fibres with the ionised form of the Ca2+-sensitive dye fura-2 . Fura-2 ( 1 mM ) in distilled H2O was introduced into the tip of the ionophoretic electrode , and the shank was then filled with 150 mM potassium acetate . Dye was ionophoresed into the muscle fibres to give a final concentration of 5–50 μM fura-2 in the cell [44] . After filling with fura-2 , the fibres were left for about 20 min before any readings were taken to allow for complete distribution of the dye in the myoplasm . The ratio of fluorescence emission intensities at 510 nm was sampled via a photo multiplier tube ( PMT ) at 250 Hz using a spectrophotometer ( Cairn ) under 340 and 380 nm excitation . However , in order to improve the temporal resolution for the investigation of single twitches ( Fig . 1 ) , a single wavelength ( 380 nm ) was used and fluorescence was sampled at 20 , 000 Hz . An isosbestic measurement was taken and this value was used to construct the ratio values; details of this methodology can be found in our earlier publication [45] . For the single wavelength recordings the gain of the PMT was adjusted on a fibre by fibre basis to improve the signal to noise ratio . The dual wavelength ratiometrically recorded resting [Ca2+]i was used to correct for this . The fibre was stimulated using a bipolar stimulating electrode placed close to the neuromuscular junction , which was visible in the light microscope as a corrugated oval on the fibre . The fibre was stimulated with pulses of 1 ms duration from 1 to 100 Hz . Shortening in response to action potential activation of the fibres was minimal . In some experiments , fibres were immobilised by application of the selective inhibitor of the ATPase activity of skeletal muscle myosin; 4-Methyl-N- ( phenylmethyl ) benzenesulfonamide ( BTS ) 25μm to the bath; in this case , the [Ca2+]i transients were not significantly different to those before application of BTS , indicating minimal interference from movement artefacts . During the experiments , the isolated fibres were superfused ( 1 mL/min ) with Krebs solution maintained at room temperature ( 22°C–24°C ) and aerated with 95% O2-5% CO2 . The fluorescence of fura-2 was converted to [Ca2+]i using our previously determined in vivo calibration curve measured in isolated fibre segments from mouse extensor digitorum longus muscle [45 , 46] using the equation determined by Grynkiewicz et al . [47] . Because of the slow binding kinetics of Fura-2 , very fast events such as Ca2+ release during muscle stimulation are not adequately captured , with a marked underestimation of the rate of Ca2+ release . This limitation can be overcome by applying a correction process [45] to the raw [Ca2+]i values . We used this correction in calculating the rise times reported in Fig . 1B . The corrected [Ca2+]i was calculated from the raw [Ca2+]i using the following equation [45]: Corrected [ Ca 2 + ] i = [ Ca 2+ ] i + d dt [ Ca 2+ ] i ⋅ 1 k −1 ( 1+ [ Ca 2+ ] i K d ) ( 1 ) where k−1 is the dissociation constant of Ca2+-fura-2 , equal to 40 s−1 [45] . The rise times of the twitch transient obtained by this equation ( see Fig . 1B ) are in good agreement with those obtained by Calderón et al [43] for type IIX fibres from mouse FDB muscle using faster , lower-affinity dyes . An example of the effect of this kinetic correction on the calculated rise time of the twitch transient is shown in Fig . 7A . Kinetics of [Ca2+]i decay were measured by fitting exponential equations to the decay phases of the twitch and tetanic transients . The decay kinetics of the twitch transient ( Fig . 1C and D ) were calculated by fitting a two-phase exponential equation of the following form: y− y ∞ y 0 − y ∞ = f 1 e − k 1 t + ( 1− f 1 ) e − k 2 t ( 2 ) where y is the value of [Ca2+]i at time t , y0 is the value of [Ca2+]i at the start of decay , y∞ is the value of [Ca2+]i at the end of decay , f1 is the fraction of the total drop in [Ca2+]i attributable to the fast phase , k1 is the rate constant of the fast phase , and k2 is the rate constant of the slow phase . An example of the double-exponential curve fitted to the decay of the twitch transient is shown in Fig . 7B . The decay kinetics of the tetanic transients during fatiguing stimulation ( Fig . 3D ) were calculated by fitting a single-phase exponential equation , which is Eqn 2 with f1 set equal to 1 . It should be noted that the exponential equations were fitted to the raw , not the corrected , [Ca2+]i data because the slower [Ca2+]i changes during decay are adequately captured by fura-2 and as a result the corrected [Ca2+]i largely follows the raw [Ca2+]i [45] . Also , during decay , the correction process introduces extra noise which makes it difficult to fit an exponential equation satisfactorily . A double-exponential function was fitted to the decay phase of the twitch transient , as described above . Then , from the slow phase of the fitted curve , the values of [Ca2+]i and −d[Ca2+]i /dt ( decay rate ) were determined at selected time points . Then the following SR pump function equation was fitted to these values [20–22]: − d dt [ Ca 2+ ] i =A [ Ca 2+ ] i N −L ( 3 ) where A reflects the rate of Ca2+ uptake by the SR pump , L represents the rate of Ca2+ leak from the SR and N is a power term indicating the cooperativity of Ca2+ binding by the SR pump [21] . Following the practice of Westerblad et . al . [22] , we set N to a value of 4 to facilitate the comparison of A and L between fibers of WT and Actn3 KO mice . Other investigators have obtained N values close to 4 when allowed to be freely fit [21 , 22] , and in this particular study we obtained N = 4 . 27 ± 0 . 09 when allowed to be freely fit . For high-speed acquisition of transmitted illumination images during shortening of intact single FDB fibres electrically field-stimulated with a single supramaximal voltage pulse of 0 . 3 ms duration and 10 V amplitude , a CMOS PCO1200hs high-speed camera ( PCO AG , Kehlheim , Germany ) was mounted to the camera side-port of the Olympus inverted microscope . The Peltier-cooled camera was connected to a PC for acquisition control and data storage . Single fibres approximately covered a 520×160 pixel area when visualised through a ×20 objective which allowed frame rates for shortening sequences to push up to ≈4 , 200 frames per second . Recordings were synchronised with the induction of a single twitch and image read-out and storage from the ring-buffer of the camera was performed offline . For offline analysis of each experiment , an image sequence of approx . 1 , 000 to 1 , 700 frames per fibre were analysed using a modification of a previously written processing algorithm in IDL language environment [27] . This algorithm allows the user to depict the first image of a sequence , reads all subsequent images in a matrix and performs segmentation after the user has defined the region-of-interest including the fibre boundaries . The algorithm extracts the maximum fibre length and runs the shortening sequence on the processed images in a movie sequence to check for online accuracy . Immunoblotting for selected proteins was performed using equally loaded WT and Actn3 KO FDB and QUAD muscle samples as determined using the Pierce BCA assay kit ( Thermo Scientific ) and EDL muscles using Stain Free gel technology ( BioRad ) . A total of 4 to 20μg of total protein was loaded per sample and separated by SDS—PAGE on 4–12% pre-cast mini-gels ( Life Technologies ) or 4–15% Criterion Stain Free gels ( BioRad ) , transferred to polyvinylidene fluoride ( Millipore ) or nitrocellulose membranes ( BioRad ) blocked with 5% skim milk/1× tris buffered saline ( TBS ) /0 . 1% Tween-20 , then probed with indicated antibodies overnight and developed with ECL chemiluminescent reagents ( Amersham Biosciences and Thermoscientific ) . Images were collected using Image Lab software ( BioRad ) for EDL blots or X-ray film for FDB and QUAD analyses . Primary antibodies for immunoblotting include; EDL muscle lysates: anti-α-actinin-3 ( ACTN3; 1:10000 , Epitomics ) , anti-calsequestrin VIIID12 ( CSQ1; 1:2000 , Abcam ) , anti-sarcoplasmic reticulum ATPase1 ( SERCA1; 1:1000 , Developmental studies hybridoma bank ( DSHB ) ) , anti-ryanodine receptor 1 ( RyR1; 1:300 , DSHB ) , anti-dihydropyridine receptor ( DHPR; 1:400 , DSHB ) , anti-Parvalbumin ( PARVALB , 1:500 , Swant ) , with secondary goat-anti-mouse IgG-horse radish peroxidase ( HRP , 1:20000 , Pierce ) , goat anti-rabbit IgG HRP ( 1:20000 , Pierce ) and rabbit anti goat IgG HRP ( Invitrogen , 1:20000 ) . FDB and QUAD muscle lysates: anti-α-actinin-3 ( ACTN3; 1:1500; gift from A . Beggs , Children’s Hospital Boston ) , SERCA1 ( 1:2500; Sigma Aldrich ) , Sarcalumenin ( SAR; 1:1000; Sigma Aldrich ) , Parvalbumin ( PARVALB; 1:1000; Abcam ) , and α-sarcomeric actin ( 5C5; 1:2000; Sigma Aldrich ) . Secondary antibodies used were sheep anti-mouse IgG-HRP conjugates ( 1:2000; GE Healthcare ) and donkey anti-rabbit IgG-HRP conjugates ( 1:2000; GE Healthcare ) [48] . Data are presented as Mean ± S . E . M . . Unless otherwise stated , all statistical tests are two-tailed t-tests at a significance level of 5% . All statistical tests and curve fitting were performed using a standard statistical software package ( GraphPad Prism Version 6 for Windows , GraphPad Software , San Diego California USA ) .
α-Actinin-3 is a protein found inside the muscles of most people around the world . It is encoded by a gene called ACTN3 , popularly known as “the gene for speed . ” In 1 . 5 billion people , a certain variation in the genetic sequence of their ACTN3 gene causes their muscles to produce no α-actinin-3 protein at all . These people have no muscle disease; however , in elite athletes , a lack of α-actinin-3 seems to be beneficial for endurance activities and detrimental to sprinting activities . Intriguingly , α-actinin-3 deficiency varies in frequency across the globe , being most common in European and Asian populations and least common in African populations . During recent human evolution , there appears to have been strong positive selection for α-actinin-3 deficiency in places where food resources are relatively scarce and climate is cold . We have previously demonstrated that α-actinin-3 deficiency in the Actn3 knockout ( KO ) mouse causes a shift towards more “energy efficient” forms of muscle metabolism which would enhance survival in times of famine , and benefit endurance performance . Our present study , using single muscle fibres from Actn3 KO mice , demonstrates changes in calcium handling that would adapt muscles to cold environments and provide a survival advantage in cold climates .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[]
2015
Altered Ca2+ Kinetics Associated with α-Actinin-3 Deficiency May Explain Positive Selection for ACTN3 Null Allele in Human Evolution
Phagocytosis of bacteria by innate immune cells is a primary method of bacterial clearance during infection . However , the mechanisms by which the host cell recognizes bacteria and consequentially initiates phagocytosis are largely unclear . Previous studies of the bacterium Pseudomonas aeruginosa have indicated that bacterial flagella and flagellar motility play an important role in colonization of the host and , importantly , that loss of flagellar motility enables phagocytic evasion . Here we use molecular , cellular , and genetic methods to provide the first formal evidence that phagocytic cells recognize bacterial motility rather than flagella and initiate phagocytosis in response to this motility . We demonstrate that deletion of genes coding for the flagellar stator complex , which results in non-swimming bacteria that retain an initial flagellar structure , confers resistance to phagocytic binding and ingestion in several species of the gamma proteobacterial group of Gram-negative bacteria , indicative of a shared strategy for phagocytic evasion . Furthermore , we show for the first time that susceptibility to phagocytosis in swimming bacteria is proportional to mot gene function and , consequently , flagellar rotation since complementary genetically- and biochemically-modulated incremental decreases in flagellar motility result in corresponding and proportional phagocytic evasion . These findings identify that phagocytic cells respond to flagellar movement , which represents a novel mechanism for non-opsonized phagocytic recognition of pathogenic bacteria . Pathogen recognition by the innate immune system is one of the first lines of defense in cellular immunity to infection [1] . However , how bacteria establish chronic infections , as observed in patients with cystic fibrosis ( CF ) , and the reasons that these infective agents cannot be eliminated by the immune system are still largely unclear [2] , [3] . A relevant example of this is Pseudomonas aeruginosa , a Gram-negative opportunistic pathogen which establishes infection in the lung tissue of CF patients and effectively evades immune clearance [2] , [3]; CF disease severity correlates with chronic infection of the pulmonary compartment by P . aeruginosa [2] , [3] . One contributing factor that enables immune evasion is the loss of bacterial flagellar motility during colonization [4]–[8] . P . aeruginosa has a single , polar , monotrichous flagellum which provides force for swimming locomotion in aqueous environments [9] . Multiple studies have found that the majority of P . aeruginosa isolates taken from chronically infected CF patients have down-regulated flagellar gene expression and are phenotypically deficient in the ability to swim [6] , [7] . The previous paradigm suggested that the loss of flagellin as a phagocytic ligand facilitates evasion of innate immune cells and results in increased bacterial burden in the CF lung [5] , [8] . Recently , with the use of flagellated and non-flagellated swimming-defective P . aeruginosa genetic mutants , we demonstrated that it is not the loss of the flagellum itself , but rather the loss of flagellar-based swimming motility that allows P . aeruginosa to avoid phagocytic clearance [4] . However , it is currently unclear how the loss of bacterial swimming motility enables phagocytic evasion from innate immune cells and , to date , no published reports have examined in detail the dynamics of non-opsonized P . aeruginosa-phagocyte association and subsequent fate as a function of bacterial swimming motility . In order to delineate how bacterial swimming contributes to phagocytic recognition and uptake , we take advantage of isogenic bacterial mutations that affect flagellar swimming motility and we identify the individual components that comprise the phagocytic process as it relates to swimming and non-swimming bacteria . Swimming motility in Gram-negative bacteria is powered by generation of an ion gradient to turn a flagellar rotor against a stationary stator complex [10] . The resultant force provides the necessary torque to turn the flagellar filament and thus propel the bacteria [10] . In these studies we utilize genetic mutants which lack structural and functional flagella due to mutations in either the flagellin monomer or the flagellar hook protein and are therefore non-swimming , and also mutants which do not produce all or part of the flagellar stator complex . These mot stator mutants all have fully assembled flagella , since loss of the Mot stator proteins does not impede construction of the flagellar filament , and are instead partially or fully defective in the ability to rotate the flagellum depending on which stator components are omitted [9] , [11] , [12] . Our previous work with these mutants found that the phagocytic response to P . aeruginosa infection depends on flagellar motility , but does not depend on the flagellum itself as an activating ligand [4] . Since loss of flagellar motility confers phagocytic resistance , these data suggest that innate immune cells have the ability to recognize bacterial movement and that swimming bacteria provide an important sensory input for phagocytic engulfment [4] . However , an alternative explanation is that bacteria change the expression of unknown secreted and/or cell-surface ligands in response to the loss of swimming motility and therefore alter their phagocytic recognition and uptake . Here we test these hypotheses and provide the first evidence that phagocytic cells utilize bacterial swimming motility as a global mechanism for bacterial recognition . Significantly , we show that alterations in swimming motility allow multiple bacterial species to evade phagocytic recognition . This is not due to measurable changes in the expression of common outer membrane proteins ( OMPs ) or known regulators of pathogen-associated molecular patterns ( PAMPs ) . Rather , we provide evidence that phagocytic cells are able to respond to bacterial swimming as a function of flagellar rotation after initial contact and , importantly , that phagocytosis is directly proportional to the flagellar torque of the bacteria . We therefore propose a model in which the step-wise loss of flagellar function confers a progressive increase in the ability of the bacteria to evade the phagocytic response of the innate immune system , which promotes an environmentally beneficial niche during infection . This selective pressure provides an explanation for the down-regulation of motility genes and phenotypic loss of swimming that is observed in isolates procured from chronic infections [4]–[8] . To determine whether phagocytic evasion through loss of swimming motility is specific to P . aeruginosa or is a mechanism shared amongst flagellated Gram-negative pathogens , we used genetically modified motility mutants in multiple bacterial backgrounds ( Table 1 ) . P . aeruginosa PA14 is a non-mucoid clinical isolate and is considered the wild-type ( WT ) in this study . All P . aeruginosa genetic mutants used in this study are on the PA14 background . All Vibrio cholerae mutants are constructed using the classical biotype O395 strain and all Escherichia coli mutants are in the K12 background . All non-flagellated strains ( which lack swimming motility ) have a mutation in either the flagellar hook gene ( flgK ) , or in the gene coding for the flagellin monomer ( flaA and fliC for V . cholerae and E . coli , respectively ) [9] , [12] , [13] . The two stator complexes ( MotAB and MotCD ) in P . aeruginosa are each composed of two proteins and are functionally partially-redundant . Importantly , deletion of all four genes ( motABmotCD ) inhibits flagellar rotation , but not flagellar assembly , resulting in a mutant that is flagellated but incapable of swimming [9] . The motAB mutant , and to a lesser extent the motCD mutant , are swimming competent , though not to the same degree as the parental WT [9] , [14] . The stator complexes in V . cholerae and E . coli are analogous to those of P . aeruginosa , though not identical in composition . The stator of V . cholerae is also composed of at least four proteins , termed PomA , PomB , MotX , and MotY [15] . The contribution of each protein to stator functionality in V . cholerae is still unclear , however loss of the motX gene results in a flagellated , but non-swimming mutant that is phenotypically similar to the P . aeruginosa motABmotCD mutant [12] , [15] . In E . coli , the stator is composed of only two proteins , MotA and MotB [13] . Loss of either gene product ( MotA in this study ) results in a similar flagellated , but non-swimming mutant [13] . We previously reported that the genetic loss of the stator complexes in P . aeruginosa PA14 confers resistance to phagocytosis in vitro and in vivo in comparison to the swimming-competent parental strain [4] . Phagocytic evasion is not dependent on flagellar assembly , as both flagellated and non-flagellated mutants were equally capable of avoiding phagocytic ingestion [4] . In order to better understand the dynamics of phagocytic resistance by strains incapable of swimming motility , we first verified that strains competent in swimming motility were as equally susceptible to gentamicin as non-swimming strains and remained equally viable during incubation ( Figure S1 and data not shown ) , and then performed gentamicin protection assays with bone marrow-derived dendritic cells ( BMDCs ) and increasing concentrations of non-swimming P . aeruginosa relative to the WT concentration . We were not able to identify a resistance threshold in either the flgK or the motABmotCD mutants where phagocytic susceptibility approximated WT levels ( Figure 1A ) . In assays where the concentration of non-swimming bacteria was increased to 100-times that of WT , we observed only a ∼30% increase in recovery relative to WT ( Figure 1A ) , indicating that the mechanism facilitating phagocytic resistance of non-swimming P . aeruginosa can only partially be overcome even in the presence of increased non-swimming bacterial concentrations . This degree of phagocytic resistance conferred by loss of bacterial motility is highlighted by the comparison to other phenotypes that have been reported to alter bacterial clearance . For example , alginate production ( mucoidy ) by P . aeruginosa has been reported to alter bacterial phagocytic susceptibility [16] , however the swimming mucoid P . aeruginosa strain FRD1 [17] exhibited only a ∼2-fold change in phagocytosis compared to non-mucoid PA14 WT ( Figure 1A ) . To test whether motility-based phagocytic recognition is specific to P . aeruginosa , or if this mechanism extends to other bacterial pathogens as well , we performed similar assays using flagellated and non-flagellated V . cholerae and E . coli genetic mutants that contain analogous mutations to the P . aeruginosa mutants described previously . In assays using V . cholerae , both the non-flagellated flaA mutant and the flagellated but non-swimming motX mutant were ∼100-fold more resistant to phagocytosis than the isogenic WT ( Figure 1B ) . In comparison , the swimming-competent tcpA and toxT mutants , which instead lack toxin co-regulated pili ( TCP ) which facilitate attachment [18]–[20] , were ingested to a similar degree as WT V . cholerae ( Figure 1B ) . In experiments using E . coli , the non-flagellated flgK and fliC strains and the flagellated but non-swimming motA strain were all significantly more resistant to phagocytosis compared to the swimming WT , although to a lesser degree than observed with P . aeruginosa and V . cholerae ( Figure 1C ) . To test if these findings also applied to human phagocytes , we tested human THP-1 cells for their preferential ability to phagocytose swimming bacteria . The human THP-1 phagocytic cell line recapitulated our observations using murine BMDCs ( Figure 1D ) which supports a general mechanism by which non-opsonized Gram-negative bacterial recognition by phagocytic cells is swimming motility-dependent and is not species-specific . In order to visualize the host-pathogen interactions that occur between P . aeruginosa and innate immune cells , and to confirm the assays presented in Figure 1 , murine peritoneal macrophages were incubated at 37°C with equal numbers of either GFP-transformed P . aeruginosa PA14 WT or motABmotCD , or V . cholerae O395 WT or motX bacteria and the non-adherent bacteria were washed away prior to counter-staining exposed cell-surfaces with Alexa-647-labeled wheat germ agglutinin ( WGA ) . Multiple images per co-incubation were generated by randomly choosing a viewing field and counting the internalized bacteria along the Z-axis of all visible cells . Representative images of co-incubations using O395 WT ( Figure 2A , left ) or motX ( Figure 2A , right ) demonstrate that bacteria with swimming motility associate with macrophages to a much higher extent than do non-swimming bacteria . In co-incubations using O395 WT or PA14 WT bacteria ( as in Figure 2A ) the quantified internalization , as assessed by bacteria within the phagocytes that do not co-localize with the WGA , is increased >10-fold over motX or motABmotCD , respectively ( Figure 2B ) . These data both further support our gentamicin protection assays and the hypothesis that loss of flagellar motility inhibits the ability of phagocytic cells to engulf bacteria . One possible explanation for our current observations is that motility or loss of motility elicits the release of an unknown soluble factor , and that this hypothetical ligand is acting to either induce phagocytosis ( if elicited in the motile bacteria ) or to impair phagocytosis ( if elicited in the non-motile bacteria ) by affecting either the neighboring bacteria or the phagocyte itself . In either scenario , we hypothesized that one bacterial strain may affect the phagocytosis of the other strain in trans . We tested this hypothesis with mixed cultures of PA14 WT and motABmotCD . Carbinicillin-resistant ( Carbr ) WT or the motABmotCD mutant were mixed in equal numbers with the Carbinicillin-sensitive ( Carbs ) version of the other strain and introduced to murine BMDCs in a standard gentamicin protection assay , after which lysates were plated on Carbinicillin-selective plates . The number of recovered Carbr-motABmotCD CFUs after co-incubation with Carbs-WT and BMDCs was not significantly different than when motABmotCD alone was incubated with BMDCs ( Figure 3A ) . Likewise , recovered CFUs of Carbr-WT when mixed with Carbs-motABmotCD did not change from what is observed when WT alone is assayed by gentamicin protection assay ( Figure 3A ) . This indicates that a swimming competent strain is not able to confer phagocytic susceptibility to a non-swimming strain , nor can a non-swimming mutant confer resistance to a swimming WT . Therefore , differences in phagocytic response as elicited by swimming verses non-swimming P . aeruginosa are not due to any soluble factor being secreted into the extracellular environment or altering the phagocytic activity of the BMDCs . Many of the regulatory pathways controlling synthesis of outer membrane proteins and peripheral structures on P . aeruginosa are still being elucidated; however phagocytosis assays with P . aeruginosa swarming mutants , type-III secretion mutants , and mucoid strains did not result in significantly increased phagocytic resistance relative to controls ( data not shown ) . Nonetheless , it is still possible that flagellar rotation is co-regulated with gain or loss of expression of an unknown extracellular PAMP or ligand that is recognized by innate immune cells . To identify if deletion of the mot genes correlates with changes in peripheral gene expression levels , we performed genome-wide microarray analysis of the WT and motABmotCD strains . Comparison of gene expression levels between WT and motABmotCD showed no significant change in any recognizable PAMP regulators , OMP genes , lipopolysaccharide synthesis elements , or known immune activating factors ( Figure 3B and Table S1 ) . Genes which did change expression more than 2-fold with loss of the mot operons are listed in Table 2 . However , swimming motility assays and preliminary phagocytic assays with PA14 strains containing transposon insertions in each of those genes identified in Table 2 did not recapitulate the phenotypes observed with motABmotCD ( data not shown ) . These data support the hypothesis that phagocytic cells are able to directly respond to swimming motility by bacteria . An alternative hypothesis to the cellular sensing of bacterial motility is that instead of non-swimming strains evading phagocytic uptake , the loss of flagellar motility renders the bacteria more susceptible to killing within the phagolysosome . While there is no prior evidence of this , we rigorously tested relative bacterial association and recovery over time by co-incubating WT or motABmotCD with adherent macrophages and then separating the cell-unassociated bacteria in the media from the macrophage-associated bacteria and plating both fractions to quantitatively assess relative CFUs in each . At all time points tested , greater CFU recovery was observed in the unassociated fraction when using motABmotCD , while in the associated fraction , significantly higher CFUs were recovered with WT ( Figure 4A ) . If intracellular killing were increased for motABmotCD , extracellular CFUs would decrease below that of WT as bacteria were removed from the system at a higher rate . We therefore conclude that microbiocidal vulnerability and bacterial death does not measurably account for the differences observed between swimming and non-swimming strains . These data support previous observations that intracellular killing of non-opsonized P . aeruginosa is <5% of available bacteria within a 45-min co-incubation time period [4] . Another alternative explanation for the current observations is that non-swimming bacterial mutants do not come into contact with phagocytes to the same degree as swimming-capable WT . To test this hypothesis we performed multiple , complementary assays . First , we performed gentamicin protection assays with WT or motABmotCD in the presence of surfactant in order to decrease surface tension that may inhibit contact between bacteria and phagocytes . In co-incubations performed with either the non-ionic detergents Tween80 or beta-octyl glucoside ( used as a biofilm inhibitor [21] ) , or the artificial lung surfactant Survanta , we did not observe any increase in motABmotCD uptake ( Figure 4B ) . Secondly , we tested whether forced contact between bacteria and phagocytes would overcome the phagocytic deficit of the non-swimming bacteria . To do so , we centrifuged bacteria onto BMDCs or macrophages and then subsequently assayed for phagocytosis . The degree of initial contact of WT or motABmotCD bacteria with the phagocytes following centrifugation was analyzed by FACS and was not different between strains ( Figure 4C , inset ) . We observed a slight increase in CFU recovery of the non-swimming P . aeruginosa flgK and motABmotCD mutants ( Figure 4C ) as well as the non-swimming V . cholerae flaA and motX mutants ( Figure 4D ) relative to the respective swimming bacterial strains when contact was artificially initiated . However , the increased internalization did not recapitulate WT levels of phagocytosis , since non-swimming strains were still at least 10-fold more resistant to uptake as compared to their respective parental strains . These data demonstrate that phagocytic recognition is not solely dependent on contact between bacteria and phagocyte and supports a role for flagellar motion in pathogen recognition and ingestion . The relative contributions of binding verses phagocytic uptake and engulfment are not well understood in non-opsonized phagocytosis . To further elucidate the individual components that promote the phagocytosis of swimming bacteria , we quantitatively assessed bacterial association with macrophages under 3 sequential conditions . We first co-incubated swimming or non-swimming P . aeruginosa with adherent murine macrophages at 4°C , which is permissive for binding but prevents both bacterial motility and phagocytic uptake , and then washed away non-associated bacteria and plated the cellular lysates . In parallel , we warmed cells and bacteria to 37° after the initial binding and washing at 4°C , thus initiating both bacterial movement and phagocytosis of bound bacteria , and then plated lysates directly , or treated with gentamicin and then plated . In co-incubations held at 4°C , recovered CFUs between WT , flgK , and motABmotCD were similar , as was expected since all bacteria were immobilized ( Figure 5A ) . Of note , this also supports that it is not an unknown bacterial cell-surface ligand , with expression altered by changes in motility , that affects bacterial binding to phagocytes . However , the difference in relative bacterial association with macrophages increased dramatically when bound-bacteria and cells were warmed to 37°C , demonstrating that binding of bacteria is a necessary but insufficient component to the differential phagocytic recognition ( Figure 5A ) . Even once associated with phagocytic cells , non-swimming P . aeruginosa evade uptake and , as evidenced by the progressively decreasing number of CFUs recovered after successive washes ( Figure 5A left ) , disassociate at a higher efficiency than WT bacteria . Treatment with gentamicin demonstrated that the remaining associated bacteria , after washing , are further differentially ingested dependent on swimming-capability ( Figure 5A ) . However , it is possible that co-incubation at 4°C distorts initial receptor-ligand interactions that nominally occur at physiological temperature . To confirm that non-swimming P . aeruginosa is impaired in its ability to bind innate immune cells , we pre-treated macrophages with cytochalasin D to inhibit phagocytic uptake and subsequently incubated WT or non-swimming mutants with these macrophages at 37o . We then washed and plated cellular lysates to quantitatively assess the bacteria that bound to the outside of the cells . In support of the previous assays , we recovered significantly fewer flgK and motABmotCD CFUs than WT ( Figure 5B ) . Visualization of these co-incubations using GFP-expressing strains and Alexa-647-stained macrophages confirmed that bacterial association is decreased in non-swimming P . aeruginosa strains ( Figure 5C ) . In order to better understand and visualize how phagocytic cells bind swimming verses non-swimming bacteria we performed live cell microscopy of adherent macrophages interacting with P . aeruginosa . Equal concentrations of either GFP-expressing WT or GFP-expressing motABmotCD were flowed across adherent macrophages at a constant rate and visualized under fluorescence and DIC . WT readily accumulated on macrophage cell surfaces with prolonged associations and visible and substantial adherence events ( Figure 6 top , Video S1 ) . The motABmotCD mutant displayed little or no accumulation on the cells , visually flowing past macrophages with appreciably shorter adherent associations ( Figure 6 bottom , Video S2 ) . These images support the previous data which show that phagocytic evasion by non-swimming bacteria is achieved through multi-faceted resistance to binding accompanied by phagocytic unresponsiveness even with contact . Our data indicate that flagellar rotation confers phagocytic recognition by innate immune cells . As a formal test of this , we hypothesized that bacterial flagellar motility would be proportional to phagocytic uptake . Motility studies with P . aeruginosa grown in media of increasing viscosity have shown that successive genetic deletions of the partially-redundant mot flagellar stator complexes result in decreases in swimming capability [9] , [14] . Specifically , swimming and flagellar-based motility in P . aeruginosa is tied to the degree of flagellar stator function , since loss of rotation from deleting motAB decreases flagellar-based motility below that of WT , while loss of motCD further decreases flagellar-based motility below that of the motAB mutant [9] , [14] , and loss of all four mot genes ( both complexes ) renders P . aeruginosa completely unable to swim or swarm ( maximal expansion of colonies of WT , motAB , motCD and motABmotD in 0 . 6% agar were previously assessed as 29 . 5 , 21 . 9 , 7 . 3 , and 6 . 3 mm , respectively [9] , [14] ) . Therefore , we used isogenic mot mutants to test if decreases in swimming ability confer proportional increases in phagocytic evasion . Total bacterial association between GFP-expressing motAB and BMDCs was significantly decreased as compared to GFP-expressing WT as measured by fluorescence-activated cell sorting ( FACS ) , while association was further decreased in GFP-expressing motCD and GFP-expressing motABmotCD ( Figure 7A ) . To more rigorously and quantitatively assess relative phagocytosis of these mutants we returned to the gentamicin protection assay . Phagocytosis of motAB was slightly but significantly decreased compared to WT ( Figure 7B ) . Further phagocytic resistance was observed in motCD , with non-swimming motABmotCD mutant being the most resistant ( Figure 7B ) . This was not due to measurable differences in binding between the mot mutants , since these all bound to cytochalasin D-treated BMDCs similarly , though binding was impaired relative to GFP-expressing WT and better than GFP-expressing flgK ( Figure 7C ) . Importantly , microarray analysis comparing gene expression profiles between WT , motAB , motCD and motABmotCD did not reveal any genetic changes that progressively correlate amongst these four strains with motility and therefore there were also no changes amongst the bacterial strains that correlated with phagocytosis ( Table S2 ) . Using methodology similar to that in Figure 5A , we next used the mot mutants to compare relative swimming ability with phagocytosis by adherent macrophages . Assessment of retained association and subsequent engulfment after initial binding revealed that all 3 mot mutants were slightly , but comparably , deficient in binding to adherent macrophages at 4°C ( Figure 7D ) . However , upon warming of cells and bound bacteria to 37°C , followed by treatment with gentamicin , a progressive loss of association relative to WT was observed where association and engulfment of WT > motAB > motCD > motABmotCD ( Figure 7D ) . This is the first evidence that the MotAB and MotCD proteins regulate phagocytic susceptibility in P . aeruginosa and that sequential loss of the Mot complexes confers increasing phagocytic evasion . Since we observed increasingly dramatic phagocytic evasion phenotypes through genetic manipulation of the bacterial stator complexes , we turned to V . cholerae for biochemical proof-of-principle in support of our genetic evidence . Flagellar torque in Pseudomonas is believed to be generated through active transport of protons across the outer and inner membranes [14] , [22] . In V . cholerae , however , flagellar torque is generated through transport of sodium ions [15] , [23] . Progressively limiting the concentration of sodium in the media leads to proportional inhibition of V . cholerae flagellar rotation , and thus its ability to swim [23] . We performed gentamicin protection assays in serum-free buffers containing successively titrated concentrations of NaCl , while maintaining a constant osmolarity by substituting choline chloride . As expected , decreasing the availability of sodium did not significantly change the degree of uptake of WT P . aeruginosa , which does not depend on sodium for flagellar motility ( Figure 7E ) . However , loss of sodium availability correlated with increased phagocytic resistance by WT V . cholerae ( Figure 7E ) . Treatment of V . cholerae or P . aeruginosa alone with sodium-limited buffers did not significantly decrease recovery of colony forming units ( data not shown ) . Importantly , reconstitution of the lowest sodium-containing buffer with 15mM NaCl , while maintaining the choline chloride concentration constant , elicited a recovery in phagocytic susceptibility of V . cholerae , thereby confirming that choline chloride itself is not responsible for increased phagocytic resistance or for death of the V . cholerae ( Figure 7E ) . Taken together , these data indicate that the step-wise loss of flagellar torsion and swimming ability , whether through genetic deletion of the stator complex or limiting ion motive force , provides for an increasing ability to evade recognition and phagocytosis by innate immune cells . In this work , we define the steps that comprise phagocytic recognition of non-opsonized bacterial pathogens and identify that changes in flagellar swimming motility can titrate the phagocytic clearance of bacteria . In support of previous results , phagocytosis is independent of flagellar assembly , since both flagellated and non-flagellated non-swimming strains are equally resistant to uptake [4] . While the majority of these studies focus on P . aeruginosa , we show that this immune resistance phenotype in swimming-defective strains is not specific to a single species , but represents a potentially widespread mechanism of immune evasion by Gram-negative bacteria . This phenotype is not due to motility-regulated secreted factors or compensatory changes in expression of bacterial genes . Likewise , phagocytic evasion is not due to avoidance of contact with phagocytic cells . Instead , as shown with P . aeruginosa , non-swimming strains avoid phagocytic recognition by disassociation after initial contact and remain resistant to phagocytosis even after being bound at the cell surface . In comparing resistance across multiple bacterial species , it is interesting that non-swimming V . cholerae mimicked P . aeruginosa in terms of scope and magnitude of phagocytic resistance , while the degree of non-swimming E . coli resistance was much less dramatic . Since loss of motility in the V . cholerae and E . coli isogenic mutants was confirmed by swimming motility assays in 0 . 3% agar ( data not shown ) , it is not immediately clear why swimming deficiency in E . coli conferred resistance to a lesser degree . Of note , both P . aeruginosa and V . cholerae have a single , polar , monotrichous flagellum under standard conditions , whereas E . coli swim via multiple peritrichous flagella [9] , [12] , [13] . However , non-flagellated flgK and fliC strains of E . coli were not significantly different than the flagellated , non-swimming motA mutant in terms of phagocytic uptake . It is therefore unlikely that multiple flagella are causative of the discrepancy; more plausible is that the recognition of alternative structures , specific to E . coli , are able to partially compensate for the resistance phenotype of non-swimming strains . The small decrease in uptake observed with a mucoid strain of swimming-capable P . aeruginosa , as well as the trending decrease in recovered V . cholerae CFUs when TCP is eliminated , suggest that such compensatory mechanisms are feasible . Even so , the significant drop in phagocytic susceptibility when E . coli loses flagellar motility , independent of flagellar assembly , supports our hypothesis of a widespread mechanism utilized by innate immune cells for phagocytosis of motile , non-opsonized pathogens . Moreover , we demonstrate that this response to motility is shared amongst phagocytic cell types , anatomical locations , and cells of human or mouse origin ( Figure 1 and [4] ) . To delineate the phagocytic process , we examined bacterial engulfment in a step-wise manner . Our data indicate that for an uptake event to occur , contact must be made between the pathogen and the phagocyte , followed by adhesion and recognition , which culminates in a stimulus to ingest . In assessing P . aeruginosa binding and association with cytochalasin D-treated BMDCs , both the non-flagellated flgK mutant and the flagellated mot mutants were modestly but significantly decreased in binding to BMDCs compared to WT , though the degree of flgK association was well below that of all three mot mutants , which were not different from each other . This is of interest since multiple reports have identified bacterial flagella as potent adhesins in physiological systems [24]–[26] . Taken together , these data point to the flagellum in P . aeruginosa as indeed having adhesive properties , but calls into question the role of flagella as direct ligands for phagocytosis since all non-swimming mutants , regardless of flagellar expression , were indistinguishable in phagocytic assays . Furthermore , artificial enforcement of contact between non-swimming P . aeruginosa or V . cholerae and phagocytes did not recapitulate WT levels of uptake . In the experiments where pathogen-host cell contact was induced , and therefore initially equal between strains , we recovered significantly fewer CFUs using non-swimming strains relative to controls and the presence of the flagellum did not change phagocytic uptake among strains that were non-swimming . Therefore , the presence of a non-rotating flagellum can only partially recover WT levels of binding to phagocytic cells , and contact alone , regardless of flagellar assembly , is necessary but insufficient for full phagocytic activation . Importantly , among those bacteria that bound to the cell-surface , we discovered that their subsequent phagocytic fate is dependent on their swimming capability . Overall dissociation after contact , and therefore also evasion of engulfment , was increased in non-swimming P . aeruginosa compared to WT . These data support an overall model where loss of flagellar rotation enables the evasion of both phagocyte binding and , importantly , recognition and response after initial contact . The evidence that the flagellum itself is not contributing to phagocytic susceptibility , but that flagellar rotation is , raises the question of how the cells preferentially recognize swimming bacteria . We previously demonstrated that loss of the MyD88 adaptor protein did not alter phagocytic uptake of P . aeruginosa [4] . Therefore , none of the MyD88-dependent toll-like receptors ( TLRs ) , specifically TLR5 which recognizes bacterial flagellin , are required for phagocytosis of P . aeruginosa . The two likely possibilities , not mutually exclusive , are that bacterial motility alters the expression of an unknown bacterially-produced factor or ligand that alters the ability of the phagocyte to recognize or ingest the bacteria; or that the phagocyte can sense the motility and that this drives the phagocytic event . Our data demonstrate that it is unlikely that phagocytic cells are sensing a motility co-regulated secreted molecule or extracellular ligand , since the mixed-culture assay did not indicate that WT could confer phagocytic susceptibility to the motABmotCD mutant in trans , nor could motABmotCD confer phagocytic resistance to WT . This inability to physiologically complement the phagocytic phenotype suggests that the extracellular environment of cells co-incubated with WT is not different than when cells are co-incubated with motABmotCD . In support of the complementation data , microarray analysis of bacterial gene expression when the mot complexes were successively deleted from WT indicate that no known immunogenic effectors are significantly altered . Most importantly , there was no significant change in gene expression pattern that correlated with successive deletion of the mot complexes and therefore no compensatory bacterial genetic changes that correlated with phagocytic susceptibility . Those genes that do change expression more than 2-fold in response to deletion of mot genes are likely bystander effects , as we could not identify phagocytic or motility phenotypes in corresponding transposon mutants . Thus , we believe it is unlikely that phagocytic evasion by motABmotCD is due to indirect effects of mot gene deletion . The alternative explanation is that leukocytes possess a mechanism that recognizes and responds to flagellar torsion as a phagocytic initiation signal . We hypothesized that , if this were the case , then step-wise decreases in flagellar torsion would result in proportional increases in phagocytic resistance . Deleting MotAB from the stator complex of P . aeruginosa results in decreased swimming speed , as it partially contributes to flagellar torque generation [9] , [14] . Our data show that this decrease in swimming capability confers a small but significant degree of resistance to phagocytosis . Further loss of flagellar motility , due to deletion of MotCD , which is a larger contributor to stator functionality than MotAB [9] , [14] , conferred a greater degree of phagocytic resistance . Complete loss of flagellar function , the phenotypic result of deletion of all four mot genes [9] , [14] , conferred the greatest degree of phagocytic resistance , equal to that of non-flagellated mutants . Once bacteria are cell-bound , subsequent dissociation and phagocytic evasion follow the same pattern , with resistance in motABmotCD > motCD > motAB > WT . This is the first demonstration of titrated phagocytic resistance in P . aeruginosa being regulated through mot gene function , and fits an infection model where the sensory mechanisms of the innate immune system provide a selective pressure for P . aeruginosa to down-regulate flagellar motility . Since the partial loss of the stator does not completely inhibit swimming capability , selective pressure to lose stator functionality would not necessarily impede P . aeruginosa colonization , but provide increasing degrees of resistance to phagocytic recognition . Indeed , the partial redundancy in the P . aeruginosa stator proteins may have evolved to provide such an advantage during infection . While a functional stator complex is required for flagellar rotation , additional requirements , such as the electro-chemical gradient that provides rotational force , are necessary for full flagellar motility [23] . Limiting the availability of ions required for flagellar rotation can selectively impede flagellar motility [23] . We found that progressively decreasing sodium availability to V . cholerae , which depends specifically on sodium ions for flagellar rotation , conferred step-wise increases in phagocytic resistance , analogous to our observations with the P . aeruginosa genetic mutants . Limiting sodium availability to P . aeruginosa did not alter phagocytic susceptibility , which fits with P . aeruginosa use of a proton motive force and not sodium for flagellar motility [14] , [22] . These data are in agreement with our results using genetically modified bacteria , indicating that loss of flagellar motility , regardless of the means , confers resistance to phagocytic uptake . While the down-regulation of flagellar motility in P . aeruginosa isolates from persistent infections has been previously documented [5]–[7] , these results provide an explanation for the observed loss of motility in clinical strains recovered from CF patients over the course of chronic infection , but which is not limited to just P . aeruginosa . Consistent with a pleiotropic mechano-sensory system , both non-swimming V . cholerae and E . coli also demonstrate phagocytic resistance . We believe it is therefore likely that innate immune cells are able sense bacterial motility , possibly through membrane depression or activation of an unknown tension receptor ( s ) , and that this mechanical perturbation , analogous to a “fish on a hook” , provides the necessary sensory stimulant for the cell to “set the hook” and initiate phagocytic uptake . Examples of cellular mechano-sensory systems exist in other physiological systems , such as cellular stretch detection in muscle sarcoma cells [27] and shear-enhanced adhesive catch bonds in rolling leukocytes [28] , but to date no reports have identified such a mechanism contributing to pathogen recognition . Since flagellar motility is a necessary virulence factor for many pathogens to effectively colonize a host [29]–[32] , it makes evolutionary sense that the innate immune system , as a first line of defense , would develop strategies to exploit this phenotype . Concomitantly , loss of flagellar motility in isolates taken from established infections corresponds to selective pressure to bypass this immune strategy . In conclusion , in this work we demonstrate that bacterial flagellar rotation is recognized as a phagocytic activator by innate immune cells . We show that this mechanism responds to at least three different species of bacteria , P . aeruginosa , V . cholerae , and E . coli , and thus likely represents a common and widespread immune strategy for bacterial recognition by direct sensing of flagellar torsion . In the P . aeruginosa model , swimming-deficient strains avoid phagocytic uptake through a combinatorial strategy of limiting prolonged association after initial contact with phagocytic cells and not eliciting uptake when bound to the cell surface . We show for the first time that phagocytic recognition is directly proportional to mot gene function as it relates to phenotypic flagellar torsion . These results provide a basis for the reported observations of non-motility in clinical strains isolated from established infections , and provide evidence of a novel strategy utilized by the innate immune system to fight bacterial infection . This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Dartmouth IACUC Committee ( Permit Number: A3259-01 ) . No surgery was performed , and all efforts were made to minimize suffering . Bone marrow-derived dendritic cells ( BMDCs ) were cultured from C57BL/6 WT mice obtained from NCI using a modification of Inaba et al . as previously described [33] . For isolation of murine macrophages , naïve C57BL/6 mice were injected i . p . with 1 ml of 4% thioglycolate and subsequently sacrificed 4 days later . The peritoneal cavity was lavaged with 6 ml of serum-free Hank's Balanced Salt Solution ( HBSS ) . The lavage fluid was centrifuged and pelleted cells were washed twice in serum-free HBSS before being resuspended in 2 ml serum-free HBSS . For these studies , the Pseudomonas aeruginosa strain FRD1 is a mucoid clinical isolate , while the non-mucoid clinical isolate PA14 , Vibrio cholerae strain O395 ( generously provided by Dr . Ron Taylor , Dartmouth ) , and Escherichia coli strain K12 ( obtained from Yale CGSC ) are the parental bacterial strains and wild type controls for all of the respective mutants studied . Bacterial strains expressing green-fluorescence protein ( GFP ) were generated by transformation of the indicated strains with a multi-copy plasmid ( pSMC21 Ampr Kanr Carbr GFP+ ) that constitutively expresses GFP under control of a derivative of the Ptac promoter [34] , [35] . FACS-based bacterial association was assayed as a modified version previous protocols [4] . Briefly , 2 . 5×105 C57BL/6 BMDCs or macrophages were incubated with the indicated non-transformed or GFP-expressing bacterial strains at an MOI of ∼10 in serum-free HBSS for 45 minutes at 37°C or 4°C as indicated in the text . Cells were washed in phosphate-buffered saline ( PBS ) and mean fluorescence intensity of the phagocyte populations were assessed and graphed to obtain relative efficiency of cellular association with the indicated bacterial strains . For bacterial binding assays , BMDCs or macrophages were pre-incubated in 10 uM cytochalasin D ( Sigma ) in serum-free HBSS for 60 minutes at 37°C . Co-incubation between phagocytes and the indicated bacterial strains took place in the presence of 10 uM cytochalasin D in serum-free HBSS or in HBSS alone for 45 minutes at 37°C . Cells were subsequently washed in serum-free HBSS or PBS and then analyzed by plating cellular lysates and counting recovered CFUs , or by FACS for the acquisition of fluorescence as a function of GFP+ bacterial association . Phagocytosis of live bacteria was performed as a modified version of published protocols [36] and as previously described [4] . Briefly , overnight cultures of P . aeruginosa , V . cholerae , or E . coli were washed and resuspended in serum-free HBSS or the indicated buffer and bacteria concentrations were determined . 2 . 5×105 BMDCs or the indicated cell type were incubated with bacteria at an MOI of ∼10 for 45 minutes at 37°C , followed by incubation in 100 µg/ml gentamicin for 20 minutes at 37°C . Recovered CFUs are normalized to input bacteria to account for variability in initial strain concentration . Where indicated , recovered CFUs are presented as a percent of the isogenic WT to compare relative degrees of phagocytosis . In experiments utilizing sodium-limited buffers , solutions were made with 0 . 9 mM CaCl , 4 mM KCl , 0 . 5 mM MgCl , 5 mM HEPES and the indicated amount of NaCl and reconstituted with choline chloride for a combined concentration of 140 mM ( pH 7 . 5 ) . For phagocytic threshold experiments , the concentration of non-swimming P . aeruginosa was successively increased 10-fold relative to the concentration of WT . For forced-contact experiments , BMDCs or macrophages were centrifuged for 5 min at 400 g . Bacteria were then layered onto pelleted cells followed by centrifugation at 4°C for 10 min at 715 g . An equal degree of cell-to-bacteria contact after centrifugation in swimming verses non-swimming strains was verified by immediately fixing cells and GFP-transformed bacteria with 4% paraformeldahyde and measuring the accumulated cellular GFP signal via FACS . For bacteria-phagocyte cell surface tension experiments , gentamicin protection assays were performed in the presence of the indicated concentrations of surfactant . RNA from P . aeruginosa strains was prepared with TRI Reagent ( Sigma ) followed by the RNeasy kit ( Qiagen ) , following manufacturer's instructions . Microarray analysis was performed on a Pseudomonas aeruginosa PA01 gene chip using raw oligonucleotide probes generated from wild-type PA14 , the motAB mutant , the motCD mutant , or the motABmotCD mutant . Each sample was analyzed in triplicate ( N = 3 ) , and summarized in one probe intensity by the Vermont Genetics Network Microarray Facility using Affymetrix GCOS software . Data analysis was performed using R [37] / BioConductor tools [38] , [39] . Probe set sample matrix expression statistics were calculated using the Robust Multichip Average ( RMA ) method of Speed and coworkers [40] , [41] , implemented in the aroma . affymetrix package of Bengtsson [42] . Quality statistics were calculated using the Simpleaffy [43] and AffyQCReport packages [44] . The linear mixed effects model was fit using the lme4 package [45] . For static imaging , BMDCs were washed twice in 400 uL of serum-free HBSS prior to a 10 minute cytospin onto glass slides at 89 . 5 g . Alternatively , primary macrophages were allowed to adhere to glass slides for 1 hour at 37°C . Cells were co-incubated with GFP-expressing P . aeruginosa or GFP-expressing V . cholerae strains as indicated for 45 minutes at 37°C at a MOI∼10 . Cells were stained with Alexa647-labeled wheat germ agglutinin ( Molecular Probes ) to delineate the cell surfaces . Cells were visualized via fluorescence microscopy on a Zeiss LSM510 Meta microscope using a 40X or 63X lens , followed by image analysis with LSM5 Image Browser software . For live cell imaging , GFP-expressing P . aeruginosa in phosphate buffer saline ( PBS ) were flowed over a monolayer of adherent macrophages at 50 mL/h for 20 min at an MOI∼10 , followed by fresh media for 10 min . Bacterial accumulation was monitored at 5 sec intervals at 60X magnification using fluorescence and DIC . Imaging was performed using a Nikon TE2000 swept field confocal microscope with 0 . 17 mm Delta TPG dishes and analysis was performed with NIS-Elements viewing software . Macrophages were allowed to adhere to the bottom of 24- or 48-well plates in serum-free HBSS for 60 min at 37°C . Adherent cells were washed twice in serum-free HBSS followed by co-incubation with P . aeruginosa strains at 4°C for 30 min . Non-associated bacteria were removed by washing with serum-free HBSS . Cell-associated bacteria were quantified by lysing cells in 0 . 1% Triton-X 100 , plating lysate on LB media for >12 hours at 37°C , and counting recovered CFUs . Alternatively , following co-incubation at 4°C and removal of non-associated bacteria , cells and associated P . aeruginosa were warmed to 37°C for 30 min and quantified as above or warmed for 30 min , washed with serum-free HBSS , treated with 100 ug/mL gentamicin , and then quantified as above . P . aeruginosa or V . cholerae cultures were grown to mid-log phase in LB broth and diluted to O . D . 600<0 . 1 in serum-free HBSS . To measure susceptibility to gentamicin , samples were treated with 100 ug/mL for 15 minutes at 37°C and then directly plated on LB agar or , alternatively , untreated samples were further diluted 1∶200 ( P . a . ) or 1∶555 ( V . c . ) and plated on LB agar and resultant CFUs were counted . To measure bacterial replication and death in HBSS , bacterial cultures were prepared as above and then plated directly , or incubated at 37°C for 60 min and quantified as above .
Flagella-driven bacterial motility , referred to as swimming , has been recognized for over 20 years to affect the ability of bacteria to infect and colonize a host . The common theme is that bacteria must be motile to colonize the host but must become non-motile to chronically persist; this has been observed in many pathogenic bacteria including species of Vibrio and Pseudomonas . Therefore it makes sense that the immune system would evolve mechanisms to exploit this virulence determinant of pathogenic bacteria . Here we present evidence that flagellar motility is recognized by innate immune cells as a phagocytic activation signal . We show that step-wise loss of flagellar motility confers a proportional ability to evade phagocytic engulfment , independent of the flagellum itself acting as a phagocytic activator . This is not due to motility- co-regulated secretions or compensatory genetic changes by the bacteria , but instead is due to a mechano-sensory response whereby phagocytic cells respond directly to flagellar motility . This represents a novel mechanism by which the innate immune system facilitates clearance of bacterial pathogens , and provides an explanation for how selective pressure may result in bacteria with down-regulated flagellar gene expression and motility as is observed in isolates taken from chronic infections .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "medicine", "immunology", "microbiology", "host-pathogen", "interaction", "bacterial", "diseases", "pseudomonas", "infections", "bacterial", "pathogens", "infectious", "diseases", "microbial", "pathogens", "biology", "immune", "response", "gram", "negative", "immunity", "innate", "immunity" ]
2011
Step-Wise Loss of Bacterial Flagellar Torsion Confers Progressive Phagocytic Evasion
Cells live in uncertain , dynamic environments and have many mechanisms for sensing and responding to changes in their surroundings . However , sudden fluctuations in the environment can be catastrophic to a population if it relies solely on sensory responses , which have a delay associated with them . Cells can reconcile these effects by using a tunable stochastic response , where in the absence of a stressor they create phenotypic diversity within an isogenic population , but use a deterministic response when stressors are sensed . Here , we develop a stochastic model of the multiple antibiotic resistance network of Escherichia coli and show that it can produce tunable stochastic pulses in the activator MarA . In particular , we show that a combination of interlinked positive and negative feedback loops plays an important role in setting the dynamics of the stochastic pulses . Negative feedback produces a pulsatile response that is tunable , while positive feedback serves to amplify the effect . Our simulations show that the uninduced native network is in a parameter regime that is of low cost to the cell ( taxing resistance mechanisms are expressed infrequently ) and also elevated noise strength ( phenotypic variability is high ) . The stochastic pulsing can be tuned by MarA induction such that variability is decreased once stresses are sensed , avoiding the detrimental effects of noise when an optimal MarA concentration is needed . We further show that variability in the expression of MarA can act as a bet hedging mechanism , allowing for survival in time-varying stress environments , however this effect is tunable to allow for a fully induced , deterministic response in the presence of a stressor . Antimicrobial drug resistance has been studied extensively due to its clinical importance . Traditionally , research has focused on heritable genetic mechanisms , but transient mechanisms , where only a subset of the population expresses resistance genes , are beginning to receive attention for their role in the recalcitrance of chronic infections [1] . Examples of transient resistance include bacterial persistence , inducible expression of antibiotic efflux pumps , and biofilm formation [1]–[3] . Although these mechanisms can provide resistance or tolerance to a broad spectrum of chemicals , they are often taxing to the cell , slowing growth or utilizing resources [4] , [5] . Importantly , transient resistance can occur within an isogenic population , where phenotypic variation can provide diversity to hedge against catastrophic events due to unpredictable fluctuations in the environment by insuring that some fraction of the population is always in a resistant state [6]–[11] . MarA , the multiple antibiotic resistance activator , is a global regulator of resistance genes . It is conserved across enteric bacteria including Klebsiella , Salmonella , Escherichia , Enterobacter , and Shigella species , but is best studied in Escherichia coli [12] . Bulk population studies have shown that MarA plays an important role in multidrug tolerance by inducing expression of over 40 genes implicated in antibiotic resistance [13]–[19] . Examples include the AcrAB multidrug efflux pump; micF , an antisense RNA that represses expression of the outer membrane porin OmpF; SodA , a manganese-containing superoxide dismutase; and the outer membrane channel TolC [15] , [20] , [21] . Expression of MarA is inducible , providing increased resistance in response to a sensed compound . As shown in Fig . 1A , marA is arranged in an operon with two other genes: marR , the multiple antibiotic resistance repressor and marB , which does not play a role in regulation [14] . The marRAB operon is activated by monomeric MarA , which binds to a single site upstream of the −35 site , and is repressed by dimeric MarR ( denoted MarR2 ) , which binds to two sites , one between the −10 and −35 sites and one downstream of the transcriptional start site of the operon [14] . A variety of chemicals including phenolic compounds , uncoupling agents , redox-cycling compounds , and aromatic acid metabolites can activate transcription of marRAB [12] , [22] , [23] . Of the known inducers of marRAB , the weak aromatic acid salicylate is the best studied and is known to bind directly to MarR [24] , [25] . Upon addition of 5 mM salicylate , transcription of marRAB increases 21-fold [19] . Not all genes in the mar regulon are activated by the same MarA concentrations , suggesting that a graded response is possible with less costly genes expressed first and more burdensome genes expressed only once high MarA levels are reached [15] , [26] . The regulatory network controlling MarA consists of interlinked positive and negative feedback loops ( Fig . 1A ) . We asked what role these opposing actions play in controlling the dynamics of MarA . Recent studies have shown that interlinked positive and negative feedback can produce a wide range of dynamic behaviors . Examples include robust oscillations , bistability , monostability , or stochastic pulsing [27]–[29] . Several synthetic oscillators have been constructed using interlinked feedback [30]–[32] and it has also been shown to be a common feature in many natural examples of biological oscillators [28] . Stochastic pulsing is emerging as an important feature in gene regulation , regulating competence , sporulation , and stress response in Bacillus subtilis [33]–[36] , persistence in bacteria [37] , [38] , calcium stress response and glucose repression in Saccharomyces cerevisiae [39] , and virulence factors in bacteria [11] , [40]–[44] . More generally , phenotypic diversity within a population has been shown to increase the net growth rate under uncertain environments [6]–[11] . Although bulk population studies have demonstrated that MarA expression can be induced by inhibition of the negative feedback loop , we asked what role the opposing interlinked loops play and how these effects are manifested at the single-cell level . To study this , we developed a stochastic model of the marRAB network . Our findings suggest that the interlinking of positive and negative feedback can produce stochastic pulses in MarA expression when the system is uninduced . Induction with salicylate leads to elevated levels of MarA and decreased variability . By comparing the native network with a reduced noise variant computationally , we show that stochastic pulsing can act as a bet hedging mechanism to insure that some fraction of the population is always expressing resistance genes . The combination of stochastic pulsing and inducible non-noisy expression of MarA can serve to tune the stochasticity of the system to hedge against environmental uncertainty , while allowing for a deterministic response when a stressor is sensed . We developed a stochastic model to study MarA expression dynamics . In the model , protein production is the result of a series of single random events [45] , including reactions for transcription , translation , and folding of MarA and MarR , dimerization of MarR to MarR2 , MarA and MarR2 association and dissociation events at the marRAB promoter , MarR2 inhibition by salicylate , and mRNA and protein degradation . Reaction rates and constants were drawn from the literature using experimentally derived values ( Methods , Table S1 ) and simulations were conducted using the Gillespie stochastic simulation algorithm [46] . We first asked how the dynamics of MarA expression change with and without induction at the single-cell level . Bulk population studies have shown that MarA expression can be induced [19] , [23] , however it is not clear whether these population-level results obscure more complex dynamics in individual cells . Using a stochastic computational model , we observed distinct pulses in expression of MarA and MarR2 in the absence of induction ( Fig . 1B ) . The pulses are caused by brief periods when both MarR2 molecules dissociate from the marRAB promoter and MarA binds , initiating expression of the marRAB genes . This is terminated when one or two copies of MarR2 bind to the operator , shutting down transcription , and resulting in a pulse in the expression of marRAB genes . Stochastic pulsing has been shown experimentally for several bacterial systems [6]–[11] , [33]–[38] , [40]–[44] . The phenomenon observed here is consistent with data from other well-characterized repressed systems such as the lactose [47] , tryptophan [48] , and arabinose [49] operons , where a transcriptional burst occurs when the repressor randomly dissociates from its binding sites . In contrast to the uninduced system , our simulations show elevated levels of MarA expression when induced , but lack the pulsing behavior observed in the uninduced state ( Fig . 1C ) . In the presence of a harmful compound , constant , high MarA expression would allow the cell to counteract the noxious effects of a stressor without dipping into a state of low tolerance or rising into a regime with unnecessary cost . When induced , the marRAB promoter spends most of the time in an active state with MarA bound and no MarR2 present , resulting in reduced noise and elevated expression of MarA . To further clarify the mechanism behind the pulsing behavior , we analyzed the corresponding deterministic system , finding a single stable fixed point for all values of salicylate ( Fig . S1 ) . We hypothesized that the stochastic pulses in MarA observed in the uninduced system were caused by the interlinked positive and negative feedback loops that control expression of the marRAB operon . To study the relationship between the feedback loops and noise dynamics we compared four variations on the marRAB operon model ( Fig . 2A ) : ( i ) Wildtype , which includes the complete operator with all binding sites intact; ( ii ) Only Positive , which eliminates both MarR2 binding sites , leaving only the positive feedback loop; ( iii ) Only Negative , which eliminates the MarA binding site , leaving the negative feedback loop; and ( iv ) No Feedback , which removes both feedback loops so that the marRAB operon is constitutively expressed . To allow for a controlled comparison between the four networks , we fixed the mean expression of MarA such that it was the same for all networks when the systems were uninduced . Our findings show that stochastic pulsing is the result of the interaction between the positive and negative feedback loops . We tested the four network variants to quantify how the individual loops influenced the dynamics of MarA ( Fig . 2B ) . In the No Feedback variant expression is constitutive and low levels of noise come from small fluctuations in the birth and death of mRNA and proteins . In the Only Positive case the random fluctuations in MarA levels are amplified . Random increases in MarA lead to further elevated levels of MarA due to positive feedback , while fluctuations that decrease protein levels lower the probability of expression , leading to slow fluctuations in MarA expression . In the Only Negative variant we observe transcriptional bursting when both MarR2 molecules dissociate from the promoter , but because the system lacks positive feedback , bursts in expression are not amplified . Finally , in the Wildtype variant transcriptional bursts created by negative feedback are amplified by positive feedback , since MarA levels increase faster than MarR2 levels [19] and the presence of MarA decreases the apparent binding rate of MarR2 [16] , likely due to steric hindrance [50] . Thus , stochastic pulsing is caused by the combination of positive and negative feedback loops , where the negative feedback loop produces pulses and the positive feedback loop serves to amplify them . To analyze the contributions of the two feedback loops in the presence of increasing levels of the inducer salicylate , we measured the coefficient of variation and noise strength of MarA for each system ( Figs . 2C and D ) . The coefficient of variation ( CV ) is the standard deviation divided by the mean . It measures the relative variation in the system , however decreases in CV can be the result of either decreased noise or increased mean [7] . Therefore , we also considered noise strength as a measure of variability . Noise strength is defined as the variance divided by the mean ( also known as the Fano factor ) ; higher noise strengths imply that the variability is high relative to the mean , giving a sensitive measure of noise [45] . As salicylate is added , mean MarA levels go up in the variants with negative feedback ( Fig . S2 ) . For the systems without negative feedback both CV and noise strength are independent of salicylate concentration . The combination of positive and negative feedback amplifies noise in the absence of induction , while allowing for tunable noise levels . Histograms of MarA expression for the four network variants show that the Wildtype system produces a long-tailed distribution of MarA , while none of the other networks show this behavior ( Fig . S3 ) . This subpopulation of cells with high MarA levels will induce resistance mechanisms , which can hedge against the sudden appearance of a stressor . In the No Feedback and Only Positive variants , both CV and noise strength are constant , with positive feedback leading to higher noise ( Figs . 2C and D ) . Interestingly , in the Only Negative case , the CV level depends upon induction , while noise strength does not . This is because salicylate produces a reduction in active MarR2 levels , which is equivalent to a reduction in the MarR2-promoter association constant kr . This parameter is independent of noise strength for a wide range of values in negative autoregulation [51] . The decrease in Wildtype variability observed in Figs . 2C and D arises from a disruption of stochastic pulsing and is not solely the result of an increase in MarA levels as the system is induced . We next asked if the Wildtype dissociation constants we derived from the literature place the system in a favorable regime that minimizes the cost of expressing burdensome resistance machinery while maximizing the chance of survival in an uncertain environment . To study this , we tested a range of association rates for MarA and MarR2 promoter binding while keeping the dissociation rate fixed and calculated both the cost of expressing MarA and the noise strength of MarA . MarA induces many genes within the mar regulon that provide resistance to stressors , but expression of these genes is taxing to the cell [4] , [5] , [15] . We calculated the cost of MarA expression by using the experimentally-derived function from [5] , which gives cost as a function of salicylate . We related salicylate levels from this function to MarA expression directly by using data from previously published studies [5] , [12] ( Fig . S4 , Text S1 ) . Increased positive feedback and decreased negative feedback strengths , given by association rates ka and kr , produce higher levels of MarA , which result in a higher cost . By this metric , the Wildtype network is in a very low cost regime ( Fig . 3A ) . We also calculated the noise strength as a function of the association rates , showing that a region of high noise strength exists when the association rates of the activator and repressor are balanced ( Fig . 3B ) . The nominal feedback strengths of the Wildtype system place the system on a plateau of high noise strength , guaranteeing stochastic pulsing and relative insensitivity to feedback strength . The curvature of the elevated noise strength regime is due to the nonlinear nature of the interactions between the binding of MarA and MarR2 to the promoter . The high noise strengths observed when the MarR2–promoter association rate kr is low are the result of very slow fluctuations in MarA and MarR2 that keep the system far from the mean . The Wildtype system is in a region with low cost and high noise strength . This combination of conditions enables the creation of MarA pulses , which can trigger the induction of antibiotic resistance genes without undue burden to the population . To study the robustness of our results , we conducted a sensitivity analysis for all model parameters to ensure that our findings were not specific to a particular set of values . For equivalent systems with 2-fold increases and decreases relative to the wildtype parameters , we calculated the number of MarA pulses and the noise strength of MarA and compared them to the results observed in the original system ( Fig . S5 ) . In all cases , results mirrored those from the original system with pulses in MarA observed with 0 mM salicylate but not with 5 mM salicylate . Additionally , we calculated the noise strength for MarA , which showed similar results: noise strength is higher in the absence of salicylate . The sensitivity analysis provides insight into the model parameters that have the largest impact on pulsing dynamics . When the transcription , translation , or degradation rates are modified , the number of pulses and the noise strength are correlated with MarA levels . In other words , when MarA levels go up due to changes in these parameters , MarA pulse numbers and noise strength increase; decreases result when the protein levels go down . Antibiotics and other harmful compounds are ubiquitous in the environments where bacteria grow , however their appearance is often non-constant and time varying . Such dynamic stress profiles have forced prokaryotes to develop mechanisms to protect themselves , including expression of pumps , superoxide dismutases , and other enzymes [52]–[54] . Cells can take several approaches when expressing resistance genes . First , they could always express the resistance genes ensuring that they will be prepared for the sudden appearance of a stressor , but the downside of this approach is that expression can be burdensome . Alternatively , cells could induce resistance genes in response to a sensed stressor . Finally , individual cells within a population could stochastically express resistance genes such that at any given time some cells in the population would be in a resistant state . Bulk population studies have demonstrated that expression of MarA and subsequent resistance is inducible . Here , we have shown computationally that in addition to this inducible resistance , expression of MarA can exhibit stochastic pulses when uninduced . We asked what benefit the combination of stochastic pulsing and inducible resistance provides to cells . For inducible resistance mechanisms , a system must respond to a sensed signal and turn on expression of resistance genes , thus , there is a delay between the time when a stressor appears and when the response in mounted . Following induction with salicylate , maximal transcription of marRAB is observed after 30 minutes [55] . MarA must activate downstream genes , further delaying appearance of the resistance phenotype , as demonstrated in experiments with the MarA homolog SoxS [56] . Because expression of resistance mechanisms is not instantaneous with an inducible system , the system is vulnerable to the sudden appearance of a stressor . Stochasticity in expression of MarA in the uninduced state would allow for some fraction of cells to always be in an elevated state of resistance , ready to counter the unexpected appearance of a stressor . We hypothesized that tunable variability would increase survival in a time-varying stress environment . To test this , we implemented a stochastic competitive growth assay to compare the fitness of the Wildtype network to a new variant with reduced noise ( Reduced Noise ) . Competition assays can be used to discriminate between genotypes in order to identify those that achieve higher population fitness [57]–[59] . We developed a stochastic competition assay by using a modified evolutionary algorithm: cells are first initialized with equal representation of each of the alternative networks , simulation are performed , the cost for each cell is calculated , and cells with poorly performing phenotypes are replaced by top performers ( Methods ) . To allow for a controlled comparison between the networks , we required that the Reduced Noise network have the same mean MarA and MarR2 expression as Wildtype for all salicylate levels and the same response time after induction with salicylate when simulations are started from the same state ( Fig . S6 ) , satisfying the equivalence requirements from [60] . The Reduced Noise network exhibits less variability than the Wildtype system , as shown in Fig . S6 , due to a reduction in the MarR2 inhibition constants and independent binding by MarA and MarR2 at the promoter ( Methods ) . Therefore , the time scale and mean levels of the induced response are identical for both variants , while the stochastic response is attenuated in the Reduced Noise variant . We found that the optimal strategy for surviving antibiotic stress depends on the frequency with which the stressor appears . We first varied the probability of antibiotic addition in a time-varying stress profile ( Fig . 4A ) . The Wildtype network outperforms the Reduced Noise network with large improvements coming when fluctuations in antibiotic levels jump from off to high in a short period of time . When high antibiotic levels are preceded by a period of low or moderate antibiotics , the Reduced Noise network is at a slight advantage because the resistance genes are already induced for both variants and the variability is lower in the Reduced Noise case . Fig . 4B summarizes the average response of the two networks as a function of the probability of antibiotic addition . For time-varying stress profiles we found that phenotypic variability allows cells with the Wildtype network to outperform the Reduced Noise variant since they are able to survive sudden , large increases in antibiotic concentration . In contrast , we found that when we competed the Wildtype and Reduced Noise variants in a constant environment the Reduced Noise variant outperformed the Wildtype system ( Fig . 4C ) . In a constant environment there is no advantage to having variability in MarA and rises and dips will send the system into states that are more costly or less fit . Findings from the constant environment demonstrate that the results shown in Fig . 4B are not the result of a systematic bias in favor of the Wildtype variant . Instead , we find that the Wildtype variant outperforms the Reduced Noise system only in fluctuating , non-constant stress environments , suggesting that variability can be helpful under certain dynamic stress profiles . Results from the competition simulations with time-varying stress show that variability in MarA is important for surviving the sudden appearance of antibiotics . We asked whether stochastic pulses in MarA expression could be used as a bet hedging strategy by a population of cells . To test this we simulated cells for an initialization period in the absence of antibiotics and then introduced a single pulse of antibiotic , quantifying the fraction of the population that was able to survive ( Fig . 4D ) . As the magnitude of the antibiotic pulse increases , the fraction of cells that survived decreases . However , the survival percentages depend upon how MarA expression is controlled . When antibiotic pulses are of high magnitude , the Wildtype populations have some cells that are in a high MarA state and are able to survive the treatment . Low amplitude pulses favor the Reduced Noise system because at any given time more cells are in a resistant state than with the Wildtype network where a larger range of MarA levels are sampled . Those cells in a low MarA state do not have enough time to mount a response when the appearance of antibiotics is sudden . Consequently , stochastic pulses help populations of cells to insure against the sudden appearance of an antibiotic where sensing-based mechanisms would be too slow to respond . The analysis presented here reveals how the combination of stochastic gene expression with inducible tolerance can serve to increase population-level survival in dynamic , time-varying stress environments . We consider the regulatory network controlling expression of the multiple antibiotic resistance activator MarA , which regulates many downstream genes that confer tolerance to antibiotics and other inhibitors . Previous studies have shown that expression of MarA can be induced by compounds like salicylate or through mutations that eliminate transcriptional repression of the marRAB operon [19] , [61] . However , the regulatory topology that controls expression of marRAB consists of a pair of interlinked positive and negative feedback loops , begging the question what role this additional regulatory structure provides , given that simple negative feedback would be sufficient to allow for inducible expression of MarA . Using a stochastic mathematical model , we studied the role of the feedback loops both separately and in combination . Our findings suggest that the negative feedback loop alone can produce inducible expression of MarA that exhibits low amplitude variability when both MarR2 molecules unbind from the promoter . Positive feedback serves to amplify this effect , creating stochastic pulses in MarA . Furthermore , we find that the nominal system parameters derived from the literature place the marRAB network in a regime with high variability and low cost . Thus , individual cells exhibit noisy MarA expression without an undue burden from expression of taxing resistance mechanisms . Phenotypic heterogeneity in isogenic populations can provide a strategy for survival in uncertain environments . Our modeling results predict that MarA expression exhibits stochastic pulsing when uninduced . This variability , as measured using the coefficient of variation and noise strength , decreases as the system is induced . In the induced state there is little need for variability and it may be detrimental , causing some cells to move into a regime with low stress tolerance or unnecessary cost . Controlling for the mean levels of MarA expression and the timing of induction , we compared the fitness of two similar marRAB regulatory networks with differing levels of noise . We found that under constant conditions , it is disadvantageous to have variable MarA expression; in contrast , when stress profiles are dynamic , increased variability places a fraction of the population in a state that can tolerate the sudden appearance of a stressor such as an antibiotic . There are several possible extensions to the findings presented here . For example , the cost of expressing MarA when the system is induced has an impact on the growth rate . Previous studies have shown that this affects processes such as protein dilution , transcription , and gene dosage [62] , [63] , all of which will have an impact on the system dynamics by introducing an additional indirect source of feedback . Other significant sources of feedback may also arise from changes in the nutrient environment or in expression of the proteolytic degradation machinery . Empirical growth laws , such as those presented in [62] , [63] , could be used to extend the model to account for these growth rate effects . In addition , it would be interesting to include the contributions of MarA homologs SoxS and Rob in our model to examine how crosstalk between the regulators affects the dynamics of MarA [14] , [64] . Future studies to test our modeling predictions in vivo , are also of immediate interest . For example , a reporter for MarA could be used to measure the dynamics of expression at the single-cell level . These results could be compared to a synthetic gene network that exhibits external equivalence to the wildtype system , such as a network with only negative feedback that has the same dynamic range and induced MarA levels . Populations of isogenic cells can exhibit phenotypic heterogeneity through a variety of dynamic processes . In our model of the marRAB network we observe stochastic pulsing without induction , but decreased variability after expression of MarA is induced . Allowing for tunable stochasticity can provide a flexible approach to stress tolerance . This strategy of integrating dynamic behaviors may prove to be a general mechanism for hedging against environmental uncertainty while allowing for well-defined sensory mechanisms that behave in a deterministic fashion . An exact , stochastic model was implemented using the Gillespie algorithm [46] and custom analysis code . Models are based on the processes described below , where the reaction rates and parameters are detailed and referenced in Text S1 and Table S1 . The model treats cell growth and division implicitly unless otherwise noted , however results are similar when cell growth and division are explicitly modeled ( Text S1 , Fig . S7 ) . Four alternative feedback combinations were created . To allow for a controlled comparison , we fixed the mean expression of MarA such that it was the same for all networks when the systems were uninduced ( 0 mM salicylate ) . This was achieved by modifying the transcription rate and MarA degradation rates , maintaining the parameters in realistic ranges . Specifically , the differences between the four alternatives are: ( i ) Wildtype: The operator region contains two identical binding sites for MarR2 and one for MarA . A total of six promoter states are modeled with distinct association and dissociation rates ( see promoter dynamics above ) . ( ii ) Only Positive: Both binding sites for MarR2 are eliminated ( kr = 0 ) . Thus , only the promoter states P00 and P10 are included in this model . ( iii ) Only Negative: The binding site for MarA is eliminated ( ka = 0 ) . The promoter states P00 , P01 , and P02 remain . ( iv ) No Feedback: The system has basal , constitutive expression ( kr and ka = 0 ) . P00 is the only promoter state available . Details on parameters are given in Text S1 . For all heat maps , the positive feedback loop strength and the negative feedback loop strength were systematically varied by modifying ka and kr in a range wide enough to include slow and fast transitions between promoter states . For each point , we calculated the noise strength and cost , using the average of six independent replicates . The cost and noise strength were calculated based on MarA levels , using data generated after the initial system transients . The cost was calculated by using the function from [5] ( given by MCost below ) , calculating equivalent salicylate to MarA levels using data from the Hill function in Fig . S4 . To calculate the number of pulses in MarA , we used the following heuristic: a pulse was defined as a period when MarA levels exceeded 2/3 of the 90th percentile of the number of molecules for at least 20 minutes . Pulses separated by less than 15 minutes were combined into a single pulse . We created a variant with the same MarA expression for all salicylate concentration , but which had reduced noise compared to the Wildtype network . To achieve this , the inhibition constants , cInh1 and cInh2 , were decreased , causing higher minimum MarR2 levels , increasing the probability of binding to the promoter and stopping the pulse at an earlier stage . In contrast to the Wildtype model , independent binding by MarA and MarR2 at the promoter is modeled , allowing MarR2 to bind easily when MarA is bound to the promoter . The parameters modified for this variant are detailed in Table S3 . To compare the two variants ( Wildtype and Reduced Noise ) in a head-to-head fashion , we simulated a competitive growth environment in the presence and absence of antibiotics . The following procedure was used to model competitive growth: Step 1 . Allow cells to grow without competition for an initialization period . Step 2: Simulate all individual cells for a fixed time , given an identical antibiotic time course . Step 3: Calculate the cost using the MarA levels for the cell . The cost of growing for the cell is the sum of the cost of expressing the resistance machinery , measured as a function of salicylate , and the cost of growing with the antibiotic , minus their product ( Bliss independence is assumed [5] ) . The effective concentration of the antibiotic is inversely related to the concentration of salicylate [5] . For our experiments we used an compound that has the same cost for the cell as tetracycline and induces MarA with the same strength as salicylate , where both relations are defined in [5] . Cells with costs above a threshold are determined to be dead and eliminated from the competition . Competitive growth results are not sensitive to the exact value of this threshold . Step 4: Calculate , for each cell , the number of replications and replace underperforming cells with those that are growing well . Here , we take into account cell growth and division such that cells with lower costs are more prevalent than those with high costs . The number of daughter cells for each variant is obtained and the ratio between variants is calculated . This ratio is used to set the proportion between variants in the new population . In other words , dead and underperforming cells are replaced by healthy cells such that the new proportion between populations is the same as the proportion between the growth of the old populations . This allows us to maintain a constant number of cells , while at the same time representing the growth of the total population . This process is then repeated by returning to Step 2 until a variant overtakes the population or a predetermined maximum number of rounds is reached . Further details are provided in Text S1 . Three competitive growth simulations were performed: ( 1 ) Growth in a fluctuating environment: After the initialization period , an antibiotic profile is selected randomly . At each round , the antibiotic was either “on” or “off” , with the probability of antibiotic being present equal to 0 , 0 . 25 , 0 . 5 , 0 . 75 , or 1 for different simulations . A round corresponds to 540 min in the absence of antibiotic and 75 minutes in its presence . If the antibiotic was “on” , concentrations were selected randomly using an exponential distribution with a mean of 3 mM and maximum concentration of 4 . 5 mM . ( 2 ) Growth in a constant environment: The antibiotic concentration was kept fixed for both the initialization period and the competition simulations . ( 3 ) Fraction of surviving cells after a pulse of antibiotic: Only Steps 1 , 2 , and 3 of the algorithm described above are performed . After an initialization period in the absence of antibiotic , a pulse of antibiotic is introduced . The number of surviving cells , as measured by calculating those with cost of MarA and antibiotic to be below the predetermined threshold , are calculated for each simulation . Further details on the growth assay simulations are given in Text S1 . The cost of growing with salicylate is defined in [5] as:From the Hill function shown in Fig . S4 we obtain a relationship between MarA and salicylate concentration . Organizing the terms and assuming a maximum MarA concentration of 10 , 000 molecules/cell we find:These two equations are combined to obtain the machinery cost . Expression of the mar regulon genes provides antibiotic resistance , an effect that can be modeled as a reduction of the intracellular concentration of antibiotic:where B ( Sal ) is defined as in [5]: The cost of growing in the presence of antibiotic is modeled as in [5]: In our computations , a compound with the same cost function as tetracycline was used . This function is similar for chloramphenicol , with n = 1 . 97 and Kc = Ktet [5] . The total cost is assumed to be Bliss independent , as described in [5]:
Cells can sense their environment and respond to changes , however the sudden appearance of a stressor can be catastrophic if the time it takes to sense and initiate a response is slow relative to the action of a stressor . A possible solution is to couple a sensory response with a stochastic , random approach . In the absence of stress , a random subset of cells expresses resistance genes , ensuring that if a stressor appears there will be some cells that are able to survive and regenerate the population; once stress is sensed all cells should respond by expressing resistance genes . Such an approach is particularly advantageous when resistance mechanisms are taxing to the cell because it limits their expression when no stress is present . We studied this phenomenon computationally using a model of the multiple antibiotic resistance activator , MarA . MarA controls over 40 resistance genes and can be induced by many harmful compounds . We show that when uninduced , the gene regulatory network controlling MarA is capable of producing stochastic pulses that can serve to hedge against sudden changes in the environment with minimal cost to the population . When induced , MarA expression is elevated and has low variability to ensure a uniform response .
[ "Abstract", "Introduction", "Results", "Discussion", "Methods" ]
[]
2013
Tunable Stochastic Pulsing in the Escherichia coli Multiple Antibiotic Resistance Network from Interlinked Positive and Negative Feedback Loops
The interaction of nuclear pore proteins ( Nups ) with active genes can promote their transcription . In yeast , some inducible genes interact with the nuclear pore complex both when active and for several generations after being repressed , a phenomenon called epigenetic transcriptional memory . This interaction promotes future reactivation and requires Nup100 , a homologue of human Nup98 . A similar phenomenon occurs in human cells; for at least four generations after treatment with interferon gamma ( IFN-γ ) , many IFN-γ-inducible genes are induced more rapidly and more strongly than in cells that have not previously been exposed to IFN-γ . In both yeast and human cells , the recently expressed promoters of genes with memory exhibit persistent dimethylation of histone H3 lysine 4 ( H3K4me2 ) and physically interact with Nups and a poised form of RNA polymerase II . However , in human cells , unlike yeast , these interactions occur in the nucleoplasm . In human cells transiently depleted of Nup98 or yeast cells lacking Nup100 , transcriptional memory is lost; RNA polymerase II does not remain associated with promoters , H3K4me2 is lost , and the rate of transcriptional reactivation is reduced . These results suggest that Nup100/Nup98 binding to recently expressed promoters plays a conserved role in promoting epigenetic transcriptional memory . The nuclear pore complex ( NPC ) is a conserved macromolecular structure that mediates the essential transport of molecules between the nucleus and the cytoplasm [1] . The NPC is an 8-fold symmetric channel derived from ∼30 proteins associated with cytoplasmic filaments and a nucleoplasmic “basket” [2] , [3] . Natively unstructured NPC proteins rich in phenylalanine-glycine repeats line the channel of the NPC and interactions of these proteins with transport factors facilitates selective transport of proteins and mRNPs [2] , [4]–[6] . Proteins that make up the basket-like structure on the nucleoplasmic face of the NPC and the fibrils on the cytoplasmic face of the NPC play key roles in regulating nuclear transport and mRNP remodeling [4] , [7] . Nuclear pore proteins also physically interact with chromatin to regulate transcription of certain genes . In Saccharomyces cerevisiae and Drosophila melanogaster , many active genes physically interact with nuclear pore proteins [8]–[11] . Interaction with the NPC has been proposed to promote stronger transcription [9] , [12]–[16] , to mediate epigenetic regulation [17]–[19] , to promote chromatin boundary activity [20] , [21] , and to provide negative feedback in signaling pathways [22] . However , the exact biochemical nature of these roles , their generality , and their conservation is unclear . In yeast , some of the inducible genes that relocate from the nucleoplasm to the NPC upon activation [such as GAL1 ( GenBank Accession CAA84962 . 1 ) and INO1 ( GenBank Accession CAA89448 . 1 ) ] remain at the nuclear periphery for multiple generations after repression , a phenomenon called epigenetic transcriptional memory [17] . The persistent association of genes with the NPC is not associated with transcription , but promotes faster reactivation [17] , [18] , [23] . In the case of the GAL genes , this leads to significantly faster reactivation compared with activation [17] , [24] . This is not always true; in the case of the INO1 gene , perhaps because of the rate at which cells sense the activating signal ( inositol starvation ) during reactivation , the rate of reactivation is slower than the rate of activation [17] , [18] . However , interaction with the NPC after repression specifically promotes INO1 reactivation because when it is lost , the rate of reactivation is slowed [18] . Active INO1 and recently repressed INO1 interact with the NPC by distinct mechanisms . Interaction of active INO1 with the NPC involves cis-acting “DNA zip codes” called gene recruitment sequences ( GRSs ) in the promoter [15] . Interaction of recently repressed INO1 with the NPC is independent of the GRSs and requires a different zip code called a memory recruitment sequence ( MRS ) , as well as the histone variant H2A . Z ( GenBank Accession CAA99011 . 1 ) and the nuclear pore protein Nup100 ( GenBank Accession CAA81905 . 1 ) [18] , which is homologous to Nup98 in metazoa . Whereas GRS-mediated interaction of active INO1 with the NPC promotes stronger transcription [15] , MRS-mediated interaction of recently repressed INO1 with the NPC promotes incorporation of H2A . Z into the promoter and allows RNA polymerase II ( RNAPII ) to bind , poising the gene for future reactivation [18] . Mutations in the MRS , loss of H2A . Z , or loss of Nup100 specifically block interaction of recently repressed INO1 with the NPC , leading to loss of RNAPII from the recently repressed promoter and slower reactivation [18] . However , these mutations have no effect on the rate of initial activation or the ultimate steady-state levels of INO1 mRNA [17] , [18] , [23] . Thus , the interaction of genes with the NPC can both promote stronger expression and , by a distinct mechanism , poise recently repressed genes for future reactivation . Stress-inducible genes utilize a related type of transcriptional memory . Previous exposure of yeast cells to high salt leads to faster activation of many genes induced by oxidative stress [19] . Similar to INO1 transcriptional memory , this effect persists for four to five generations , suggesting that salt stress establishes an epigenetic change that promotes the rate of activation of these genes . The faster rate of activation of these genes is dependent on the NPC protein Nup42 ( GenBank Accession EEU07798 . 1 ) [19] . MEME analysis of the promoters of 77 genes exhibiting stress-induced transcriptional memory identified a DNA element very similar to the INO1 MRS element [19] . GAL gene transcriptional memory has been suggested to depend on the NPC-associated protein Mlp1 ( GenBank Accession CAA82174 . 1 ) [23] , [25] . Therefore , although there are some gene-specific features , aspects of the molecular mechanism of INO1 transcriptional memory are shared by diverse yeast genes . Nuclear pore proteins also interact with metazoan genes to promote their transcription . Inhibiting histone deacetylase activity using trichostatin A in human cells leads to derepression of hundreds of genes , many of which physically interact with the NPC [8] . In Drosophila , nuclear pore proteins interact with the hsp70 locus , the X chromosome in male flies [26]–[28] , and genome-wide , thousands of genes [9] . However , in metazoans , some nuclear pore proteins localize both at the NPC and in the nucleoplasm and genes that interact with nuclear pore proteins can localize either at the nuclear periphery or in the nucleoplasm [16] , [29] . In Drosophila , of the 18 , 878 genes that interact with the nuclear pore protein Nup98 , 3 , 810 interacted exclusively with NPC-associated Nup98 and 11 , 307 interacted exclusively with nucleoplasmic Nup98 ( GenBank Accession NP_651187 . 2 ) [9] . As in yeast , the interaction of genes with nuclear pore proteins also promotes transcription in flies [9] , [16] . Here we sought to explore the role of nuclear pore interactions with genes in promoting transcriptional memory in humans . HeLa cells treated with IFN-γ show much faster and stronger expression of certain target genes if they have previously encountered IFN-γ [30] . This effect persists for up to four cell divisions ( 96 h ) , suggesting that it is epigenetically inherited through mitosis [31] . This phenomenon is also associated with changes in chromatin structure; dimethylated histone H3 lysine 4 ( H3K4me2 ) remains associated with the promoter of the interferon-γ ( IFN-γ ) -inducible gene HLA-DRA for up to 96 h after treatment with IFN-γ [31] . Here , we have determined the scope of IFN-γ transcriptional memory in human cells and compared it with the molecular mechanisms of INO1 transcriptional memory in yeast . Hundreds of the genes that are induced by IFN-γ exhibit transcriptional memory . Following expression , yeast and human genes that exhibit transcriptional memory are marked by H3K4me2 and associate with both a poised RNAPII and Nup100/Nup98 ( GenBank Accession AAH12906 . 2 ) for up to four generations . Loss of Nup100 in yeast , or transient knockdown of Nup98 in HeLa cells , leads to loss of RNAPII and H3K4me2 from recently expressed promoters and a slower rate of reactivation of genes that exhibit memory . Thus , Nup100/Nup98 is required for epigenetic transcriptional memory , a mechanism conserved from yeast to humans . After yeast cells are shifted from medium lacking inositol into medium containing inositol , INO1 transcription is rapidly repressed and the mRNA returns to baseline within ∼30 min [17] , [18] , [23] . RNAPII dissociates from the body of the gene after addition of inositol , but it remains associated with the INO1 promoter for up to four generations after repression [17] , [18] , [23] . This form of RNAPII is unphosphorylated on the carboxy terminal domain ( CTD ) on serine 5 ( associated with transcription initiation ) and serine 2 ( associated with transcription elongation ) , suggesting that it represents a preinitiation form [18] . To explore the nature of RNAPII that is associated with the recently repressed INO1 promoter , we monitored the association of preinitiation complex ( PIC ) components before , during , and after expression of INO1 . We performed ChIP using strains expressing Tandem Affinity Purification ( TAP ) -tagged components of TFIID , TFIIA , TFIIB , TFIIF , TFIIE , TFIIH , TFIIK , TFIIS , and Mediator from cells grown in long-term repressing ( +inositol ) , activating ( −inositol ) , or recently repressed ( −ino→+ino , 3 h ) conditions . None of these PIC components bound to the long-term repressed INO1 promoter ( Figure 1A ) . However , like RNAPII , the PIC components TFIID , TFIIA , TFIIB , TFIIF , TFIIE , and TFIIH bound to the promoter both when the gene was active and after repression ( Figure 1A ) . In contrast , three PIC components interacted with the active promoter , but not the recently repressed promoter: the TFIIK component Kin28 ( GenBank Accession CAA64904 . 1 , the kinase that phosphorylates serine 5 on the CTD ) [32] , [33] , the TFIIS component Ctk1 ( GenBank Accession CAA81980 . 1 , the kinase that phosphorylates serine 2 on the CTD ) [34]–[36] , and the Mediator component Gal11 ( GenBank Accession CAA99056 . 1 ) [35] , [36] . This is consistent with the conclusion that the RNAPII that binds to the recently repressed INO1 promoter is not phosphorylated on Ser2 or Ser5 of the CTD [18] . We confirmed that Ser5 phosphorylated RNAPII did not remain associated with the INO1 promoter after repression using a monoclonal antiphospho Ser5 CTD antibody ( mAb 4h8; Figure S1 ) . Together , these results suggest that a novel , partially assembled PIC associates with the recently repressed INO1 promoter . Furthermore , binding of these components is not sufficient to induce transcription , suggesting that INO1 reactivation is regulated by controlling the association of Mediator , TFIIK , and/or TFIIS . INO1 transcriptional memory requires an 11 base pair cis-acting element called the MRS in the promoter [18] . Mutation of the MRS blocks interaction of recently repressed INO1 with the NPC , incorporation of the histone variant H2A . Z , and binding of RNAPII to the recently repressed INO1 promoter , resulting in a slower rate of reactivation of INO1 [18] . When inserted at an ectopic locus , the MRS is sufficient to induce both H2A . Z incorporation and interaction with the NPC [18] . To test if the MRS was also sufficient to induce the association of a poised RNAPII at an ectopic locus , we inserted the MRS adjacent to the URA3 locus ( GenBank Accession AAB64498 . 1 ) [18] and performed ChIP for RNAPII . We fixed and harvested cells that had been shifted from activating to repressing conditions for 3 h so that we could simultaneously monitor the recovery of the endogenous INO1 locus as an internal positive control . Although RNAPII associated with the recently repressed INO1 promoter under these conditions , it did not associate with URA3 or URA3:MRS or a negative control locus ( the GAL1 promoter; Figure 1B ) . Therefore , the MRS is not sufficient to recapitulate all facets of INO1 transcriptional memory and assembly of the PIC requires other features of the promoter . This suggests that the interaction with the NPC and the incorporation of H2A . Z occur upstream of , and presumably promote , assembly of the PIC . The HLA-DRA gene in HeLa cells ( GenBank Accession CAG33294 . 1 , encoding the HLA class II histocompatibility antigen DRα chain ) exhibits a form of transcriptional memory in response to IFN-γ . Cells previously treated with IFN-γ induce HLA-DRA more rapidly and more robustly in response to subsequent exposure to IFN-γ ( Figure 2A ) [31] . Not all IFN-γ-inducible genes behave this way; another IFN-γ-inducible gene , CIITA ( GenBank Accession NP_000237 . 2 ) , does not display transcriptional memory [31] . Similar to INO1 transcriptional memory , this type of transcriptional memory is epigenetically inherited , persisting through at least four cell divisions in HeLa cells ( 96 h ) [31] . These similarities led us to ask if these two systems utilize related molecular mechanisms . We used ChIP to examine the association of RNAPII with the HLA-DRA promoter before ( uninduced ) , during ( induced ) , or at various times after treatment with IFN-γ [48 h ( ∼2 cell divisions ) and 96 h ( ∼4 cell divisions ) postinduction] . Prior to IFN-γ treatment , RNAPII was not associated with the promoter or the coding sequence of HLA-DRA and CIITA ( Figure 2B ) . This is consistent with the undetectable levels of HLA-DRA and CIITA mRNA before IFN-γ treatment ( Figure 2A ) . During IFN-γ treatment , RNAPII associated strongly with the promoter and the coding sequence of both HLA-DRA and CIITA ( Figure 2B ) . After removing IFN-γ , RNAPII remained associated with the HLA-DRA promoter , but not the coding sequence , for up to 96 h ( Figure 2B ) . RNAPII did not associate with CIITA after removing IFN-γ and the levels of RNAPII associated with GAPDH ( GenBank Accession AAH83511 . 1 ) promoter and coding sequence were consistent under all three conditions ( Figure 2B ) . Therefore , HLA-DRA memory correlates with persistent RNAPII binding to the previously induced promoter through at least four cell divisions . Following treatment of cells with IFN-γ , the signaling and transcriptional response can persist even after washing , presumably because of persistent association of IFN-γ with the IFN-γ receptor and signaling through the JAK/STAT pathway ( Figure S2A and B ) [37] . For this reason , we trypsinized and split the cells after removing IFN-γ in all of our experiments , which leads to the levels of HLA-DRA mRNA returning to baseline levels within 6 h ( Figure S2A ) . Likewise , treatment of HeLa cells with IFN-γ immediately after trypsinizing did not result in expression of HLA-DRA ( Figure S2B ) , suggesting that trypsin digestion blocks IFN-γ signaling . Thus , the association of RNAPII with the HLA-DRA promoter is not due to persistent expression . This is consistent with loss of RNAPII from the HLA-DRA coding sequence after removing IFN-γ and splitting ( Figure 2B ) . We also tested if previous expression of HLA-DRA is necessary for transcriptional memory . Exposure of cells to IFN-γ for 2 h , followed by splitting the cells , does not result in significant HLA-DRA expression ( Figure S2B ) . However , this brief exposure to IFN-γ is sufficient to induce a faster rate of reactivation 48 h later ( ∼2 cell divisions; Figure S1C ) . Thus , previous expression of HLA-DRA is not necessary to induce future transcriptional memory . In metazoans , transcription is regulated both by blocking RNAPII recruitment and by blocking RNAPII elongation [38]–[42] . In the latter case , RNAPII binds to the promoter , initiates transcription , and then pauses at the 5′ end of the gene due to regulation by negative elongation factor ( NELF; GenBank Accession AAI10499 . 1 ) and DRB sensitivity-inducing factor ( DSIF; GenBank Accession BAA24075 . 1 ) [43] . This paused RNAPII is phosphorylated on Ser5 of the CTD , but unphosphorylated on Ser2 [38] , [44] , [45] . Transcription of such genes is stimulated by recruitment of the kinase P-TEFb , which phosphorylates Ser2 and allows elongation [46] . In yeast , ChIP using a monoclonal anti-phospho serine 5 antibody ( mAb 4h8 ) recovers active , but not recently repressed INO1 promoter ( Figure S2 ) . We performed ChIP using mAb 4h8 to ask if the RNAPII associated with the previously expressed HLA-DRA promoter is postinitiation or preinitiation . Ser5 phosphorylated RNAPII bound to the active HLA-DRA promoter in cells exposed to IFN-γ , but not with the previously expressed HLA-DRA promoter after removal of IFN-γ ( Figure 2C ) . Therefore , similar to yeast INO1 , the HLA-DRA promoter associates with a preinitiation form of RNAPII for several cell divisions after removing IFN-γ . In yeast , distinct Nups interact with active [11] and recently repressed INO1 [18] . To test if HLA-DRA interacts with Nups , we performed ChIP using the mAb 414 monoclonal antibody , which recognizes Phe-x-Phe-Gly repeats present in several nuclear pore proteins [47] , [48] . We observed strong interaction of Phe-x-Phe-Gly repeat proteins with the active HLA-DRA promoter and a weaker interaction after removing IFN-γ ( Figure 3A ) . This pattern was very similar to the interaction of the Phe-x-Phe-Gly repeat protein Nup2 ( GenBank Accession AAB67259 . 1 ) with the INO1 promoter in yeast [18] . The interaction was also specific; mAb 414 did not recover the HLA-DRA coding sequence ( not shown ) nor the promoters of CIITA , GAPDH , and β-ACTIN ( Figure 3A ) . This suggests that Phe-x-Phe-Gly Nups interact with the active and recently expressed HLA-DRA promoter . The yeast nuclear pore protein Nup100 interacts with the INO1 promoter specifically after repression , and not during activation [18] . We performed ChIP using an antibody against Nup98 , a human homologue of Nup100 [1] , and analyzed the interaction with the HLA-DRA promoter . Nup98 did not interact with the HLA-DRA promoter before or during IFN-γ treatment ( Figure 3B ) . However , for up to 96 h ( ∼4 cell divisions ) after removal of IFN-γ , we observed a clear and specific association of Nup98 with the HLA-DRA promoter . Therefore , similar to the specific interaction of Nup100 with the recently repressed INO1 promoter , Nup98 interacts specifically with the recently expressed HLA-DRA promoter . In Drosophila , genes interact with Nups both at the NPC and in the nucleoplasm [9] , [16] . In particular , Nup98 has been shown to localize both at the NPC and the nuclear periphery [49] . To test if the HLA-DRA interaction with Nup98 occurs at the NPC , we localized the HLA-DRA gene with respect to the nuclear periphery using DNA fluorescence in situ hybridization ( FISH ) . Cells were fixed before ( uninduced ) , during ( induced ) , or after ( 48 h postinduction ) treatment with IFN-γ and processed for DNA-FISH using fluorescent probes generated by nick translation of bacterial artificial chromosomes ( BACs ) . Using confocal microscopy , we measured the distance from the individual HLA-DRA foci to the edge of the Hoescht fluorescence within individual z slices ( Figure 3C ) . The distribution of these distances was plotted for ∼300 foci . Under all three conditions , the HLA-DRA gene localized in the nucleoplasm ( Figure 3D ) . Active HLA-DRA was somewhat more nucleoplasmic than the preinduced HLA-DRA ( p = 0 . 004 , two-tailed t test ) or postinduced HLA-DRA ( p = 0 . 001 ) . The position of CIITA with respect to the nuclear periphery did not change under these conditions ( Figure S4 ) . This suggests that HLA-DRA interacts with Nups away from the NPC , in the nucleoplasm . To probe the generality of IFN-γ-induced transcriptional memory throughout the human genome , we performed expression microarrays on cDNA from cells treated with IFN-γ . We compared samples from time points either during initial activation or during reactivation after 48 h without IFN-γ ( ∼2 cell divisions , as in Figure 2A ) . The log2 ratios relative to the initial time point ( 0 h ) were calculated by averaging between replicates and , for genes with multiple probes , between probes . Based on the initial activation after addition of IFN-γ , we identified a subset of 664 genes that were induced ≥2 fold between 6 h and 24 h ( Table S1 ) . Gene ontology ( GO ) analysis revealed that this subset of genes was highly enriched for terms related to innate immunity: “regulation of immune system process” ( p = 7 . 76×10−23 ) , “response to interferon gamma” ( p = 3 . 99×10−18 ) , and “response to cytokine stimulus” ( p = 1 . 16×10−17; Table S2 ) [50] . We then used k means clustering to organize this subset into clusters on the basis of their behaviors during activation and reactivation ( Figure 4A and Table S1 ) . Cluster 1 includes 218 genes that were modestly induced during activation and more strongly induced during reactivation ( see average behavior in Figure 4B ) . This cluster was strongly enriched for genes involved in inflammation and genes regulated by infection ( Table S3 ) . Cluster 2 includes 403 genes that were induced equivalently during activation and reactivation ( Figure 4C ) . This cluster was enriched for GO terms associated with innate immunity ( Table S4 ) . Cluster 3 includes 42 of the most strongly induced genes that were , nonetheless , induced more rapidly during reactivation ( Figure 4D ) . This cluster includes HLA-DRA ( Figure 4A , arrow ) and was highly enriched for GO terms associated with cytokine signaling generally and IFN-γ signaling in particular ( Table S5 ) . Many of the genes in Cluster 1 and most of the genes in Cluster 3 displayed mRNA profiles consistent with transcriptional memory . We analyzed genes from each of these clusters by RT qPCR to confirm this behavior . The Cluster 1 gene HLA-DQB1 ( GenBank Accession AAA59770 . 1 ) and the Cluster 3 genes HLA-DPB1 ( GenBank Accession AAA59837 . 1 ) and OAS2 ( GenBank Accession AAH10625 . 1 ) displayed significantly faster and/or stronger activation in cells previously exposed to IFN-γ ( Figure S5B–D ) . HLA-DQB1 and HLA-DPB1 encode the HLA class II histocompatibility antigen DQα and DPβ chains , respectively , and OAS2 encodes a 2′-5′-oligoadenylate synthetase [51] , [52] . However , the clustering algorithm did not result in perfect segregation of genes with memory from genes without memory; the gene CIITA , which does not exhibit obvious transcriptional memory ( Figure S5A ) [31] , was within Cluster 1 . Regardless , these results suggest that a large subset of the genes induced by IFN-γ exhibit stronger or more rapid induction in response to IFN-γ if the cells have been previously exposed to IFN-γ . To test if other genes that exhibit memory are regulated by the same mechanism as HLA-DRA , we used ChIP against RNAPII and Nup98 before , during , and after IFN-γ treatment . Both RNAPII ( Figure S5E ) and Nup98 ( Figure 4E ) bound to the promoters of HLA-DQB1 , HLA-DPB1 , and OAS2 for up to 96 h ( ∼4 cell divisions ) after removing IFN-γ . Neither RNAPII ( Figure S5E ) nor Nup98 ( Figure 4C ) bound to the coding sequences of these genes after removal of IFN-γ . Therefore , the interaction of RNAPII and Nup98 with recently expressed promoters is a general feature of IFN-γ memory . Transcriptional memory in yeast and humans is associated with changes in chromatin . In yeast , the histone variant H2A . Z is incorporated into a single nucleosome in the INO1 promoter after repression and loss of H2A . Z blocks INO1 transcriptional memory [17] , [18] . It is unclear if H2A . Z is involved in IFN-γ transcriptional memory; HLA-DRA memory is associated with a very slight increase in H2A . Z incorporation into the promoter after removal of IFN-γ ( Figure S5F ) . However , HLA-DRA transcriptional memory is associated with persistent dimethylation of histone H3 lysine 4 ( H3K4me2 ) [31] . Histone H3 in nucleosomes at the 5′ end of actively transcribed genes are trimethylated on lysine 4 ( H3K4me3 ) [53] , [54] . Whereas the H3K4me3 mark is lost from the promoter of HLA-DRA after removal of IFN-γ , the H3K4me2 mark remains ( Figure S6A and B ) [31] . In contrast , both marks are lost from the CIITA promoter after removing IFN-γ ( Figure S6A and B ) [31] . To test if H3K4me2 was associated with INO1 transcriptional memory in yeast , we examined the association of H3K4me3 and H3K4me2 with the INO1 promoter under long-term repressing , activating , or recently repressed conditions . As a control , we used a strain in which the MRS had been mutated and INO1 transcriptional memory is blocked [18] . H3K4me3 was only associated with the active INO1 promoter ( Figure 5A ) . However , H3K4me2 was associated with both the active and recently repressed INO1 promoters ( Figure 5B ) . The persistence of H3K4me2 after repression required the MRS ( Figure 5B ) . Therefore , INO1 transcriptional memory is also associated with dimethylation of H3K4 . We next asked if the machinery responsible for methylation of H3K4 was required for other aspects of INO1 transcriptional memory; namely , poised RNAPII association and localization at the nuclear periphery after repression . In yeast strains lacking either the histone methyltransferase Set1 ( GenBank Accession AAB68867 . 1 ) or E2 ubiquitin-conjugating enzyme Rad6 ( GenBank Accession CAA96761 . 1 ) , all di- and tri-methylation of H3K4 is lost [55] . Loss of these enzymes did not affect the localization of active INO1 to the nuclear periphery ( Figure 5C ) or interaction of RNAPII with active INO1 ( Figure 5D ) . However , loss of either Set1 or Rad6 specifically disrupted both localization of recently repressed INO1 at the nuclear periphery ( Figure 5C ) and RNAPII binding to the recently repressed INO1 promoter ( Figure 5D ) . This suggests that H3K4me2 at the INO1 promoter is required for INO1 transcriptional memory . The MRS is necessary for both incorporation of H2A . Z [18] and the persistent dimethylation of H3K4 ( Figure 5B ) at the recently repressed INO1 promoter . Integration of the MRS at ectopic sites is sufficient to induce H2A . Z deposition [18] . Therefore , we asked if the MRS was also sufficient to induce dimethylation of H3K4 at an ectopic locus . We performed ChIP against H3K4me2 in a strain in which either the MRS or the nonfunctional mrs mutant was integrated beside URA3 [18] . We observed a robust signal for H3K4me2 associated with URA3:MRS but not with URA3:mrsmut ( Figure 5E ) or a control locus ( the coding sequence of the repressed gene PRM1; not shown ) . We also observed a small but reproducible increase in H3K4me3 at URA3:MRS compared with URA3:mrsmut , although this level was significantly lower than the level associated with the active INO1 promoter ( Figure S6C ) . Therefore , the MRS is sufficient to induce both H2A . Z incorporation and dimethylation of H3K4 , recapitulating the chromatin changes associated with INO1 transcriptional memory . Loss of H2A . Z or H3K4 dimethylation leads to loss of INO1 transcriptional memory and both of these modifications require the MRS ( Figure 5 ) [17] , [18] . We next asked if the methylation of H3K4 at the recently repressed INO1 promoter required H2A . Z . In wild-type and htz1Δ strains , we observed similar levels of H3K4me3 ( Figure S6D ) and H3K4me2 ( Figure 5F ) at the active and recently repressed INO1 promoter . Therefore , dimethylation of H3K4 at the recently repressed INO1 promoter requires the MRS , but not H2A . Z . Furthermore , although strains lacking H2A . Z show slower INO1 reactivation kinetics , loss of peripheral localization of recently repressed INO1 , and loss of RNAPII from the INO1 promoter after repression [18] , the INO1 promoter is still marked by H3K4me2 . This suggests that dimethylation of H3K4 occurs upstream of , or independent of , H2A . Z deposition to promote INO1 transcriptional memory . INO1 transcriptional memory requires Nup100 for both rapid reactivation and RNAPII association after repression [18] . Because we observed a specific physical interaction of Nup98 with the previously induced HLA-DRA promoter , we asked if Nup98 was required for IFN-γ transcriptional memory . We used transient siRNA knockdown to reduce the levels of Nup98 prior to expression and ChIP analysis ( schematized in Figure 6A ) . Transient knockdown reduced Nup98 during the time course of the experiment; 5 d after transfection , Nup98 protein levels were still reduced , while at earlier times Nup98 was not detected ( Figure 6B ) . Both nuclear pore-associated Nup98 and nucleoplasmic Nup98 were depleted by this treatment ( Figure S7 ) . We did not observe a significant change in the growth rate or morphology of the cells subjected to this treatment . Knockdown of Nup98 had no apparent effect on RNAPII binding to active HLA-DRA ( Figure 6C ) , on the rate of initial activation of HLA-DRA ( Figure 6D ) , or on the association of RNAPII with the CIITA gene ( Figure 6C ) . However , knockdown of Nup98 blocked binding of RNAPII to the HLA-DRA promoter following removal of IFN-γ ( Figure 6C ) and dramatically reduced the rate of reactivation of HLA-DRA ( Figure 6D ) . This suggests that Nup98 is required for HLA-DRA transcriptional memory . Because Nup98 bound to the promoters of the Cluster 1 gene HLA-DQB1 and the Cluster 3 genes HLA-DPB1 and OAS2 after removal of IFN-γ ( Figure 4E ) , we tested the effect of Nup98 knockdown on the transcriptional memory of these genes . Transient knockdown of Nup98 reduced the rate of reactivation of all three genes ( Figure 6E , F , and G ) . In the cases of HLA-DQB1 and OAS2 , this effect was specific for reactivation . However , in the case of HLA-DPB1 , we also observed a slower rate of activation ( Figure 6F ) . Therefore , knockdown of Nup98 affects the transcriptional memory of several human genes , although this effect may not be memory-specific in all cases . To explore the role of Nup98 in regulating chromatin structure , we asked if the dimethylation of H3K4 associated with transcriptional memory required Nup98 . We performed ChIP against H3K4me2 in cells knocked down for Nup98 and quantified the enrichment of this mark at the promoters of HLA-DRA , HLA-DPB1 , HLA-DQB1 , OAS2 , and CIITA ( Figure 7A ) . We observed H3K4me2 at the recently expressed promoters of HLA-DRA , HLA-DPB1 , HLA-DQB1 , and OAS2 , and this mark was lost when Nup98 was knocked down ( Figure 7A ) . Consistent with the impaired activation of HLA-DPB1 in the absence of Nup98 ( Figure 6G ) , we also observed a slight decrease in the level of H3K4me2 associated with the active HLA-DPB1 promoter when Nup98 was knocked down ( Figure 7A ) . Therefore , Nup98 is required for dimethylation of H3K4 at the promoters of genes with transcriptional memory . To confirm that the effects of NUP98 knockdown are specific , we tested the effect of knockdown of NUP107 , a component of the core channel of the NPC [56] , on H3K4 dimethylation of IFN-γ-inducible promoters . Using the same strategy to knockdown Nup107 , the protein was effectively depleted ( Figure 7B ) . H3K4me2 levels associated with the promoters of HLA-DRA , HLA-DQB1 , HLA-DPB1 , and OAS2 after removal of IFN-γ remained high in the absence of Nup107 ( Figure 7C ) . Therefore , the effects of Nup98 knockdown on transcriptional memory were specific . We next asked if dimethylation of H3K4 at the recently repressed INO1 promoter requires yeast Nup100 . We grew wild-type and nup100Δ yeast strains in repressing , activating , and recently repressed conditions and performed ChIP against H3K4me2 and H3K4me3 ( Figure 7D and Figure S8 ) . Cells lacking Nup100 exhibited normal trimethylation ( Figure S8 ) and dimethylation of H3K4 at the active INO1 promoter but did not maintain H3K4me2 at the recently repressed INO1 promoter ( Figure 7D ) . Therefore , in yeast and human cells , Nup98/Nup100 is required for maintenance of histone H3 dimethylation during transcriptional memory . Dimethylation of H3K4 is generally associated with the 5′ coding sequences of actively transcribed genes [57] . However , H3K3me2 of promoter regions , often associated with ncRNAs , leads to recruitment of the Set3 histone deacetylase complex ( Set3C ) and transcriptional repression [58] . We wondered if Set3C had any role in INO1 transcriptional memory . This is somewhat complicated by the poor expression of INO1 in mutants lacking Set3 ( GenBank Accession EEU07596 . 1 ) , suggesting that Set3 might have multiple effects on INO1 expression [59] . However , we tested if Set3 plays a role in binding of RNAPII to the recently repressed INO1 promoter . In mutant strains lacking Set3 , RNAPII failed to remain associated with the INO1 promoter after repression ( Figure 7E ) . This suggests that recognition of H3K4me2 by Set3 is required for INO1 transcriptional memory . Our work suggests that transcription can be regulated at three distinct stages: RNAPII recruitment , transcription initiation , and transcription elongation . In yeast , the primary mechanism by which transcription is regulated is through recruitment of RNAPII/PIC to the promoter and inducible genes tend to be devoid of RNAPII when uninduced or repressed [61]–[64] . However , under certain circumstances , a preinitiation form of RNAPII can associate with inactive yeast promoters . For example , in stationary phase cells , unphosphorylated RNAPII is associated with hundreds of inactive promoters , and this has been suggested to poise these genes for future activation [65] . Likewise , in metazoans , transcription can be regulated both at the level of PIC assembly and after initiation , at the level of transcription elongation [38] , [44] , [66]–[68] . Promoter-proximal pausing requires NELF and DSIF and is relieved by recruitment of pTEF-b [46] , [69] , [70] . Brewer's yeast lacks a homologue of NELF , and there is no conclusive evidence for RNAPII pausing [71] . Therefore , promoter-proximal pausing may be a metazoan-specific form of regulation [64] . Our results suggest that , for certain genes , the mechanism of regulation depends on the history of the cells . Transcription of such genes is regulated by either preventing RNAPII/PIC recruitment ( under long-term repressing conditions ) or allowing RNAPII/PIC recruitment but preventing transcription initiation ( under recently repressed conditions ) . In the case of the INO1 gene , whereas none of the PIC components bound to the long-term repressed INO1 promoter , most of them bound to the recently repressed INO1 promoter ( Figure 1 ) . This form of PIC is distinct from the PIC that associates with active INO1: TFIIK , Mediator , and TFIIS are absent and RNAPII remains unphosphorylated and fails to initiate . Thus , while the regulation of long-term repressed INO1 prevents binding of RNAPII and the rest of the PIC , the regulation of recently repressed INO1 occurs at a subsequent step . This suggests that the rate-limiting step in derepression is different for long-term repressed INO1 and recently repressed INO1 . In both yeast and humans , transcriptional memory is inherited through cell division . In the case of yeast , the INO1 gene remains at the nuclear periphery , associated with the NPC for ∼3–4 generations ( ≥6 h ) in both the mother and daughter cells [17] . Likewise , RNAPII remains associated with the INO1 promoter over the same number of generations [18] . HeLa cells exposed to IFN-γ exhibit faster and more robust activation of IFN-γ-inducible genes for up to ∼4 generations ( 96 h ) after removing IFN-γ [31] . Binding of RNAPII and Nup98 to , and dimethylation of histone H3 lysine 4 over , the promoters of genes exhibiting transcriptional memory persists over the same number of generations . Therefore , the poised , preinitiation state is heritable through several generations , suggesting that it represents an epigenetic state . Changes in chromatin composition and modification are necessary for transcriptional memory and presumably allow binding of RNAPII/PIC to recently expressed promoters in yeast and humans . For genes that display transcriptional memory in both yeast and humans , histone H3 within promoter nucleosomes was unmethylated on lysine 4 prior to induction ( Figures 5 and 7 ) . After expression , histone H3 within promoter nucleosomes was dimethylated on lysine 4 ( H3K4me2; Figures 5 and 7 ) [31] . In human cells , this correlates with lower nucleosome occupancy of the recently expressed HLA-DRA promoter compared with the uninduced promoter [31] . In yeast , a cis-acting element necessary for INO1 transcriptional memory ( the MRS ) was necessary for dimethylation of H3K4 after repression and insertion of the MRS at an ectopic locus is sufficient to induce both H2A . Z incorporation [18] and dimethylation of H3K4 ( Figure 5 ) . Finally , loss of the enzymes responsible for H3K4 methylation ( Set1 or Rad6; Figure 5 ) or recognition of H3K4me2 ( Set3; Figure 7 ) led to loss of INO1 transcriptional memory . Although it is still formally possible that Set1 methylates another protein required for transcriptional memory , the connection to the cis-acting MRS element and the requirement for Rad6 and Set3 ( Figure 7 ) suggest that Set1 methylation of H3K4 is required for transcriptional memory . Binding of nuclear pore proteins to the promoters of genes impacts both their transcription and their epigenetic regulation . In yeast and Drosophila , nuclear pore proteins interact with the promoters of active genes and this interaction is required for their full expression [9] , [12]–[16] . We have found another role for these interactions . The yeast nuclear pore protein Nup100 is required for INO1 transcriptional memory and the homologous human protein Nup98 is required for IFN-γ-mediated memory . One complication of any experiment manipulating Nup98 levels is that both Nup98 and Nup96 are generated from a single transcript [72] . After nuclear import , this protein undergoes autocatalytic cleavage , producing two proteins [73] . Indeed , knockdown of Nup98 also leads to depletion of Nup96 ( Figure S9 ) . Therefore , our results raise the possibility that both Nup98 and Nup96 impact transcriptional memory , especially since Nup96 has been implicated in promoting expression of interferon-responsive genes in mouse [74] and the Nup96 homologue Nup145C ( GenBank Accession P49687 ) is required for localization of recently repressed INO1 at the nuclear periphery in yeast [18] . Both Nup96 and Nup98 localize in the nucleoplasm and at the NPC [49] , [75] . Our data suggest that Nup98 plays a direct and specific role: Nup98 binds to the promoters of genes that exhibit transcriptional memory specifically after removal of IFN-γ and knockdown affects reactivation rate without affecting activation rate . Also , although loss of Nup96 is lethal [74] , [76] , we did not observe a strong defect in the growth of cells transiently knocked down for Nup98 , suggesting that Nup96 function was not completely depleted . And finally , knockdown of Nup107 , another component of the same subcomplex of the NPC as Nup96 , had no effect on H3K4 dimethylation of promoters of primed genes . We conclude that our data support a role for Nup98 , and potentially Nup96 , in transcriptional memory . Future work will be required to separate these two roles . Phe-x-Phe-Gly repeat proteins that are recognized by mAb 414 interact with the promoter of both active HLA-DRA and recently expressed HLA-DRA . Nup98 , which possesses related repeated motif ( Gly-Leu-Phe-Gly ) , interacts specifically with the promoter of recently expressed HLA-DRA . This suggests that active HLA-DRA and recently expressed HLA-DRA interact with two distinct sets of nuclear pore proteins . This conclusion is very similar to what we have observed for INO1; active INO1 and recently repressed INO1 interact with different Nups , and localization of active and recently repressed INO1 at the nuclear periphery requires different Nups and different DNA elements [18] . The interaction of active and recently expressed HLA-DRA with Nups occurs in the nucleoplasm . Consistent with previous work [49] , this suggests that in both Drosophila and human cells there are two pools of nuclear pore proteins: a pool at the NPC and a pool in the nucleoplasm . Although the interaction between genes and Nup100 occurs at the NPC in yeast and the interaction with Nup98 occurs in the nucleoplasm in human cells , the biochemical outputs of these interactions are conserved . After removing IFN-γ , the HLA-DRA gene shows increased colocalization with PML bodies , nuclear “dots” that are enriched for the promyelocytic leukemia factor ( PML ) [31] . PML bodies increase in number after IFN-γ treatment and depletion of PML led to a decrease in both the rate of HLA-DRA reactivation and loss of H3K4me2 after removing IFN-γ [31] . This suggests that relocalization of HLA-DRA to these structures is required for IFN-γ memory . Foci of Nup98 in the nucleoplasm do not colocalize with PML bodies [49] , suggesting that HLA-DRA may not colocalize with Nup98 foci . It will be important to understand how PML bodies and nuclear pore proteins impact each other in this process . The role of H2A . Z in transcriptional memory is controversial . Whereas H2A . Z is required for INO1 transcriptional memory and is intimately connected to MRS function [18] , loss of H2A . Z affects both the rate of activation and reactivation of GAL genes and its role in GAL gene transcriptional memory has been challenged [77]–[79] . The MRS from the INO1 promoter is sufficient to induce both incorporation of H2A . Z and dimethylation of H3K4 . However , loss of H2A . Z did not block dimethylation of H3K4 , suggesting that H3K4me2 occurs upstream of , or in parallel to , H2A . Z deposition . In HeLa cells , we observed only a slight increase in H2A . Z levels associated with the HLA-DRA promoter after removing IFN-γ ( Figure S5F ) . Although a large increase in H2A . Z association is not necessary for H2A . Z to play a role in memory , it is possible that H2A . Z functions as a gene-specific regulator of a more general system to promote reactivation . Our results suggest that , under certain circumstances , Nup98 regulates H3K4 methylation of a gene that colocalizes with PML bodies . Translocations that result in fusion of PML with the retinoic acid receptor α lead to loss of PML bodies , altered transcription , and acute promyelocytic leukemia [80] , [81] . Translocations that result in fusion of Nup98 to transcription factors lead to acute myeloid leukemias [82]–[85] . Finally , translocations that result in fusions of >60 different genes with the H3K4 methyltransferase MLL result in acute lymphoblastic leukemia , in part through altered Hox gene expression [86]–[88] . These striking similarities raise the possibility that these translocations impact the expression of an overlapping set of genes through similar mechanisms , perhaps involving transcriptional priming . Transcriptional memory plays a broad role in gene priming of IFN-γ-responsive genes ( Figure 4 ) . Hundreds of genes displayed faster or stronger induction kinetics in cells that have previously been exposed to IFN-γ and that this effect persists for days in rapidly doubling HeLa cells . This suggests that transcriptional memory can qualitatively alter the response of a system to a particular stimulus . If so , it is possible that the response of cells to other stimuli can be modulated by transcriptional memory . For example , similar to the phenomenon of stress cross-protection in yeast [19] , the rate of induction in response to one cytokine could also be qualitatively or quantitatively altered by previous exposure to a different cytokine . Because transcriptional memory is epigenetically inherited through several cell divisions , it could alter the response of cells , tissues , or whole organisms to persistent or episodic stimuli over long timescales . If so , then it might play an important role in pathological inflammation [89] , [90] . Unless otherwise noted , chemicals used were obtained from Sigma Aldrich and enzymes were from New England Biolabs . BACs were from Invitrogen . 8WG16 antibody was obtained from Covance , anti-Ser5P CTD ( cat no . ab5408 ) , anti-Phe-x-Phe-Gly m414 ( cat no . ab50008 ) , anti-Nup98 ( cat no . ab45584 ) , anti-Nup96 ( cat no . ab124980 ) , anti-Nup107 ( cat no . ab85916 ) , anti-H2A . Z ( cat no . ab4174 ) , anti-H3K4me2 ( cat no . ab32356 ) , and anti-H3K4me3 ( cat no . ab1012 ) were from AbCam . IFN-γ was from PBL Biomedical . Yeast strains used in this study are listed in Table S6 . Strains with the MRS or the mrs mutant elements have been described [18] and were created as described [15] . For yeast experiments , ChIP was performed as described [18] . For TAP-tagged ChIP experiments , Pan Mouse IgG Dynabeads from Invitrogen were used . For HeLa experiments , cells were trypsinized and fixed using 1% formaldehyde for 15 min at 25°C . Cross-linking was quenched using 150 mM glycine , and cells were harvested by centrifugation and washed twice with ice-cold PBS . Cells were lysed in 10 ml MC lysis buffer ( 10 mM NaCl , 10 mM Tris-HCl , 3 mM MgCl2 , 0 . 5% NP-40 ) and nuclei were recovered by centrifugation at 1 , 350 rpm twice and snap frozen in liquid nitrogen . Fixed nuclei were resuspended in 1 ml lysis buffer ( 10 mM Tris-HCl , 100 mM NaCl , 1 mM EDTA , 0 . 5 mM EGTA , 0 . 5% N-lauroylsarcosine ) with protease inhibitors ( Roche ) and sonicated using a Branson 450 microtip 16 times for 15 s at setting 5 to generate ∼500 bp average fragment size . 1% Triton X-100 and 0 . 1% sodium deoxycholate were added back and then chromatin was spun at ∼16 , 100× g at 4°C for 15 min . The supernatant fraction was added to antibody and Dynabeads overnight at 4°C . The beads were recovered and washed four times with lysis buffer+Triton X-100 and sodium deoxycholate . Immunoprecipitated chromatin was eluted in 100 µl TE+1% SDS at 65°C for 15 min . Input and IP fractions were treated with 50 µg RNase A and 100 µg Proteinase K for 1 h at 42°C , before reversing crosslinks overnight at 65°C . DNA was extracted with phenol∶chloroform∶isoamyl alcohol , and chloroform and 2 µg linear acrylamide was added prior to ethanol precipitation . Samples were washed with 70% ethanol and resuspended in 30 µl TE . qPCR was performed as described [12] using oligonucleotides listed in Table S7 . For reactivation experiments , HeLa cells were grown to ∼50% confluence , treated with 50 ng/mL of IFN-γ in DMEM supplemented with calf serum and antibiotics for 24 h , washed extensively with PBS , trypsinized , and seeded to plates at appropriate densities that would lead to the same confluence when the cells were harvested . Transfections were performed using Lipofectamine 2000 ( Invitrogen ) and siRNA smart pools for Nup98 , Nup107 , or scrambled ( Thermo Fisher ) according to the manufacturer's recommendations . RNA was harvested using Trizol Reagent ( Invitrogen ) according to the manufacturer's recommendations . HeLa cells were treated with IFN-γ as indicated . Cells were then trypsinized and adhered to polylysine-treated slides . Cells were fixed with formaldehyde for 15 min at 25°C and then washed with PBS+0 . 5% Triton X-100 several times . Slides were then treated with 0 . 1 M HCl on ice for 15 min , and then in 50% formamide/2× SSC for 30 min at 80°C . Fish probes were generated from BACs using FISH Tag DNA kit 488 ( Invitrogen ) . Probes were added to coverslip and then cells were covered , sealed with rubber cement , and heated at 80°C for 4 min , followed by incubation overnight at 37°C in the dark . Slides were then washed 3 times with 2× SSC at 37°C , 3 times with 0 . 1× SSC , and stained with Hoechst in 0 . 1× SSC at 25°C for 10 min , followed by 4× SSC/0 . 2% Tween-20 , mounted in Vectashield , sealed with nail polish , and z stacks of images were obtained using a Leica SP5 confocal microscope with a 100× objective . Measurements of the distance from FISH probe signal to nuclear periphery were made using ImageJ for individual z slices . HeLa cells were treated with IFN-γ for 0 , 6 , or 24 h for initial activation or after a previous 24 h treatment followed by a 48 h rest period ( reactivation ) . RNA samples were isolated using Trizol ( Invitrogen ) . RNA was DNase I treated and then reverse transcribed using Superscript III ( Invitrogen ) . For qPCR experiments , primer locations are shown in Figure S3 . For microarrays , the second strand was synthesized using second strand synthesis kit ( New England Biolabs ) . cDNA from two biological replicates was then labeled and hybridized to Agilent 128×135K arrays using human genome build hg18 . Log2 ratios were generated using DNAstar Arraystar software ( Roche ) . Averaged , normalized array data for the subset of genes that were induced ≥2 fold on average between 6 h and 24 h were organized using k means clustering by Cluster and visualized using Treeview . Media was removed from cells , and cells were scrapped off of plates in PBS using a rubber scraper . Cells were pelleted at 1 , 500 rpm at 4°C . Pellets were resuspended in whole cell extract buffer ( 50 mM Tris , 280 mM NaCl , 0 . 5% NP-40 , 0 . 2 mM EDTA , 2 mM EGTA , 10% glycerol ) with DTT , sodium vanadate , and protease inhibitors . Lysates were incubated on ice for 20 min and then spun at 13 , 200 rpm at 4°C . Supernatant was harvested , and protein concentration was quantified using BCA assay ( Pierce ) and frozen in liquid nitrogen . 75 µg of each sample was separated on a 10% SDS Tris-MOPS gel , transferred to nitrocellulose , and incubated overnight with antibodies against Nup98 GAPDH in TBST+5% skim milk at 4°C . Blots were then washed twice with TBST , incubated with secondary antibody conjugated to HRP , and exposed to Enhanced Chemiluminescence reagents ( Pierce ) and imaged using a UVP BiospectrumAC Imaging System . Yeast strains were harvested , fixed with methanol , and processed for microscopy as described [91] . Cells were harvested by centrifugation , washed 3 times with PBS , and adhered to polylysine-treated slides for 3 min at RT . Cells were fixed with 4% paraformaldehyde in PBS at RT for 15 min , washed twice with PBS , and then permeabilized with PBS+0 . 5% Triton X-100 at RT for 30 min . Cells were then blocked with PBS+3% BSA+0 . 1% Triton X-100 for 1 h at room temperature , and then incubated with primary antibodies ( either m414 with Nup96 or m414 with Nup98 ) overnight at 4°C . The following day , cells were washed twice with PBS and then incubated with secondary antibody ( Goat anti Rabbit Alexafluor488 or Goat anti Mouse Alexafluor 594 ) in blocking buffer for 2 h at room temperature , washed twice with PBS , and mounted with Vectashield . Cells were imaged on a Leica SP5 confocal microscope .
Cells respond to changes in nutrients or signaling molecules by altering the expression of genes . The rate at which genes are turned on is not uniform; some genes are induced rapidly and others are induced slowly . In brewer's yeast , previous experience can enhance the rate at which genes are turned on again , a phenomenon called “transcriptional memory . ” After repression , such genes physically interact with the nuclear pore complex , leading to altered chromatin structure and binding of a poised RNA polymerase II . Human genes that are induced by interferon gamma show a similar behavior . In both cases , the phenomenon persists through several cell divisions , suggesting that it is epigenetically inherited . Here , we find that yeast and human cells utilize a similar molecular mechanism to prime genes for reactivation . In both species , the nuclear pore protein Nup100/Nup98 binds to the promoters of genes that exhibit transcriptional memory . This leads to an altered chromatin state in the promoter and binding of RNA polymerase II , poising genes for future expression . We conclude that both unicellular and multicellular organisms use nuclear pore proteins in a novel way to alter transcription based on previous experiences .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "model", "organisms", "genetics", "immunology", "biology", "genomics", "molecular", "cell", "biology", "genetics", "and", "genomics" ]
2013
A Conserved Role for Human Nup98 in Altering Chromatin Structure and Promoting Epigenetic Transcriptional Memory
Chaperonins are a class of molecular chaperones that assist in the folding and assembly of a wide range of substrates . In plants , chloroplast chaperonins are composed of two different types of subunits , Cpn60α and Cpn60β , and duplication of Cpn60α and Cpn60β genes occurs in a high proportion of plants . However , the importance of multiple Cpn60α and Cpn60β genes in plants is poorly understood . In this study , we found that loss-of-function of CPNA2 ( AtCpn60α2 ) , a gene encoding the minor Cpn60α subunit in Arabidopsis thaliana , resulted in arrested embryo development at the globular stage , whereas the other AtCpn60α gene encoding the dominant Cpn60α subunit , CPNA1 ( AtCpn60α1 ) , mainly affected embryonic cotyledon development at the torpedo stage and thereafter . Further studies demonstrated that CPNA2 can form a functional chaperonin with CPNB2 ( AtCpn60β2 ) and CPNB3 ( AtCpn60β3 ) , while the functional partners of CPNA1 are CPNB1 ( AtCpn60β1 ) and CPNB2 . We also revealed that the functional chaperonin containing CPNA2 could assist the folding of a specific substrate , KASI ( β-ketoacyl-[acyl carrier protein] synthase I ) , and that the KASI protein level was remarkably reduced due to loss-of-function of CPNA2 . Furthermore , the reduction in the KASI protein level was shown to be the possible cause for the arrest of cpna2 embryos . Our findings indicate that the two Cpn60α subunits in Arabidopsis play different roles during embryo development through forming distinct chaperonins with specific AtCpn60β to assist the folding of particular substrates , thus providing novel insights into functional divergence of Cpn60α subunits in plants . Chaperonins are a class of molecular chaperones that are characterized by a barrel-shaped architecture formed by two stacked oligomeric rings consisting of several subunits of approximately 60 kDa . Two types of chaperonins have been identified: type I chaperonins are found in eubacteria , chloroplasts , and mitochondria; and type II exist in archaea and eukaryotic cytosol . The main difference between them is that type I chaperonins require co-chaperonins consisting of seven 10 kDa subunits for substrate encapsulation , whereas type II chaperonins have a built-in lid that plays the same role [1] . The GroEL/GroES complex in Escherichia coil has been studied extensively as the prototype of type I chaperonins . GroEL is a homo-oligomer which consists of two stacked heptameric rings . The folding cycle in GroEL/GroES has been surveyed in detail , and the canonical view suggested that the complex operates through the asymmetric “bullet” cycle . In this asymmetric cycle , unfolded/misfolded substrates first bind to the hydrophobic cavity lining of one ring ( cis ring ) , utilizing the exposed hydrophobic residues , and then the binding of ATP causes large conformational changes of the cis ring that further trigger the binding of co-chaperonins . Binding of co-chaperonins initiates further conformational changes and caps the cis ring , and consequently encapsulates the substrates into an expanded cavity with a hydrophilic lining , which assists the substrates to refold into their native states . Following the hydrolysis of ATP in the cis ring , ATP and other non-native proteins bind to the opposite ring ( trans ring ) , resulting in the dissociation of refolded substrates , ADP , and co-chaperonins in the cis ring [2–4] . Moreover , an alternative model , known as the symmetric “football” model , was recently also proposed . In this model , the exchange of ADP to ATP is extremely rapid in the presence of abundant substrate protein , resulting in formation of a symmetric “football” intermediate that has GroES bound to both rings and can assist in protein folding simultaneously in both rings . This “football” intermediate would be reverted to the “bullet” conformation upon ATP hydrolysis [5–7] . In addition , although Escherichia coil only contains one chaperonin gene , a survey of 669 complete bacterial genomes showed that nearly 30% contain two or more chaperonin genes , and a degree of subfunctionalization has occurred in the chaperonin subunits encoded by these duplicated genes [8] . Moreover , in the previous study , Wang and coworkers found that some specific mutations of GroEL can improve the folding of GFP , but the mutated GroEL has a reduced ability to function as general chaperones , suggesting a conflict between the increased ability of GroEL to fold particular substrates and its general ability to fold a wide range of substrates , and this conflict would be resolved by duplication and variation of chaperonin genes [9] . Chloroplast chaperonin ( Ch-Cpn60 ) was first found as a homolog of GroEL that could bind to the chloroplast Rubisco large subunit and assist the assembly of Rubisco , a key rate-limiting enzyme in the process of carbon dioxide fixation [10] . In contrast to GroEL , ch-Cpn60s contain two different types of subunits , Cpn60α and Cpn60β , which only share approximately 50% identity . Ch-Cpn60s composed of Cpn60α and Cpn60β are considered to be the native form of chloroplast chaperonins in vivo , because ch-Cpn60s purified from Pisum sativum , Brassica napus , Arabidopsis thaliana and Spinacia oleracea were all shown to be hetero-oligomers consisting of nearly equal amounts of Cpn60α and Cpn60β [11–13] . In Arabidopsis thaliana , there are two Cpn60α genes and four Cpn60β genes , which encode three dominant subunits: AtCpn60α1 , AtCpn60β1 and AtCpn60β2; and three minor subunits: AtCpn60α2 , AtCpn60β3 and AtCpn60β4 ( the nomenclature used in this article is in accordance with The Arabidopsis Information Resource database ) . Among them , AtCpn60α1 and AtCpn60α2 share only about 57% identity , and AtCpn60β1/2/3 share 90%-95% identity , while AtCpn60β4 is only 60% identical to the other AtCpn60β subunits [14–15] . AtCpn60α1 was the first chaperonin gene studied in detail , and its mutant , schlepperless ( slp ) , showed retardation of embryo development before the heart stage , and defective embryos with highly reduced cotyledons [16] . Then a T-DNA insertion mutant of AtCpn60α2 , emb3007 , showed the embryo development arrested at the globular stage in the SeedGenes database ( http://www . seedgenes . org/ ) , suggesting that AtCpn60α2 is also possibly an embryo-defective gene [17–18] . A T-DNA mutant lacking the AtCpn60β1 transcript , len1 , had impaired leaves and showed systemic acquired resistance ( SAR ) under short-day condition [19] . It was also reported that a weak mutant of AtCpn60α1 and a strong mutant allele of AtCpn60β1 both showed impaired chloroplast division and reduced chlorophyll levels , and the AtCpn60β1 AtCpn60β2 double mutant led to an albino seedling similar to slp , suggesting that AtCpn60β1 and AtCpn60β2 are redundantly required for normal chloroplast function , together with AtCpn60α1 [20] . In addition , a recent report also showed that the ch-Cpn60 containing the AtCpn60β4 subunit played a specific role in the folding of NdhH , a subunit of the chloroplast NADH dehydrogenase-like complex ( NDH ) , indicating that the particular type of AtCpn60β subunit could contribute to the folding of some specific substrates [21] . Embryogenesis is the beginning of plant development . During Arabidopsis embryo development , chloroplast biogenesis is a temporary process . Proplastids in the whole embryo first begin to differentiate into chloroplasts at the transition stage , and then the mature chloroplasts degenerate to undifferentiated eoplasts during seed maturation [22–24] . For decades , through forward and reverse genetic screens in Arabidopsis , numerous chloroplast proteins crucial for embryo development were discovered . Interestingly , nearly all embryo defects caused by chloroplast dysfunction displayed premature arrest at the globular stage , indicating that the formation of impermanent chloroplasts in Arabidopsis embryos is especially crucial for the transition of globular embryos to heart-shaped embryos [25–27] . Here , we provided genetic evidence to show that the two AtCpn60α genes in Arabidopsis affect the embryonic development at different stages . Further studies revealed that CPNA2 could form a functional chaperonin with AtCpn60β2 and AtCpn60β3 subunits to specifically assist the folding of KASI ( β-ketoacyl-[acyl carrier protein] synthase I ) , and KASI could not be folded by the functional chaperonin containing CPNA1 . Moreover , we found that the KASI protein level was largely reduced due to loss-of-function of CPNA2 , and the reduction in KASI protein level possibly caused the abnormality of the cpna2 embryos . Our results showed that Cpn60α2 and Cpn60α1 in Arabidopsis can form functional chaperonin complexes with specific AtCpn60β subunits to assist the folding of particular substrates , indicating that functional divergence of chloroplast Cpn60α subunits has occurred in higher plants . To elucidate the molecular mechanisms that control embryo development in Arabidopsis , we ordered the stock CS76507 , which is a set of 10 , 000 T-DNA lines , from Arabidopsis Biological Resource Center ( ABRC , http://abrc . osu . edu/ ) . From the stock , we obtained a mutant that showed obvious seed abortion , with a frequency of 25 . 55% ( n = 2270 ) . We found that the T-DNA insertion of this mutant is located in the first exon of AT5G18820 ( Fig 1A ) using thermal asymmetric interlaced PCR [28] . We named the gene CPNA2 because it encodes the chaperonin subunit AtCpn60α2 , and named the mutant cpna2-2 ( The emb3007 mutant described previously was designated as cpna2-1 here ) . PCR analysis of cpna2-2/+ progeny showed that no homozygous mutant plant existed , and the ratio of heterozygote to wild type was nearly 2:1 ( S1 Table ) . Reciprocal crosses between heterozygote and wild-type plants further demonstrated that the transmission efficiency of gametophytes was not affected by loss-of-function of CPNA2 ( S1 Table ) . Moreover , we obtained another T-DNA mutant ( cpna2-3 , SALK_144574 ) from ABRC , and found that the mutant cpna2-3/+ also showed seed abortion , with a frequency of 26 . 09% ( n = 1196 ) ( Fig 1A and 1B ) . To further confirm that CPNA2 is responsible for seed abortion in cpna2-2/+ and cpna2-3/+ plants , we performed a complementation test . A genomic fragment , including CPNA2 , 1548 bp upstream of the start codon and 673 bp downstream of CPNA2 , was introduced into cpna2-2/+ and cpna2-3/+ . The result showed that the fertility was restored in the siliques of CPNA2pro:gCPNA2 transgenic plants ( Fig 1B ) . Together , these results indicated that seed abortion in Arabidopsis could be caused by loss-of-function of CPNA2 . To investigate the cause of seed abortion of cpna2-2/+ and cpna2-3/+ , we first examined the processes of embryo development in the seeds of cpna2-2/+ and cpna2-3/+ using the whole mount clearing technique . We could not distinguish abnormal embryos in the siliques from the zygote stage to the globular stage . However , when most embryos in the seeds of cpna2-2/+ and cpna2-3/+ reached the transition stage , some embryos showed the irregular globular shape and the start of abnormal cell division ( Fig 1C ) . While wild-type embryos progressed into further stages , the abnormal embryos still stayed at the globular stage , and finally degraded along with the collapse of seeds ( Fig 1C ) . The phenotype of these abnormal embryos is consistent with the mutant emb3007 in SeedGenes ( http://www . seedgenes . org/; [17–18] ) . In addition , since CPNA2 was predicted to encode a chloroplast chaperonin subunit and chloroplast chaperonins had been reported to be involved in the folding and assembly of many chloroplast proteins [29] , we wondered whether the abortion of cpna2 embryos was due to impaired chloroplast development . To investigate this , we first examined the subcellular localization of CPNA2 in mesophyll protoplasts isolated from transgenic plants carrying the 35Spro:CPNA2-GFP construct . As shown in Fig 2A , the protoplasts containing CPNA2-GFP fusion protein displayed GFP signals that overlapped with chlorophyll autofluorescence , confirming that CPNA2 is located in chloroplasts . Then we observed the ultrastructure of chloroplasts using transmission electron microscopy . In the 6 DAP siliques of cpna2-2/+ plant , we observed that mature chloroplasts in wild-type embryos contained organized thylakoid membranes stacked into grana ( Fig 2B and 2C ) . In contrast , only abnormal chloroplasts that lacked thylakoid membranes and contained a deeply stained mass were found in cpna2-2 mutant embryos ( Fig 2D and 2E ) . Collectively , these results suggested that loss-of-function of CPNA2 impeded the process by which proplastids differentiate into mature chloroplasts during embryo development , thereby causing the arrest of the cpna2 embryos and seed abortion of cpna2-2/+ and cpna2-3/+ . It had been reported that CPNA2 has an extremely low signal in all tissues and developmental stages using the Genevestigator program , and the CPNA2 protein could not be detected in proteomics studies [14 , 30] . To further investigate the expression pattern of CPNA2 , we examined CPNA2 transcript levels in different Arabidopsis tissues using quantitative real-time PCR ( qRT-PCR ) . The result demonstrated that CPNA2 is expressed in all tissues and is especially highly expressed in the 5 DAP ( day after pollination ) siliques ( Fig 3A ) . Then , we performed a GUS assay in the transgenic plants carrying the CPNA2pro:GUS construct , and observed a very strong signal in the SAM and a weaker signal in vascular bundles of 7 DAG ( day after germination ) seedlings , which largely declined in 14 DAG seedlings ( Fig 3B and 3C ) . No signal was found in mature leaves , flowers , inflorescences and siliques , possibly due to low abundance and dispersion of GUS signals . To investigate the expression pattern of CPNA2 during embryo development , we obtained transgenic plants carrying the CPNA2pro:H2B-GFP construct , and observed the fluorescent signal in the dissected embryos . No GFP ( green fluorescent protein ) signal was detected in the globular embryos , but GFP signals began to sporadically appear in the transition stage embryos ( Fig 3D ) . When the embryos reached the heart stage , fluorescent signals were located on the adaxial sides of cotyledons ( Fig 3D ) . Next , the signals were mainly detected in cotyledons of the torpedo and cotyledon stages , and were still strongest on the adaxial sides of cotyledons ( Fig 3D ) . Together , these results showed that CPNA2 is highly expressed in the SAM of early seedlings and embryonic cotyledons . Hill and Hemmingsen reported that CPNA2 is the paralog of CPNA1 [15] , thus it is possible that they have redundant functions . However , a CPNA1 mutant , slp ( schlepperless ) , showed an embryo-defective phenotype that mainly appeared at the heart stage and thereafter , which is different from the cpna2 mutants [16] . To confirm the previous study , we obtained another AtCpn60α1 mutant ( cpna1 , SALK_006606 ) ( S1A Fig ) , and then observed embryo development in the siliques of cpna1/+ . As expected , nearly a quarter of the examined embryos ( 25 . 71% , n = 579 ) showed the abnormal phenotype . The abnormal embryos had highly reduced cotyledons , a larger angle between cotyledons , and developed more slowly from the torpedo stage ( S1B Fig ) , which was similar to the phenotype of the slp embryos . As shown above , cpna1 and cpna2 mutants had very different embryo-defective phenotypes , which could be caused by functional divergence and/or different expression patterns of the two genes . To investigate the expression pattern of CPNA1 during embryo development , we obtained transgenic plants carrying the CPNA1pro:H2B-CFP construct , and observed CFP ( cyan fluorescent protein ) signals in the dissected embryos . The fluorescent signal was originally detected in protoderm cells at the globular stage , and then concentrated in the SAM at the transition stage ( Fig 3E ) . When the embryos developed into heart , torpedo , and cotyledon stages , most CFP signals were specifically redistributed on the adaxial sides of cotyledons ( Fig 3E ) . As the above results demonstrated , CPNA1 has a similar expression pattern to CPNA2 during embryo development , while it is expressed more widely . This indicates that the different embryo-defective phenotypes of the cpna1 and cpna2 mutants are likely to be caused by functional divergence of the two genes , but not due to differences in expression pattern . Moreover , these findings also suggested that CPNA1 mainly plays a role at the torpedo stage and thereafter , whereas CPNA2 is crucial to reach the heart stage for Arabidopsis embryos . Ch-Cpn60s had been considered to be hetero-oligomers consisting of equal amounts of Cpn60α and Cpn60β , based on several studies conducted in Pisum sativum , Brassica napus , Arabidopsis thaliana , and Spinacia oleracea [11 , 12 , 13 , 31] . Moreover , because CPNA2 and CPNA1 could play different roles during embryo development , we wondered which AtCpn60β subunits could interact with CPNA2 or CPNA1 to form specific chaperonins . Using AtPID ( Arabidopsis thaliana Protein Interactome Database ) [32] , we first predicted the functional partners of CPNA2 and CPNA1 . The result showed that AtCpn60β1 and AtCpn60β2 had much higher scores than AtCpn60β3 and AtCpn60β4 among the predicted functional partners of CPNA1 ( S2 Table ) . In contrast , AtCpn60β3 and AtCpn60β2 were the top two predicted functional partners of CPNA2 , and AtCpn60β3 had a far higher score than the other candidates ( S2 Table ) . These results implied that CPNA2 and CPNA1 could possibly interact with different AtCpn60β subunits to form specific functional chaperonins . To further clarify the functional partners of CPNA2 and CPNA1 , we obtained T-DNA insertion mutants of AtCpn60β1 ( CPNB1 ) , AtCpn60β2 ( CPNB2 ) , AtCpn60β3 ( CPNB3 ) , and AtCpn60β4 ( CPNB4 ) from ABRC ( S2A Fig ) . Homozygous mutant plants of all AtCpn60β genes could be obtained and had normal fertility . Through reverse transcription PCR ( RT-PCR ) analysis , we also confirmed the complete loss of the corresponding transcripts in the cpnb2 , cpnb3 and cpnb4 mutants , and the enormous reduction of the CPNB1 transcript in the cpnb1 mutant ( S2B Fig ) . We then crossed these mutants pairwise to obtain double heterozygous plants . In all double heterozygous plants , we only observed aberrant seeds in siliques of the cpnb1/+ cpnb2/+ and cpnb2/+ cpnb3/+ plants ( S2C Fig ) , implying that the cpnb1 cpnb2 and cpnb2 cpnb3 double homozygous embryos were possibly abnormal . We also obtained cpnb1/+ cpnb2 and cpnb2 cpnb3/+ plants in the self-crossed progenies of the cpnb1/+ cpnb2/+ and cpnb2/+ cpnb3/+ mutants , respectively . As expected , siliques of the cpnb1/+ cpnb2 and cpnb2 cpnb3/+ plants contained numerous aberrant seeds , at a frequency of 25 . 43% ( n = 1050 ) and 25 . 64% ( n = 1166 ) , respectively , suggesting that the cpnb1 cpnb2 and cpnb2 cpnb3 embryos were probably abnormal . To clarify the cause for the abnormality of the cpnb1 cpnb2 and cpnb2 cpnb3 embryos , we examined the developmental processes of these double homozygous embryos in the aberrant seeds . In siliques of the cpnb1/+ cpnb2 plants , the cpnb1 cpnb2 embryos had a similar shape to wild-type embryos at the heart stage , apart from slightly smaller cotyledons and a larger angle between cotyledons . However , the cpnb1 cpnb2 embryos began to display highly reduced cotyledons at the torpedo stage and thereafter compared to the wild-type embryos ( Fig 4A ) . In contrast , we observed that the majority of the cpnb2 cpnb3 embryos ( 70 . 61% , n = 228 ) were arrested at the globular stage in the siliques of the cpnb2 cpnb3/+ plants , while the other cpnb2 cpnb3 embryos showed various phenotypes , including remarkable retardation of embryo development , and variation of cotyledon shape and number ( Fig 4B ) . Overall , we found that cpnb1 cpnb2 embryos had a very similar phenotype to cpna1 embryos , echoing the previous study that showed that cpnb1 cpnb2 seedlings were analogous to cpna1 seedlings [20] . In addition , although the penetrance was incomplete possibly due to partial complement of CPNB1 , the majority of the cpnb2 cpnb3 embryos were shown to phenocopy the cpna2 embryos . Combined with the prediction from AtPID ( S2 Table ) , this result further suggested that CPNA1 is likely to form a functional chaperonin with CPNB1 and CPNB2 during Arabidopsis embryo development , whereas CPNB2 and CPNB3 were the functional partners of CPNA2 . Moreover , since the cpnb1/+ cpnb3/+ plants showed normal seed development , it seemed that CPNB2 was a versatile AtCpn60β subunit which could form functional chaperonins with both CPNA1 and CPNA2 , and was sufficient to support embryo development alone . As the previous results demonstrated , we found that CPNA2 and CPNA1 have nonredundant functions during embryo development . Moreover , it had been widely reported that some chaperonins containing specific subunits had unique substrates and played an important role under certain circumstances [21 , 33 , 34 , 35] . Based on these results , it is possible that the chaperonin containing CPNA2 has some specific substrates that cannot be folded by the chaperonin containing CPNA1 , thus affecting the specific developmental process of Arabidopsis embryos . To examine this possibility , we first introduced two chimeric genes encoding an HA ( influenza hemagglutinin protein epitope ) tag fused to the C-terminus of CPNA2 or CPNA1 into cpna2-2/+ and cpna1/+ mutants , respectively . The CPNA2pro:CPNA2-HA and CPNA1pro:CPNA1-HA constructs fully restored the fertility of cpna2-2/+ and cpna1/+ plants , respectively ( Fig 5A ) , indicating that HA-tag has no effect on the functions of CPNA2 and CPNA1 . Since expression of the CPNA2 gene in vegetative tissues is very low [14] , we also obtained transgenic lines carrying the 35Spro:CPNA2-HA or 35Spro:CPNA1-HA construct , and conducted Co-IP assay in 7 DAG transgenic and wild-type seedlings using the μMACS HA isolation kit ( Miltenyi Biotec ) . The immunoprecipitates were separated by SDS-PAGE and then analyzed by liquid chromatography-tandem mass spectrometry ( LC-MS/MS ) ( Fig 5B ) . The MS analysis showed that KASI , a protein involved in de novo fatty acid synthesis , had more peptides and much higher scores in CPNA2 immunoprecipitation fractions than in CPNA1 and WT immunoprecipitation fractions ( Table 1 and S1 Data ) . These results suggested that KASI was a possible specific substrate of the chaperonin containing CPNA2 . Moreover , the MS data also showed that CPNB3 was not detected in CPNA1 immunoprecipitation fractions , but had high scores in CPNA2 immunoprecipitation fractions ( Table 1 ) , echoing the previous genetic results demonstrating that CPNB3 is a functional partner of CPNA2 but not CPNA1 . To further confirm that KASI is a specific substrate of the chaperonin containing CPNA2 , we also performed a proteinase K protection assay . This assay takes advantage of the formation of a highly stable cis-ternary complex consisting of substrate , chaperonin and co-chaperonin in the presence of ADP [36 , 37] . This cis-ternary complex could sequester a substrate into the cavity of a chaperonin , thus protecting the substrate from digestion by proteinase K . Recombinant CPNA1 , CPNA2 , CPNB1 , CPNB2 , CPNB3 , Cpn20 ( a co-chaperonin subunit in Arabidopsis ) , and KASI proteins were overexpressed in Escherichia coli and then purified on Ni-NTA agarose resin . Subsequently , the various chaperonins consisting of AtCpn60α and AtCpn60β subunits were reconstituted according to previously described method [31] . After purification by gel-filtration chromatography , the reconstituted chaperonins were used in proteinase K protection assays of denatured KASI protein . As shown in Fig 5C , 5D and 5E , KASI was digested by proteinase K in the presence of Cpn60A1-B1 , Cpn60A1-B2 or Cpn60A1-B3 , indicating that KASI was not likely to form stable cis-ternary complexes with Cpn20 and chaperonins containing CPNA1 . In contrast , in the presence of Cpn60A2-B2 or Cpn60A2-B3 , almost all of the KASI protein was protected from digestion by proteinase K ( Fig 5G and 5H ) , which was likely due to the formation of the stable cis-ternary complexes . Taken together , these results showed that denatured KASI protein could only be captured and refolded by Cpn60A2-B2 and Cpn60A2-B3 , but not by chaperonins containing CPNA1 , confirming that KASI is a specific substrate of the functional chaperonins containing CPNA2 . In addition , as shown in Table 1 , CPNB1 had very high scores in both CPNA2 immunoprecipitation fractions , indicating that CPNB1 could likely interact with CPNA2 . To know whether CPNB1 was a functional partner of CPNA2 , we also reconstituted the chaperonin consisting of CPNA2 and CPNB1 ( Cpn60A2-B1 ) and performed a proteinase K protection assay of denatured KASI using Cpn60A2-B1 . As shown in Fig 5F , KASI was not protected from digestion by proteinase K in the presence of Cpn60A2-B1 , indicating that Cpn60A2-B1 was not a functional chaperonin that can assist the folding of KASI . This result showed that although CPNB1 could interact with CPNA2 when the CPNA2 protein was overexpressed in vivo , it could not form a full-functional chaperonin with CPNA2 alone , providing further evidence supporting the above genetic findings that suggested CPNB1 was not a functional partner of CPNA2 . As demonstrated above , KASI is a specific substrate of the functional chaperonin containing CPNA2 . To know whether the level of KASI protein was reduced in the cpna2 homozygous mutant , we first rescued the development of cpna2-2 homozygous embryos using a ABI3pro:CPNA2-HA construct . Since the ABI3 gene is specifically expressed in seeds [38 , 39] , we obtained cpna2-2 homozygous seedlings from ABI3pro:CPNA2-HA transgenic lines ( Fig 6A ) . The abnormal seedlings had the white cotyledons and could not develop true leaves even on the fourteenth day after germination ( Fig 6A ) . Genotypic analysis of the abnormal seedlings also confirmed that they were cpna2-2 homozygous mutants partially rescued by the ABI3pro:CPNA2-HA construct ( Fig 5B ) . Then we examined KASI protein levels in 7 and 14 DAG seedlings of WT and cpna2-2 by immunoblotting . As shown in Fig 6C and 6D , KASI protein levels in 7 and 14 DAG seedlings of cpna2-2 were reduced to approximately one-tenth of the level in contemporaneous WT seedlings . This result indicated that the KASI protein level could be largely reduced due to loss-of-function of CPNA2 in vivo . In addition , Wu and Xue reported that KASI was crucial for embryo development and KASI deficiency resulted in disrupted embryo development before the globular stage [40] . Therefore , it is possible that the decline of KASI protein level in cpna2 embryos causes abortion of the mutant embryos . To examine this possibility , we constructed a CPNA2pro:amiR-KASI vector to specifically reduce the expression level of KASI in the embryos of transgenic lines at the transition stage and thereafter . In T1 generation transgenic plants , we chose three lines for follow-up studies . As shown in Fig 7A , we found that the expression levels of KASI in lines 18 and 23 were reduced by almost half , whereas the expression level of KASI in line 16 remained unchanged . Then we carefully examined embryo development in ovules of wild type , line 16 , line 18 , and line 23 . In the ovules of line 16 , embryos developed similarly to wild type ( Fig 7B ) . In contrast , when wild-type embryos reached the heart stage , almost all the embryos of line 18 and line 23 still stayed at the globular stage , and reached the heart stage when normal embryos entered the torpedo stage ( Fig 7B ) . Embryos in the ovules of line 18 and line 23 ultimately could reach the cotyledon stage ( Fig 7B ) , and the fertility of line 18 and line 23 plants was not affected . Moreover , we also counted the percentages of all embryonic morphologies in the 3 , 4 , 5 , and 7 DAP siliques of wild type and transgenic lines ( S3 Table ) , further confirming the delayed embryo development in lines 18 and 23 , consistent with the morphological observations . By observing the embryo development of the KASI knock-down transgenic lines , we found that reduction of the expression level of KASI clearly delayed the process by which globular embryos develop into heart-shaped embryos . This finding suggested that the KASI level is crucial to reach the heart stage for Arabidopsis embryos , implying that abnormality of the cpna2 embryos is likely to be caused by a decrease of correctly folded KASI protein in the mutant embryos . To investigate the evolutionary relationship of Cpn60α1 and Cpn60α2 , we obtained protein sequences of CPNA1 and CPNA2 , excluding the transit peptides predicted by TargetP [41] , and then searched for homologous proteins in various species using BLAST ( Basic Local Alignment Search Tool , http://blast . ncbi . nlm . nih . gov/Blast . cgi ) . By sequence alignment , we constructed the phylogenetic tree of Cpn60α1 and Cpn60α2 . The tree showed that Cpn60α1 orthologs exist in monocotyledons , dicotyledons , gymnosperms ( Picea sitchensis ) , bryophytes ( Physcomitrella patens ) and algae ( Chlamydomonas reinhardtii ) , whereas Cpn60α2 orthologs form a separate cluster and only exist in monocotyledons and dicotyledons ( S3 Fig ) . The result suggested that Cpn60α1 is more primitive than Cpn60α2 , and Cpn60α2 probably originated from gene duplication and variation of Cpn60α1 in angiosperms . As the above results showed , the functional chaperonin containing CPNA2 could specifically assist in the folding of KASI and could play a unique role during Arabidopsis embryo development . To clarify the structural basis of functional specialization of CPNA2 , we first analyzed the three-dimensional ( 3-D ) structures of GroEL , CPNA1 , and CPNA2 through homology modeling . As shown in Fig 8A , compared with GroEL and CPNA1 , CPNA2 lacked strands 2 and 3 . To confirm the importance of strands 2 and 3 , we also did homology modeling of the CPNA2 orthologs in Brassica napus , Vitis vinifera , and Morus notabilis . Surprisingly , these orthologs all possess strands 2 and 3 ( Fig 8B ) , suggesting that the lack of strands 2 and 3 is not likely to be necessary for functional specialization of CPNA2 . Moreover , Ile 150 ( I150 ) and Asp 398 ( D398 ) , which are crucial for ATP/ADP binding in GroEL , are also highly conserved in CPNA1 and CPNA2 ( Fig 8A ) . In previous studies , a few positions in the apical domain of GroEL had been proposed as substrate binding sites , which are highly conserved in many species [42–44] . To know whether these positions led to functional specialization of CPNA2 , we first examined the corresponding positions in the homologous proteins of CPNA1 and CPNA2 by protein sequence alignment ( S4 Fig ) . The result showed that the highly conserved hydrophobic residue Ala 259 in the orthologs of CPNA1 is converted to a hydrophilic residue Glu or Ser in the orthologs of CPNA2 , while the conserved positively charged residue Arg 267 in Cpn60α1 is mostly replaced by an uncharged residue Gln or Asn in Cpn60α2 ( S4 Fig ) . Furthermore , Glu 259 and Gln 267 of the CPNA2 protein are also unique in all the Arabidopsis ch-Cpn60 subunits ( Fig 8C ) , implying that the two positions are likely the cause for functional specialization of CPNA2 . To confirm this conjecture , Glu 259 and Gln 267 of CPNA2 were converted to Ala 259 and Arg 267 , respectively , through site-directed mutagenesis , and then a CPNA2pro:CPNA2 , CPNA2pro:CPNA2E259A , CPNA2pro:CPNA2Q267R or CPNA2pro:CPNA2E259A/Q267R construct was introduced into cpna2-2/+ plants . Unexpectedly , cpna2-2/+ plants carrying the CPNA2pro:CPNA2 , CPNA2pro:CPNA2E259A , CPNA2pro:CPNA2Q267R or CPNA2pro:CPNA2E259A/Q267R construct all had normal fertility ( Fig 8D ) , suggesting that the two conserved residues in the orthologs of CPNA2 are not crucial for the functional specialization of CPNA2 . These results indicated that CPNA2 is likely to utilize a few new positions to bind KASI , and that detailed structural information of the chaperonin containing CPNA2 is required to further elucidate the mechanism of KASI folding . Chloroplast chaperonins are composed of two types of chaperonin subunits , Cpn60α and Cpn60β , which is different from the chaperonins in bacteria and mitochondria . Several previous studies demonstrated that ch-Cpn60s consisting of nearly equal amounts of Cpn60α and Cpn60β were the native form of chloroplast chaperonins in vivo , although the Cpn60β subunit could also form the chaperonin complex in reconstitution experiments alone [11 , 12 , 13 , 31] . Among the four Cpn60β subunits in Arabidopsis , AtCpn60β1 , AtCpn60β2 , and AtCpn60β3 have more than 90% identity , while AtCpn60β4 shares only 60% identity with the other three AtCpn60β subunits . However , the homo-oligomers reconstituted with AtCpn60β1 , AtCpn60β2 or AtCpn60β3 have unique physicochemical properties , different preferences for various co-chaperonins , and distinct abilities of folding substrates , implying the functional divergence of Cpn60β1/2/3 in Arabidopsis [45] . In this study , we analyzed the phenotypes of different combinations of AtCpn60β double mutants , and found that cpnb1 cpnb2 and cpnb2 cpnb3 double mutant embryos phenocopied cpna1 and cpna2 embryos , respectively . This finding suggested that CPNA1 plays a role in embryo development together with CPNB1 and CPNB2 , while CPNA2 can function with CPNB2 and CPNB3 . Moreover , we found that CPNB3 could not be detected in the CPNA1 immunoprecipitation fractions , whereas it was abundant in the CPNA2 immunoprecipitation fractions ( Table 1 ) . This result indicated that CPNA1 had far lower affinity for CPNB3 compared with CPNA2 , further confirming that CPNB3 is the functional partner of CPNA2 but not CPNA1 , consistent with the genetic results . In addition , we found that the chaperonin complex reconstituted with CPNA2 and CPNB1 could not protect KASI from proteinase K ( Fig 5F ) , thus suggesting that CPNB1 is the functional partner of CPNA1 but not CPNA2 , consistent with the genetic results . These findings provided evidence that different AtCpn60α subunits could bind specific AtCpn60β subunits as their functional partners , indicating the functional divergence of Cpn60α subunits in Arabidopsis . Moreover , we also found that although CPNB1 and CPNA2 do not appear to function together in the folding of KASI , CPNB1 was abundant in the CPNA2 immunoprecipitation fractions ( Table 1 ) . It was also reported that AtCpn60β1/2/3 and AtCpn60α1 usually formed the native chaperonin together in vivo [21 , 30] , even though we did not detect CPNB3 in CPNA1 immunoprecipitation fractions possibly due to low affinity of CPNB3 for CPNA1 . These results suggested that all the AtCpn60β1/2/3 subunits are usually mixed into native chaperonins containing the Cpn60α subunit in vivo , although they have different affinity for specific Cpn60α subunit . Moreover , although it had been reported that the native chaperonin containing Cpn60β4 in Arabidopsis is composed of seven Cpn60α1 , two Cpn60β4 , and five Cpn60β1/2/3 [21] , the exact proportions of AtCpn60β1 , AtCpn60β2 , and AtCpn60β3 in native chaperonins are difficult to determine due to similar molecular weights and high identity of AtCpn60β1/2/3 . Further study is still needed to clarify the stoichiometry of subunits in native ch-Cpn60s of Arabidopsis . Although multiple chaperonin genes are present in a high proportion of prokaryotes and eukaryotes , the biological significance of duplication and variation of chaperonin genes has yet to be fully elucidated . Recently , a study on the type II chaperonin of Sulfolobales showed that three different chaperonin subunits ( α , β , γ ) could form three types of chaperonins at different temperatures , and specific chaperonins could fold a distinct range of substrates to adapt to environmental changes [33] . Moreover , it was also reported that GroEL1 in Mycobacterium smegmatis specifically interacts with KasA ( a key component of type II Fatty Acid Synthesis ) to affect mycolic acid synthesis and biofilm formation , whereas GroEL2 provides the housekeeping chaperone function [35] . In the field of chloroplast chaperonins , Zhang and coworkers recently determined the crystal structure of the apical domains of Cpn60α and Cpn60β1 in Chlamydomonas reinhardtii , and elucidated the structural basis for why Cpn60α and Cpn60β subunits have different affinity for substrates and co-chaperonins [46] . Additionally , in line with the divergence of protein sequence , the Cpn60β4 subunit in Arabidopsis has a unique structure and the chaperonin containing Cpn60β4 could specifically assist the folding of NdhH [21] . These findings revealed that duplication and variation of chaperonin genes could extend the function of chaperonins in various species . Here , we found that KASI , a protein involved in de novo fatty acid synthesis , was far more abundant in CPNA2 immunoprecipitation fractions than in CPNA1 immunoprecipitation fractions , implying that KASI was likely to be a specific substrate of the chaperonin containing CPNA2 . To confirm this conjecture , we conducted the proteinase K protection assay of KASI in the presence of different chaperonin complexes . It was shown that both Cpn60A2-B2 and Cpn60A2-B3 could perfectly protect KASI from digestion by proteinase K , whereas all the chaperonins containing CPNA1 could not protect KASI . This result further showed that the functional chaperonins containing CPNA2 could specifically assist in the folding of KASI , suggesting that CPNA2 , a minor Cpn60α subunit , has a unique function in Arabidopsis . Moreover , we also examined two conserved positions proposed as substrate binding sites in the orthologs of CPNA2 , and found that they were not responsible for the functional specialization of CPNA2 . Hence , detailed structural analysis of the chaperonin containing CPNA2 is required to further elucidate the mechanism of KASI folding . Additionally , it was reported that chaperonins in various species had a wide range of substrates [1] , therefore we cannot exclude the possibility that the chaperonin containing CPNA2 has other specific substrates in addition to KASI . Moreover , since we conducted Co-IP assay in 7 DAG seedlings but not in embryos due to technology limitations , it is possible that there are some unknown specific substrates of the chaperonin containing CPNA2 that only exist in embryos . The detection of more specific substrates would further contribute to the functional elucidation of CPNA2 in Arabidopsis . A number of genes involved in de novo fatty acid synthesis are essential for early embryo development in Arabidopsis . GURKE , a gene encoding the acetyl-CoA carboxylase ACC1 , is required for partitioning the apical part of globular embryos in Arabidopsis [47] , and loss-of-function of CAC1A , a gene encoding the biotin carboxyl-carrier protein BCCP1 , obviously delayed embryo development from the early globular stage [48] . Moreover , KASI deficiency was also found to result in arrested development of most kasI embryos before the globular stage , and delayed development of few kasI embryos [40] . Additionally , in Arabidopsis microarray data sets [49] , we found that the expression level of KASI has a dramatic increase when embryos reach the heart stage , implying that KASI is likely to be crucial for the transition of globular embryos to heart-shaped embryos . To confirm this conjecture , we specifically reduced the expression level of KASI in the embryos at the transition stage and thereafter by transforming the CPNA2pro:amiR-KASI vector . In the transgenic lines , we observed that the process of globular embryos reaching the heart stage was delayed , confirming that KASI plays an important role in the formation of heart-shaped embryos . Since cpna2 embryos are arrested at the globular stage and loss-of-function of CPNA2 could result in a significant decrease in the KASI protein level that is crucial for the transition of globular embryos to heart-shaped embryos ( Figs 1 and 6 ) , the arrest of cpna2 embryos is likely due to the reduction of the well-folded KASI protein level in mutant embryos . However , we did not find any CPNA2pro:amiR-KASI transgenic line in which embryo development is arrested at the globular stage , perhaps because it would be difficult to obtain transgenic lines in which the expression of KASI is nearly knocked out since KASI is an embryo-lethal gene as reported by Wu and Xue [40] . Additionally , as previously mentioned , other unknown specific substrates of the chaperonin containing CPNA2 might exist in Arabidopsis embryos , and these substrates are also possible to play an important role in embryo development . Hence the elucidation of why cpna2 embryos are arrested at the globular stage still needs further research . During plastid development , proplastids develop highly organized thylakoid membrane to differentiate into mature chloroplasts , and formation of the thylakoid membrane requires coordinated synthesis and assembly of proteins , pigments , and glycerolipids . In chloroplasts , de novo fatty acid ( FA ) synthesis produces 16:0 and 18:0 FAs that are the building blocks of membrane glycerolipid production [50] . Therefore , FA synthesis is crucial for the formation of the thylakoid membrane and for chloroplast biogenesis . In this study , we found that CPNA2 deficiency could result in a significant decrease in the KASI protein level ( Fig 6 ) . KASI is a key condensing enzyme involved in de novo FA synthesis [50] , and therefore , it is likely that CPNA2 deficiency also disrupts FA synthesis in chloroplasts , thus impeding formation of the thylakoid membrane and chloroplast biogenesis . In accordance with this conjecture , we found that abnormal chloroplasts in cpna2-2 embryos lacked thylakoid membranes and contained a deeply stained mass ( Fig 2D and 2E ) , indicating that chloroplast biogenesis in cpna2-2 embryos is severely disrupted . Moreover , the result of GUS staining showed that CPNA2 is highly expressed in the SAM of Arabidopsis seedlings ( Fig 3B and 3C ) , implying that CPNA2 may also play an important role in chloroplast biogenesis in the SAM . This idea was further supported by the result that the cpna2-2 homozygous seedlings could not develop true leaves ( Fig 6A ) . Taken together , these results suggest that CPNA2 is crucial for chloroplast biogenesis , and thus affects the developmental processes of Arabidopsis embryos and seedlings . In the process of biological evolution , gene duplication and variation usually extend the function of original genes to adapt to environmental changes . In this study , we found that CPNA2 in Arabidopsis belongs to a unique type of Cpn60α subunits that only exist in angiosperms . Functional chaperonins consisting of CPNA2 and specific Cpn60β subunits could specifically assist in the folding of KASI , and play an important role in the transition of globular embryos to heart-shaped embryos in Arabidopsis . This neofunctionalization of Cpn60α subunits in Arabidopsis provides a novel insight into the significance of multiple Cpn60α genes in plants , and reveals the relationship between duplication and functional specialization of chaperonin genes . The Columbia ecotype of Arabidopsis thaliana was used as the wild type in this study . The T-DNA insertion mutants were obtained from ABRC ( Arabidopsis Biological Resource Center ) , including CS76507 , SALK_144574 ( cpna2-3 ) , SALK_006606 ( cpna1 ) , SAIL_852_B03 ( cpnb1 ) , SALK_014547 ( cpnb2 ) , SALK_099972 ( cpnb3 ) and SALK_064887 ( cpnb4 ) . The cpna2-2 mutant was obtained from the stock CS76507 using thermal asymmetric interlaced PCR [28] . The T-DNA flanking sequences of the mutants were determined by PCR using specific primer of T-DNA left border ( LB ) and specific genomic primers ( LP and RP ) . All plants were grown in a greenhouse under long-day condition ( 16 h light/8 h dark ) at 22°C . To construct CPNA2pro:gCPNA2 , the 5000 bp CPNA2 genomic fragment was amplified from wild-type genome , and then cloned into pCambia1300 vector ( Cambia ) . To construct CPNA2pro:GUS , the promoter of CPNA2 was amplified and cloned into pCambia1381Xb vector ( Cambia ) . To construct CPNA2pro:H2B-GFP , CPNA1pro:H2B-CFP , 35Spro:GFP and 35Spro:CPNA2-GFP , we first obtained the pC1300-GFP and pC1300-CFP vectors using the operation procedure described by Ren et al . [51] . Then the promoters of CPNA2 and CPNA1 were amplified and inserted into the above vectors to produce CPNA2pro:GFP and CPNA1pro:CFP , while the 35S promoter was cloned into pC1300-GFP to produce 35Spro:GFP . Finally , the H2B coding sequence was amplified and cloned into CPNA2pro:GFP and CPNA1pro:CFP to obtain CPNA2pro:H2B-GFP and CPNA1pro:H2B-CFP , while the CPNA2 coding sequence was inserted into 35Spro:GFP to obtain 35Spro:CPNA2-GFP . To construct 35Spro:CPNA2-HA , 35Spro:CPNA1-HA , CPNA2pro:CPNA2-HA , ABI3pro:CPNA2-HA and CPNA1pro:CPNA1-HA , the 35S promoter , the CPNA2 promoter , the CPNA1 promoter and ABI3 promoter were first cloned into pCambia1300 vector , respectively , to produce pC1300-35pro , pC1300-CPNA2pro , pC1300-CPNA1pro and pC1300-ABI3pro . Then the CPNA2 coding sequence fused to HA tag ( CPNA2-HA ) and the CPNA1 coding sequence fused to HA tag ( CPNA1-HA ) were amplified and cloned into pC1300-35pro to produce 35Spro:CPNA2-HA and 35Spro:CPNA1-HA , while CPNA2-HA and CPNA1-HA were cloned into pC1300-CPNA2pro and pC1300-CPNA1pro , respectively , to produce CPNA2pro:CPNA2-HA and CPNA1pro:CPNA1-HA . Moreover , CPNA2-HA was also cloned into pC1300-ABI3pro to produce ABI3pro:CPNA2-HA . To construct CPNA2pro:amiR-KASI , we first obtained one amiRNA sequence ( TGATGTAATTTACCTCCGCAG ) designed for targeting the KASI gene using the Web MicroRNA Designer ( WMD3; http://wmd3 . weigelworld . org/cgi-bin/webapp . cgi ) [52] . Then the amiRNA foldback fragment was generated by overlap extension PCR using four specific primers provided by WMD3 and pRS300 vector as a template . Finally , the amiR-KASI foldback fragment was inserted into the pC1300-CPNA2pro vector to produce CPNA2pro:amiR-KASI . To construct CPNA2pro:CPNA2 , CPNA2pro:CPNA2E291A , CPNA2pro:CPNA2Q299R and CPNA2pro:CPNA2E291A/Q299R vectors , CPNA2E291A , CPNA2Q299R and CPNA2E291A/Q299R were amplified from the CPNA2 coding sequence by site-directed mutagenesis using overlap extension PCR . Then these mutant sequences together with the CPNA2 coding sequence were cloned into the pC1300-CPNA2pro vector to obtain CPNA2pro:CPNA2 , CPNA2pro:CPNA2E291A , CPNA2pro:CPNA2Q299R and CPNA2pro:CPNA2E291A/Q299R . After sequencing , all the constructs were transformed into Arabidopsis plants using the floral dip method [53] . After screened on Murashige and Skoog medium with 10 mg/L hygromycin , positive transformants were identified by PCR and used for subsequent analysis . All the primers for cloning were listed in S4 Table . Fresh ovules were first dissected from siliques using two needles and cleared with Hoyer’s solution following the protocol described by Yadegari et al . [54] . Then the embryos in the cleared ovules were observed under the Olympus TH4-200 microscope with differential interference contrast ( DIC ) optics and photographed by a SPOT Xplorer Camera ( Diagnostic Instruments ) . GUS staining was conducted according to the method described by He et al . [55] . The various tissues of CPNA2pro:GUS plants were incubated in GUS solution for 2 to 3 days at 37°C , and then observed by Olympus SZX12 stereomicroscope and photographed with a digital camera ( Cool SNAP , RS Photometric ) . To observe the fluorescent signals of embryos in the CPNA2pro:H2B-GFP and CPNA1pro:H2B-CFP plants , fresh embryos were isolated from ovules through enzymolysis ( 1% cellulose and 0 . 8% macerozyme dissolved in 13% mannitol , enzymolysis for 0 . 5 h at 37°C ) , mounted in 10% glycerol , and then observed under a confocal microscope ( Fluoview1000; Olympus ) . The images were obtained under EGFP fluorescence channel ( excitation , 488 nm; emission , 505–530 nm ) and ECFP fluorescence channel ( excitation , 440 nm; emission , 505–530 nm ) . Total RNA of various Arabidopsis tissues were extracted using Trizol reagent ( Sigma ) and then reverse-transcribed into cDNA with a Reverse Transcription System ( TOYOBO ) . The cDNAs of rosette leaves of the cpnb1-4 homozygous mutants were used as the templates for PCR analysis with the gene-specific primers . qRT-PCR of CPNA2 was performed using TransStart Top Green qPCR SuperMix ( TransGen , China ) with a Rotor-Gene 6000 machine ( Corbett Research ) and the relative expression levels normalized to GAPDH were analyzed by the double standard curves method as described previously [56] . qRT-PCR of KASI was performed using TransStart Top Green qPCR SuperMix ( TransGen , China ) with a Bio-rad CFX Connect machine ( BIO-RAD ) and the relative expression levels normalized to GAPDH were analyzed by the comparative CT method as described previously [57 , 58] . Three biological and three technical replicates of each sample were made for qRT-PCR analysis . Primers used in the experiments were listed in S4 Table . The mesophyll protoplasts of 35Spro:GFP and 35Spro:CPNA2-GFP transgenic plants were isolated according to the method described previously [59] , and then observed under a confocal microscope ( Fluoview1000; Olympus ) . A 488 nm argon ion laser line was used for excitation of GFP and chlorophyll , while 505–530 nm and 650–675 nm emission filters were used for capturing GFP and chlorophyll autofluorescence , respectively . The wild-type and cpna2 embryos in 6 DAP siliques of cpna2-2/+ plants were fixed , embedded and sectioned as described by Deng et al . [59] . The ultrathin sections were examined and photographed under a transmission electron microscope ( Hitachi HT7700 ) . Chaperonin-substrate complexes were isolated from the 35Spro:CPNA2-HA and 35Spro:CPNA1-HA transgenic plants with the μMACS HA isolation kit ( Miltenyi Biotec ) according to the procedure described previously [21] . In brief , intact chloroplasts were first isolated from 7 DAG seedlings of the transformants by the method described previously [60] . Then the freshly isolated chloroplasts were ruptured in lysis buffer ( 50 mM Tris-HCl pH 8 . 0 , 0 . 01% Tween 20 , 10 mM MgCl2 , 20 mM glucose , 30 U/ml hexokinase ) plus protease inhibitor cocktail ( Biotool ) . After lysis of chloroplasts , ADP ( Sigma ) was added into the lysates to reach a concentration of 10 mM , and then the lysates were centrifuged at 20 , 000 g for 10 min . The supernatants were transferred to new tubes and then NaCl was added into the supernatants to reach a final concentration of 150 mM . After incubating with 50 μl anti-HA Microbeads for 2 . 5 h at 4°C , the mixture was transferred to columns placed in a magnetic field . After rinsing four times with 200 μl washing buffer I ( 50 mM Tris-HCl pH 8 . 0 , 1% Triton X-100 , 0 . 5% Sodium deoxycholate , 150 mM NaCl , 5 mM ADP ) , twice with 200 μl washing buffer II ( 50 mM Tris-HCl pH 8 . 0 , 1% Triton X-100 , 150 mM NaCl , 5 mM ADP ) and once with washing buffer III ( 25 mM Tris-HCl pH 7 . 5 , 5 mM ADP ) , the immunoprecipitates were then eluted with 50 μl elution buffer ( 50 mM Tris-HCl pH 6 . 8 , 50 mM DTT , 1% SDS , 1 mM EDTA , 0 . 005% bromophenol blue , 10% glycerol ) . After elution , the immunoprecipitates were in-gel digested and analyzed by mass spectrometry as described by Wang et al . [61] with minor modification . In brief , the total protein was loaded to the gel and SDS-PAGE was conducted . After electrophoresis , the gel was stained , sliced and in-gel digested by trypsin , and then the desalted peptides were dissolved in 0 . 1% formic acid/2% acetonitrile/98% H2O , loaded onto a C18 trap column ( Thermo Scientific ) , and subsequently eluted from the trap column over the self-packed C18 analytic column in a 120 min gradient . The LC-MS/MS analysis was performed by using a Q Exactive HF instrument ( Thermo Scientific ) equipped with an Easy-nLC 1000 system . MS data was acquired and submitted to Proteome Discoverer 1 . 4 ( Thermo Scientific ) to perform protein identification and quantitation utilizing its integrated SEQUEST HT search engine and Percolator algorithm . The peptide mass tolerance was set to 10 ppm and 20 mmu for MS/MS . Carbamido methylation of cysteine was set as a fixed modification , and oxidation of methionine and deamidation of N , Q as a dynamic modification . A high confidence dataset with less than 1% FDR ( false discovery rate ) was used for peptide filtering . Files from the samples were searched against the Arabidopsis proteome database of Swiss-Prot ( http://www . uniprot . org/ ) . The coding sequences ( CDS ) , excluding the portion of the transit peptides , of CPNA1 , CPNA2 , CPNB1 , CPNB2 , CPNB3 , Cpn20 , and KASI in Arabidopsis were cloned into pET-28a ( + ) vector ( Novagen ) . Then all the proteins were overexpressed in E . coli expression strain BL21 following induction with isopropyl β-D-thiogalactoside ( IPTG ) , and the BL21 cells were harvested and resuspended in lysis buffer ( 50 mM Na2HPO4 pH 8 . 0 , 0 . 3 M NaCl , 1% Triton X-100 , 5% glycerol , 2 mM PMSF ) . Following sonication of the BL21 cells , the fusion proteins were purified by High Affinity Ni-NTA Resin ( Genscript ) . For the proteinase K protection assay , Cpn60s were reconstituted with Cpn60α and Cpn60β according to the method described previously [31 , 62] . 15 μM Cpn60α , 15 μM Cpn60β and 10 μM Cpn20 were mixed in the incubation buffer ( 50 mM Tris-HCl pH 8 . 0 , 0 . 3 M NaCl , 10 mM MgCl2 , 16 mM KCl , 2 mM DTT and 5 mM ATP ) , and then incubated for 2 h at 30°C . After centrifugation , the supernatant fraction of the reconstitution mixture was collected and loaded on a Enrich Size Exclusion Column 650 ( Bio-Rad ) . Then the reconstituted Cpn60s were purified and collected by gel-filtration chromatography . The proteinase K protection assay was performed according to the procedure as described previously [62] with some modification . The various purified Cpn60s ( 1 μM ) and denatured substrate KASI ( 0 . 64 μM ) were incubated in refolding buffer ( 50 mM Tris-HCl pH 7 . 5 , 50 mM KCl , 5 mM MgCl2 , 1 . 9 μM Cpn20 and 1 mM ADP ) for 20 min at 25°C , and then proteinase K ( sigma ) was added to a final concentration of 2 . 0 μg/mL . After incubation for 0 , 5 , 10 , 15 and 20 min at 25°C , the proteolysis was stopped by adding PMSF ( 2 mM ) . Finally , the content of the substrates in reaction mixtures were analyzed by SDS-PAGE and immunoblotting with His-tag antibody ( Genscript ) . Total protein in 7 and 14 DAG seedlings of WT and cpna2-2 homozygous mutant carrying ABI3pro:CPNA2-HA vector was extracted according to the method described previously [59] . Then the concentrations of total protein were normalized by immunoblotting analysis of ACTIN using anti-ACTIN ( ABclonal ) . 20 μl normalized protein samples were loaded on 12% SDS-PAGE gels and analyzed by immunoblotting using KASI antibody ( ABclonal ) to detect the KASI protein levels . The relative KASI protein levels in the seedlings of WT and cpna2-2 homozygous mutant were quantified by ImageJ software . Multiple sequence alignment of Cpn60α proteins in various species was generated with ClustalX 1 . 83 [63] . Then the alignment result was used for building the phylogenetic tree with MEGA 5 . 1 [64] . The neighbor-joining method was used with a bootstrap ( 1000 replicates ) test of phylogeny . The predicted structural models of CPNA1 , CPNA2 , BnaC02g08340D , LOC100257653 and L484_018489 were obtained by SWISS-MODEL ( http://www . swissmodel . expasy . org/ ) , while the crystal structure of GroEL ( 1AON , Chain A ) was used as the template . The finished models were visualized using Swiss-Pdb Viewer 4 . 1 . 0 [65] . Sequence data in this article can be found in TAIR ( The Arabidopsis Information Resource ) under these accession numbers: CPNA1 ( AT2G28000 ) , CPNA2 ( AT5G18820 ) , CPNB1 ( AT1G55490 ) , CPNB2 ( AT3G13470 ) , CPNB3 ( AT5G56500 ) , CPNB4 ( AT1G26230 ) , Cpn20 ( AT5G20720 ) , KASI ( AT5G46290 ) .
Chaperonins are large oligomeric complexes that are involved in the folding and assembly of numerous proteins in various species . In contrast to other types of chaperonins , chloroplast chaperonins are characterized by the hetero-oligomeric structure composed of two unique types of subunits , Cpn60α and Cpn60β , each of which is present in two or more paralogous forms in most of higher plants . However , the functional significance underlying the wide array of subunit types and complex oligomeric arrangement remains largely unknown . Here , we investigated the role of the minor Cpn60α subunit AtCpn60α2 in Arabidopsis embryo development , and found that AtCpn60α2 is important for the transition of globular embryos to heart-shaped embryos , whereas loss of the dominant Cpn60α subunit AtCpn60α1 affects embryonic cotyledon development . Further studies demonstrated that AtCpn60α2 could form functional chaperonins with AtCpn60β2 and AtCpn60β3 to specifically assist in folding of the substrate KASI , which is important for the formation of heart-shaped embryos . Our results suggest that duplication of Cpn60α genes in higher plants can increase the potential number of chloroplast chaperonin substrates and provide chloroplast chaperonins with more roles in plant growth and development , thus revealing the relationship between duplication and functional specialization of chaperonin genes .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]
[ "biotechnology", "plant", "anatomy", "plant", "growth", "and", "development", "plant", "cell", "biology", "plant", "embryo", "anatomy", "brassica", "chloroplasts", "developmental", "biology", "plant", "science", "model", "organisms", "genetically", "modified", "plants", "experimental", "organism", "systems", "seedlings", "embryos", "plants", "cellular", "structures", "and", "organelles", "cotyledons", "(botany)", "genetic", "engineering", "arabidopsis", "thaliana", "research", "and", "analysis", "methods", "embryology", "genetically", "modified", "organisms", "plant", "embryogenesis", "plant", "development", "embryo", "development", "eukaryota", "plant", "and", "algal", "models", "plant", "cells", "cell", "biology", "phenotypes", "embryogenesis", "genetics", "biology", "and", "life", "sciences", "cellular", "types", "plant", "biotechnology", "organisms", "fruit", "and", "seed", "anatomy" ]
2017
Functional divergence of chloroplast Cpn60α subunits during Arabidopsis embryo development
Mycobacterium ulcerans ( M . ulcerans ) is a necrotizing skin infection endemic to the Bellarine Peninsula , Australia . Current treatment recommendations include 8 weeks of combination antibiotics , with adjuvant surgery if necessary . However , antibiotic toxicity often results in early treatment cessation and local experience suggests that shorter antibiotic courses may be effective with concurrent surgery . We report the outcomes of patients in the Barwon Health M . ulcerans cohort who received shorter courses of antibiotic therapy than 8 weeks . A retrospective analysis was performed of all M . ulcerans infections treated at Barwon Health from March 1 , 1998 to July 31 , 2013 . Sixty-two patients , with a median age of 65 years , received < 56 days of antibiotics and 51 ( 82% ) of these patients underwent concurrent surgical excision . Most received a two-drug regimen of rifampicin combined with either ciprofloxacin or clarithromycin for a median 29 days ( IQR 21–41days ) . Cessation rates were 55% for adverse events and 36% based on clinician decision . The overall success rate was 95% ( 98% with concurrent surgery; 82% with antibiotics alone ) with a 50% success rate for those who received < 14 days of antibiotics increasing to 94% if they received 14–27 days and 100% for 28–55 days ( p<0 . 01 ) . A 100% success rate was seen for concurrent surgery and 14–27 days of antibiotics versus 67% for concurrent surgery and < 14 days of antibiotics ( p = 0 . 12 ) . No previously identified risk factors for treatment failure with surgery alone were associated with reduced treatment success rates with < 56 days of antibiotics . In selected patients , antibiotic treatment durations for M . ulcerans shorter than the current WHO recommended 8 weeks duration may be associated with successful outcomes . Mycobacterium ulcerans ( M . ulcerans ) infection , or Buruli ulcer , is a necrotizing infection of the skin and subcutaneous tissue and is the third most common mycobacterial infection in immunocompetant people after tuberculosis and leprosy . It has been reported in 33 predominantly subtropical and tropical countries , with west and central Africa being the worst affected regions [1] . In Australia endemic foci of infection are found in tropical Far North Queensland and temperate regions of southern Victoria [2] . Up until 2004 , the World Health Organisation ( WHO ) recommended wide surgical excision as treatment for M . ulcerans lesions , with no role for antibiotics which were thought to be ineffective [3] . However surgical treatment was often difficult to access in resource-limited settings [4] , was limited by high recurrence rates [5 , 6] and in severe cases caused significant cosmetic morbidity and increased costs [7 , 8] . Mounting clinical evidence of the effectiveness of antibiotics [9–14] has resulted in a paradigm shift to a predominantly medical approach . The latest WHO recommendations are for eight weeks combination antibiotic therapy with intramuscular streptomycin and oral rifampicin . Surgical intervention is recommended only to hasten healing of more extensive ulcers , if antibiotics are contraindicated or not tolerated , or at a patient’s request . In addition , if surgery is required , an initial four weeks of antibiotics prior to surgery is recommended [2 , 15] . Clinicians at Barwon Health began using antibiotics to treat M . ulcerans infections from the Bellarine Peninsula in 1998 [5] . Since this time the proportion of patients receiving antibiotics has increased , resulting in fewer recurrences and permitting more conservative , and thus less reconstructive , surgery [12] . Oral antibiotics have subsequently reduced hospitalisations and the cost of treatment [7] . Our current treatment practice for the majority of M . ulcerans lesions is an oral combination of rifampicin with either ciprofloxacin or clarithromycin for eight weeks . Surgery is used for debriding necrotic wounds or closing large tissue defects to increase the rate of wound healing , for patients unable or unwilling to take antibiotics , and for those preferring the more rapid healing of small lesions that surgical excision and direct closure enables compared with the often prolonged healing of lesions treated with antibiotics alone [2] . However , these protracted antibiotic treatment regimens are not without toxicity , and 16–33% of patients , often elderly , cease them early due to side effects [12 , 13] . In addition , shorter courses of adjunctive antibiotics ( 4–6 weeks ) have been offered for smaller lesions treated with surgical excision and direct closure based on previous experience where cure was obtained for 100% of 21 patients who received between 12 and 30 days of antibiotics combined with surgical excision [12] . The aim of this study was to review the experience and outcomes of patients in the Barwon Health M . ulcerans observational cohort who received a shorter course of antibiotic therapy than the standard recommended duration of 8 weeks . This is an observational , retrospective cohort study with analysis performed on anonymised data . Verbal patient consent , or consent from a parent or guardian in the case of a minor , was gained for collection of data and noted in the patient medical record . Barwon Health’s Human Research and Ethic Committee have approved this analysis . From March 1 , 1998 to July 31 , 2013 there were 252 patients diagnosed with M . ulcerans infection at Barwon Health . Forty-one patients who were not treated with antibiotics were excluded from this analysis , two were excluded due to unrelated deaths during follow-up and two were lost to follow-up . Therefore 207 patients were included; 110 ( 53% ) were male and the median age was 59 years ( IQR 36–75 years ) . Sixty-two ( 30% ) patients received less than 56 days of antibiotic treatment ( short-course therapy ) whilst 145 ( 70% ) patients had 56 days or more ( long-course therapy ) ( Table 2 ) . Those receiving short-course antibiotics were more likely to be female , ≥ 60 years of age , have WHO category 1 lesions , and were less likely to have had major surgery or have positive margins ( Table 2 ) . However there were no significant differences in the proportions that were diabetic , the type or site of lesions , or the duration of symptoms prior to diagnosis ( Table 2 ) . The median age of the patients was 65 years ( range 2–94 years ) and their lesion was present for a median of 42 days prior to diagnosis ( IQR , 24–70 days ) . Fifty-one ( 82% ) patients had their lesions surgically excised , with the defect directly closed in 25 ( 49% ) cases , and closed with split skin grafts in 21 ( 41% ) , a vascularised tissue flap in 4 ( 8% ) and both a split skin graft and a vascularised tissue flap in 1 case ( 2% ) . Thirty-nine ( 76% ) of those who had surgery had previously commenced antibiotics for a median of 8 days ( IQR , 4–18 days ) . Twenty of these patients had specimens sent for M . ulcerans culture and their median duration of antibiotics prior to surgery was 12 days ( IQR 5–28 days ) . Six of these patients ( 30% ) had positive mycobacterial cultures; 4 had received 8 days or less of antibiotics , one had received 17 days and one 18 days of antibiotics prior to surgery ( Fig . 1 ) . The regimens used were predominantly rifampicin-based in combination with ciprofloxacin in 41 ( 66% ) , clarithromycin in 12 ( 19% ) , clarithromycin/ethambutol in 3 ( 5% ) and moxifloxacin in 2 ( 3% ) patients . Other combinations included clarithromycin with either ethambutol in 2 patients ( 3% ) or ciprofloxacin ( 1 patient; 2% ) and clarithromycin alone in one patient ( 2% ) . The median duration of treatment was 29 days ( IQR 21–41 days ) . Cessation secondary to side effects , often attributed to more than one of the drugs in a combination , occurred in 34 ( 55% ) patients . The most common side effects included nausea , vomiting , diarrhoea , hepatitis , rash , joint aches and acute renal failure . In twenty-two ( 36% ) patients the clinician’s decision to cease treatment was based on presumed adequate treatment when combined with surgical management of the lesions . Two patients ceased due to interactions with concomitant medications or patient’s wishes in each of the surgically treated and antibiotic only groups . Fifty-nine of 62 ( 95% ) short-course patients experienced treatment success , including the 5 WHO category 3 patients . Details of the three patients who failed treatment are listed in Table 3 . This compares with a 99% success rate ( 144/145 ) for patients who received long-course treatment . Outcomes according to the duration of antibiotic treatment are presented in Table 4 and Fig . 2 . Overall , those who received less than 14 days of therapy had a 50% success rate which increased to 94% if they received 14–27 days of treatment and 100% for 28–55 days treatment ( p<0 . 01; Table 4 , Fig . 2A ) . The success rate for those receiving < 28 days compared to ≥ 28 days was reduced ( 86% versus 100% , p = 0 . 04 ) . There was a 98% ( 50/51 cases ) treatment success rate in those receiving antibiotics and surgery ( Table 4 , Fig . 2 ) . Cure rates were significantly increased in those who received longer durations of therapy ( p<0 . 001 ) . All 23 patients treated with antibiotics and surgery for 14–28 days achieved treatment success , in comparison to 67% ( 2 patients ) if they received antibiotics for less than 14 days ( p = 0 . 12 ) . When only cases with positive margins were included the cure rates remained higher for treatment durations of 14–28 days compared with < 14 days ( 8/8 patients versus 0/1 patients , p = 0 . 11 ) . Although the numbers are small , there was an 82% ( 9/11 cases ) success rate for those treated with antibiotics alone . Cure rates were significantly increased in those who received a duration of therapy greater than 28 days ( p = 0 . 05 ) . Two of three patients receiving antibiotics alone for less than 28 days failed treatment , whilst the 6 patients receiving 28–42 days of treatment were cured ( p = 0 . 08; Table 4; Fig . 2B ) . There were no significant differences in recurrence rates for those patients with positive margins , age ≥ 60 years , duration of symptoms > 75 days , immunosuppression or distal lesions ( Table 5 ) . In selected patients , antibiotic treatment durations for M . ulcerans shorter than the current WHO recommended 8 weeks duration may be associated with successful outcomes . Success may be influenced by the duration of treatment and the use of surgical excision .
Buruli ulcer is a necrotizing skin and subcutaneous tissue infection caused by Mycobacterium ulcerans and is the third most common mycobacterial infection , behind tuberculosis and leprosy , world-wide . In recent years , the World Health Organisation has modified its guidelines for M . ulcerans treatment , moving from predominantly surgical to predominantly medical based management . It now recommends the combination of oral rifampicin and intramuscular streptomycin for a period of eight weeks as first-line therapy , with surgery as adjunctive therapy if necessary . The Barwon Health experience from south-eastern Australia has demonstrated that the entirely oral combination of rifampicin with either ciprofloxacin or clarithromycin for eight weeks can be an effective treatment option . However , these antibiotics are often toxic leading to early cessation , especially in the elderly . In addition , clinicians have been using a shorter duration of therapy for smaller lesions that have also been surgically managed . This study reviews our experience treating M . ulcerans with antibiotic durations of less than 8 weeks and demonstrates that successful outcomes can be achieved in selected patients , with success rates influenced by the duration of treatment and the use of surgical excision . This finding needs confirmation in further studies , but could have significant benefits in terms of reducing toxicity and improving adherence associated with Buruli ulcer antibiotic treatment .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[]
2015
Mycobacterium Ulcerans Treatment – Can Antibiotic Duration Be Reduced in Selected Patients?
Complex interactions between genes or proteins contribute substantially to phenotypic evolution . We present a probabilistic model and a maximum likelihood approach for cross-species clustering analysis and for identification of conserved as well as species-specific co-expression modules . This model enables a “soft” cross-species clustering ( SCSC ) approach by encouraging but not enforcing orthologous genes to be grouped into the same cluster . SCSC is therefore robust to obscure orthologous relationships and can reflect different functional roles of orthologous genes in different species . We generated a time-course gene expression dataset for differentiating mouse embryonic stem ( ES ) cells , and compiled a dataset of published gene expression data on differentiating human ES cells . Applying SCSC to analyze these datasets , we identified conserved and species-specific gene regulatory modules . Together with protein-DNA binding data , an SCSC cluster specifically induced in murine ES cells indicated that the KLF2/4/5 transcription factors , although critical to maintaining the pluripotent phenotype in mouse ES cells , were decoupled from the OCT4/SOX2/NANOG regulatory module in human ES cells . Two of the target genes of murine KLF2/4/5 , LIN28 and NODAL , were rewired to be targets of OCT4/SOX2/NANOG in human ES cells . Moreover , by mapping SCSC clusters onto KEGG signaling pathways , we identified the signal transduction components that were induced in pluripotent ES cells in either a conserved or a species-specific manner . These results suggest that the pluripotent cell identity can be established and maintained through more than one gene regulatory network . A major goal in biology is to understand the evolution of complex traits , such as the development of multicellular body plans or an organism's physical state as it ages [1] . To a certain extent , complex traits are governed by regulated gene expression , and considerable plasticity exists such that the same or a similar phenotypic outcome may arise from the same or vastly different gene regulatory programs across species [1] , [2] . Methods for identifying evolutionarily conserved as well as alternative gene regulatory pathways underlying a biological trait should enable deeper mechanistic understanding of the processes that shaped the trait . Cross-species comparative analyses have made fundamental contributions to biology , most remarkably exhibited by comparative analysis of genomic sequences [2] . With the growing availability of functional genomic data , comparative paradigms are now being extended to the study of other functional attributes , most notably gene expression ( e . g . , [3] , [4] , [5] , [6] , [7] reviewed in [8] ) . Major advantages of gene expression comparison include but are not limited to pinpointing the genes and tissues whose expression tends to evolve at an accelerated or reduced rate [4] , [9] , improving functional gene annotation [3] , discovering conserved genetic modules and pathways [5] , [7] and tracing phenotypic diversity by differential expression of specific regulatory genes [3] , [4] . More recently , cross-species expression data have been used for inferring the evolution of interaction and regulatory networks [4] , [10] . A major challenge in comparing expression data between organisms is that gene expression is not static and the level of expression is influenced by external conditions . This difficulty was circumvented in the special cases in which identical perturbations could be applied across species , as in comparisons of the sexes across species [11] . In the absence of identical perturbations , the co-expression between gene pairs remains comparable across species [8] . Therefore co-expression based analysis has been widely applied to compare gene expression datasets across phylogenetically close [3] , [10] and distant species [5] , [7] , [12] . These efforts often focused on identifying conserved co-expression modules , groups of genes whose expression profiles were correlated in multiple species . Because the co-regulatory relationship of these genes was conserved , they were considered to function in a coordinated manner . The methods to identify these modules were based on first applying preset thresholds to expression correlation in each species and then intersecting the groups of orthologous genes across species [5] , [12] , [13] , [14] , [15] , [16] . Such approaches were straightforward but often strongly influenced by subjective inputs from the researcher , for example , in the choice of correlation thresholds . An exception to the ad hoc thresholds was that Ramani et al . used known interaction proteins to train a threshold of co-expression [16] . This approach worked for protein-protein interaction analysis , but would require a lot empirical data to train similar thresholds for the analyses of other regulatory relationships , such as the relationships between transcription factors ( TFs ) and their target genes . Another limitation of the methods discussed above is that these methods do not uncover species-specific co-expression patterns , which may be important for explaining and understanding the evolution of novel features , e . g . , the unique liver genes in human as opposed to other primates [17] . Automatic clustering algorithms , such as K-means and hierarchical clustering , have been widely used in gene expression data analysis to discover co-expression patterns that can be translated to biological knowledge or new hypotheses [18] . It is thus a natural step to extend these algorithms to cross-species analysis . However , clustering remains a difficult problem , as exemplified by ad hoc criteria for choosing optimal clusters and results being sensitive to the initial conditions . Naively applying these algorithms to multiple species , for example , by clustering each species separately and then combining the clustering results , will likely amplify the computational errors made in each species . A better approach is to customize the clustering methods for cross-species analysis , taking advantage of the evolutionary context to minimize clustering errors . Two methods , DCA [3] and CoherentCluster [7] , were proposed recently in this direction . However , these two methods lacked statistical models and did not maximize the use of data . For example , the expression of one species was used for constraining the clustering of the other species , but not vice versa . Ideally , some iterative schemes , such as are common for many machine learning algorithms , would be implemented to simultaneously cluster genes in multiple species . We have developed a statistical model for cross-species clustering analysis . The model allows each species to create its own clusters of the genes but also encourages the species to borrow strength from each others' clusters of orthologous genes . The result is a pairing of clusters , one from each species , where the paired clusters share many but not necessarily all orthologous genes . The clustering and degree of overlap are chosen by the data through maximum likelihood estimation . The model-based approach not only reduces subjective influence but also enables effective use of evolutionary dependence . A model-based Soft Cross-Species Clustering ( SCSC ) method was developed . The rationale of this model stems from the following observations and intuitions . First , clusters of co-expressed genes may be conserved across a large evolutionary distance , in the sense that the orthologous genes also exhibit correlated expression [19] . Empirically , this is consistent with the observation that shared cis-regulatory elements and cis-regulatory modules that regulate a set of co-expressed genes in one species are often found to be enriched in the regulatory regions of the orthologous sets of genes in other species [20] . The conservation of clusters also makes evolutionary sense because a cluster may correspond to a regulatory program that is functionally important and thus resistant to change [21] . Second , rewiring across clusters , i . e . , the change of cluster membership of orthologous genes , is also observed in phylogenetically related species [22] , [23] . This rewiring process can reflect simple sequence changes such as gain or loss of transcription factor binding sites . The difference of regulatory programs across species is believed to be an important source of evolutionary diversity or novelty [17] , [22] , [23] . Finally , the expression patterns of orthologous clusters may not be conserved , reflecting either a change in the activities of the trans-acting factors ( thus all the genes in a cluster will change their expression pattern , but their co-regulatory relationship is maintained ) [22] or differences in experimental conditions across species . We formulated the above observations and intuitions into a probabilistic model , with certain simplifications that made the model mathematically tractable . First , we assumed that in every species there are a certain number of clusters that can be mapped one-to-one ( called orthologous clusters ) , with each cluster corresponding to an essential regulatory program . However , the mean profiles of orthologous clusters were assumed to be independent . Second , the expression of a gene in a cluster was assumed to be a sample from a Gaussian distribution , which was the characteristic or mean profile of this cluster . This assumption is commonly made in model-based clustering analysis [18] . Third , a gene tends to belong to the orthologous clusters in the two species . In other words , the prior probability that a gene belongs to the clusters ( i , i′ ) where i and i′ are the indices of the corresponding clusters in two species respectively , was larger than that for non-orthologous clusters . This intuition was formally represented by a logistic regression of prior probabilities over the cluster indices ( see Methods ) . Overall , the model captured the main observations discussed above: that cluster structure tends to be conserved , that change of cluster membership should be allowed ( as G5′ in Figure 1 ) , and that the mean expression profiles of orthologous clusters are relatively independent . The performance of SCSC was compared with that of DCA , K-means , hierarchical clustering , MCLUST , WGCNA and CLICK clustering [24] on six synthetic datasets ( Table S1 , Text S1 ) . Because the performance of K-means , hierarchical clustering , MCLUST , WGCNA and CLICK algorithms were optimized within each species , if the information of conservation of co-clustering did not help , they should outperform SCSC and DCA . In four of the six synthetic datasets , CLICK and SCSC outperformed the other algorithms on the center-scatter score ( Figure S1 ) , which is consistent to CLICK's capability of filtering out singleton genes and identifying very tight clusters . In all the rest comparisons , SCSC outperformed K-means , hierarchical clustering MCLUST , WGCNA , and CLICK , which in turn outperformed DCA ( Figure S1 ) . These results suggest that although conserved co-clustering information could help to improve clustering performance , the power of such information is released in a model-based approach ( SCSC ) but shackled in a heuristic algorithm ( DCA ) . DCA essentially sequentially performs two hierarchical clustering in the two species , with no iteration . To mimic errors in the orthology map or the scenario where some orthologous genes have divergent functional roles in two species , we permuted a proportion ( 10%–30% ) of the orthologous relations into wrong matches in the first synthetic dataset . SCSC , DCA , and K-means were executed on these datasets with orthology errors ( Table S2 ) . As the proportion of misplaced orthology links increased , all four algorithms showed decreased performance as expected . However , SCSC demonstrated robustness against orthology errors in that its performance on the dataset with 30% orthology errors was better than those of the other three algorithms under 0% orthology errors . The biological process that inspired the SCSC model is cellular differentiation , a fundamental process occurring universally in multicellular organisms . Embryonic stem ( ES ) cells were used as a tool to study this process . ES cells are characterized by the ability to self-renew and differentiate into every cell type found in the mature organism . We are interested in determining the extent to which molecular circuits that underlie ES cell phenotypes and the processes of commitment and differentiation are conserved across species . Human and mouse ES ( hES and mES ) cells share the critical properties of ES cells but do not employ the identical set of transcription factors . For example , transcription factor FOXD3 is required for mES cell self-renewal [25] , but its expression appears to be non-essential for hES [26] . Similarly , STAT3 , a transcription factor downstream to LIF signaling , is also required for self-renewal and the maintenance of pluripotency of mES cells , but it seems to be dispensable in hES cells [27] . We hypothesize that the pluripotent cell identity can be established and maintained through more than one gene regulatory network . These regulatory networks share core components that are universally indispensable for pluripotency . Peripheral components , though critical for cell fate , can be implemented using alternative designs . If this hypothesis is verified , the conserved and species-specific ES cell gene clusters may reveal the essential and peripheral components of gene regulatory networks underlining pluripotency , which may in turn assist the search for gene sets that are capable of reprogramming adult cells into a pluripotent state with higher efficiency [28] , [29] . We generated detailed time-course microarray data during a differentiation process of mES cells ( GEO accession number: GSE12550 , see Methods ) . Using SCSC , we jointly analyzed them with four datasets of undifferentiated and differentiated hES cells . The mES cell data were generated at eight time points during differentiation , with an average of six biological replicates at each time point ( see Methods ) . The four human datasets included undifferentiated and differentiated cells from multiple American and European ES cell lines [30] , [31] , [32] together with two differentiation pathways of adult stem cells [33] ( Figure S2A ) . We ran SCSC on 6 , 088 pairs of probe sets , representing an unbiased selection of orthologous genes that may best reflect the gene regulatory networks in mES and hES cells ( Text S2 ) . SCSC generated a result of clusters ( Figure 2 ) . Clusters ( 2 , * ) FF and ( 3 , * ) were upregulated in mES cells , and Cluster ( * , 3 ) was upregulated in hES cells ( Figure 2 ) . Here * denotes all the indices from 1 to 6 . For example , ( * , 3 ) includes the clusters ( 1 , 3 ) , ( 2 , 3 ) … ( 6 , 3 ) . The other mouse and human clusters had increasing expression patterns during differentiation . Clusters ( 2 , 3 ) and ( 3 , 3 ) had conserved upregulation in mES and hES cells . The part of the gene regulatory circuit that is conserved between mES and hES cells was represented in these two clusters ( Table S3A ) . The gene pairs that belonged to Clusters ( 2 , * ) and ( 3 , * ) but did not belong to Clusters ( 2 , 3 ) and ( 3 , 3 ) were specifically expressed in mES cells . These genes represent the part of the gene regulatory network that are expressed in mES cells , but is disrupted in hES cells ( Table S3B ) . Finally , gene pairs belonging to ( * , 3 ) but not Clusters ( 2 , 3 ) and ( 3 , 3 ) were specifically expressed in hES cells ( Table S3C ) . To explore which signaling pathways and what components of these signaling pathways are induced in hES and mES cells , we mapped the genes that were induced in either hES or mES cells , i . e . , Clusters ( 2 , * ) , ( 3 , * ) and ( * , 3 ) , onto all known signaling pathways documented in the KEGG pathway database [34] . The ES-induced components of these signaling pathways were plotted to highlight the hES-specific , mES-specific , and the conserved components ( Figure 3 ) . Among 1 , 113 genes involved in transcriptional regulation ( GO: 0003700 ) and included in this analysis , 448 clustered in either mES or hES upregulated clusters ( ( 2 , * ) , ( 3 , * ) and ( * , 3 ) ) , indicating that a very large proportion ( 40% ) of the transcriptional regulators were utilized in ES cells . Among these 448 transcription regulators , 85 ( 19% ) exhibited conserved upregulation in mES and hES cells ( in clusters ( 2 , 3 ) and ( 3 , 3 ) ) , representing a core set of regulators with higher expression in undifferentiated than differentiated ES cells ( Table S4 ) . Among these regulators , OCT4 and SOX2 are indispensable for maintaining an ES cell phenotype and for reprogramming [28]; NANOG , UTF1 , and polycomb group proteins EED and PHC1 either promote self-renewal or inhibit differentiation . Repression of lineage-specific differentiation genes is critical for maintaining the undifferentiated state [35] . Conserved transcriptional repressors and corepressors included DNA methylation enzymes DNMT1 and DNMT3B , Polycomb group factors EED and PHC1 , histone deacetylase SAP30 , and transcription factors SUPT4H1 , E2F8 , TGIF1 and CTBP2 . In addition , certain aspects of DNA replication and cell cycle regulation were also conserved in ES cells , as exemplified by conserved expression of CDK2 , RAD51 , E2F8 , MYST2 , POLYA1 and TERF1 . KLF2 , KLF4 and KLF5 belong to the Krüppel-like factor ( KLF ) family of evolutionarily conserved zinc finger transcription factors that regulate numerous biological processes , including proliferation , differentiation , development and apoptosis [36] . We previously demonstrated that simultaneous depletion of KLF2 , KLF4 and KLF5 led to differentiation of mES cells [37] . Consistent with this result , in mES cells , KLF2 , KLF4 and KLF5 co-clustered with other pluripotency regulators ( Clusters ( 2 , * ) and ( 3 , * ) ) , including OCT4 , SOX2 and NANOG . Chromatin immunoprecipitation coupled to microarray assay ( ChIP-chip ) data showed that KLF2 , KLF4 and KLF5 proteins all bind upstream of their own coding genes as well as upstream of OCT4 , SOX2 and NANOG [37] . NANOG and SOX2 ChIP-chip data demonstrated that they both bind to KLF2 , KLF4 and KLF5 [38] . The co-clustering result together with the published RNA knockdown data and ChIP-chip data suggest that KLF2 , KLF4 and KLF5 form a regulatory module that is coupled with the OCT4-SOX2-NANOG regulatory module in mES cells ( Figure 4A ) . The mES cell expression of the three KLF factors was not conserved in humans . Human KLF2 , KLF4 and KLF5 were clustered in Cluster ( * , 1 ) , which exhibited low expression in hES cells and a steady increase during spontaneous and lineage-specific differentiation . This led to the hypothesis that the KLF2/4/5 module was decoupled from the OCT4-SOX2-NANOG module in the transcription network of hES cells . To explore the decoupling hypothesis , we first re-examined the mouse data for potential clues . In mES cells , the KLF2/4/5 regulatory module and the OCT4-SOX2-NANOG regulatory module were firmly established , because every factor bound to every other gene within the module . A maximum of 30 regulator-target links among the six transcription factors were allowed ( Figure 4A ) . All except four of the allowed regulator-target links were confirmed by ChIP-chip data . The four missing links were OCT4->KLF2/4/5 and SOX2->KLF2 . All of these missing edges were between the two modules , which seemed to poise them for decoupling . Second , we checked if the inter-module regulatory links were preserved in hES cells . Human ChIP-chip data [39] showed that three out of the five inter-module regulatory links were dissociated ( SOX2->KLF4 , NANOG->KLF4 , NANOG->KLF2 ) . The two observations above and co-expression result were consistent with the hypothesis that the two regulatory modules were decoupled in hES cells . If the KLF2/4/5 module was mouse-specific , it should specifically regulate other regulatory factors in mES cells . Therefore , the existence of species-specific targets of KLF2/4/5 could be further evidence for the decoupling hypothesis . Besides the three KLF genes themselves , ESRRB , FOXD3 and SOCS3 were among their specific targets in mES cells . ESRRB [40] , FOXD3 [25] and SOCS3 [41] were all related to self-renewal and inhibiting differentiation in mES cells . FFFF In mice , KLF2 , KLF4 , and KLF5 and ESRRB , FOXD3 , and SOCS3 all exhibited high expression in undifferentiated ES cells , and their expression decreases during differentiation . Moreover , ESRRB , FOXD3 and SOCS3 upstream regions were bound by KLF2 , KLF4 and KLF5 in mES cells [37] . In humans , the expression levels of ESRRB and FOXD3 dropped below a detectable level in all measured ES cells . Human KLF2 , KLF4 , KLF5 and SOCS3 were clustered in Cluster ( 1 , * ) , implying that their expression increases as hES cells differentiate . In summary , with the decoupling of the KLF module from the OCT4-SOX2-NANOG module in hES cells , the upregulation of ESRRB , FOXD3 and SOCS3 in undifferentiated hES cells was diminished ( Figure 4B ) . Another group of KLF target genes in mice exhibited conserved upregulation in hES cells . ChIP-chip and RNAi data [37] confirmed that this group included OCT4 , SOX2 , NANOG , LIN28 and NODAL ( Figure 4A ) . In particular , LIN28 and NODAL were upregulated by KLFs in mES cells , because KLFs bound to these genes in vivo and knocking-down KLFs substantially decreased their expression levels . Since KLF2 , KLF4 and KLF5 themselves were not upregulated in hES cells , the maintenance of upregulation of LIN28 and NODAL in hES cells may require rewiring of the transcription networks [23] . In other words , the upregulation of LIN28 and NODAL in hES cells had to be achieved by transcription factors other than the KLFs . Consistent with this hypothesis , ChIP-chip data [37] , [39] showed that OCT4 , SOX2 and NANOG bound to LIN28 in hES cells but not in mES cells; NANOG bound to NODAL in hES but not in mES cells ( Figure 4B ) . As controls , none of ESRRB , FOXD3 or SOCS3 upstream was bound by OCT4 , SOX2 or NANOG in hES cells . In summary , the mouse-specific KLF2/4/5 regulatory module upregulated a set of key mES cell regulators . This module was not conserved in humans and therefore represented a peripheral component of the pluripotency maintaining regulatory networks . KLF4 was included in the set of genes for reprogramming both mouse [42] and human cells [28]; However , KLF4 was dispensable for maintaining the ES cell phenotype [28] , [42] . This fact supports our hypothesis that genes involved in peripheral components of ES cell transcription networks should be capable of assisting but may not be essential for reprogramming . To what extent do gene clusters reflect functionally related gene groups ? Although we do not expect a generic answer to this question , well-deliberated quantitative analyses may provide useful empirical data . Two sets of co-regulated genes were derived from an independent functional analysis , where seven regulatory proteins were knocked down by RNAi in mES cells [40] . To evaluate the consistency between the clustering result and the independently identified co-regulated genes , we applied a recently developed metric called the biological homogeneity index ( BHI ) [43] . BHI is the average proportion of gene pairs that are consistently allocated to both the same cluster and the same functional group in the gold standard dataset . A greater BHI reflects a higher consistency between the clusters and the functional groups . Because the gold standard datasets were generated from mES cells , we compared SCSC results with K-means clustering and hierarchical clustering performed on the same genes in the mES cell differentiation dataset ( Figure S3 ) . For a fair comparison , the same number of clusters were generated from K-means and hierarchical clustering as from SCSC . We gave K-means the advantage of starting from multiple initial values , minimizing the chances of being trapped by local maxima . In both comparisons , SCSC generated far more consistent gene groups with the functional groups defined by the independent RNAi studies , supporting the original intuition behind SCSC , that functional gene groups could be better revealed by comparative transcriptome analysis . Similar to KLF4 , LIN28 , a transcriptional target of the KLF2/4/5 regulatory module [37] , was also used as a reprogramming factor , but it was non-essential [29] . MYC , a transcription factor down-regulated during mES cell differentiation but not during hES cell differentiation ( Figure 2 ) , was another non-essential reprogramming factor [28] , [42] . These examples highlight the power of cross-species analysis to distinguish core versus peripheral components of a transcription network for maintaining a particular phenotype . Compared to differentiated cells , relatively few signal transduction factors were produced in ES cells . Comparing within the clusters that were upregulated in either mES or hES cells , i . e . , among Clusters ( 2 , * ) , ( 3 , * ) and ( * , 3 ) , genes involved in NOTCH , WNT , TGFβ , JAK-STAT and MAPK pathways were all depleted in the conserved clusters ( ( 2 , 3 ) and ( 3 , 3 ) , p-value <4*10−5 ) . The lack of shared signal transduction factors in the conserved clusters suggests that these signaling pathways either do not present in one of the two ES cells or they utilize alternative implementations in them ( Table 1 ) . JAK-STAT and NOTCH were present in mES cells , but no typical signaling transducers of these pathways appeared to be present in hES cells . It has long been known that mES cells remain undifferentiated in the presence of Leukemia Inhibitory Factor ( LIF ) , and the activation of Signal Transducer and Activator of Transcription 3 ( STAT3 ) via LIF-JAK signaling appears sufficient for maintenance of pluripotency of mES cells . However , LIF is unable to maintain the pluripotent state of hES cells [44] . The mechanism behind this apparent discrepancy is not fully understood , although the activation of human STAT3 alone does not sustain self-renewal of hES cells [44] . As summarized in Table 1 , none of the key components of the JAK-STAT pathway active in mES cells were present in hES cells , including key kinases JAK3 and TK2 and a family of five STAT transcription factors . This indicates that the JAK-STAT pathway is poised to receive the LIF signal in mES cells . Although only STAT3 is known as a downstream factor of LIF signaling in mES cells , our data predict that the other four STAT transcription factors may also contribute to maintaining the mES cell phenotype , since all of these genes are downregulated during differentiation . TGFβ , WNT and MAPK pathways appeared to be present in both mES and hES cells . However , our data suggest that mouse and human ES cells do not always use orthologous factors in these pathways . The non-orthologous components of these signaling pathways appeared to share two common features . First , paralogous members of the same gene family could serve as surrogates of an orthologous component . Using the WNT pathway as an example , growth factors FRIZZLED9 ( mES ) , FRIZZLED3 ( hES ) , receptors LRP5 ( mES ) and LRP6 ( hES ) , and transcription regulators HHTLE4HH ( mES ) and TLE1 ( hES ) were alternative members of the same gene family that appeared to assume orthologous functions in mES and hES cells ( Table 1 ) . Second , the alternatively implemented signaling transduction routes in the two species appeared to share the same regulatory logic . For example , TGFβ signaling in mES cell is inhibited by SMAD7 [45] and SKILl at the receptor and transcriptional levels [46] , whereas in the hES cell , another inhibition mechanism appeared to be present through the interaction of SMAD2 and TGIF1 [47] . Also , WNT signals to HHTLE4HH and TLE1 in mES and hES cells , respectively , for probably the same purpose of transcriptional repression [48] . The characterization of species-specific signaling pathways and alternative routes of signaling transduction facilitates understanding how pluripotency is maintained in mES and hES cells and why a signal could induce seemingly different and even reverse responses from these cells ( BMP: [49] , [50] , WNT: [51] , [52]; LIF: [44] ) . mES and hES cells are similar in the sense that they are both derived from the inner cell mass of blastocyst embryos , and are both pluripotent . Besides mES cells , pluripotent stem cells were also derived from the late epiblast layer of post-implantation mouse embryos ( mEpiS cells ) [53] . Compared to mES cells , mEpiS cells are functionally more similar to hES cells in the following ways . Both hES and mEpiS cells , but not mES cells , can differentiate into trophoblast upon exposure to Bmp4; display very limited capacity for chimera formation when injected or aggregated with mouse preimplantation embryos; form relatively large and flat colonies when grown as monolayers; do not survive well as individual cells . Importantly , the pluripotency of hES cells and mEpiS cells , can be maintained via Activin/Nodal signaling [53] , [54] , whereas Activin induces mES cells to differentiate into mesendoderm [55] . Thus , the alternative implementations of gene regulatory networks between hES can mES cells may reflect their functional differences and indicate the differences of their seemingly comparable temporal origins during embryonic development . Total RNA for transcriptional profiling was obtained from B6 mES cells at various stages of differentiation , including undifferentiated ( 0 day ) , 4 , 8 , 12 , 21 and 31 days of differentiation . Six biological samples were analyzed at each time point . B6 mouse ESC were cultured on mouse embryonic feeders ( MEFs ) using standard methods as previously described [56] in 15% FCS supplemented with LIF . Undifferentiated ES cell samples were obtained by trypsinising near confluent plates of ES cells and depleting the MEFs by plating the cells onto gelatin coated plates for 2×20 min . The ES on gelatin samples were MEF depleted ES cells seeded on gelatin coated dishes and cultured until they reached ∼70% confluency . To ensure the undifferentiated ES cell samples were free from MEF contamination , MEF depleted ES cells that passaged once on gelatin were used as 0-day ES cell samples . To make EBs , the ES cells on gelatin were seeded into non-adherent petri dishes , and LIF was withdrawn to induce differentiation . Half of the EB media was changed every 3–4 days . The formation of EBs was consistent with previous studies [57] , [58] . After 8 days , numerous cystic structures were observed and became progressively larger over time . After about 10 days , beating foci of cardiac myocytes could be observed in some EBs , indicating the terminal differentiation of some cell types . Total RNA was extracted from the different samples using the RNeasy kit ( Quiagen ) and amplified using a two-round linear amplification strategy as previously described [56] . The labeled RNA was then hybridized to Affymetrix MgU74Av2 microarrays according to the manufacturer's instructions . Normalization and probe-level modeling were done with dChip software [59] . The expression value of an orthologous gene pair is denoted as ( gi , gi′ ) , where i and i′ index two orthologous genes . The goal of SCSC is to assign a cluster label ci , i′ to every orthologous gene pair ( i , i′ ) . The range of ci , i′ goes from ( 1 , 1 ) to ( K , L ) , where K and L are the maximum numbers of clusters allowed in the two species . Without loss of generalizability , we assume there are no more than K clusters in either of the two species; i . e . , , and then ( K , K ) are the largest possible values ci , i′ can take . The following statistical model does not assume K = L . However K = L is used in the SCSC program implementation . SCSC takes a model-based approach . The cluster labels are assumed to be generated according to probabilities and that conform to a multinomial logit model [60]: ( 1 ) where . and capture the independent co-expression information contributed by each species , i . e . , row and column effects in Figure 2 . is an 0–1 indicator function . represents the degree of dependence between correspondent clusters between the two species . When , cluster is deemed as the correspondent cluster to cluster k . The order of the result clusters in a clustering analysis is usually arbitrarily determined . SCSC orders its clusters in the two species in a way that the clusters in the two species with the largest intersection of orthologous genes are given the same numerical indicator ( See below ) . Given the cluster indicator of a gene pair , for example , the model for complete data is: ( 2 ) Here denotes a Gaussian distribution; and are the mean vectors of the kth and the th clusters in the two species , respectively; and are their corresponding covariance matrices . A generative probabilistic model for two species gene expression data is: ( 3 ) where the product enumerates all gene pairs ( i , i′ ) ; is the probability of gene pair ( i , i′ ) coming from cluster ; is the likelihood of gene i given it comes from cluster k in one species , and is the likelihood of gene i′ given it comes from cluster of the other species . An iterative maximization algorithm was developed to fit the SCSC model ( Figure S4 , Text S3 ) . Because SCSC uses a likelihood maximization approach based on the EM algorithm , the local maximum issue that is general to EM algorithm applies . The SCSC program is available both as a downloadable program and as a web application at: http://biocomp . bioen . uiuc . edu/SCSC .
A major goal in biology is to understand the evolution of complex traits , such as the development of multicellular body plans . To a certain extent , complex traits are governed by regulated gene expression . The comparison expression data between species requires extra considerations than sequence comparison , because gene expression is not static and the level of expression is influenced by external conditions . Considering that co-expression patterns are often comparable across species , we developed a statistical model for cross-species clustering analysis . The model allows each species to create its own clusters of the genes but also encourages the species to borrow strength from each others' clusters of orthologous genes . The result is a pairing of clusters , one from each species , where the paired clusters share many but not necessarily all orthologous genes . The model-based approach not only reduces subjective influence but also enables effective use of evolutionary dependence . Applying this model to analyze human and mouse embryonic stem ( ES ) cell data , we identified the transcription factors and the signaling proteins that are specifically expressed in either human or mouse ES cells . These results suggest that the pluripotent cell identity can be established and maintained through more than one gene regulatory network .
[ "Abstract", "Introduction", "Results", "Discussion", "Methods" ]
[ "computational", "biology/evolutionary", "modeling", "genetics", "and", "genomics/gene", "expression", "developmental", "biology/stem", "cells", "computational", "biology/transcriptional", "regulation" ]
2010
Modeling Co-Expression across Species for Complex Traits: Insights to the Difference of Human and Mouse Embryonic Stem Cells
The rice blast fungus Magnaporthe oryzae is one of the most significant pathogens affecting global food security . To cause rice blast disease the fungus elaborates a specialised infection structure called an appressorium . Here , we report genome wide transcriptional profile analysis of appressorium development using next generation sequencing ( NGS ) . We performed both RNA-Seq and High-Throughput SuperSAGE analysis to compare the utility of these procedures for identifying differential gene expression in M . oryzae . We then analysed global patterns of gene expression during appressorium development . We show evidence for large-scale gene expression changes , highlighting the role of autophagy , lipid metabolism and melanin biosynthesis in appressorium differentiation . We reveal the role of the Pmk1 MAP kinase as a key global regulator of appressorium-associated gene expression . We also provide evidence for differential expression of transporter-encoding gene families and specific high level expression of genes involved in quinate uptake and utilization , consistent with pathogen-mediated perturbation of host metabolism during plant infection . When considered together , these data provide a comprehensive high-resolution analysis of gene expression changes associated with cellular differentiation that will provide a key resource for understanding the biology of rice blast disease . The ascomycete fungus Magnaporthe oryzae is the causal agent of rice-blast disease , which can destroy up to 18% of the annual rice harvest [1] . Because more than half of the global population depends on rice as a staple food crop , rice blast disease represents a significant factor that impacts upon global food security [2] . The genetic tractability of the fungus and availability of a genome sequence also make the organism an excellent experimental model for the study of plant pathogenesis [3] . In common with many plant pathogenic fungi , including rusts and powdery mildews , M . oryzae enters its host plant using a specialised infection structure known as an appressorium [1] . Upon landing on a rice leaf , the three-celled asexual spore ( called a conidium ) germinates , producing a germ tube from one of the apical cells . The end of the germ tube soon swells to form a dome-shaped appressorium , which becomes melanised as it matures [4] . Accumulation of glycerol in the developing appressorium leads to an influx of water by osmosis and the consequent development of hydrostatic turgor of up to 8 MPa [5] . Such high pressure enables the fungus to penetrate the plant cuticle and cell wall by physical force and enter underlying epidermal cells . Differentiation of functional appressoria is tightly linked to genetic regulation of the cell-cycle . A DNA replication-dependent checkpoint , for instance , is essential for initiation of appressorium formation [6] and entry into mitosis is a pre-requisite for development of a functional appressorium [7] . One of the daughter nuclei from the single mitotic division , which occurs prior to appressorium development , migrates into the developing appressorium , after which septation occurs , separating appressorium from germ tube [8] . The remaining daughter nucleus migrates back to the conidium , which eventually collapses and dies due to infection-associated autophagy [7] , [9] . Appressorium formation by M . oryzae can be studied away from living plants on artificial , hydrophobic surfaces . Along with the development of methods for routinely performing targeted gene deletions and replacements , this has enabled discovery of important signalling pathways involved in appressorium development , including both cyclic-AMP dependent and mitogen-activated protein ( MAP ) kinase pathways [10] , [11] . Central to appressorium development is the Pmk1 MAP kinase pathway [1] , [12] , composed of a MAP kinase Pmk1 , activated by a MAP kinase kinase ( MAPKK ) Mst1 , which in turn is activated by the Mst11 MAPKK kinase ( MAPKKK ) . The pathway is regulated by the Mgb beta-subunit of a heterotrimeric G-protein and the recently described Msb2 and Sho1 upstream activators [13] . Mutant strains of M . oryzae , in which Pmk1 has been deleted , are unable to develop appressoria or grow invasively in planta , even when inoculated directly into wounded plant tissue , although growth in axenic culture is largely unaffected [11] , [12] , [14] . Appressorium formation in M . oryzae occurs under conditions where there are no exogenous nutrients available and , therefore , formation of the appressorium and synthesis of large quantities of glycerol involves mobilisation of compounds stored in the conidium . Rapid Pmk1-dependent mobilisation of lipids and glycogen occurs during appressorium development [14] , accompanied by an increase in triacylglycerol lipase activity , which liberates glycerol from storage lipids . Fatty acid beta-oxidation has also been shown to be important for appressorium formation , in addition to the glyoxylate cycle , to enable utilization of acetyl-CoA through gluconeogenesis [15] , [16] . The importance of the acetyl-CoA pool during appressorium formation is highlighted by the fact that mutants impaired in carnitine acetyl transferase activity are non-pathogenic [17] , [18] . Acetyl-CoA is , for instance , needed for synthesis of melanin , cell wall chitin and glucans , as well as potentially being used to synthesise glycerol , and may therefore be pivotal to biosynthetic pathways essential for appressorium function [14] , [19] . In order to define the reservoir of gene functions needed for appressorium-mediated plant infection by M . oryzae , a systematic analysis of the global patterns of transcriptional activity is necessary . Previous studies have begun to examine this problem by using microarray analysis or , alternatively , massively parallel signature sequencing ( MPSS ) and serial analysis of gene expression ( SAGE ) , using Sanger sequencing . Each of these studies has , however , focused on only a restricted set of conditions . Donofrio and co-workers [20] demonstrated that a number of known genes involved in pathogenicity were up-regulated under nitrogen starvation when transcripts were analyzed by oligonucleotide-based microarrays , following growth under nitrogen-limiting conditions . However , no direct observations of transcript abundance were made during appressorium development . Gowda and colleagues [21] compared transcript abundance in samples grown as mycelial cultures , or following appressorium development , but included only a single time-point ( 24 h ) , by which time the appressorium is fully developed and developmental dynamics are complete . Transcriptional changes have also been compared on inductive and non-inductive surfaces , as well as following addition of exogenous cAMP [22] . However , only two developmental time points were chosen , seven and twelve hours after spore germination . Most recently , a microarray study compared gene expression levels in M . oryzae mycelium grown under different stress conditions with those of the fungus growing in planta [23] . The authors concluded that during invasive growth M . oryzae may grow under conditions of nutrient starvation , consistent with earlier studies that made similar conclusions [24] , [25] . However , each of these data sets , while providing valuable information , presented expression patterns for only a sub-set of M . oryzae genes and a restricted set of time-points . In this study we have taken advantage of the utility of next generation-sequencing ( NGS ) to perform a comprehensive analysis of gene expression throughout appressorium development in M . oryzae at much greater sensitivity than was hitherto possible using either microarray or tag-based approaches . There are two evolving methods to apply NGS sequencing to measure gene expression changes , RNA-Seq , in which whole transcripts are sequenced [26] and tag-based methods such as Digital Gene Expression ( DGE ) and High Throughput ( HT ) -SuperSAGE [27] . We employed both RNA-Seq and HT-SuperSAGE and found that HT-SuperSAGE provides data that corresponds well with RNA-Seq from the same tissues , but at a much higher throughput and reduced cost . Subsequently , we used HT-SuperSAGE to analyse global patterns of gene expression during appressorium development of M . oryzae . Here , we present transcript profiles of 10 , 591 genes , 96% of the total predicted genes of M . oryzae , thus providing the most complete coverage of the transcriptome in M . oryzae published studies . This has enabled identification of genes that are highly expressed at specific stages of appressorium development . We have used these data to compile a publicly accessible database ( http://cogeme . ex . ac . uk/supersage/ ) as part of the COGEME suite of databases [28] to provide expression values for any specified gene in the M . oryzae genome during appressorium morphogenesis . We present the most significant changes in gene expression and reveal major metabolic and physiological changes associated with infection-related development by the rice blast fungus . Two alternative high-throughput methods exist for generation of transcriptomic data . In RNA-Seq , sequences are derived from total RNA , reverse transcribed to cDNA , fragmented and sequenced using next-generation DNA sequencing ( NGS ) technology [26] . The short reads produced by NGS sequencing are then assembled after alignment to a reference genome and in this way the complete sequence of each transcribed gene can be obtained , allowing identification of splice sites , un-translated regions , alternatively spliced transcripts and complete gene coding sequences . It is also possible to use these data to quantify abundance of each transcript in the cDNA library by calculating the frequency of short reads that align to each gene [29] . These values are normalised to take account of differing lengths of genes and the total number of short reads obtained from each library and are generally expressed as fragments per kilobase of exon , per million fragments mapped ( FPKM ) . An alternative method for quantifying levels of transcript abundance is HT-SuperSAGE [27] . In this method cDNA is prepared from each tissue sample . Twenty-six base sequence tags are then independently generated from each transcript in these libraries and NGS technology used to sequence tags [27] . Sequence tags are aligned back to a reference genome and the number of tags from each gene calculated to provide a measure of gene expression . Values are normalised to take into account the total number of tags sequenced from each library and are typically expressed as a fraction of the total number of sequenced tags ( tags per million or TPM ) . The advantage of HT-SuperSAGE over RNA-Seq for the analysis of transcript abundance is that a lower depth of sequencing is required . It has been estimated , for example , that to achieve 90% coverage of the human transcriptome , 40 million reads would be required using RNA-Seq , compared to less than 5 million reads using HT-SuperSAGE [30] . This makes it feasible to run multiple HT-SuperSAGE samples simultaneously , using unique 4-base bar codes to distinguish between tags from different samples and reducing costs further [27] . In order to make a comparison between these two techniques , we analysed transcript abundance in M . oryzae mycelium grown in complete medium ( CM ) , compared to M . oryzae grown in glucose minimal medium ( MM ) for 36 h . Each RNA-Seq sample required one lane in an Illumina flowcell , whereas four HT-SuperSAGE samples were analysed per lane . The number of individual transcripts identified using each of these two techniques was very similar; 9 , 985 using RNA-Seq and 9 , 989 using HT-SuperSAGE from at least one of the two samples of mycelium ( CM and MM ) . A Pearson correlation coefficient of 0 . 57 was recorded when data from both methods were compared . A previous study comparing quantitative gene expression values in appressorium and mycelium from M . oryzae using MPSS , robust-long SAGE ( RL-SAGE ) and microarray analyses produced pair-wise correlation coefficients ranging from only 0 . 068 to 0 . 59 using unfiltered data sets . These correlation coefficients could be increased by filtering data sets either by removing genes with low levels of expression or removing outliers [22] . The use of NGS methodologies overcame such limitations . Therefore the two methods produced very similar levels of sensitivity , but HT-SuperSAGE proved much more cost effective . Having established the sensitivity and accuracy of HT-SuperSAGE , we applied the method to reveal global patterns of gene expression during appressorium development by M . oryzae . RNA was extracted from conidia germinated on a hydrophobic glass slide for 4 , 6 , 8 , 14 and 16 h , respectively ( Figure 1A ) . Two replicates for each time-point were taken and HT-SuperSAGE of cDNA was used to measure transcript abundance , representing individual genes at each time point . Time points were chosen to target discrete developmental stages associated with appressorium morphogenesis . Two broad stages can be readily discerned during appressorium formation: a development phase from approximately 4 to 8 h after germination , during which the germ tube tip swells forming a characteristic hemispherical shape and cellular components begin to migrate into this nascent appressorium , which subsequently becomes melanin-pigmented , and a maturation phase after 8 to 16 h during which the appressorium becomes pressurised due to the increased production of solutes and conidial cells undergo autophagy and cell death [1] . To define major transcriptional changes associated with appressorium development sampling was carried out at 4 , 6 and 8 h , while the maturation phase was further monitored at 14 and 16 h . For comparison , HT-SuperSAGE data were generated from mycelium grown in rich complete medium ( CM ) or under conditions of nutrient limitation in glucose minimal medium ( MM ) . To define further the gene expression profiles specific to appressorium morphogenesis , we analysed a mutant lacking the PMK1 MAP kinase-encoding gene , which is vital for appressorium formation and pathogenicity [11] . Strains lacking PMK1 cannot form appressoria and are non-pathogenic even when spores are injected directly into plant tissue [11] , [12] , [14] . HT-SuperSAGE data was used to compare transcript abundance in germinating conidia 4 h after they were placed on an inductive surface . Figure 1B shows that at this time-point , an appressorium is already developing in Guy11 strain whereas no infection cell development occurs in an isogenic Δpmk1 mutant [11] . To assess the overall similarity between data sets , the Euclidean distance was calculated between each sample , using transcript abundance values for all genes . Data sets were clustered based on these distances and a heat map generated ( Figure 1C ) . The results show that data sets generated from mycelium are highly distinctive from transcripts associated with appressorium development . Within appressorium datasets , the Δpmk1 mutant showed global patterns of gene expression that formed a separate clade from datasets generated from wild-type M . oryzae , suggesting that the PMK1 MAP kinase affects the expression level of a significant number of genes associated with cellular differentiation . The time-points at 4–8 h also form a separate clade from those at 14 and 16 h , consistent with there being two distinct phases of appressorium development . We were specifically interested in identifying gene expression patterns during appressorium development , suggesting physiological or signalling pathways important in cellular morphogenesis . As a baseline for comparison , we looked at gene expression during growth of mycelium in rich medium in which no comparable cellular differentiation occurs . A total of 1026 genes were identified that were significantly up-regulated ( adjusted P-value< = 0 . 05 ) at all time-points between 4 and 8 h when compared to expression in mycelium grown in CM ( Table S1 ) . An analysis was performed on this data set to predict functions of each gene identified based on gene ontology ( GO ) categories using Blast2GO [31] . GO categories over-represented within appressorium differentially-expressed genes were identified using Fisher's exact test ( adjusted P-value< = 0 . 05 , Table S1 ) . Over-represented GO categories were consistent with increased carbohydrate metabolism , expression of a large set of glycosyl hydrolases and sugar transporter-encoding genes and induction of secondary metabolic pathways . We also identified genes encoding proteins important in cell cycle control such as the G2-specific protein kinase-encoding gene NIMA ( MGG_03026 ) [6] , [7] , a homologue of cdc14 from Schizosaccharomyces pombe ( MGG_00757 ) , which is part of the complex that controls septation and cytokinesis [32] , a homologue of tinA from Aspergillus nidulans ( MGG_00763 ) , a gene encoding a protein that interacts with NimA [33] , and homologues of the kinetochore protein-encoding gene Mis14 ( MGG_00906 ) and sudA ( MGG_04988 ) from Aspergillus nidulans which are involved in chromosome segregation during mitosis [34] . The homologues of cdc14 , Mis14 , tinA and sudA all show significant up-regulation during early appressorium development at 4 and 6 h after conidial germination . In fact , the Mis14 homologue is significantly up-regulated throughout appressorium development and the sudA homologue is also significantly up-regulated at 8 h . NIMA is only significantly up-regulated 8 hours after the start of conidial germination . The data are also consistent with NIMA and the homologues of sudA and cdc14 being under transcriptional control of the Pmk1 MAP-kinase pathway . Appressorium development in M . oryzae is regulated by cell cycle control and temperature sensitive mutants in NIMA , for instance , are unable to produce appressoria at restrictive temperatures [6] , [7] . We also found evidence for co-regulation and differential expression of autophagy-associated genes ( Figure S1 ) . Hierarchical clustering grouped twelve of these genes into a clade showing up-regulation during early stages of appressorium formation . Infection-associated autophagy is necessary for appressorium function and deletion of any one of sixteen genes involved in non-selective macroautophagy in M . oryzae leads to loss of pathogenicity [9] . A total of 1492 genes were identified as significantly up-regulated ( adjusted P-value< = 0 . 05 ) at both 14 h and 16 h , when compared to mycelium grown in CM ( Table S2 ) . GO categories over-represented in this dataset included those involved in carbohydrate metabolism , specifically the large and diverse set of glycosyl hydrolases encoded by M . oryzae , transmembrane transport , particularly of sugars , developmental processes such as cell wall biogenesis and the response to different external stimuli ( including , for example , cAMP ) . In total , 481 genes were identified showing significantly lower levels of expression in a Δpmk1 mutant when compared to the wild-type at 4 h ( adjusted P-value< = 0 . 05 ) ( Table S3 ) . These genes are therefore likely to be positively regulated by the presence of an active PMK1 MAP kinase pathway . GO categories over-represented in this dataset were those specifically involved in response to exogenous stimuli , including two CFEM-domain containing receptor proteins ( Table S3 ) and a large set of 15 transporter-encoding genes , as well as 16 putative transcription factor-encoding genes differentially expressed as a consequence of loss of PMK1 . During formation of the appressorium in vitro , the germinating conidium is under nutrient limited conditions and therefore a number of genes may be up-regulated solely as a response to starvation stress . To identify genes up-regulated by nutrient limitation , expression levels were compared between M . oryzae mycelium grown in glucose minimal medium ( MM ) and complete medium ( CM ) . In this way 298 genes were identified that were up-regulated in mycelium grown in MM compared to CM ( Table S4 ) . The GO categories over-represented in the data set were involved in transmembrane transport , redox control and developmental processes . The Venn diagrams in Figure 1 provide an illustration of the overlaps between each distinct transcriptionally-defined gene set . For example , of the 481 genes that are down-regulated in a Δpmk1 mutant , nearly half ( 238 ) are also up-regulated during the early stages ( 4–8 hours ) of appressorium development . A smaller number of Δpmk1 down-regulated genes ( 174 ) are differentially regulated during the later stages of appressorium development ( 14–16 hours ) . The overlap between genes up-regulated by nutrient limitation and those up-regulated during appressorium development is , however , much lower ( 15% and 24% of the MM up-regulated genes are also up-regulated during early and late appressorium development , respectively ) . This suggests that nutrient limitation acts as an inducing signal for only a small proportion of genes up-regulated during appressorium development . As a consequence of the importance of the acetyl-CoA biosynthesis and metabolism to appressorium development [14]–[19] , we next selected 31 genes encoding enzymes that putatively utilise or produce acetyl-CoA . Table S5 shows the HT-SuperSAGE data for this population of genes during appressorium development in Guy11 , in mycelium grown in both CM and MM , and in germinating conidia of a Δpmk1 mutant at 4 h after being placed on an inductive surface . The results are summarised by each pathway in Figure 2 . The predicted pathways in which the enzymes that utilise/produce acetyl-CoA have a higher or lower expression ( adjusted P-value< = 0 . 05 ) during appressorium formation ( at 4 h ) when compared to mycelial growth , is shown in Figure 2A . Genes encoding enzymes from the pathway that oxidises fatty acids to produce acetyl-CoA , for example , show increased expression during appressorium formation , as do carnitine acetyl transferases , which transport acetyl-CoA between sub-cellular compartments . The enzyme acetyl-CoA carboxylase , that synthesises malonyl-CoA from acetyl-CoA [35] , shows increased expression during appressorium formation , while acetyl-CoA-utilising enzymes in the fatty acid , mevalonate and lysine biosynthesis pathways all showed lower expression . Figure 2B shows pathways in which the enzymes that utilise/produce acetyl-CoA have a higher or lower expression ( adjusted P-value< = 0 . 05 ) during appressorium formation ( at 4 h ) in Guy11 compared to a Δpmk1 mutant . Acetyl-CoA carboxylase , glyoxylate cycle genes , pyruvate dehydrogenase and carnitine acetyl transferase genes were all reduced in expression in a Δpmk1 mutant when compared to the isogenic Guy11 . In contrast , genes encoding acetyl-CoA producing/utilising enzymes involved in lysine biosynthesis showed greater expression in a Δpmk1 mutant . Overall , these results confirm that acetyl-CoA plays a central role in appressorium morphogenesis , being mainly produced from beta-oxidation of fatty acids and then used for biosynthesis of malonyl-CoA or the glyoxylate shunt . Only acetyl-CoA carboxylase and the carnitine acetyl-transferases appear to be differentially regulated as a consequence of the presence of the Pmk1 MAP-kinase pathway . Peroxisomal beta-oxidation of fatty acids [16] and subsequent transport of acetyl-CoA out of the peroxisome by carnitine acetyltransferase have both been shown to be necessary for formation of functional appressoria [17] . Appressorium formation is furthermore delayed in strains in which the glyoxylate cycle enzyme isocitrate lyase is deleted [15] . To follow the likely fate of acetyl CoA during appressorium differentiation we next analysed expression of genes associated with malonyl CoA synthesis and metabolism . Acetyl-CoA carboxylase is highly up-regulated during appressorium development and differentially regulated by the presence of PMK1 . This enzyme synthesises malonyl-CoA from acetyl-CoA [35] . Malonyl-CoA is used as a substrate by both polyketide and fatty acid synthases . In order to determine the likely fate of malonyl CoA during fatty acid metabolism , HT-SuperSAGE data were analysed for polyketide synthase expression , as well as genes involved in fatty acid biosynthesis ( Table S6 ) . Nine polyketide synthase and four hybrid polyketide synthase / non-ribosomal peptide synthases were significantly up-regulated ( adjusted P-value< = 0 . 05 ) in at least one time-point during appressorium development . These included ALB1 , which encodes a polyketide synthase that catalyses the first step in melanin biosynthesis [36] . Consistent with the importance of malonyl-CoA synthesis during appressorium formation , is the up-regulation of the malonyl CoA-acyl carrier protein transacylase gene , which transfers malonyl-CoA thioesters from solution to fatty acid synthases or polyketide synthases [37] . Expression of malonyl-CoA utilisation genes was visualised as a heat map ( Figure 3A ) , created using moderated log2-fold changes of transcript abundance during appressorium development , compared to expression in mycelium . Genes showing similar patterns of expression were grouped together by hierarchical clustering . The gene encoding acetyl-CoA carboxylase , for example , clusters together with the gene encoding ALB1 , while the rest of the melanin biosynthesis pathway genes cluster with the gene encoding malonyl CoA-acyl carrier protein transacylase ( Figure 3 ) . This suggests a strong link between melanin biosynthesis pathway and malonyl-CoA metabolism . Differential co-ordinated expression of fatty acid synthases , putatively associated with generation of very long chain branched fatty acids , such as mycocerosic acid ( MGG 04775 and MGG 08285 ) , was also observed along with a large clade of 13 co-ordinately-regulated polyketide synthases and a separate clade of hybrid polyketide synthase , non-ribosomal peptide synthetases , including the ACE1 avirulence gene [38] . To investigate gene expression associated with appressorial mobilisation of lipids [14] and their subsequent metabolism [16] , we next selected the entire set of predicted lipid metabolic genes . Table S7 shows transcriptional profiling data for genes encoding enzymes from these pathways and Figure 3B shows a heat map to illustrate some of the principal changes in appressorium-associated expression . Most genes involved in beta-oxidation of fatty acids are significantly up-regulated during appressorium development , with those encoding the multifunctional beta-oxidation enzyme MFP1 ( MGG_6148 ) and an acyl-CoA dehydrogenase ( MGG_15041 ) being most up-regulated . In a heat map these two genes cluster with two other genes encoding specific enzymes of the glyoxylate shunt , such as isocitrate lyase ( MGG_04895 ) and malate synthase ( MGG_02813 ) , consistent with activation of the pathway during appressorium maturation ( Figure 3B ) . The cytosolic isozyme of malate dehydrogenase ( MGG_08835 ) does not show such high relative levels of expression during appressorium development , but is also involved in shuttling oxaloacetate from mitochondria to the cytosol [39] and may therefore not be specifically induced during appressorium formation . The glyoxylate cycle also requires a non-mitochondrial citrate synthase , but analysis of the M . oryzae genome shows only one putative citrate synthase ( MGG_07202 ) and one methylcitrate synthase-encoding gene ( MGG_02617 ) , likely involved in propionate metabolism [40] , both of which are predicted to be mitochondrial . It may be that different transcripts encoding isozymes of citrate synthase with different locations can be synthesised from the same gene , as observed for NADP-dependent isocitrate dehydrogenases from Aspergillus nidulans [41] . If this is the case , it is not surprising that the citrate synthase gene does not show the same pattern of expression as glyoxylate cycle-specific genes . The glyoxylate cycle enables acetyl-CoA produced by beta-oxidation of fatty acids to be fed into gluconeogenesis allowing glycerol , glucans and chitin to be synthesised [15] . Intracellular transport of acetyl-CoA produced during beta-oxidation by the peroxisomal carnitine acetyltransferase PTH2 is necessary for appressorium function [17] , [18] . HT-SuperSAGE revealed that PTH2 ( MGG_01721 ) is highly expressed between 4–8 h and likely to be under the control of the Pmk1 MAP kinase pathway . Our data therefore independently confirm that fatty acid beta-oxidation , melanin biosynthesis and the glyoxylate shunt are pivotal processes during appressorium development , consistent with gene functional studies [1] , marking major changes in metabolism that are necessary for infection cells to develop and function correctly . Figure 4 shows expression patterns of the key enzymes in each of these pathways . Figure 4A shows the core peroxisomal fatty acid beta-oxidation pathway in which nearly all genes show higher levels of expression during appressorium development ( black bars ) than in mycelium grown axenically . Only the multi-functional beta-oxidation enzyme showed reduced expression in a Δpmk1 mutant ( red bar ) compared to the wild-type , suggesting that it might be controlled by the Pmk1 MAP kinase pathway . Expression profiles of genes encoding enzymes of the dihydroxynaphthalene ( DHN ) melanin biosynthesis pathway are shown in Figure 4B . They all show very similar patterns of expression , with high levels at 4 and 6 h when the appressorium is developing , but reducing from 8 h onwards at the onset of maturation . Expression profiles of genes encoding enzymes of the glyoxylate cycle are shown in Figure 4C . Three of the four enzymes showed higher level of expression throughout appressorium development and are also significantly reduced in expression in a Δpmk1 mutant compared to the wild-type Guy11 . The gene encoding cytosolic malate dehydrogenase , however , showed a different expression profile which may be due to the fact that cytosolic malate dehydrogenase is a component of both the glyoxylate cycle and the malate-aspartate shuttle [39] . The latter pathway is responsible for translocating reducing equivalents in the form of NADH produced by glycolysis across the mitochondrial inner membrane for oxidative phosphorylation [39] . This pathway is likely to be active during both mycelial growth and development of the appressorium , and it is therefore not surprising that the malate dehydrogenase gene is highly expressed in both tissue types . Gene expression profiles within mature appressoria at the point of penetration peg emergence would be expected to predict the likely repertoire of gene functions associated with initial growth in plant tissue and may therefore be valuable in identifying the principal substrates used by the fungus during its growth in plant cells . Phytopathogenic fungi are osmotrophic micro-organisms reliant on the secretion of a broad repertoire of depolymerising enzymes and a range of transporters to acquire nutrients from their host , as well as to export toxins and remove anti-fungal compounds produced by the plant . We identified expression data for 206 genes encoding secreted enzymes that breakdown carbohydrates ( Table S8 ) . Of these , 72 were significantly up-regulated during appressorium development , and only 30 down-regulated . The classes of enzymes that showed differential up-regulation of expression during appressorium development included many that potentially degrade components of the plant cell wall , for example , cutinases , endo-1 , 4-beta-xylanases , a polygalacturonase , cellulases , a rhamnogalacturonan acetylesterase and alpha-L-arabinofuranosidases . In addition , genes encoding enzymes involved in the extensive fungal cell wall remodelling that goes on during appressorium development , for example , chitinases and beta-hexosaminidases were differentially expressed . In parallel , a search for M . oryzae proteins with Pfam motif PF00083 , a signature of saccharide ( and other ) transporters of the major facilitator superfamily ( MFS ) of membrane transporters , identified 71 genes ( Table S9 ) , of which 29 were differentially expressed during appressorium formation . When considered together , these data suggest that the maturing appressorium expresses genes leading to rapid secretion of a large repertoire of enzymes to break down plant oligosaccharides and a range of other plant cellular components into monosaccharides and simple monomers , with expression of cognate transporters to import these products into the invading pathogen . Among sugar transporter-encoding genes we noted a family of four putative quinate permease genes ( MGG_07779 , MGG_14136 , MGG_09778 and MGG_04225 ) , differentially expressed at all stages of appressoria formation . Only one of the quinate permeases , MGG_09778 is expressed in a Δpmk1 mutant at 4 h post germination . Four other quinate permeases were also detected at levels not significantly different from mycelium growing in CM , suggesting that distinct families of the transporter may be deployed in mycelial growth and plant infection . Interestingly , quinate can serve as sole carbon source for several fungi and the pathway has been studied in detail in Neurospora crassa [42] and Aspergillus nidulans [43] . Quinic acid is a cyclic polyol and an abundant carbon source that can account for up to 10% of decaying leaf litter [43] . Interestingly , a recent metabolite profiling study of rice blast-infected leaves noted an increase in quinate at early stages of M . oryzae infection and suggested that the invading fungus may modulate host metabolism to divert metabolites , such as dehyroquinate and dehydroshikimate that are shared between the quinate and shikimate pathways to quinate production , thereby reducing defensive phenylpropanoid production through the shikimate pathway [44] . Quinate produced in such a way could serve as a very good source of carbon for M . oryzae , which is less readily utilizable by the rice host . To test this idea , we investigated whether M . oryzae genes encoding enzymes required for quinate metabolism were also differentially expressed in developing appressoria ( Figure 5 , Table S10 ) . We found that quinate dehydrogenase , 3-dehydroquinase and 3-dehydroshikimate dehydratase are also differentially expressed during appressorium maturation . Quinate metabolism is also subject to catabolite repression , is induced by quinic acid , and co-regulated at the transcriptional level by activator ( MGG07777 ) and repressor proteins ( MGG1842 , MGG14813 ) , which we also found to be differentially expressed during appressorium formation ( Table S10 ) . Furthermore , expression of genes involved in the anabolic shikimate pathway encoding the penta-functional AROM protein , chorismate synthase and 3-deoxy-D-arbinoheptulosonate-7-phosphate synthase were not significantly up- regulated during appressorium formation , providing further evidence for the quinate metabolic pathway being active . Taken together , these observations strongly suggest a role for quinate as a carbon source for M . oryzae during plant infection . Protocatechuic acid , the end product of quinate pathway may be further degraded via the β-ketoadipate pathway into succinate and acetyl CoA and enter the TCA cycle [45] . However , in fungi the pathway has only been studied biochemically and only one gene for beta-carboxy-cis , cis-enzyme has been cloned from Neurospora crassa [46] . Interestingly , the M . oryzae homolog of this gene , MGG 1335 is significantly expressed during appressorium development at 6 h . In contrast to the large number of sugar transporters up-regulated during appressorium formation , only 4 of the 38 putative organic acid transporter genes ( GO annotation; includes all genes containing Pfam motif PF000324 for amino acid permeases ) were significantly up-regulated at any time point during appressorium formation ( Table S11 ) . Of the four , only one , a proline-specific permease ( MGG_02899 ) , was specifically expressed in appressoria at all time points . Furthermore , ten of the organic acid transporters were down-regulated in appressoria during both development ( 4–8 hrs ) and maturation phases ( 14–16 hrs ) . This group included proline and lysine specific permeases ( MGG_04216 , MGG_08129 , MGG_14937 ) , GABA permease ( MGG_14115 ) , another excitatory amino acid transporter ( MGG_07639 ) and an orthologue of isp4 from Schizosaccharomyces pombe , an oligopeptide transporter , which is up-regulated in fission yeast in response to nitrogen starvation [47] . Overall , our observations suggest that amino acid uptake is unlikely to be a significant process during appressorium development and the initial stages of plant infection . The other major group of transporters we investigated were the ABC transporters , MFS transporters and multidrug and toxin extrusion ( MATE ) family of transporters , that are often annotated as drug transporters ( see , for example , Blast2Go or the Magnaporthe genome database at http://www . broadinstitute . org/annotation/fungi/magnaporthe/ ) . Ninety one such transporter genes were analyzed for expression during appressorium development ( Table S12 ) . We found that 35 putative drug transporter genes were significantly up-regulated during appressorium development , and 18 down-regulated . Only three of the transporters were also up-regulated in mycelium growing on MM and another two in a Δpmk1 mutant , indicating that the majority of these transporters ( 31 ) are expressed specifically during appressorium function and may be deployed to deliver secondary metabolites into the host or to protect the fungus from plant defence compounds during pathogenesis . Six of the drug transporters ( MGG_11025 , MGG_13762 , MGG_09976 , MGG_03557 , MGG_10336 and MGG_10534 ) were significantly up-regulated at all stages of appressorium development . The MGG_13762 gene encodes the previously characterised ABC3 transporter gene , which is required for host penetration [48] . Two other previously reported ABC transporters , ABC1 ( MGG_13624 ) [49] and ABC4 ( MGG_00937 ) [50] implicated as pathogenicity factors in M . oryzae , were also differentially expressed in appressoria . Consistent with the differential expression of putative efflux pumps , 18 key secondary metabolic pathway enzymes are also significantly up-regulated in at least one time-point during appressorium development , consistent with an overall increase in secondary metabolite synthesis during appressorium development . Fungal hydrophobins are small , hydrophobic proteins secreted by fungi and are essential for the formation of aerial structures and mediate the attachment of the fungus to hydrophobic surfaces such as the rice leaf surface [51] . The class I hydrophobin MPG1 [24] , [25] and the class II hydrophobin MHP1 [52] are both required for full pathogenicity of M . oryzae . Two other class II hydrophobin encoding genes have also been discovered in the genome of M . oryzae [52] . Another secreted fungal protein that binds to hydrophobic surfaces is encoded by HsbA from Aspergillus oryzae [53] . This secreted protein binds to artificial polybutylene succinate-co-adipate ( PBSA ) hydrophobic surfaces and has been shown to recruit a polyesterase which degrades the PBSA , enabling the fungus to use it as a carbon source [53] . Eight homologues of HsbA were discovered in the genome of M . oryzae , based on occurrence of the HsbA Pfam motif ( PF12296 ) . Analysis of HT-SuperSAGE data ( Table S14 ) suggests that four of these HsbA encoding genes are differentially up-regulated throughout appressorium development and a further two are up-regulated only at later stages ( 14–16 hours ) . These data suggest that HsbA-like genes are likely to have a specific role during appressorium development . It may be worth speculating that they are involved in attachment of the developing appressorium to the rice surface and might recruit secreted enzymes that degrade constituents of the plant epidermis ( such as , for example , cutinases ) . MPG1 ( MGG_10315 ) is expressed at high levels throughout appressorium development , but also during mycelial growth . No transcripts were detected for MHP1 ( MGG_01173 ) , consistent with published data showing very low expression during mycelial growth and appressorium development , but high expression during growth in planta [52] . The two other hydrophobin encoding genes did not show differential expression during appressorium development . The HT-SuperSAGE data described in this study have been made easily accessible to the wider research community by submission to Genbank , but also by creation of an online database ( http://cogeme . ex . ac . uk/supersage/ ) , as part of the suite of COGEME databases . The user enters the ID of any M . oryzae gene and the database will provide HT-SuperSAGE data for the time course of appressorium development , as well as data from a Δpmk1 mutant and mycelial growth for comparison . In this way , the expression profiles of more than 96% of the M . oryzae genome can be evaluated during infection-related development . NGS has revolutionised transcriptomic analysis , allowing study of gene expression with a hitherto unattainable level of resolution . RNA-Seq is a powerful tool for visualising transcriptome complexity , enabling genome-wide identification of coding sequences , gene structures , alternative splicing and non-coding RNAs [26] . It can also be used to quantify transcript abundance . Digital gene expression ( DGE ) , in which 21 base tags from 3′-ends of genes are generated directly from cDNA and sequenced using NGS , is more affordable for comparative gene expression studies [54] . A previous comparison between the two techniques estimated that for 90% coverage of the human transcriptome , more than eight times as many RNA-Seq reads would be needed as compared to DGE reads [30] . In fact , the two technologies are complementary and in a transcriptomic study of a bacteria-challenged marine fish ( Lateolabrax japonicus ) , RNA-Seq was used first to identify the structure and variation in the transcriptome and DGE then used to quantify expression levels of individual genes [54] . In this study we used a variation of DGE , known as HT-SuperSAGE that generates longer 26 base tags , thus facilitating unambiguous matching of tags to gene sequences [27] . The protocol allows multiple samples to be analysed on a single lane of an Illumina flow-cell , using 4 bp ‘bar-codes’ to identify tags from different samples , thus reducing running costs . We found this an effective means of determining the transcriptional profile of more than 96% of the predicted gene set from Magnaporthe oryzae during appressorium formation . In common with many important plant pathogenic fungi , M . oryzae elaborates a specialised infection structure , the appressorium , to enable it to penetrate the host epidermis [19] . The appressorium develops from the end of a germ tube that grows from a three-celled spore , the conidium , which adheres to the surface of a rice leaf . The appressorium generates high turgor , which is used to create mechanical force to penetrate the plant cuticle and enter the underlying epidermal cells . In this study , we used HT-SuperSAGE to analyse global patterns of gene expression during appressorium formation and elucidate physiological pathways important for appressorium development and function . We analysed appressorium differentiation on artificial surfaces so that all gene expression data generated would be exclusively from M . oryzae rather than its rice host . Our rationale for doing this was because we wanted to define appressorium-associated gene expression primarily , as a first step in understanding global patterns of gene expression during plant infection by the fungus . It is technically difficult to identify M . oryzae gene expression during the early stages of rice infection due to the paucity of fungal material present compared to rice tissue . Our coverage of 96% of the predicted genes means an almost complete coverage of the M . oryzae genome that , coupled with the extreme depth provide by the NGS technologies employed , has provided for a level of statistical rigor that cannot be approached by studies using other presently available transcriptomic platforms . Additionally , this has allowed us to identify components of complete metabolic pathways of potential interest . In due course , we will need to analyse these data sets within the wider context of pathogen and host gene expression during infections on living rice plants , but it is clear that the experimental design and methods employed have allowed us to identify the most significant changes in gene expression associated with formation of a functional appressorium by M . oryzae . Germination of conidia and formation of appressoria occurs in the absence of exogenous nutrients and therefore relies on conidial storage compounds for cell growth and the synthesis of compatible solutes , such as glycerol , necessary for development of turgor in the appressorium . During appressorium development , Pmk1-dependent mobilisation of lipids and glycogen has been observed [14] . This is accompanied by an increase in triacylglycerol activity , which liberates glycerol from stored lipids . An addition , both the beta-oxidation pathway and glyoxylate cycle are important for the formation of functional appressoria [15] , [16] . Together these two pathways allow fatty acids to be broken down and the carbon units from these compounds can be used to synthesise sugars and glycerol via gluconeogenesis . Acetyl-CoA can be inferred to be an important compound in the metabolic changes that occur during appressorium formation , being the link between the beta-oxidation pathway and the glyoxylate shunt and is also needed for synthesis of melanin ( which is necessary for the generation of turgor in the appressorium ) and chitins and glucans necessary for cell wall biogenesis . The importance of acetyl-CoA in appressorium morphogenesis has been confirmed by studies showing that mutants of M . oryzae lacking carnitine acetyl transferase activity are unable to undergo appressorium-mediated plant infection [17] , [18] . Oh , et al . , [22] noted the significance of the altered expression of genes related to fatty acid catabolism and the potential importance of the peroxisome in appressorium maturation . By analysing differential expression of genes encoding enzymes that either utilise or produce acetyl-CoA , we have presented evidence here that during appressorium formation acetyl-CoA is synthesised mainly by beta-oxidation of fatty acids . Acetyl-CoA is likely used to synthesise polyketides ( particularly melanin ) and also fed into gluconeogenesis via the glyoxylate cycle . Carnitine acetyl transferase encoding genes are also differentially expressed during this process , providing further evidence of the importance of acetyl-CoA movement across peroxisomal and mitochondrial membranes during appressorium formation [17] , [18] . Consistent with the major role of the Pmk1 MAP-kinase pathway in controlling appressorium morphogenesis , is the observation that genes encoding melanin biosynthetic enzymes , the multi-functional beta-oxidation enzyme MFP1 and carnitine acetyl transferases were reduced in expression in a Δpmk1 mutant compared to Guy11 during early stages of appressorium formation . This study has also provided evidence that a large set of transporter-encoding genes is differentially expressed during appressorium formation . Sugar transporter genes and secreted oligosaccharide-degrading enzymes are up-regulated , suggesting that M . oryzae uses the appressorium to prepare for tissue invasion and use of host plant carbohydrates as a source of nutrition . In particular , we were interested to find clues to the likely major substrates used by M . oryzae during plant infection . The observation that quinate permeases are up-regulated as well as genes from the quinate utilisation cluster , strongly suggests that quinate produced in rice cells may be used by M . oryzae as a major carbon source . This is consistent with a metabolomic study which identified significant increases in quinate within blast-infected leaf tissue , suggesting pathogen-mediated alteration of host plant metabolism to increase synthesis of quinate during infection [44] . Quinate is converted to protocatechuate by three reactions , catalyzed by quinate dehydrogenase , dehydroquinate dehydratase , and dehydroshikimate dehydratase , respectively . Subsequently , protocatechuate is metabolized through the β-ketoadipate pathway . All of the M . oryzae quinate utilization genes are differentially expressed during appressorium development . Critically , dehydroquinate and dehydroshikimate are also intermediates of the shikimate pathway , which leads to branched pathways of biosynthesis of various aromatic amino acids , vitamins , and quinones , as well as plant defense compounds via the phenylproanoid pathway . Diverting the shikimate pathway to produce quinate , for uptake and metabolism by M . oryzae , provides a means of potentially suppressing plant defense . Recent evidence has shown that such metabolic priming may play a significant role in effector-mediated suppression of plant defenses [55] . In the corn smut fungus Ustilago maydis , for instance , a chorismate mutase is deployed by the fungus to reduce salicylic acid biosynthesis [55] . M . oryzae expresses an isochorismatase that might also serve such a purpose ( Table S2 ) , as well as possessing a chorismate mutase . However , the systematic , co-ordinated regulation of quinate permeases and quinate metabolic enzymes provides strong evidence for an effective means of suppressing plant defense and fueling fungal growth by the rice blast fungus that will need to be tested by gene functional analysis . Another striking observation was the up-regulation of a wide range of sugar transporters and putative efflux pumps and ABC transporters during appressorium maturation . This fact points to large-scale deployment of fungal secondary metabolites during plant tissue colonization and utilization of a significant family of membrane-bound pumps to contend with corresponding plant defense compounds . The repertoire is likely to be distinct from those used by M . oryzae during mycelial growth given the extensive pattern of differential gene expression . The analysis presented in this paper has highlighted a group of hydrophobic surface binding proteins of the same family as HsbA from A . oryzae [53] that show significant up-regulation of expression during appressorium development , suggesting they may play an important role in this process , potentially by recruiting hydrolytic enzymes to the fungal cell surface . This study has demonstrated the value of NGS sequencing technologies in studying gene expression during a morphogenetic process that is vital for fungal pathogenesis . We have used this data to create a publicly available resource that can be accessed at http://cogeme . ex . ac . uk/supersage/ , providing the means for any M . oryzae gene to be readily interrogated for its expression profile during infection-related development . M . oryzae strains Guy11 [56] and Δpmk1 [11]were used in this study . For RNA preparation , mycelia were grown in shaking culture in complete medium , CM [24] for 36 h at 25°C , 200 rpm and harvested by filtering through 3 layers of Miracloth ( EMD Biosciences ) , washed and frozen in liquid nitrogen . For growth in glucose minimal medium ( GMM ) , mycelia growing in CM for 36 hrs were washed , transferred to GMM , grown for an additional 16 hrs and harvested as above . Conidia were harvested from 14-day old CM agar plates and washed three times with sterile water . For germination , conidia were diluted in sterile water to 7 . 5×105 conidia / ml in the presence of 50 ng/µl 1 , 16-Hexadecanediol . This solution was then used to flood plastic coverslips ( Cole-Parmer ) previously glued to square petri plates ( Greiner Bio One ) . Formation of appressoria was monitored under a light microscope and samples were collected at 4 , 6 , 8 , 14 and 16 h by scraping the surface of the coverslips with a sterile razor blade . Recovered samples were immediately frozen in liquid nitrogen , lyophilized and stored at −80°C until needed . Total RNA was extracted from mycelia or germinating conidia using the Qiagen RNeasy Plant Mini kit according to manufacturer's instructions . RNA was eluted in RNase-free water and checked for integrity and quantity on an Agilent 2100 Bioanalyzer according to manufacturer's instructions . RNA with integrity number of at least 6 . 5 was used for library preparations . RNA was prepared from at least two biological replicates and used for independent library preparations . Sequencing libraries were prepared using mRNA-Seq Sample Preparation kit from Illumina from 9 µg of total RNA according to the manufacturer's instructions . Libraries were quantified and checked for quality on Agilent 2100 Bioanalyzer using a DNA 1000 chip kit . Each library was diluted to 10 nM in Elution Buffer ( Qiagen ) and used for sequencing using an Illumina Genome Analyser GX II platform . Individual sequencing libraries were prepared for germinating conidia at each individual time point as well as mycelia grown in CM and GMM ( two biological replicates for each sample ) . Additional libraries were also prepared for germinating conidia from the Δpmk1 mutant harvested at 4 hr after plating . These libraries were prepared from 10 µg total RNA according to the method described previously [27] with minor modifications . Libraries of tagged cDNA fragments were PCR amplified using Hot Start Phusion DNA polymerase and GEX-1 and -2 primers ( Table S13 ) for 15 cycles according to the following parameters; initial denaturation at 98°C for 1 min followed by 15 cycles of 98°C for 10 sec; 62 . 5°C for 20 sec; 72°C for 30 sec and a final extension at 72°C for 2 min . PCR products were ethanol precipitated , re-suspended in 25 µl LoTE and size separated on 8% non-denaturing polyacrylamide gels using TAE buffer . Products were visualised by ethidium bromide staining and 123–125 bp sized products were excised from the gel . Products were then extracted from gel in EB , quantified on Agilent Bioanalyzer 2100 using DNA 1000 chip kit and adjusted to 10 nM final concentration in EB . Products were ligated to indexed Adapters-1 ( Table S13 ) . These adapters contain defined index sequences for sample identification ( Table S14 ) , enabling four samples to be analysed per lane of an Illumina flow cell . Four libraries thus prepared were pooled and used for sequencing as with the RNA-Seq libraries . Tophat software [57] was used to align short reads to the published genome of Magnaporthe oryzae , version 6 ( http://www . broadinstitute . org/annotation/genome/magnaporthe_grisea/MultiHome . html ) [3] and to predict exon splice sites . Cufflinks software [58] was used to analyse this data ( using reads with a mapping quality >30 ) from both biological replicates along with gene annotations from M . oryzae , resulting in normalised counts ( expressed in fragments per kilobase of exon model per million mapped fragments – FPKM ) for each gene . The Cuffdiff component of the Cufflinks package was used to look for significant differences in FPKM between different samples . FASTX Barcode Splitter from the FASTX-Toolkit ( http://hannonlab . cshl . edu/fastx_toolkit/download . html ) was used to separate the samples from the same lane using the four base barcode . FASTA/Q trimmer from the FASTX-Toolkit was used to remove the 4 base barcode from the sequence and then to remove the sequence from position 27 to the end of the sequence leaving a 26 base tag sequence . Any remaining adapter sequences were removed using FASTA/Q clipper from the FASTX-Toolkit . FASTQ-to-FASTA from the FASTX-toolkit was used to convert the tag sequences to a FASTA format . The frequency of each tag was calculated using custom perl scripts . Tags were mapped to predicted transcripts from the published genome of Magnaporthe oryzae ( version 6 ) using Bowtie [59] , allowing one base mismatch . For each transcript , the frequencies of all the tags mapped to that gene were summed . Statistical analysis or data was performed using DESeq [60] . Transcript abundances for each gene were expressed as a weighted mean of counts from each replicate normalised to overall library size ( known as ‘base mean’ ) . P-values ( adjusted for false discovery rate ) were generated for each gene in pair-wise comparisons between different condtions . In our analyses , we used an adjusted P-value of < = 0 . 05 as a criteria for identifying significant differences in gene expression . HT-SuperSAGE data ( raw counts and TPM ) obtained from Guy11 mycelium grown in CM , time course of appressorium development in Guy11 and Δpmk1 mutant conidia left to germinate for 4 hours was stored in a MySQL database . A publicly available web-based front end was constructed for this database which can be accessed at http://cogeme . ex . ac . uk/supersage/ . HT-SuperSAGE and RNA-Seq data described in this paper has been submitted to Gene Expression Omnibus ( GEO ) at NCBI ( http://www . ncbi . nlm . nih . gov/geo/ ) , accession numbers GSE30069 , GSE30256 , GSE30327 .
The fungus Magnaporthe oryzae causes a disease of rice , known as rice blast . Half the world's population depends on rice as a staple food source and rice blast disease destroys 18% of the rice harvest annually . It is therefore important to develop methods to control blast as a means of ensuring global food security . The rice blast fungus spreads rapidly from infected to uninfected plants using a spore known as a conidium . When a conidium lands on the surface of a rice leaf , it develops a specialised structure called an appressorium which is used to penetrate the tough outer cuticle of the rice leaf , enabling the fungus to enter plant tissue . In this study , we have used new sequencing technologies to identify genes that are actively expressed during appressorium formation by looking at relative levels of their transcripts . We have also compared levels of gene expression in a wild-type strain of the fungus to a mutant that is unable to make appressoria and therefore cannot infect plants . The study has enabled us to identify key metabolic processes that are activated during appressorium formation and to understand how fungal metabolism and physiology are dramatically altered during infection-related development .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "developmental", "biology", "plant", "science", "plant", "biology", "biology", "genomics", "microbiology", "genetics", "and", "genomics" ]
2012
Genome-wide Transcriptional Profiling of Appressorium Development by the Rice Blast Fungus Magnaporthe oryzae
Incidences of infection-related cancers are on the rise in developing countries where the prevalence of intestinal nematode worm infections are also high . Trichuris muris ( T . muris ) is a murine gut-dwelling nematode that is the direct model for human T . trichiura , one of the major soil-transmitted helminth infections of humans . In order to assess whether chronic infection with T . muris does indeed influence the development of cancer hallmarks , both wild type mice and colon cancer model ( APC min/+ ) mice were infected with this parasite . Parasite infection in wild type mice led to the development of neoplastic change similar to that seen in mice that had been treated with the carcinogen azoxymethane . Additionally , both chronic and acute infection in the APCmin/+ mice led to an enhanced tumour development that was distinct to the site of infection suggesting systemic control . By blocking the parasite induced T regulatory response in these mice , the increase in the number of tumours following infection was abrogated . Thus T . muris infection alone causes an increase in gut pathologies that are known to be markers of cancer but also increases the incidence of tumour formation in a colon cancer model . The influence of parasitic worm infection on the development of cancer may therefore be significant . Colon cancer is one of the leading causes of death within the western world and prevalence in developing countries has increased in the last decade [1] . There exists a strong link between inflammation and cancer [2] . This is emphasized in the colon where individuals with inflammatory bowel disease are predisposed to the development of colorectal cancer [3–5] . Furthermore , chronic infection and the resultant long-term exposure to inflammatory stimuli heighten the risk of neoplastic change . A number of chronic bacterial , viral and parasitic infections are associated with predisposition to neoplasia; Helicobacter pylori is associated with gastric cancer [6] , Hepatitis B and C with liver cancer [7] , Clonorchis sinensis with cholangiocarcinoma [8] and Schistosome infection with bladder cancer incidence [9] . Gastrointestinal worms , comprising Ascaris lumbricoides , Trichuris trichiura , Necator americanus and Ancylostoma duodenale species infect over 2 billion people worldwide and account for considerable morbidity and a loss of 5 . 2 million DALYS [10–12] . Individuals in endemic areas build up chronic infections due to repeated exposure with few people completely resolving infection . This chronic insult on the intestine is associated with intestinal inflammatory changes and it is now well understood that gut dwelling nematodes can manipulate the immune system ( reviewed in [13 , 14] ) . Indeed , the therapeutic potential of worms in IBD [15 , 16] , allergy [17 , 18] and inflammatory disease are apparent [19] . Trichuris muris ( T . muris ) is a natural parasite of mice and is extensively utilised as a laboratory model for the study of human whipworm infection , T . trichiura [20 , 21] . In susceptible hosts , the persistence of T . muris in the large intestine is characterised by the development of a strong type 1 ( Th1 ) response , dysregulation of epithelial homeostasis and upregulation of inflammatory cytokines [22 , 23] . The generation of crypt cell hyperplasia is driven by an expansion of the proliferative compartment of the intestinal epithelium and is under immune control . The intestinal pathology associated with chronic T . muris infection closely resembles that seen in Crohn’s disease in humans and in human trichuriasis and is under the control of the regulatory cytokine IL-10 [24 , 25] . Given the heightened risk of IBD patients to colorectal carcinoma due to intestinal dysplasia and other genetic factors [26] and the global prevalence of intestinal helminth infection , it is clear that the nematode-neoplasia link warrants investigation . Here we assess the effects of a natural model of chronic intestinal helminth infection on the development of intestinal neoplasia . Experiments were performed under the regulations of the Home Office Scientific Procedures Act ( 1986 ) , Project licence 70/8127 and subject to review by the University of Manchester Animal Welfare and Ethical Review Body ( AWERB ) . The experiments conform to the ARRIVE guidelines . Male wild type ( WT ) C57BL/6 mice aged 6–8 weeks were purchased from Envigo , U . K . APCmin/+ mice were obtained from the Paterson Institute , Christie Hospital , Manchester , U . K . for initial d42 post infection ( p . i . ) studies , then from Birmingham University for all subsequent studies . C57BL/6 animals were used at 6–8 weeks of age and were housed for 7 days prior to experimentation . Both sexes of APCmin/+ C57BL/6 mice were housed in the same facilities and infected at 12 weeks of age with group sizes of 8–12 mice . All animals were euthanized using a rising concentration of CO2 . MLN and spleen cells were removed , cultured and restimulated for 24 hours under conditions previously described [27] . We measured concentrations of TNF-α , IFN-γ , IL-6 and IL-10 in the culture supernatants using a cytokine bead assay ( CBA , BD Biosciences , UK ) performed according to the manufacturer’s instructions . ELISA plates were coated with 5 μg/ml of overnight E/S in 0 . 05 M carbonate/bicarbonate buffer , pH 9 . 6 and incubated overnight at 4°C . Plates were blocked for 1 hour with 150 μl PBS/Tween-20 ( PBST ) , 3% BSA at room temperature . Eight serial two-fold dilutions of sera in PBST were conducted from 1/20 to 1/2560 and transferred to the ELISA plates ( 50 μl/well ) for 90 minutes at room temperature . Parasite specific IgG1 and IgG2a were detected using biotinylated rat-anti mouse antibodies ( Pharmingen , UK and Serotec , UK respectively ) diluted in PBST , 50 μl/well for 1 hour at room temperature . Streptavidin peroxidase was added at 75 μl/well for 1 hour and ABTS substrate was added at 100 μl/well . Plates were read after approximately 20 minutes at 405nm on a VersaMax microplate reader ( Molecular devices , UK ) . Proportions of CD4+ , CD25+ and FoxP3+ cells in the MLN and spleen of WT animals were analysed using flow cytometry at day 80 p . i . FITC anti-CD3 ( ε ) in combination with streptavidin-allophycocyanin , ( Caltag Laboratories , Burlingame , CA ) FoxP3 and a CD25 FL3 ( PharMingen ) were used for surface marker staining . Cells were analysed using CellQuest Pro software ( BD Biosciences , UK ) . In subsequent APCmin/+ mice experiments , cells were analysed on a MACSQuant ( Miltenyi Biotec , UK ) using FITC-CD4 , PE-CD25 and APC-FoxP3 ( BD Biosciences , UK ) . Both small and large intestine were removed from animals at autopsy , gently flushed out using saline , slit longitudinally and cut into 2 cm pieces . Sections were pinned out on wax coated petri dishes with the luminal face of the intestine facing upwards , and fixed with 4% formaldehyde for 24 hours . Sections of intestine were stained whilst pinned out with methylene blue to allow the visualisation of tumours ( 5 minutes at room temperature ) . For regional analysis of tumour burden in the small intestine , the intestine was divided into 3 equal length sections-from the duodenum to the ileum and labelled SA to SC respectively . Within each region , the intestine was divided into 2 cm pieces , which were assessed for tumour burden . The area of tissue , number and size of tumours was determined using a computer assisted Zeiss Axiohome™ microscope system under x 40 magnification . Samples of cecum were removed and flushed out using saline . Samples were fixed intact in carnoy’s fixative for 30 minutes prior to storage in 70% ethanol . Tissues were prepared using the gut bundle technique [28] . Tissues were then paraffin embedded using standard histological techniques and 3μm sections were cut . Sections were stained with haematoxylin and eosin ( H&E ) to allow the visualisation of apoptotic cells . Such cells are detected on the basis of their morphology using light microscopy , a method that has been used extensively [29–32] . Typically , apoptotic cells appear pink , circular , with crescent shaped nucleus , and are bubbled up out of the plane of focus . TUNEL labelling is another method which has can be used to detect apoptosis in the intestinal epithelium . However , this technique is prone to false positive and false negative results when compared with morphological assessment , as well as failing to distinguish between DNA cleaved by apoptosis and DNA fragments cleaved by other processes [33] . For the purpose of this investigation , therefore , morphological analysis was deemed the most reliable method to use . Groups of 4 mice were treated by i . p . injection with 10mg BrdU ( Sigma , Poole , U . K . ) 40 minutes prior to sacrifice . All animals were killed at the same time- within and between experiments to minimise any differences in proliferation attributable to variation in circadian rhythm . Detection of nuclei that had incorporated BrdU was performed by immunohistochemistry , using a monoclonal anti-BrdU antibody ( Mas 250b , Harlan Sera Laboratories , Loughborough , U . K . ) as described [34] . Sections were analysed by scoring 50 caecal crypts per mouse , 4 mice per group . Full-length longitudinal sections of crypts were selected for analysis . The blinded scoring commenced with the cell at the mid-point at the base of the crypt , which was designated as position 1 and continued until the crypt-crypt table was reached . This method of scoring allows the generation of statistically valid results [34] and was used to determine the levels of apoptotic and proliferating cells . In this way both the position and overall numbers of apoptopic or proliferating cells in the cecum can be determined . Area of epithelium was assessed using the computer assisted Zeiss Axiohome™ Microscope system to mark around the area of interest . Analysis was performed on H&E stained sections , 4 mice per group , 3–4 circumferences per mouse . Circumference of the lumen was subtracted from circumference of the muscularis to give the area of epithelium . Individual crypt length and widths were determined using the same system , selecting well-orientated crypts and measuring from the base of the crypt to the lumen for crypt length , and the widest area of the crypt for width analysis . 50 crypts per mouse were measured , 4 mice per group . Aberrant crypts were detected on the basis of their morphology on H&E stained cross sections of intestine as described [35] . A scoring system was devised to assess the severity of aberrant crypts , aberrant crypt foci [multiple aberrant crypts] , epithelial hyperplasia and adenoma formation . Scores were assigned on the basis of number of aberrant crypts , the number aberrant crypt foci ( clusters ) per circumference as well as the degree of area of epithelium affected . A score of 0 indicates no detectable change and 4 the highest level of severity . Statistical analysis was performed using Students t test . A value of p<0 . 05 was considered to be significant . Here we demonstrate that low-level chronic T . muris infection promoted the development of intestinal neoplasia to a level that is comparable to that induced by a chemical carcinogen . Moreover , the observation that T . muris increased the neoplastic change seen in AOM treated mice and promoted tumour formation in APCmin/+ mice identified that gut dwelling nematode infection can induce simultaneous activation of local and systemic dysplasia . We have also identified that the T . muris induced Treg response that accompanies infection may negatively influence the neoplastic change in WT mice and tumour development in APCmin/+ mice . Genetic changes such as activated oncogenes or altered tumour suppressor genes ( such as APC ) in tumour cells are responsible for many aspects of neoplasia , indeed , over 80% of colorectal cancer cases are proposed to be due to the loss of APC [50] . It is now established that an inflammatory environment also plays a role [2 , 51 , 52] . During chronic T . muris infection in C57BL/6 animals there is intestinal inflammation , a large influx of inflammatory cells into the intestine and elevated levels of pro-inflammatory cytokine production in the MLN and spleen ( Fig 1A–1D ) . This infection-induced inflammation may be promoting the development of epithelial neoplasia in these mice ( Fig 1E–1G ) and indeed , other intestinal infections have been shown to promote tumour formation in an inflammation dependent manner [53] . In order to quantify the neoplastic change seen with infection we used the AOM model of intestinal cancer . T . muris infection induced an increase in aberrant crypt foci and in hyperplasia as compared to AOM alone ( Fig 2C and 2D ) . However , infection and AOM in combination did not show additional changes over infection alone . Thus we can conclude that T . muris infection initiates neoplastic changes in the gut that are significantly increased when compared to those seen with a commonly used chemical carcinogen . To assess the effect of T . muris on a model of spontaneous neoplastic change rather than chemical induced tumours , we used the APCmin/+ mouse model of colon cancer . Interestingly , T . muris , a nematode that resides in the large intestine , was able to exacerbate intestinal neoplasia throughout the intestinal tract in these animals ( Fig 3A–3F ) . This clearly demonstrates that a caecal nematode infection can potentiate neoplasia in both a localized and systemic manner . Even within 18 days of infection , significant changes were seen within the lower ileum and in the number of smaller tumours found . This progressed to significant changes seen throughout the small intestine by day 42 p . i . with even more size categories of tumours affected . The finding that the greatest increase in tumour number was in tumours of the smaller size category ( Fig 3G ) strongly suggested that T . muris infection was acting to promote new tumour formation rather than enhancing the growth of well differentiated preexisting tumours . There was no difference between mean tumour size in naïve and infected animals ( Fig 3C & 3D ) , again supporting the hypothesis that infection does not significantly affect the growth of pre-existing tumours . T . muris is known to cause epithelial dysregulation in the intestine with increased epithelial proliferation and apoptosis [22 , 47] , both mechanisms which could lead to tumour formation [54] . However , changes in these mechanisms were only found within the caecum , the parasite niche , and not in the small intestine , which is the site of most neoplastic change ( Fig 3H & 3I ) . Therefore , although epithelial homeostasis may play an important role in the development of worm-induced dysplasia in the large intestine , it appears to have minimal impact in the small intestine . Typical inflammatory cytokines associated with chronic T . muris infection were seen in both the MLN and spleen of infected APCmin/+ mice ( S2 Fig ) . This complements studies by Rao et al [53] that demonstrates that H . hepaticus infection promotes tumour development both locally in the intestine as well as systemically in mammary tissue in APCmin/+ mice due to inflammatory cytokine production and studies on the cytokine microenvironment in these mice [55] . Furthermore , the administration of dextran sulphate sodium ( DSS ) to APCmin/+ mice exacerbates adenoma formation , highlighting the importance of intestinal inflammation in promoting adenoma formation in this system [56] and the ablation of inflammatory cytokines leads to a decrease in adenomas [57] . Additionally , an increased pro-inflammatory cytokine production seen in T . muris infected mice over naïve mice treated with AOM alone may explain the increased neoplastic change ( S1 Fig ) . However , a T . muris infection also promotes a robust Treg response that protects the host from damage [48] . Indeed , a key cytokine produced by Treg cells , IL-10 , is critical in host survival during T . muris infection [37] . The successful use of T . muris to counter allergy and to protect against colitis in mouse models has been postulated to be due to this induced Treg response and its ability to immune modulate . In other systems , immune suppression can promote cancer through the down regulation of the anti-tumour immune response [reviewed in [49]] although paradoxically , in colon cancer Tregs are found to play a protective role [58–60] and this may be down to the type of Tregs that are found [61] or indeed the balance of cytokine production and Tregs [62] . Using anti-CD25 monoclonal antibody treatment to depress the number of Treg cells in vivo during the course of T . muris infection significantly reduced the number of CD4+CD25+FoxP3+ cells in the MLN and spleen of treated APCmin/+ mice . In the isotype treated animals , infection increased the number of tumours ( Fig 4C ) in the mice as demonstrated previously ( Fig 3A & 3B ) . However , the mean tumour area ( Fig 4D ) and total tumour area ( Fig 4E ) was also increased as compared to the previous study where it was unchanged after infection ( Fig 3C & 3D ) suggesting an effect of T . muris on the growth of the tumours rather than initiation . It is worthwhile to note that this colony developed a significantly higher number of tumours and showed clinical signs of disease much earlier than the previous colony suggesting an earlier advancement of the disease . This raises the interesting question of whether the timing of infection in the context of tumour development is important . It was clear however that T . muris infection still induced neoplastic change . In contrast , there were no differences in any neoplastic change readout between the infected and naïve groups of the anti-CD25 treated mice , supporting a role for Tregs in suppressing tumour control in infected mice . Anti-CD25 treatment of naïve animals did increase the numbers of tumours and the mean tumour area as compared to isotype treated animals suggesting a role for CD25+ cells in protection against spontaneous neoplastic change in the APCmin/+ mouse . The role of Tregs in APCmin/+ is complex and findings differ between studies [63–66] . This may in part be due to the phenotype of the T reg present and the microenvironment [61 , 62] and would certainly warrant further investigation in the context of T . muris infection . Regardless of the effects in naïve mice , the data here strongly supports a role for parasite-infection induced CD25+ T cells in suppressing anti-tumour immunity . To confirm whether observations were specific to T . muris infection or a reflection of intestinal helminth infection in general , APCmin/+ mice were infected with H polygyrus . H . polygyrus is a small intestinal dwelling parasite that presents as a chronic primary infection and at day 28 p . i . does not induce a marked CD4+CD25+FoxP3+ response ( S3 Fig ) [67] . This parasite model had the added benefit of allowing assessment of any neoplastic change as a result of mechanical damage by the worm at the site where neoplastic changes were evident . H . polygyrus did not induce any significant changes in any of the parameters assessed . Additionally H . polygyrus did not elicit as strong a proinflammatory environment in the MLN or spleen as observed with T . muris ( S4 Fig ) . Whether a more prolonged infection would result in such changes requires further investigation . The importance of the T . muris induced Treg response being detrimental , in terms of tumour control , to the host is important as the regulatory response induced by the parasite has been suggested to be beneficial by controlling colitis and allergy in mice [15–17] . We propose that this infection induced Treg response actually has detrimental consequences for both WT mice and APCmin/+ mice . The promotion of neoplasia by T . muris has important connotations given that 600 million people [11] harbor chronic infection with this genus . Ultimately , the impact of such infections warrant further investigation , particularly when considering the rising trend of cancer prevalence and , in particular , infection-induced cancers in developing countries [68 , 69] .
It is estimated that now 2 billion people currently live with chronic parasitic worm infections . As the incidences of cancer increase worldwide , the importance of these chronic inflammatory conditions on the development of cancer becomes more important . Several bacterial , viral and parasitic infections are already known to influence cancer development but as colon cancer is particularly prevalent worldwide , we wanted to assess the effect of a large intestinal dwelling worm , Trichuris muris ( T . muris ) on its aetiology . This whipworm is a natural infection of mice and has significant homology to human whipworm . From our studies , we showed that chronic infection alone induced changes in the caecum of the mouse that were comparable to those seen with a well-known carcinogen . In addition to this , T . muris infection was also able to increase the development of adenomas in the small intestine of mutant mice that spontaneously develop tumours . This change was abrogated if a T regulatory cell type was blocked during infection . The T regulatory cell type that arises during infection has been shown to play an important role in protecting the host from damage caused by the parasite and the immune response to it . The present study using the mouse model however , suggests that regulatory T cells can have negative effects , at least in terms of the development of bowel cancer . As so many people live with chronic , regulated parasitic infections , the importance of the parasites in cancer development may therefore be significant .
[ "Abstract", "Introduction", "Materials", "and", "methods", "Results" ]
[ "innate", "immune", "system", "medicine", "and", "health", "sciences", "immune", "physiology", "cytokines", "cancer", "treatment", "immunology", "parasitic", "diseases", "nematode", "infections", "oncology", "animal", "models", "developmental", "biology", "model", "organisms", "gastroenterology", "and", "hepatology", "experimental", "organism", "systems", "molecular", "development", "digestive", "system", "research", "and", "analysis", "methods", "parasitic", "intestinal", "diseases", "mouse", "models", "gastrointestinal", "infections", "immune", "system", "gastrointestinal", "tract", "anatomy", "physiology", "biology", "and", "life", "sciences" ]
2017
Chronic Trichuris muris infection causes neoplastic change in the intestine and exacerbates tumour formation in APC min/+ mice
Chagas disease , caused by Trypanosoma cruzi , is endemic in southern parts of the American continent . Herein , we have tested the protective efficacy of a DNA-prime/T . rangeli-boost ( TcVac4 ) vaccine in a dog ( Canis familiaris ) model . Dogs were immunized with two-doses of DNA vaccine ( pcDNA3 . 1 encoding TcG1 , TcG2 , and TcG4 antigens plus IL-12- and GM-CSF-encoding plasmids ) followed by two doses of glutaraldehyde-inactivated T . rangeli epimastigotes ( TrIE ) ; and challenged with highly pathogenic T . cruzi ( SylvioX10/4 ) isolate . Dogs given TrIE or empty pcDNA3 . 1 were used as controls . We monitored post-vaccination and post-challenge infection antibody response by an ELISA , parasitemia by blood analysis and xenodiagnosis , and heart function by electrocardiography . Post-mortem anatomic and pathologic evaluation of the heart was conducted . TcVac4 induced a strong IgG response ( IgG2>IgG1 ) that was significantly expanded post-infection , and moved to a nearly balanced IgG2/IgG1 response in chronic phase . In comparison , dogs given TrIE or empty plasmid DNA only developed high IgG titers with IgG2 predominance in response to T . cruzi infection . Blood parasitemia , tissue parasite foci , parasite transmission to triatomines , electrocardiographic abnormalities were significantly lower in TcVac4-vaccinated dogs than was observed in dogs given TrIE or empty plasmid DNA only . Macroscopic and microscopic alterations , the hallmarks of chronic Chagas disease , were significantly decreased in the myocardium of TcVac4-vaccinated dogs . We conclude that TcVac4 induced immunity was beneficial in providing resistance to T . cruzi infection , evidenced by control of chronic pathology of the heart and preservation of cardiac function in dogs . Additionally , TcVac4 vaccination decreased the transmission of parasites from vaccinated/infected animals to triatomines . Trypanosoma cruzi ( T . cruzi ) is a pathogenic protozoan that belongs to the Trypanosomatidae family . It is the etiologic agent of Chagas disease [1] . Approximately , 5% of the infected humans develop a lethal acute infection , and 30–40% progress to a chronic debilitating illness of the cardiac system , characterized by clinically irreversible and progressive myocardial hypertrophy and tissue destruction that eventually leads to heart failure [1] . It is an important health issue in most of the Latin American countries and due to human migration; it has become an important health issue in the United States and Europe [2] . Vector control programs have not been able to completely prevent parasite transmission [3]; the available anti-parasite drugs are not sufficiently safe or effective [4 , 5]; and no vaccines are currently available . Several investigators have shown the potential utility of T . cruzi surface antigens as vaccine candidates in mice and dogs ( reviewed in [6 , 7] ) . Our group has performed computational screening of T . cruzi sequence databases reported in GenBank , and identified genes encoding glycosylphosphatidylinositol ( GPI ) -anchored proteins TcG1 , TcG2 and TcG4 as potential vaccine candidates . These antigens were chosen after an unbiased computational/bioinformatics screening of the T . cruzi genome sequence database that led to the identification of 11 potential candidates [8]Through rigorous analysis over a period of several years , we determined that three candidates ( TcG1 , TcG2 , TcG4 ) were maximally relevant for vaccine development [9] . These three candidates were phylogenetically conserved in clinically important T . cruzi strains , expressed in infective and intracellular stages of the parasite [8 , 9] , and recognized by immunoglobulins and CD8+T cells in multiple T . cruzi-infected hosts [9 , 10] . When individually delivered as a DNA-prime/DNA-boost vaccine along with adjuvants ( IL-12- and GM-CSF-encoding plasmids ) in mice , these antigens elicited a significant trypanolytic antibody and Th1 cytokine ( IFN-γ ) response , a property that has been associated with immune control of T . cruzi [8] . Co-delivery of these antigens as DNA vaccine ( TcVac1 ) induced additive immunity and higher degree of protection from T . cruzi infection than was observed with single vaccine candidates in mice [9] . When tested in dogs , TcVac1 elicited a parasite-specific IgM and IgG ( IgG2>IgG1 ) response but phagocytes’ activity was suppressed resulting in parasites’ escape and dissemination to tissues [10] . Consequently , TcVac1-immunized dogs moderately controlled the chronic parasite persistence and histopathologic cardiac alterations , and remained infective to triatomines [10] . Recent studies have tested several other antigenic candidates as DNA vaccine for their prophylactic and therapeutic efficacy against Chagas disease [11 , 12] . Results of these vaccines are encouraging . However , till to date no anti-T . cruzi vaccine has reached the expected results of producing sterile immunity in dogs . In this study , we chose to test the protective efficacy of a DNA-prime/inactivated T . rangeli-boost vaccine ( TcVac4 ) against T . cruzi infection and Chagas disease in dog model . The use of heterologous DNA-prime/inactivated microorganism-boost vaccine [13] or inactivated microorganism-prime/DNA-boost vaccine [14] has been previously reported with promising results . We included inactivated T . rangeli as a booster vaccine dose for several reasons: One , T . cruzi lysates have been previously tested and shown to provide limited or no protection . Though reason for inefficacy of a T . cruzi epimastigote-based vaccine is not known , it is likely that diversity in the protein expression pattern in epimastigote versus infective/intracellular stages of T . cruzi and the presence of large family of proteins ( e . g . trans-sialidase and mucins ) may result in a lack of protective immunity . Two , T . rangeli exhibits significant homology ( >60% ) with T . cruzi proteome [15 , 16] but is non-pathogenic for mammals [17 , 18] and , thus , require no specific biosafety lab facility for culturing in large batches . Three , mice immunized with glutaraldehyde-fixed T . rangeli elicited B and T responses that recognized T . cruzi antigens [19 , 20] . Consequently , T . rangeli-immunized mice were equipped to control challenge infection with T . cruzi evidenced by a significant reduction in mortality and parasitemia , and absence of histopathological lesions [19 , 20] . T . rangeli based vaccine was also tested in dogs with positive results; dogs immunized with glutaraldehyde-inactivated T . rangeli epimastigotes exhibited reduced parasitemia after challenge infection with T . cruzi , and subsequently were less infective to triatomines as compared to controls [21] . We discuss the protective immunity to TcVac4 in dogs and potential utility of this vaccine composition in controlling parasite dissemination in the domestic cycle of transmission . Twenty-one mongrel dogs ( 10 males and 11 female , 3–4 months old ) were acquired locally and kept in the animal facility at the UAEM Research Center . Animals were included in the experiment when they were > 8-months old ( weight: 8–12 kg ) . All dogs were confirmed to be free of cardiac abnormalities by electrocardiography ( EKG ) and free of T . cruzi infection by microscopic examination of blood smears and serological evaluation of anti-T . cruzi antibodies using an enzyme-linked immunosorbent assay ( ELISA ) [10] . During the adaptation period , dogs were vaccinated against the regional infectious diseases ( Canine distemper , Parvovirus infection , Canine hepatitis , Leptospirosis , and Rabies ) and treated against worms . Animals received commercial dog food , according to their physiologic development and water ad libitum . All experimental protocols were conducted under the technical specifications for the production , care and use of laboratory animals from the Norma Oficial Mexicana ( NOM-62-ZOO-1999 ) [22] , and the council for international Organizations of Medical Science . Dogs were sedated and euthanized according to the Norma Oficial Mexicana ( NOM-033-Z00-1995 ) [23] . All protocols were approved by the Laboratory Animal Care Committee at the Facultad de Medicina Veterinaria y Zootecnia of the Universidad Nacional Autónoma de México ( UNAM ) . Trypomastigotes of T . cruzi ( SylvioX10/4 ) were maintained and propagated by continuous in vitro passage in C2C12 cells . Animals were immunized with DNA-prime/inactivated T . rangeli-boost ( TcVac4 ) vaccine . For the DNA vaccine , cDNAs for TcG1 , TcG2 and TcG4 ( SylvioX10/4 isolate , Genbank: AY727914 , AY727915 and AY727917 , respectively ) were cloned in the eukaryotic expression plasmid pcDNA3 . 1 [8] . Plasmids encoding canine interleukin ( IL ) -12 ( p40 and p35 subunits fused to express heterodimeric protein ) and canine granulocyte- macrophage colony stimulating factor ( GM-CSF ) have been previously described [10] . Recombinant plasmids were transformed into E . coli DH5-alpha-competent cells , grown in LB-broth containing 100-μg/ml ampicillin , and purified by anion exchange chromatography using the Qiagen maxi prep kit ( Qiagen , Chatsworth , CA ) . For booster doses , epimastigotes of T . rangeli ( Guatemala strain , a kind gift from Dr . Jorge Ricardez Esquinca at Facultad de Medicina Humana , Universidad Autónoma de Chiapas , México ) were maintained in axenic culture and propagated in LIT media [24] . T . rangeli epimastigotes ( 1x109/ml ) were inactivated with 0 . 1% glutaraldehyde solution ( Sigma-Aldrich ) , washed thrice with PBS , and emulsified in PBS containing 500-μg/mL saponin adjuvant ( Sigma-Aldrich ) [21] . To evaluate the prophylactic efficacy of TcVac4 against acute T . cruzi infection and Chagas disease , we randomly assigned dogs to the following groups: a ) pcDNA3 . 1/no Tc ( empty plasmid DNA and no challenge infection , n = 6 ) ; b ) pcDNA3 . 1/Tc ( empty plasmid followed by Tc challenge infection , n = 6 ) ; c ) TcVac4/Tc ( two doses of DNA vaccine followed by two doses of TrIE and challenge infection , n = 6 ) ; and d ) TrIE/Tc ( two doses of TrIE followed by challenge infection , n = 3 ) . Each dose of DNA vaccine was delivered at two sites; intramuscular ( 180 μg each DNA in 0 . 9 ml PBS ) and intradermal ( 20 μg each DNA in 0 . 1 ml PBS ) . TrIE vaccine was also delivered at two sites; subcutaneous ( 0 . 9x109 TrIE in 0 . 9 ml PBS-saponin ) and intradermal ( 1x108 TrIE in 0 . 1 ml PBS- saponin ) . All vaccine doses were given at 15 days interval , and challenge infection with T . cruzi ( 3 . 5x103 culture-derived trypomastigotes/kg body weight , intraperitoneally ) was performed six-weeks after the last vaccine dose . The selected dose of the parasites was sufficient to produce acute parasitemia within 1–2 weeks of inoculation , and electrocardiographic changes within 6–8 weeks post-infection [10 , 25] . All dogs were observed daily for general physical condition , at weekly intervals for clinical condition , and at 2-week intervals for cardiac function . Blood samples were collected beginning day 5 pi , on alternate days up to 50 days pi; and at two-week intervals thereafter . Parasitemia was measured using hemocytometer counts of 5 μl blood mixed with equal volume of ACK red blood cell lysis buffer . Xenodiagnostic analysis was performed as previously described [10] . Briefly , stage 4 naive triatomines ( M . longipennis ) were fed on dogs ( 6 nymphs per dog ) from all treatment groups on days 35 pi . Fecal samples from triatomines were collected at day 60 after feeding , and analyzed by light microscopy to detect epimastigote and/or metacyclic trypomastigotes . One hundred microscopic fields were analyzed for each fecal sample , and triatomines were considered T . cruzi positive when at least one parasite was detected . Sera samples were obtained before immunization and at two-week intervals after each immunization and challenge infection . Flat bottom 96-well plates ( High binding , Costar ) were coated overnight with soluble fraction of T . cruzi SylvioX10/4 trypomastigotes lysate ( 5-μg protein/100-μl/well , diluted in NaHCO3 solution , pH 9 . 6 ) . Plates were washed , and then incubated at 37°C for 2 h each with sera samples ( 1:100–1:1000 dilution , 100-μl/well ) and horseradish peroxidase-conjugated sheep polyclonal-anti-dog IgG , goat polyclonal-anti-dog IgG1 or sheep polyclonal-anti-dog IgG2 antibody ( 1:1000 dilution , 100-μl/well ) . All antibodies were purchased from Bethyl Laboratories , and diluted in PBS-0 . 1% Tween-20 ( PBS ) containing 0 . 5% NFDM . Color was developed by incubation with 100-μl/well Sure Blue TMB substrate ( Kirkegaard & Perry Labs ) at room temperature for 10 min , reaction was stopped with 2N sulfuric acid , and change in color monitored at 450 nm using an Epoch microplate reader and Gene 5 ( v . 2 . 0 ) software ( Biotek , VT , USA ) . Sera samples from chronically-infected dogs with confirmed T . cruzi infection and from healthy domestic dogs were used as positive and negative controls , respectively . The cut off value for ELISA was established as mean O . D . at 450 nm from negative controls ± 2 S . D . [10] . Cardiac parameters were monitored for all dogs before they were included in the study , and then after challenge infection , at 2-week intervals up to 8-weeks and at monthly intervals thereafter . We used electrocardiograph ( Stylus , EK-8 , USA ) setting at 120 V , 60 Hertz , 20 amps , and 25 Watt in all experiments . Six leads ( I , II , III , aVr , aVL and aVF ) of the electrocardiogram were considered at 1-mV/cm , and a 25 mm/sec paper speed [10]| . Necropsy was performed on the day animals died due to infection or after humanitarian sacrifice at day 60 pi corresponding to acute infection phase and at day 365 pi corresponding to chronic phase of disease development . Postmortem studies were conducted using standard protocols with emphasis on macroscopic findings related to Chagas disease in heart tissue . Also , liver and spleen were carefully inspected [10 , 25] . For histological analysis , tissue samples were fixed in 10% buffered formalin for 24 h , dehydrated in absolute ethanol , cleared in xylene , and embedded in paraffin . Tissue sections ( 5-μm thick ) were stained with hematoxylin and eosin , and evaluated by light microscopy ( magnification: 100x and 400x ) [10 , 25] . Tissue parasites were identified by nested PCR . Briefly , total DNA was isolated from cardiac tissue sections using Wizard SV genomic DNA purification System Kit ( Promega ) following the manufacturer’s instructions . For first PCR amplification , amplicon ( 350 bp ) was obtained from the conserved region of the mini exon sequence ( GenBank accession # X62674 ) [26] . The reaction mixture consisted of Taq buffer ( 750 mM Tris-HCL , pH 8 . 8 , 200 mM NH4SO4 and 0 . 1% Tween 20 ) , 0 . 2 mM dNTPs , 3 mM MgCl2 , and 1 . 25 U/50 μl Taq polymerase ( Go Taq Flexi DNA Polymerase ) . The reaction was started with addition of 1 μM of each oligonucleotide ( TC2F: 5′-CCTGCAGGCACACGTGTGTGTG-3′ and TCR: 5′-CCCCCCTCCCAGGCCACACTG-3′ ) and 0 . 1 μg DNA template; and PCR was performed as follows: initial denaturation at 94°C/4 min , followed by 30 cycles of denaturation ( 94°C/1 min ) , annealing ( 55°C/30 sec ) and extension ( 72°C/1 min ) , and a final incubation at 10°C/5 min . For the second round of nested PCR , internal primers ( Tc2F: 5′-GCACGGTGTTCTGTCTTGTC-3′ and Tc2R: 5′-ATCAGCGCCACAGAAAGTGT-3′ ) , designed using the Primer3plus software [27] were included in the reaction to amplify a 197 bp band . The reaction was initiated using 1 μl of the amplicon from the 1st PCR reaction as template DNA , and amplification for nested PCR was performed as follows: initial denaturation 94°C/4 minutes , followed by 30 cycles of denaturation ( 95°C/30 sec ) , annealing ( 51°C/30 sec ) and extension ( 75°C/1 min ) , and a final incubation at 10°C/5 minutes . Amplicons were resolved by agarose gel ( 3% ) electrophoresis , and gels were stained with ethidium bromide ( 0 . 5 μg/mL ) , and visualized and imaged using a UV transilluminator mounted with a digital Kodak DC120 camera . Serological and parasitemia data were analyzed in duplicate and expressed as mean ± SD . Data were assessed for normal distribution by Kolmogorov-Smirnov ( K-S ) test [28] and checked by histograms and Q-Q plots . Data did not fit normal distribution , and therefore , were analyzed by a 1-way analysis of variance ( ANOVA ) . The mean differences were determined by Tukey’s post-hoc test ( comparison of multiple groups ) . Statistical analysis was performed using Statistical Analysis Systems ( SAS ) Software version 9 . 0 [29] . Differences were considered significant at p<0 . 05 . Electrocardiographic findings were graded according to the severity of the abnormalities from 0 ( normal ) to 10 ( most severely affected ) [10] . We noted no apparent clinical signs of cardiac dysfunction in dogs during routine evaluation in the adaptation period or during the immunization period . After challenge infection with T . cruzi , all dogs , except TcVac4-vaccinated and negative control ( pcDNA3 . 1/no Tc ) dogs , displayed a degree of electrocardiographic alterations during 30–60 days pi , corresponding to the acute infection phase ( Table 1 ) . Up to 67% of the pcDNA3 . 1/Tc dogs exhibited severe EKG abnormalities ( average grade 6 . 8 ) in response to T . cruzi infection , and were diagnosed with one or more of the following cardiac abnormalities: deviation of the electrical axel , low voltage complexes , and inter-ventricular conduction problems . TrIE/Tc dogs exhibited moderate level of cardiac dysfunction ( average grade 2 . 6 ) , and displayed repolarization problems and low voltage complexes . In comparison , TcVac4/Tc dogs exhibited no T . cruzi-induced changes in cardiac hemodynamics and their EKG profile was similar to that noted in pcDNA3 . 1/no Tc mice ( Table 1 ) . During the chronic phase of disease development ( 365 days pi ) , all dogs from the pcDNA3 . 1/Tc group exhibited Chagas disease associated cardiac abnormalities evidenced by deviation of axel to the right , 2° degree type II Mobitz block , and low voltage complexes ( average grade 6 . 3 ) . In comparison , TcVac4/Tc dogs presented normal electrocardiograms , and only one dog in this group developed mild electrocardiographic alterations with low voltage R complex ( average grade 2 . 6 ) ( Table 1 ) . TrIE/Tc dogs were not followed in the chronic phase of infection . These data suggest that TcVac4 vaccinate was efficacious in preserving the cardiac function that otherwise was severely compromised in response to T . cruzi infection and Chagas disease development . Sera levels of T . cruzi-specific antibodies were determined by an ELISA test . All dogs were seronegative before vaccination was initiated . T . cruzi-specific antibody response ( IgGs , 1:100-dilution ) was detectable after first vaccine dose , and gradually increased with subsequent doses . The antibody response examined after last vaccine dose is shown in Fig 1 . TcVac4/Tc dogs exhibited 2 . 2–4 . 9-fold higher level of T . cruzi-specific IgGs as compared to that noted in TrIE/Tc dogs ( Fig 1A , p<0 . 05 ) . Likewise , TcVac4/Tc dogs exhibited >3-fold increase in IgG1 and IgG2 levels ( IgG2/IgG1 ratio: 2 . 23 ) when compared to that noted in sera of TrIE/Tc dogs ( Fig 1B and 1C , p<0 . 05 ) . All animals , irrespective of vaccination status , responded to challenge infection by a significant expansion of antibody response . During the acute infection phase , the IgG and IgG1 levels were increased by 3 . 5 fold , and a trend was observed as following: TcVac4 > TrIE = pcDNA3 . 1 only ( Fig 1A and 1B ) . The IgG2 levels were increased by >5-fold in pcDNA3 . 1/Tc dogs while TcVac4/Tc and TrIE/Tc dogs exhibited 2-3-fold increase in IgG2 levels when compared to normal controls ( Fig 1C ) . The IgG2/IgG1 ratios were 3 . 2 , 0 . 95 and 2 . 2 in acutely infected dogs immunized with pcDNA3 . 1 , TcVac4 , and TrIE , respectively ( Fig 1B and 1C ) . At 360 days pi corresponding to chronic phase of disease development , the IgG ( >10-fold ) and IgG2 ( >3-fold ) responses remained substantially high in TcVac4/T dogs as well as in pcDNA3 . 1/Tc dogs ( Fig 1A and 1C ) . TcVac4/Tc dogs also maintained a high level of IgG1 response resulting in a balanced IgG subtypes ( IgG1 = IgG2 ) while chronically infected control dogs ( pcDNA3 . 1/Tc ) exhibited a significantly lower level of IgG1 subtype ( IgG2/IgG1 = 3 . 76 ) ( Fig 1B and 1C ) . Together , the results presented in Fig 1 suggested that TcVac4 elicited a high level of T . cruzi specific antibody response ( IgG2>IgG1 ) that was expanded in response to challenge infection and maintained during chronic phase with a balanced level of IgG1 and IgG2 subtypes . The TrIE-induced antibody response was not associated with protection from chronic disease . Parasitemia was detected in all experimentally infected dogs . In general , pre-patent period lasted until 16 days pi and peak parasitemia was reached between days 32 and 37 pi in all infected dogs ( Fig 2 ) . The TcVac4/Tc dogs exhibited 4-fold lower level of peak parasitemia than was noted in TrIE/Tc or pcDNA3 . 1/Tc dogs . Further , in TcVac4/Tc dogs , parasitemia became undetectable at day 40 pi that was four days earlier than was noted in animals from other infected groups ( Fig 2 ) . Xenodiagnostic studies were performed to assess if TcVac4 vaccine altered the infectivity of dogs to triatomines . For this , triatomines were fed on dogs during the acute infection phase when peak parasitemia was observed ( i . e . , day 36 pi ) , and feces were analyzed at day 60 post-feeding for the detection of parasites by light microscopy . Interestingly , triatomine survival frequency was affected by the infection status of the host evidenced by 100% , 72% , 83% and 72% survival rate of insects fed on pcDNA3 . 1/no Tc , pcDNA3 . 1/Tc , TcVac4/Tc , and TrIE/Tc dogs , respectively ( Table 2 ) . Considering only the surviving bugs , the rate of infection ( i . e . positive for parasite detection ) was observed to be 0% , 89% , 50% and 69% of triatomines fed on pcDNA3 . 1/no Tc , pcDNA3 . 1/Tc , TcVac4/Tc and TrIE/Tc dogs , respectively ( Table 2 ) . Together the results presented in Fig 2 and Table 2 suggested that neither of the vaccine compositions were effective in preventing infection or early rise in acute parasitemia . However , TcVac4 was most effective in reducing the peak parasitemia , time-course of parasitemia , and dogs’ infectivity to triatomines . Dogs in all groups , before or after challenge infection , presented no apparent signs of clinical illness during the routine physical exam . All dogs in TcVac4/Tc and pcDNA3 . 1/Tc groups survived well through the acute phase of infection . On day 60 pi , three dogs from these groups were harvested for anatomical and pathological evaluation and remaining animals in these groups survived till day 365 pi when the experiments were terminated . One dog in TrIE/Tc group succumbed to challenge infection on day 40 pi , and other 2 dogs in this group were harvested at day 60 pi for pathologic evaluations . Since amastigote nests are normally not observed during the chronic phase of infection by histological techniques and end-point PCR is not highly sensitive in detecting low levels of tissue parasite burden , we performed nested PCR to evaluate tissue-parasite burden in chronically-infected dogs . Diagnostics of parasite persistence in cardiac tissue by a nested PCR demonstrated that all infected animals were positive for parasite DNA . These data , along with those presented in Fig 2 and Table 2 , suggested that immunization with TcVac4 was effective in reducing the acute parasitemia and parasite transmission to triatomines; however , parasite persistence in tissues was not abrogated by TcVac4 vaccine . Anatomopathological analysis of the heart , performed at 60 days pi ( acute infection phase ) , showed biventricular dilated cardiomyopathy and focal hemorrhages and pale zones indicative of tissue damage and fibrosis in both ventricles . Animals from all infected groups displayed similar type of pathologies irrespective of the vaccination status . However , heart lesions in response to acute infection were milder in TcVac4/Tc dogs than those noted in pcDNA3 . 1/Tc or TrIE/Tc dogs ( Fig 3A and Table 3 ) . Further , moderate splenomegaly and hepatomegaly , associated with pale zones resembling infarct areas were presented in acutely-infected pcDNA3 . 1/Tc or TrIE/Tc dogs . TcVac4/Tc dogs exhibited severe splenomegaly indicative of strong immune response but only mild lesions in the liver ( Table 3 ) . Histological studies validated the macroscopic observations at day 60 pi . We observed severe focal myocardial lymphoplasmacytic inflammation in TcVac4/Tc dogs , while moderate diffused myocardial lymphoplasmacytic inflammation was recorded in the myocardium of pcDNA3 . 1/Tc and TrIE/Tc dogs during the acute infection phase ( Fig 3B ) . The extent of infiltration of lymphocytes and polymorphonuclear leucocytes in the heart in acute infection phase was maximally noted in TcVac4/Tc dogs followed by TrIE/Tc and pcDNA3 . 1/Tc dogs . Likewise , cardiomyocyte necrosis was observed more frequently in TcVac4/Tc and TrIE/Tc dogs than in pcDNA3 . 1/Tc dogs that presented a moderate amount of necrotic cells . In contrast , amastigote nests associated with acute infection were abundant ( 3–6 foci of pseudocysts/microscope field ) in cardiac tissue of pcDNA3 . 1/Tc and TrIE/Tc dogs , and scarce ( 0–1 foci of pseudocysts/mf ) in TcVac4/Tc dogs ( Fig 3B and Table 3 ) . These data suggested that immunization with TcVac4 vaccine resulted in an immune response associated with extensive inflammatory infiltrate in the heart upon challenge infection; and subsequently tissue parasite burden was controlled . In comparison , TrIE vaccine composition was not effective in controlling the acute tissue parasite burden . At one-year post-challenge infection , mild splenomegaly and hepatomegaly were present in all chronically-infected dogs ( Table 3 ) . The lesions presented in the heart of chronically infected pcDNA3 . 1/Tc dogs were more severe than that noted in TcVac4/Tc dogs ( Fig 3C and Table 3 ) . Histological studies showed the microscopic lesions constituted of focal myocardial lymphoplasmacytic inflammation and necrotic cardiomyocytes in chronically infected dogs ( Fig 3D ) . The pcDNA3 . 1/Tc dogs exhibited severe damage , i . e . , moderate diffused myocardial lymphoplasmacytic inflammation and severe infiltration of lymphocytes and polymorphonuclear leukocytes ( Fig 3 ) . These lesions were fewer and milder in chronically infected TcVac4/Tc dogs ( Fig 3D and Table 3 ) . Together , these data suggested that the TcVac4-vaccinated dogs were better equipped in controlling the chronic parasite persistence and pathological inflammation in the heart as well as the associated pathology in other organs that was otherwise evident in chronically-infected control dogs . In this study , we have tested the efficacy of a DNA-prime/TrIE-boost ( TcVac4 ) vaccine in dogs . The candidate antigens TcG1 , TcG2 , and TcG4 used as DNA vaccine in the study are conserved in multiple , clinically-relevant isolates of T . cruzi , expressed in infective and intracellular stages of T . cruzi , recognized by antibody and T cell immunity in infected mice [8 , 30] , and recently shown to be serologically reactive in infected humans [31] . IL-12 and GM-CSF expression plasmids were used as adjuvants because these cytokines induce antigen presentation and B and T cell responses [32] . Our previous experience with testing the efficacy of a multi-component DNA-prime/DNA-boost vaccine ( TcVac1 ) constituted of TcG1 , TcG2 , and TcG4 in dogs was moderately encouraging [10] . Dogs vaccinated with TcVac1 elicited antigen-specific IgM and IgG ( IgG2>IgG1 ) antibodies , and upon challenge infection , responded by a rapid expansion of antibodies but a moderate level of CD8+ T cell proliferation and IFN-γ production , and suppression of phagocytes’ activity . Subsequently , TcVac1 provided an early control of acute parasitemia but no significant benefits in controlling the myocardial parasite burden , electrocardiographic abnormalities or histopathologic cardiac alterations , the hallmarks of acute Chagas disease [10] . These data indicated that , even though TcVac1 primed an initial immune response against T . cruzi infection , the vaccine needed improvements . We chose to strengthen the TcVac1 efficacy with heterologous booster dose constituted of T . rangeli inactivated epimastigotes . Our decision was based on the fact that T . rangeli is nonpathogenic to humans [18] , exhibits ~60% cross-reactive antigenicity with T . cruzi [17] , and immunization with T . rangeli-based vaccines haven been shown to elicit partial protection against experimental T . cruzi infection in dogs and mice [19–21 , 33 , 34] . T . rangeli-based protection from T . cruzi was associated with potent parasite-specific antibodies and elevated serum levels of proinflammatory cytokines ( IL-12 , IFN-γ > IL-6 , TNF-α > IL-10 ) that subsequently resulted in absence of histopathological lesions in mice [19] . Others have shown the DNA prime/inactivated microorganism vaccine approach was significantly better than the individual components in providing protection against Mycobacterium tuberculosis in mice [14] and rabies [13] . Importantly , we have noted that subunit vaccine delivered by heterologous DNA-prime/protein-boost approach [30] was more efficacious in eliciting potent immunity to T . cruzi infection than was observed with DNA-prime/DNA-boost homologous vaccine in mice [9] . Herein , we report that immunization with TcVac4 , delivered as two doses of subunit DNA vaccine and two doses of TrIE , elicited a strong parasite-specific IgG response with predominance of IgG2 subtype ( IgG2/IgG1 = 2 . 7 ) . Upon challenge infection , IgG1 response was expanded and IgG2 response barely changed , resulting in a balanced IgG2/IgG1 ratio ( 0 . 95 ) in TcVac4/Tc dogs . A strong and balanced antibody response ( IgG1 = IgG2 ) was maintained in TcVac4 vaccinated dogs during the chronic phase ( 365 days pi ) , while dogs from pcDNA3 . 1/Tc group exhibited an IgG2 biased response ( IgG2/IgG1 = 3 . 76 ) . DNA vaccines have previously been shown to trigger IgG2 biased antibody response [6 , 10 , 11 , 35] . In dogs , IgG2 antibodies are known to support Fc region mediated effector functions , such as antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity [36]] . Yet , our results suggest that quality of the IgG2 antibodies and a strong level of IgG1 antibodies are more important than the mere IgG2-biased response in controlling infection . This notion is supported by the observation that TcVac4-induced balanced antibody responses were effective in controlling blood parasitemia , tissue parasite replication , and parasite dissemination to triatomines , while a highly IgG2 biased antibody response in pcDNA3 . 1/Tc and TrIE/Tc dogs provided no protection from acute infection [10 , 11] . Guedes et al . [37] have reported that a strong IgG2-biased antibody response was associated with cardiomegaly in 50% of beagle dogs chronically infected with T . cruzi Berenice-78 strain and 100% of dogs infected with Y or ABC T . cruzi strains , thus suggesting that a strong IgG2 response ( without IgG1 antibodies ) caused more cardiac damage than control of infection . TcVac4 had a dramatic effect on parasitemia levels during the acute phase of infection with 500 parasites/mL blood , in comparison with 2000 parasites/mL found in control animals; additionally the duration of the parasitemia was also reduced in TcVac4/Tc dogs . Others have also reported a degree of parasitemia control by DNA or protein vaccine in dogs . For example , Rodriguez-Morales et al . [6] and Arce-Fonseca et al . [11] using TcSSP4- and TcSP-encoding DNA vaccine , and Quijano–Hernandez et al . [12] using TSA-1/Tc24-encoding DNA vaccine , have found a reduction in acute parasitemia and the duration of parasitemia in vaccinated dogs . Also in a previous study , using the TcVac1 multicomponent vaccine , we found a reduction in duration of microscopically detectable parasitemia although a reduction in number of circulating parasites/mL was not observed during the acute phase of infection [10] . Although all these vaccines , including TcVac4 , have provided some protection in reducing parasitemia during the acute phase of the infection , future vaccine improvements will be necessary as sterile immunity was not achieved . We found that electrocardiographic abnormalities associated with acute parasite burden were prevented in TcVac4/Tc dogs , evidenced by normal electrocardiographic readings in all animals in this group . During the chronic phase , TcVac4/Tc dogs continued to exhibit a normal electrocardiogram , and only one dog in this group developed aberrant QRS wave . The pcDNA3 . 1/Tc dogs displayed severe cardiac abnormalities , such as high axis deviation , interventricular conduction problems , and low voltage complex in response to acute infection , and these abnormalities persisted during the chronic disease phase when dogs also exhibited other EKG problems , such as axis deviation to the right; AVB , Type II ( Mobitz ) 2nd degree atrio-ventricular block ( Table 1 ) . This information indicates that even if damage was not completely controlled , TcVac4/Tc animals were better equipped than the pcDNA3 . 1/Tc in preventing the chronic infection associated cardiac dysfunction . Reduction in abnormal EKG readings have also been reported by the use of TSA-1/Tc24 DNA vaccine [12] wherein authors reported that 33% of vaccinated/infected animals and 71% of the non-vaccinated/infected dogs had EKG alterations . Rodriguez-Morales et al . [35] have shown vaccination with TcSSP4 provided complete control of EKG alterations induced by T . cruzi Ninoa isolate . Although results with TcSSP4 seem more encouraging that the results found with TcVac4 , direct comparison of these studies can not be done because Ninoa , the strain used by Rodriguez-Morales group , is not as pathogenic to dogs as Sylvio X10/4 T . cruzi strain . The observation of an intense focal myocardial lymphocytic and polymorphonuclear leukocyte infiltration in acutely-infected TcVac4/Tc dogs suggests that TcVac4 also primed cell-mediated immune response that was significantly expanded to control intracellular infection . It is important to note that TcVac4 efficacy was observed against SylvioX10/4 that we have found is very pathogenic in dogs [10 , 12] . During the chronic phase , TcVac4/Tc dogs exhibited a remarkable reduction in macroscopic lesions as well as inflammatory infiltrate in the myocardial tissue , and these animals mostly had normal cardiac hemodynamics and LV function in comparison with that observed in chronically-infected dogs that were not vaccinated . Similar findings of enhanced inflammatory lesions in acute phase followed by control of chronic inflammation , albeit to a lesser scale , have been reported by others using TSA-1/Tc24 DNA vaccine [12] and TcSSP4 protein vaccine [35] in dogs . We also studied the capacity of TcVac4 to block parasite transmission from infected dogs to the vector ( Meccus longipennis ) . Our data indicated that triatomines were sensitive to infection with SylvioX10/4 strain of T . cruzi , because a relatively large proportion of insects fed on infected dogs died few days after feeding , while triatomines fed on naïve dogs had no postprandial mortality ( Table 1 ) . Nevertheless , mortality and infection rate of triatomines fed on TcVac4/Tc dogs , animals that had shortest peak of parasitemia , was reduced as compared with the triatomines from all other infected groups ( Table 2 ) . These data suggest that TcVac4 is not only effective in providing protection to dogs from T . cruzi infection but is also effective in partially reducing the dogs’ infectivity to triatomines . Our results suggest the vaccination of dogs , via blocking the parasite transmission to triatomines , will potentially be useful in preventing human infection and provide us an impetus to further improve the vaccine efficacy to interrupt the domestic cycle of parasite transmission . T . rangeli has previously been reported to induce protection against an experimental infection with T . cruzi in dogs [21] . Thus , a limited protection afforded by vaccination with TrIE in this study was unexpected . Differences in experimental outcomes between our and previously published literature could probably be explained by methodology dissimilarities . For example , Colombian field isolate of T . rangeli , used as vaccine by Basso et al . [19 , 20] might be from a different lineage than the Guatemalan isolate that we used in this study; and the antigenic differences between the two isolates of T . rangeli [38] might have influenced their protective capacity when used as vaccine against T . cruzi . Differences in virulence of Tulahuen isolate used by Basso et al . [21] versus SylvioX10/4 isolate of T . cruzi used by us might also explain the observed differences in T . rangeli-based vaccine efficacy . In our experience , SylvioX10/4 is highly pathogenic to dogs [10 , 12 , 25] . Though the pathogenicity of Tulahuen in dogs was not discussed , the fact that Basso et al . [21] challenged the dogs with 10 , 000 parasites/kg of body weight , while in the present study 3500 parasites/kg of body weight were used , suggest that Tulahuen is less pathogenic than the SylvioX10/4 in dogs . Moreover , number of booster doses could also have influenced the protective capacity of the vaccine . We immunized dogs with two doses of T . rangeli-based vaccine while Basso et al [19 , 20] immunized dogs thrice . The two-dose TrIE immunization protocol was not sufficient to elicit protective immune response; these animals exhibited low antibody response predominated by IgG2 subtype and only a weak IgG1 antibody response was mounted during immunization as well as post-challenge period . Again , in agreement with Guedes et al [37] , a weak IgG1 response observed in TrIE-vaccinated dogs could be related to a poor protection against infection . In summary , we have shown that TcVac4 vaccine induced a predominant IgG2-biased antibody response that was replaced by a balanced IgG ( IgG1 = IgG2 ) response after challenge infection . TcVac4-vaccinated animals were equipped to reduce parasitemia , heart tissue parasite burden , macroscopic heart damage and electrocardiographic alterations during acute phase of the infection . During the chronic phase , TcVac4-vaccinated animals exhibited few inflammatory foci , no parasite nests and preserved cardiac function . Our data provide impetus to further improve the TcVac4 efficacy and examine its potential utility as a veterinary vaccine .
Chagas disease , is an illness caused by Trypanosoma cruzi , is endemic in southern parts of the American continent . We tested the protective efficacy of a DNA-prime/T . rangeli-boost ( TcVac4 ) vaccine in a dog ( Canis familiaris ) model . Dogs were immunized with two-doses of DNA vaccine followed by two doses of inactivated T . rangeli epimastigotes ( TrIE ) and challenged with a highly pathogenic strain of T . cruzi . TcVac4 induced a protective antibody response in comparison with dogs from the control groups . Blood parasite burden , parasite transmission to triatomines and electrocardiographic abnormalities were significantly lower in TcVac4-vaccinated dogs than was observed in dogs given TrIE or empty plasmid DNA only . Macroscopic and microscopic alterations were significantly decreased in the myocardium of TcVac4-vaccinated dogs . We conclude that TcVac4 induced immunity was beneficial in providing resistance to T . cruzi infection , evidenced by control of chronic heart damage and preservation of cardiac function in dogs . Additionally , TcVac4 vaccination decreased the transmission of parasites from vaccinated/infected animals to triatomines .
[ "Abstract", "Introduction", "Materials", "and", "Methods", "Results", "Discussion" ]
[]
2015
Immune Protection against Trypanosoma cruzi Induced by TcVac4 in a Canine Model
Varicella zoster virus ( VZV ) is the etiological agent of chickenpox and shingles , diseases characterized by epidermal skin blistering . Using a calcium-induced keratinocyte differentiation model we investigated the interaction between epidermal differentiation and VZV infection . RNA-seq analysis showed that VZV infection has a profound effect on differentiating keratinocytes , altering the normal process of epidermal gene expression to generate a signature that resembles patterns of gene expression seen in both heritable and acquired skin-blistering disorders . Further investigation by real-time PCR , protein analysis and electron microscopy revealed that VZV specifically reduced expression of specific suprabasal cytokeratins and desmosomal proteins , leading to disruption of epidermal structure and function . These changes were accompanied by an upregulation of kallikreins and serine proteases . Taken together VZV infection promotes blistering and desquamation of the epidermis , both of which are necessary to the viral spread and pathogenesis . At the same time , analysis of the viral transcriptome provided evidence that VZV gene expression was significantly increased following calcium treatment of keratinocytes . Using reporter viruses and immunohistochemistry we confirmed that VZV gene and protein expression in skin is linked with cellular differentiation . These studies highlight the intimate host-pathogen interaction following VZV infection of skin and provide insight into the mechanisms by which VZV remodels the epidermal environment to promote its own replication and spread . Replication in skin and mucosa is central to the pathogenesis of varicella zoster virus ( VZV ) , a member of the alphaherpesvirus subfamily that causes chickenpox ( varicella ) upon a primary infection and shingles ( herpes-zoster ) following reactivation from a neuronal latent state . In both diseases , VZV replication in the epidermal layer of skin results in the formation of large polykaryocytes and the development of blisters containing infectious cell-free virus . The epidermis is a continually regenerating tissue layer that develops a stratified structure , which is maintained by keratinocytes , specialized cells which produce a network of keratin filaments anchored to intracellular junctions to provide structural support to the tissue . As keratinocytes transit from the stem-cell rich basal to the uppermost layer of the epidermis , they undergo a program of terminal differentiation . Each stratum ( basal , spinous , granular , lucidum and cornified ) [1] identified within the stratified epidermis is associated with established signature patterns of gene expression [2] [3] . This process is tightly regulated by homeostatic mechanisms that involve calcium gradients , microRNAs , developmental signalling pathways and proteolytic cascades [4] , [5] , [6] , [7] , [8] , [9] . Although VZV infects primary cultured keratinocytes [10] little is known about the interaction between VZV replication and epidermal differentiation . Previous work has shown that VZV replication in skin differs from monolayer cultures in that certain VZV proteins , such as ORF10 and ORF11 , are not required for replication in melanoma monolayer cultures but are necessary for optimal replication in foetal skin xenografts of SCID-hu mice , [11] , [12] . Additionally , the live attenuated VZV vaccine , vOKA , replicates well in tissue culture but is attenuated for replication in skin but not in lymphoid or neuronal xenografts in SCID-hu mouse models [13] . In the present study we used an in vitro calcium induced model of epithelial differentiation [5] and analysed the transcriptome of uninfected and VZV-infected primary keratinocytes using RNA-seq . This approach identified not only the effect of VZV on keratinocytes but also the consequence of keratinocyte differentiation on VZV replication and maturation . Together our data provides intriguing new insights into host–pathogen interactions . As keratinocytes differentiate they lose basally expressed cytokeratins ( KRT5/14/15 ) and increase the expression of differentiation markers e . g . suprabasal cytokeratins ( KRT1/10 ) and involucrin ( IVL ) . The addition of calcium to primary keratinocytes in culture , mimics the calcium gradient across the epidermis and the process of epidermal differentiation [5] . To assess the effect of calcium on primary keratinocytes we measured by qPCR the change in the expression of selected keratinocyte markers known to be altered by differentiation ( KRT10 , KRT15 and IVL ) ( Figure S1 ) and confirmed our findings by immunoblotting for KRT10 and IVL ( Figure S1 ) . In our hands , the addition of calcium to 1 . 2 mM increased the expression of the suprabasal ( KRT10 ) and granular marker ( IVL ) as well as reducing the expression of the basal marker ( KRT15 ) , demonstrating that we could use calcium to induce keratinocyte differentiation . The ability of VZV to infect primary human keratinocytes has previously been assessed [10] . Sexton and colleagues noted that maintaining the keratinocytes in a low calcium media prior to VZV infection resulted in a higher initial infection . We were able to infect cells grown in low calcium [0 . 6 mM] ( −calcium ) and high calcium [1 . 2 mM] ( +calcium ) media with VZV as detected by an infectious centre assay ( Figure 1A–B ) . Over the course of a 5 day infection we observed an increase in the number of VZV foci , indicating cell to cell spread , full replication and production of infectious virus ( Figure 1C ) . As previously observed [10] when VZV was added to cells cultured in high calcium medium , the number of foci was less and plateaued after one day , suggesting reduced replication and spread . To optimise a model with which to investigate the interaction of VZV and keratinocyte differentiation , we next examined the effect of adding calcium [1 . 2 mM] to cells already infected with VZV . By immunohistochemistry , the VZV plaque size was comparable to the −calcium cells ( Figure 1A–B ) , but the number of foci counted was still less when calcium was added 3 days p . i . ( Figure 1C ) . Primary keratinocytes in culture are known to change size [14] , develop tight junctions and form clusters [15] when treated with calcium . To ensure that this did not affect the number of VZV plaques counted , the infected keratinocytes and the associated supernatants were transferred onto MeWo cells and the VZV titres calculated . VZV was not detected in the supernatants of any sample ( data not shown ) . The VZV titre in the +calcium cells at days 4–5 p . i . was significantly less than in the −calcium cells . However , by adding the calcium to the cells 3 days after VZV infection , the VZV titres were higher than in the +calcium cells and by day 5 no significant difference was seen in the VZV titres between the −calcium cells and the cells which the calcium had been added 3 days p . i . ( Figure 1D ) . Flow cytometry analysis of VZV keratinocytes confirmed that keratinocytes cultured in high calcium media ( +calcium ) express fewer VZV proteins than the cells culture in low calcium media ( −calcium ) but this could be increased by adding calcium at 3 days p . i . ( Figure S2 ) . Moreover , this finding was independent of whether cell-associated or cell-free VZV was used to infect cells . VZV gene expression ( ORF29 ) was compared by real time PCR in cells grown in low calcium media ( −calcium ) and cells switched to a high calcium media at day 3 post VZV infection ( Fig . 1E ) . The expression of ORF29 peaked at 48 hrs in both the comparisons with increased expression seen in the samples which had calcium added at day 3 . From the combined data above showing peak of VZV gene expression at 48 hrs following the calcium switch and the data showing increased expression of host differentiation markers at 24–48 hours following the addition of calcium ( Figure S1 ) , we determined that the optimal time point at which to examine both host and viral gene expression together was 48 hrs after calcium-induced differentiation of keratinocytes infected with VZV 3 days previously . Using this model , we compared undifferentiated ( −calcium ) and differentiated ( calcium added at day 3 post infection ) keratinocytes as well as studying the effect of the keratinocyte differentiation on the viral transcriptome ( Figure 1F ) . To summarise , primary human keratinocytes were plated out at day 0 and infected/mock infected with VZV at an m . o . i of 0 . 2 at day 2 . Cells were then incubated at 34°C until day 5 before either maintaining the cultures in a low calcium media or switching to a high calcium media at day 3 p . i . Total RNA was harvested at day 7 ( 48 hrs after changing media and 5 days p . i . ) for all four experimental conditions ( K; Keratinocytes , KV; Keratinocytes and VZV , KC; Keratinocytes and Calcium and KCV; Keratinocytes , Calcium and VZV ) as illustrated in Figure 1H . The cDNA was sequenced using the Illumina RNA-seq platform . Between 15–36×106 reads were generated per lane of which 4 . 8–12×106 mapped to the human transcriptome once duplicate reads had been removed ( Homo sapiens ( release 37 ) reference sequence GRCh37/hg19: Table S1 ) . Similar distributions of reads per gene were found across all samples before normalisation with no major outliers ( Figure S3 ) . The number of duplicate reads per sample varied between 21–45% , with higher levels of duplication observed in the samples from batch 3 , presumably due to a PCR batch effect . However , estimated library sizes ( 10–32×106 ) were independent of batch and average quality scores rose from 31 for batches 1 and 2 to 38 for batch 3 , corresponding to updated Illumina reagents and protocol . Post-normalisation , clustering transcriptome profiles by Spearman's rank correlation coefficient gave tight clusters for the calcium treated samples ( KC and KCV ) , whilst those for the KV and K samples were more dispersed with clustering being more heavily dependent on keratinocyte batch . The exceptions were samples KV2 and KV5 , which clustered tightly with the KC samples ( Figure S3 ) . To ensure that no bias was introduced into the cDNA library , ten genes were amplified by real time PCR , and shown to have good overall correlation to RNA-seq reports when comparing the effect of virus in calcium-treated ( KCV/KC ) and untreated ( KV/K ) cells ( Pearson's ρ = 0 . 88 and 0 . 75 respectively , ( Figure S3 ) . A negative binomial generalised log-linear model was fitted to the TMM-normalised read counts of 17463 human genes with reads above a threshold of 1 Count Per Million ( CPM ) in at least 3 samples . From this , likelihood-ratio tests were performed , identifying 3863 differentially expressed genes across 6 comparisons of interest ( KC/K , KV/K , KCV/KC , KCV/KV , KCV/K and KV/KC ) , ( Figure 2A–B ) . The greatest degree of differential expression was seen by the addition of calcium to uninfected keratinocytes ( KC/K ) . A total of 1786 genes were altered ( 463 genes up-regulated , 1323 genes down-regulated ) , the pattern of which was consistent with the induction of keratinocyte differentiation with a decrease in the expression of basally expressed cytokeratins and an increase in the expression of most but not all of genes expressed in both the suprabasal and granular layers ( Figure S4 ) and in keeping with the known limitations of the keratinocyte calcium-switch model , we did not see changes in genes that are expressed in the cornified layer . In contrast , relatively few genes were significantly altered by viral infection alone ( KV/K , 110 upregulated , 53 downregulated ) . Although approximately the same viral titres were achieved in both conditions , ( Figure 1D ) significantly more genes were differentially expressed in the KCV/KC comparison ( 1049 upregulated , 371 downregulated ) . Of the 107 genes differentially expressed following viral infection in both differentiated and undifferentiated keratinocytes ( KCV/KC and KV/K ) , 99 were found to be upregulated and 8 downregulated in both comparisons ( Figure 2E ) , suggesting a common role for these genes in host response to viral infection regardless of differentiation . As we could not achieve 100% infection in either the KCV or KV samples ( Figure 1 ) , we must consider that the uninfected bystander cells in these samples could also contribute to the overall gene expression changes observed . Only 31 genes were found to be differentially expressed in both virally infected ( KV/K ) and calcium treated ( KC/K ) conditions ( 19% KV/K , 2% KC/K ) ( Figure 2B ) , strongly suggesting that viral infection does not drive differentiation . A similar analysis of the KCV/KC and KC/K comparisons identified 238 genes as differentially expressed in both contrasts ( 17% KCV/KC , 13% KC/K ) . However of these genes , 159 were upregulated in the KCV/KC comparison but downregulated in KC/K , whilst 73 were downregulated in the KCV/KC but upregulated in the KC/K comparison . The direction of changes was the same for only 3% ( 6/238 ) of those genes differentially expressed in both conditions . This finding further supports the contention that VZV infection does not drive differentiation and raises the possibility that VZV may in fact interfere with or hinder it . Some of the effects seen in the VZV infected samples that were calcium switched ( KCV ) were also apparent in VZV infected samples that were untreated ( KV ) . However the differences were not due solely to the effect of calcium on keratinocytes . If this were the case , we ought to see good agreement between those genes differentially expressed in both KCV/KV and KC/K . That this overlap is relatively small ( 85 genes: 5% total KC/K; 9% total KCV/KV ) is in keeping with the observation that the KCV samples were transcriptionally distinct from both KC and K samples and reinforces the notion of an interaction between VZV infection and the process of keratinocyte differentiation . In addition , there is only a relatively small overlap between genes differentially expressed following addition of calcium and those differentially expressed upon addition of calcium and viral infection ( 215 genes , 12% KC/K , 16% KCV/K ) . Of these genes , the direction of fold change is identical for almost all ( 91% , 50 genes upregulated in both comparisons , 145 genes downregulated in both comparisons ) suggesting the KCV samples possess a more differentiated keratinocyte phenotype than the K samples . However , the vast majority of genes found to be differentially expressed in the KCV/K comparison ( 1113 genes , 84% KCV/K ) are not significantly up- or downregulated by the addition of calcium alone , further supporting the notion that changes in gene expression between the K and KCV samples are not solely a consequence of keratinocyte differentiation . Gene set enrichment analysis using the online DAVID functional annotation resource [16] identified significantly enriched functional groups ( PBH<0 . 05 ) altered by either calcium ( KC/K ) ( Figure 3A ) , by VZV infection of calcium differentiated cells ( KCV/KC ) ( Figure 3B ) or by VZV infection of undifferentiated cells ( KV/K ) ( Figure 3C ) . Genes upregulated by calcium treatment of uninfected keratinocytes ( KC/K ) showed enrichment for several functional groups including cell cycle ( GO:0007049 ) and cell division ( SwissProt PIR keyword ) whilst those that were downregulated in this comparison included regulation of transcription ( GO:0045449 ) and negative regulation of gene expression ( GO:0010629 ) . The categories identified in the KCV/KC comparison were more varied with enrichment in the upregulated genes for several functional groups including cell junction genes ( GO:0030054 ) , the ECM-receptor interaction pathway ( KEGG hsa04512 ) and serine protease inhibitors ( SwissProt keyword ) . Enrichment was also observed for additional groups such as cell adhesion ( GO:0007155 ) , epidermis development ( GO:0008544 ) , serine proteases ( SwissProt PIR keyword ) and the integrin-mediated signaling pathway ( GO:0007229 ) . Although relatively few genes were significantly altered by viral infection alone ( KV/K ) , the upregulated genes showed functional enrichment for epidermis development ( GO:0008544 ) and serine-type endopeptidase activity ( GO:0004252 ) as observed for KCV/KC . Although several interferon-stimulated genes were significantly changed after VZV infection , the corresponding GO terms ( GO:0071357 ) were not identified as being significantly enriched in either of the VZV infected conditions ( KCV or KV ) . A hallmark of epidermal differentiation are the changes that occur in cytokeratin ( KRT ) expression [6] . Basal cytokeratins ( KRT5/14/15 ) are lost and suprabasal cytokeratins ( KRT1/10 ) are gained as the keratinocytes differentiate and migrate outwards . Gene-annotation enrichment analysis using the online DAVID database of both the KCV/KC and KV/K deduced that infection with VZV profoundly affected the development of the epidermis ( Figure 3B–C ) . Specifically , we show here that VZV alters the expression of several epidermal cytokeratins , regardless of the differentiation status of the keratinocyte ( Figure S5 , Table S2 ) . The effect of VZV infection on the epithelial cytokeratins was independently verified by qPCR and the fold change calculated for a direct comparison to the RNA-seq data ( Figure 4A–B ) . A good concordance was seen between the methods . VZV infection increased the expression of KRT15 , a stem cell marker located in the hair follicle isthmus in both the KCV/KC and KV/K comparisons , although other stem cell markers ( ITGB1 , CD34 and CD200 ) ( data not shown ) and other basal layer cytokeratins ( KRT5/14 ) were not altered . At the same time , VZV either down-regulated or prevented up regulation of the suprabasally expressed cytokeratin heterodimers KRT1 and KRT10 , which are the major cytokeratins associated with keratinocyte differentiation [17] in both the KCV/KC and KV/K comparisons . EM images of differentiated keratinocytes infected with VZV show an abundance of cytokeratin structures ( Figure S5 ) . This finding may be related to the upregulation of KRT4/13 ( 131-fold and 34-fold respectively for the KCV/KC comparison , figure 4A ) , KRT4/13 are normally present as heterodimers in the suprabasal layers of mucosal but not stratified epithelium [18] , and are thought to function like KRT1/10 to maintain cellular architecture . Upregulation of KRT4/13 may therefore have compensated structurally for the reduced expression of KRT1/10 . To test whether VZV replication was responsible for the reduction of KRT1/10 expression , the qPCR experiment was repeated with UV-inactivated VZV ( Figure 4C–D ) . We again saw a downregulation of the KRT1 and KRT10 gene expression , but these changes were partially abolished by pre-treatment of the viral inoculum by UV irradiation . We also determined the effect of VZV infection on KRT10 and KRT15 protein expression by western blotting . KRT10 expression was increased by the addition of calcium by 24 hrs . However , in the VZV infected cells the KRT10 levels were reduced and this reduction was more pronounced by 48 hrs p . i . in the undifferentiated cells , confirming that the virus downregulates KRT10 regardless of the differentiation status of the cell . KRT15 expression was upregulated by VZV infection at 48 hrs p . i . and again this effect was not dependent on the addition of calcium . Further examination of the VZV infected keratinocytes by immunofluorescence confirmed that not all cells were infected at day 5 p . i . but , KRT10 expression was absent in the ORF23GFP expressing cells . However , KRT15 expression was widespread and not necessarily confined to the VZV infected cells , confirming that not all the gene changes seen in our transcriptome data was a direct result of VZV infected cells and that changes in the bystander cells also contribute to the changes seen ( Figure 4F ) . The transcriptome data was confirmed for KRT10 using a keratinocyte cell line , nTERTs . As with the primary keratinocytes , we were unable to achieve100% infection of the nTERTs even at an m . o . i . = 2 ( as measured in MeWo cells ) after 5 days and it was easier to establish VZV infection in sparsely plated nTERTs ( data not shown ) . As the nTERTs become more densely populated , the expression of KRT10 increased over the course of the experiment as measured by real time PCR . However infection of these cells with VZV significantly reduced KRT10 gene expression ( Figure 5A ) . The downregulation of KRT10 by VZV in nTERTs was also observed at the protein level ( Figure 5B ) . At both 24 and 48 hrs post infection the expression of KRT10 was reduced in VZV infected nTERTs compared to the mock infected controls and pre-treatment of the viral inoculum with PAA , which inhibits VZV DNA polymerase and viral replication restored KRT10 expression ( Figure 5C ) . To assess the expression of KRT10 in the presence of VZV infection , the cells were examined by immunofluorescence . The nTERTs are a heterogenous cell population and not all the cells express KRT10 , as shown in the mock infected control ( Figure 5D ) . However , KRT10 expression in VZV infected cells was diminished , particularly in infected cells expressing the late protein ( gE ) ( Figure 5F ) . Closer examination of the VZV infected showed that a number of cells where the expression of ORF62 was confined to the nuclei , which is indicative of an early VZV infection [19] , still expressed KRT10 ( Figure 5G–H ) , which in addition to the UV-treated virus and PAA data indicates that the effect of VZV on KRT10 is dependent on viral replication . To determine whether the VZV associated downregulation of KRT10 seen in VZV infected keratinocyte monolayers occurred in more physiologically representative skin models , keratinocyte organotypic rafts were infected with VZV . Organotypic raft cultures are an in vitro system that recapitulates epithelial differentiation and have previously been used to study VZV replication in keratinocytes [20] . H&E staining revealed intact but swollen cells in the VZV infected raft , which are typically seen in early VZV skin lesions ( Figure 6A ) . KRT10 expression was confined to a continuous layer in the suprabasal region of the mock-infected raft ( Figure 6A vii ) but disrupted in the VZV infected raft ( Figure 6A viii ) , with no expression of KRT10 seen in the VZV infected pocket ( indicated by VZV gE expression ) . These findings were also confirmed in skin biopsy samples from VZV cases ( Figure 6B–G ) . As previously observed , KRT10 expression occurred in the suprabasal layers of the epidermis and gross examination suggested downregulation of KRT10 expression restricted to VZV antigen positive infected areas ( Figure 6B–C ) . KRT10 mean intensity was compared in uninfected and VZV infected cells within the suprabasal layer of the epidermis . Ten VZV positive and ten VZV negative cells were selected within the suprabasal layer of the epidermis and the fluorescence of the KRT10 staining ( red ) was measured using ImageJ and found to be significantly less in the cells staining positive for VZV gE ( Figure 6D ) . As KRT10 forms heterodimers with KRT1 within the suprabasal layer of the epidermis [18] , we also examined KRT1 staining in a biopsy sample and carried out the same analysis of the fluorescence intensity in VZV positive and negative cells within the suprabasal layer ( Figure 6E–F ) . In agreement with the KRT10 result , KRT1 expression was also substantially reduced in VZV infected cells ( Figure 6G ) . KRT1/10 bundles provide the cytoskeletal structure in the suprabasal layers of the epidermis by interacting with desmosomal proteins [21] . Detailed analysis of the intracellular structures ( GO:0030054 and GO:0007155 , data not shown ) indicate that VZV infection altered and downregulated the expression of desmosomal components ( Figure 7A , Table S3 ) , particularly Desmoglein1 ( DSG1 ) and Desmocollin1 ( DSC1 ) . Both of these genes are intrinsically involved in the formation of tight junctions and the process of epidermal differentiation normally increases their expression . From our trancriptome analysis we see that they were upregulated by the addition of calcium ( KC/K ) by 9-fold and 5-fold respectively ( Figure S4 , Table S3 ) , but both genes were significantly downregulated by VZV infection ( Figure 7A ) . In contrast to Human Papillomavirus , another epitheliotropic virus which downregulates β4 integrin to dysregulate epidermal differentiation [22] , VZV infection had no effect on the basal hemidesmosomal proteins ( Table S3 ) . Transcriptome data on the changes in desmosomal genes were verified by qPCR ( Figure 7B ) and we confirmed that VZV infection reduced the expression of both DSG1 and DSC1 at the protein level by immunoblotting ( Figure 7C ) . In the presence of the VZV gE protein the expression of both DSG1 and DSC1 was reduced in comparison to the mock infected cells . As seen for changes in cytokeratin expression , downregulation of DSG1 and DSC1 was dependent on VZV gene expression , and could be ablated by UV-treatment and inactivation of the VZV viral inoculum ( data not shown ) . Desmosomes provide intercellular adhesive strength required for the integrity of epidermis , and electron microscopy imaging of uninfected ( Figure 7D+G ) , early VZV infection , as denoted by the presence of viral envelopes but not intact virions ( Figure 7E+H ) and late VZV infection , where virus particles accumulate at cellular boundaries ( Figure 7F+I ) revealed that desmosomal junctions were no longer observed when the VZV infection was well developed . Addition of phosphonoacetic acid ( PAA ) , which inhibits VZV DNA polymerase and viral replication , also abrogated the down regulation of DSG1 and DSC1 in VZV infected cells as measured by qPCR ( Figure 7J–K ) . Since late but not immediate early viral gene expression is modulated by PAA treatment , the reduction in desmosomal proteins in VZV infected cells may be due to proteins expressed late in the replication cycle . Serine proteases as a group were significantly enriched in both the VZV infected samples ( KC and KV ) ( Figure 3B–C ) . A heatmap of the serine peptidases and non-peptidase homologues group ( IPR001314 ) ( Figure 8A ) displays the differential expression of these genes under our four different conditions . Overall , the majority of the genes are significantly upregulated in the KCV samples , with a similar but less pronounced upregulation also seen in the KV samples compared to K and KC ( Table S4 ) . Epidermal serine proteases , such as the tissue kallikreins participate in desquamation , the natural process by which individual corneocytes are shed from the surface of the epithelium [23] . Mutations that reduce the ability of the antagonist to inhibit the activity of the proteases , result in uncontrolled proteolytic activity associated with inflammatory skin conditions e . g . Nethertons syndrome [24] . Our data demonstrates that VZV upregulated the expression of the majority of the kallikrein genes ( Figure 8B ) . The upregulation of KLK5 and −7 was of particular interest due to their role in cleaving DSC1 and DSG1 [25] . The upregulation of these genes by VZV could augment the downregulation of DSG1 and DSC1 thereby further reducing cell-cell adhesion and the strength of the epidermal barrier to withstand mechanical trauma . The upregulation of the KLK5 and −7 genes was validated by qPCR ( Figure 8C–D respectively ) and the effect of VZV on the KLK5 and −7 proteins was confirmed by immunoblotting in concentrated supernatants from mock and VZV-infected differentiated keratinocytes ( Figure 8E ) . RNA-seq enabled analysis of both host and viral transcripts within the same sample . Paired-end reads from VZV infected samples were mapped to the pOKA genome ( accession number AB097933 ) . Between 1 . 4×105–1 . 2×106 reads were mapped to the VZV genome ( Table S1 ) with the exception of KV1 where the number of mapped reads was at least ten fold lower . Visualisation of the VZV transcripts for all infected samples using IGV ( Figure 9A ) revealed that all viral genes were expressed in all infected samples , indicating that lytic viral replication was occurring under all conditions . This was also established by electron microscopy examination of the infected cells , which revealed the presence of highly cell associated virus particles in both VZV infected cells treated or untreated with calcium ( Figure S6 ) . Overall , the pattern of VZV gene expression was similar for untreated and calcium treated cells , but the average number of mapped reads was approximately 9-fold higher in differentiated cells ( KCV1–3 ) ( Figure 9B ) . Significantly higher viral expression was observed in the differentiated cells compared to the undifferentiated cells for every viral ORF without exception and regardless of temporal classification ( all ORFs up-regulated between 4 and 15 fold; pFDR<0 . 01 ) . Six viral ORFs were significantly changed by the addition of calcium ( p<0 . 01 ) although only ORF14 ( gC ) and ORF55 were significant ( pFDR<0 . 01 ) following correction for multiple testing ( Table S5 ) and when KV1 was excluded due to low VZV reads only ORF14 remained significant . The increase in viral gene expression was independently investigated by qPCR analysis for the three temporal classes of herpesvirus genes . No difference was seen in the expression of the IE gene ( ORF63 ) at 48 hrs after the addition of calcium ( Figure 9C ) , however the expression of the early ( ORF29 ) and late ( ORF14 ) viral genes was significantly increased in cells where calcium was added 3 days after VZV infection as per our model ( Figure 9D–E ) . The qPCR data did not reflect the degree of change and increase in VZV gene expression seen in the KCV samples in RNA-seq data in comparison to the KV samples . We were able to demonstrate an effect of calcium on the viral DNA and observed a three-fold increase in VZV DNA as measured by real time PCR after the addition of calcium to infected keratinocytes on day three ( Figure 9F ) . Taken together , our data , whereby calcium induced differentiation of primary keratinocytes increases VZV DNA replication and gene transcription , both of which are required for the production of progeny virions implies that the process of differentiation increases VZV replication , but , we were unable to demonstrate that calcium differentiation induced an increase in the number of VZV particles or replication by either IF , infectious VZV foci or EM ( data not shown ) . In common with other members of the herpesvirus family , VZV gene expression occurs in a temporally regulated cascade , which can be categorized as; immediate early ( IE ) , early ( E ) and late ( L ) . When viral reads were normalised separately to human reads , distinct patterns of relative viral gene expression were observed for the undifferentiated ( KV ) and differentiated ( KCV ) samples within each sample ( Figure 10A ) . A high degree of agreement was observed between the differentiated samples ( KCV1–3 ) indicating that late viral genes were more highly expressed than immediate early genes in the differentiated samples . Undifferentiated samples KV3 and KV4 also showed good agreement , where in contrast to KCV1–3 , the immediate early genes were more highly expressed than the late genes . However , samples KV2 and KV5 , despite not having been treated with calcium , had similar viral gene expression patterns to the differentiated cells ( KCV1–3 ) . Analysis of 1463 host genes which are differentially expressed after treatment with calcium but not changed by VZV infection ( i . e . representative of keratinocyte differentiation ) showed clustering of KV2 and KV5 host gene expression profiles with the calcium-shifted differentiated keratinocyte samples KCV1–3 ( Figure 10B ) . This finding was supported by Spearman's rank correlation coefficient ( Figure S3 ) and principal component analysis of host gene expression profiles ( Figure S3 ) , which again clustered samples KV2 and KV5 with the calcium treated samples . Since KV2 and KV5 were not treated with calcium , it is likely that these replicates underwent spontaneous differentiation , something that is known to happen when primary keratinocytes contact each other [26] . These data illustrate the impact of keratinocyte differentiation on VZV gene expression as distinct from the effect of calcium . To test our hypothesis that it is differentiation and not the addition of calcium that is responsible for the increase seen in VZV gene expression , the NOTCH pathway was activated by the addition of the agonist , jagged-1 and inhibited by the addition of DAPT . The canonical NOTCH pathway acts as a switch between the basal and suprabasal genes and is a key regulator of keratinocyte differentiation and its activation causes stem cells to exit their niche and start the process of terminal differentiation [27] . NOTCH activation and ablation influenced gE expression as measured by western blotting ( Figure 10C ) . gE expression was increased in comparison to the untreated keratinocytes , when the NOTCH pathway was activated . Conversely a decrease gE protein expression was observed relative to the untreated keratinocytes when the NOTCH pathway was ablated . To examine the effect of cellular differentiation on the regulation of VZV genes , we used a recombinant viruses expressing either luciferase or renilla reporter cassettes under various VZV promoters ( Figure 10D–G ) . Keratinocytes were infected with one of the three viruses as per our model and the reporter activity measured over the course of 6 days . Reporter activity for ORF4 , ORF14 and ORF9 increased over the timepoints taken whereas ORF63 remained relatively level . In all viruses , the reporter activity was altered by the addition of calcium at day 3 post infection . With the reporter driven by an immediate early promoter ( Figure 10D and F ) there was a decrease in its activity after the addition of calcium whereas the converse was true for the reporters driven by the late promoters ( Figure 10E and G ) . The increase in ORF4 promoter activity in undifferentiated cells and not in the calcium treated cells as well as the increase in the late viral promoter activity and viral early and late gene products in the calcium differentiated cells suggests that there was a relative block to viral replication which was overcome by keratinocyte differentiation . To investigate if the expression of the VZV proteins is affected by differentiation in the epidermis , skin organ cultures were intradermally injected with VZV to model skin infection and stained for IE and late proteins 3 days post infection ( Figure 10H ) . Although epidermal infection throughout the section was evident , the explant model did not demonstrate epidermal blistering unlike the in vivo archival specimens ( Figure 6B–G ) . VZV IE63 staining was found predominantly in the nuclei throughout the epidermis and the expression of late viral glycoprotein gE was largely cytoplasmic with increased expression of gE seen in the uppermost layers of the epidermis . Together with the observed differential gene expression as keratinocytes differentiate under calcium , these results indicate that VZV gene expression is tied to regulated keratinocyte differentiation , with a switch from more efficient early gene expression at the undifferentiated stage to late gene expression as differentiation ensues . Keratinocytes , the predominant cell type found in the epidermis , are a major target of VZV replication in skin . The calcium switch method delivered a dynamic model of epidermal differentiation which can be manipulated to allow investigation of viral and host interactions during synchronized keratinocyte differentiation without the presence of other cell types . A number of alternative systems have been described to investigate the biology of skin , including 3D raft cultures and explants [20] , [28] . A further model , using SCID-hu mice , has previously been successfully used to study the infection dynamics of VZV in vivo [13] . Both the 3D raft cultures and the explants show a greater degree of differentiation , formation and definition of the structural layers characteristic of skin in contrast to the calcium shift model utilised in this study . Furthermore , the explant system also contains the presence of specialised structures , such as the sebaceous glands and hair follicles that are absent from raft cultures and the monolayer system . However neither the explant nor the SCID-hu mouse model was suitable for this study as neither system can be manipulated experimentally to allow the effects of differentiation on VZV gene expression to be examined . In contrast , the development of 3D organotypic rafts whilst offering a valid alternative model to allow investigation into the effects of virus infection on cellular differentiation and vice versa was not found to be reliable enough , particularly in the presence of virus , for use in the RNA-seq part of our study . The model has certain disadvantages e . g . the genes associated with the stratum lucidum and stratum corneum are not expressed . The titre of virus produced in primary keratinocytes is at least 2 logs lower than is seen in MeWo cells and remains highly cell associated as we saw in our EM images . Thus although we were able to show that calcium induced increases in gene expression and viral genome replication , these were not associated with an increase in the formation of infectious particles . Nonetheless , our data clearly demonstrate that , in common with the archetypal alphaherpesvirus HSV-1 [29] and previous observations [10] , [20] , VZV appears to preferentially infect undifferentiated keratinocytes . In epithelia , undifferentiated keratinocytes are localised to the stem-cell rich basal layer of the epidermis and adnexal structures . Our findings are consistent with histological evidence of early Herpes Zoster lesions that shows VZV infecting the stem-cell rich isthmus region of the hair follicle [30] , following reactivation from latency and prior to the onset of the epidermal infection or distinctive cutaneous rash . By adding calcium to VZV infected undifferentiated keratinocytes our model therefore mimics the likely sequence of events in the skin , with infection of less differentiated basal cells followed by differentiation of infected cells . The fact that VZV spreads more easily in undifferentiated cells fits with the model of cell associated virus inhabiting basal epidermal layers while cell free virus in the upper epidermis is necessary for transmitted infection [31] . Virus adapted for cell to cell spread in the basal epithelium has been shown to differ from cell-free virus . For example , VZV can spread efficiently cell to cell in MeWo monolayers despite low levels of envelope glycoprotein C [32] a protein that is necessary for the formation of cell free virus and VZV replication in skin [13] , [33] . This fits with our results showing increased late viral protein expression in the suprabasal layers where assembly of cell free virus occurs [31] . One model to explain our results may therefore be that the generalised increase in viral gene and protein expression seen with keratinocyte differentiation is directed to maturation of cell-free virions from the immature viral particles formed in the basal layers , rather than a large increase in numbers of virions . Over time , as keratinocyte maturation continues , high titers of cell-free virions accumulate in the growing blister . The blister lesions formed in VZV infections are by and large discrete , although coalescence of mature lesions may occur . While production of interferon by bystander cells has been shown to limit viral spread in the skin [34] it is also possible that cell-to-cell spread is limited by the physical barriers associated with keratinocyte differentiation , thus explaining the reduced spread of virus observed in differentiated cells . Alternatively , differentiation may result in the loss of a cellular receptor rendering keratinocytes less permissive to VZV infection . Of the three known putative cellular receptors for VZV , mannose 6 phosphate receptor , reported to be critical for the accumulation of cell free virus in vesicles and Heparan sulphate are expressed only in basal layers and the expression of both are lost as keratinocytes differentiate [31] [35] . Both are therefore candidates for viral spread in less differentiated but not differentiated cells . In contrast , expression of the third putative receptor , insulin degrading enzyme ( IDE ) , increases as keratinocytes differentiate ( Jones M . unpublished data ) . Notwithstanding the reduced cell-to-cell spread in differentiated keratinocytes , our analysis revealed a quantitative increase in viral gene and viral DNA expression after calcium induced differentiation and by manipulation of the NOTCH pathway as well as by calcium and contact induced differentiation we were able to show that this was dependent on differentiation and not just the addition of calcium to our cells . As previously outlined , these results are not necessarily contradictory but are consistent with a model by which the virus is able to enter and spread in undifferentiated keratinocytes , but once infected , optimal replication and production of mature virions requires the presence or loss of cellular factors present as cells differentiate . Although VZV does not persist in the skin , it shows a pattern common to other skin tropic viruses . Human Papillomavirus ( HPV ) which maintains its genome as a stable episome in basal cells until differentiation of the host cell occurs [36] and the gamma-herpesviruses Kaposi Sarcoma Herpesvirus ( KSHV ) , whose lytic cycle is also known to be activated by keratinocyte differentiation [37] . In our model , expression of all VZV ORFs was evident in both untreated and calcium treated keratinocyte samples , confirming that VZV undergoes full lytic infection in both conditions . However , the distinct differences in the pattern of expression were clearly apparent , with relatively more immediate early genes ( ORFs 4 , 62 and 63 ) in the undifferentiated cells and relatively more late genes ( e . g . viral glycoproteins ) expressed in the keratinocytes that had undergone synchronized differentiation . The apparent association of each condition with IE or late viral gene expression suggests that the state of the host cell may impact on the regulation of the molecular switch controlling the classical temporal pattern of herpesvirus gene expression . Though not definitive proof , this hypothesis is supported by our observations . First , the expression of the VZV promoters in the recombinant viruses clearly establishes differential activity of the reporters in response to calcium induced keratinocyte differentiation . This block in production of early and late viral proteins in undifferentiated keratinocytes is consistent with a requirement for cellular factors present in differentiated cells to regulate the switch to early/late gene expression . Secondly , the expression of gC , which is essential for viral replication in skin [13] , was significantly greater in the calcium switched cells . This result corroborates previous work where increased gC expression was observed in MeWo cells treated with hexamethyl bisacetamide ( HMBA ) , a known inducer of cellular differentiation [32] . Finally , using immunohistochemistry we were also able to show that another late protein , gE , was more highly expressed in the upper layers of the epidermis in explants . Of interest , several VZV ORFs which , based on their HSV-1 orthologues are presumed to be late genes , were more highly expressed in the undifferentiated cells . These include ORFs 17 ( virion host shutoff ) , 64 ( tegument US10 ) , 46 ( tegument UL14 ) , 27 ( nuclear phosphoprotein UL31 ) , 60 ( gL ) and 23 ( capsid ) . Further work is required to determine whether these findings reflect true differences in VZV temporal gene expression or whether they are indicative of keratinocyte specific differences in the gene expression patterns . Analysis of the host transcriptome confirmed VZV does not drive keratinocyte differentiation . Instead the virus clearly alters the normal pattern of gene expression associated with differentiation , generating a signature associated with skin blistering , which itself is a characteristic feature of VZV disease . VZV downregulated or prevented the expression of the suprabasal genes , KRT1 and KRT10 , but was not shown to alter the expression of other differentiation markers such as involucrin . KRT1 and KRT10 are known to play a role in maintaining the integrity of the epidermis and are mutated in other blistering diseases such as epidermolytic ichthyosis [38] . KRT10 has also been shown to inhibit proliferation and cell cycle progression of basal keratinocytes [39] , [40] and its loss is also associated with increased cell turnover [41] . However , through its interactions with desmosomes , KRT1/10 form a dynamic scaffold in the cell and play an important role in maintaining epithelial structure [42] . Autoantibodies to desmosomal proteins , DSG1 and DSC1 proteins are a hallmark of blistering skin conditions including pemphigus foliaceus and IgA pemphigus [43] . As a group , the desmosomal genes , especially DSG1 and DSC1 were identified as being significantly altered by VZV infection while other junctional proteins were unaltered . These findings support the notion that the interaction of VZV with keratinocytes drives the pathognomonic blistering phenotype . The accompanying upregulation of the serine proteases by VZV may contribute to the observed reduction of DSG1 protein as well as inducing a desquamative phenotype , which may promote the dissemination of the virus . An unusual finding which is not typical of blistering disorders was the upregulation of the mucosal cytokeratins 4 and 13 , a phenotype previously observed in keratinocytes with impaired SMAD 2 , 4 and 7 signalling [44] . Jones et al . previously observed such changes in the TGF beta pathway in a VZV infected SCID-hu mouse model [45] . Although we observe the same gene pattern changes as Buschke et al . in that we observed downregulation of KRT1/10/DSG1 and upregulation of KRT4/13 , we did not find a significant enrichment of the TGF-beta pathway as a whole . It is possible that our transcriptome analysis , which was a single snapshot late on in VZV infection , failed to detect early signalling changes responsible for KRT4/13 changes . Alternatively , VZV may act via different pathways in this system . Other observed changes in cytokeratin expression which do not usually form part of a blistering signature include the upregulation of KRT15 and KRT19 which are associated with stem cells in adnexal skin compartments , a region which histologically is positive for VZV early on in infection . KRT15 is also associated with wound healing , but other markers of keratinocyte activation in wound healing ( e . g . KRT6 , 16 and 17 ) [46] were unaltered in our transcriptome data . In summary , we have shown by combined analysis of host and pathogen gene expression at a single time point that VZV gene expression is linked to keratinocyte differentiation . VZV replication , in turn , alters the structure of stratified squamous epithelium , driving a blistering , desquamative phenotype to form the typical skin vesicles , which are essential to VZV pathogenesis . The major functional groups studied in the manuscript i . e . the alterations affecting cytokeratins , desmosomes and proteases , are controlled by a number of regulatory pathways and further work is underway to untangle the complex molecular interactions between VZV and keratinocyte differentiation . While the data presented here are only a snapshot of this complex process , they provide a roadmap for further exploration of how VZV interacts with a target cell central to its pathogenesis . Human skin from cosmetic reductive surgery was obtained with written informed consent under the approval of the East Central London Research Ethics Committee 1 ( 10/H0121/39 ) . Neonatal primary human epidermal keratinocytes ( HEKn , Life technologies , Paisley , UK ) were cultured on mouse collagen IV ( 0 . 67 µg/cm2 BD Biosciences , Oxford , UK ) coated surfaces in keratinocyte defined media containing epithelial growth factor ( KDM and EpiLife , Life technologies ) . Differentiation was induced by shifting the cells to a high calcium media containing [1 . 2 mM] calcium chloride . Neonatal Human Dermal Fibroblasts ( HDFn , Life technologies ) were cultured in medium 106 supplemented with low serum growth supplement ( Life technologies ) . MeWo cells were cultured in MEM ( Sigma , Dorset , UK ) supplemented with 10% ( w/v ) FBS and 1% non-essential amino acids . nTERTs cells were cultured in 3∶1 DMEM∶Ham's F12 supplemented with 10% FBS , 1% L-glutamine ( 200 mM ) and Ready Mix Plus ( 0 . 4 µg/ml hydrocortisone , 5 µg/ml insulin , 10 ng/ml EGF , 5 µg/ml transferrin , 8 . 4 ng/ml cholera toxin and 13 ng/ml liothyronine ) . All uninfected cells were cultured at 37°C , 5% CO2 . Segments of human skin ( less than 2 cm2 ) were intradermally inoculated with approx . 1×105 infectious units of cell-free virus and cultured at the air-liquid interface in Dulbecco's modified Eagle's and Ham's F12 medium ( 3∶1 ) , supplemented with 10% foetal bovine serum , 1% L-glutamine , and supplemented with RM+ ( 0 . 4 µg/ml Hydrocortisone , 5 µg/ml Insulin , 0 . 01 µg/ml EGF , 0 . 0084 µg/ml Cholera toxin , 5 µg/ml Transferrin and 0 . 0013 µg/ml Lyothyronine ) for 10 days . Mock-injected segments were cultured in parallel as a control . Skin samples were fixed in 4% paraformaldehyde and embedded in paraffin . Infections for transcriptome experiments were carried out using pOka and validation experiments were carried out using a strain named THA , a low passage clade 3 clinical isolate . The VZV ORF23 GFP , expressing an N terminal tag to the capsid OR23 protein , has been detailed previously [47] . A recombinant VZV expressing luciferase ( VZVLUC ) driven by ORF4 promoter was developed by cloning the ORF4 promoter upstream of the luciferase gene in the vector PGL3 ( Promega Corp ) , followed by PCR amplification of the entire cassette and cloning into the pOka based cosmid pspe23 at the unique AvrII site located between OR65 and 66 . This was then developed into recombinant VZV with additional pOka cosmids as described previously [48] . The ORF14-luciferase virus reports luciferase as a T2A directed ribosome skipping motif fusion protein . Luciferase was amplified by PCR from the plasmid pGL3basic ( Promega Corp ) using the primers 5′ GAGGGATCCGGTTCCGGAGAGGGCAGAGGA AGTCTGCTAACATGCGGTGACGTCGAGGAGAATCCTGGCCCAATGGAAGACGCC AAAAACATA-3′ and 5′ AATTCGAATTCGCGCGCAGATCTTTACACGGCGATCTTTCCGCCCTTCTTGGC-3′ . The resulting fragment was digested with EcoRI and BamHI and cloned into the vector pmCherryC1 cut with EcoRI and BglII ( underlined in primers ) , resulting in plasmid pmCherryT2Aluc which contained mCherry fused in frame to luciferase , separated by the T2A 22 amino acid ribosome skipping motif . The expression of functional luciferase ( containing a single residue added to the amnio terminal end ) and mCherry ( with a 21 amino acid T2A C terminal addition ) were confirmed in plasmid transfected HEK293T cells ( data not shown ) . The plasmid was cut with EcoRI and BglII ( both sites downstream from the T2A luciferase gene ) and a zeomycin resistance cassette was inserted following its generation by PCR with primers to add bglII and EcoRI flanking sequences ( underlined in the following primers 5′ AGATCTAGATCTCGAGTAATGGAACGGACCG TGTTGA C-3′ and 5′ – GCTGAC GTCGACGAATTCTGATCACTCAAGTTTCGAGGTCGAGGTG 3′ ) . The resulting plasmid was used as the template for the PCR amplification of the entire T2A-Luciferase-Zeo cassette using primers with 40 bp flanking homology arms to allow recombination into ORF14 in the pOka BAC , so it was an in frame fusion with the terminal residue of gC ( ORF14 ) : using the primers gClucF2 5′ CTTATCGCAGTTATC GCAACCCTATGCATCCGTTGCTGTTCAATGGACGAGCTGTACAAG-3′ and gClucR2 5′ ATAAAATGATATACACAGACGCGTTTGGTTGGTTTCTGTCTCGAGTATGATCAG TTATC 3′ . The PCR product was amplified , gel purified and transformed for recombineering into pOka BAC [49] using pGS1783 bacterial host ( a kind gift of Gregory Smith Northwestern University IL ) , a VZV pOka BAC detailed previously [50] and recombineering methods detailed by [51] . Chloramphenicol resistant BACs also showing zeomycin resistance were validated for DNA integrity and correct insertion into ORF14 . Virus was derived by cotransfection of the BAC purified DNA into MeWo cells as previously described [50] . VZV containing both ORF63-T2A luciferase and ORF9-T2A renilla reporters were generated similarly . Renilla gene was first PCR amplified to add the T2A motif to the N terminal end using the following primers and the template pSV40 RL ( Promega Corp ) 5- AGAGGATCC GGTTCCGGAGAGGGCAGAGGAAGTCTGCTAACATGCGGTGACGTCGAGGAGAATCCTGGCCCAATGACTTCGAAAGTTTA TG -3′ and 5′ GAATTCGAATTCTGTTCATTTTTGAGAACTCGCTCAA-3′ . The EcoRI and BamHI digested product was cloned into pEGFP-C1 cut with bglII and EcoRI , to generate the plasmid pEGFPT2ARen . A kanamycin resistance cassette was amplified from pEPS kan 2 [51] using primers that added flanking repetitive sequences to allow subsequent recombineering removal of the cassette , using 5′ AGATCTAGATCTAGGATGACGACGATAAGTAGG G-3′ and ATTCGAATTCCGATGAACTCAGTAGCATTATTGTTCATTTTTGAGAACTCGCTCAACGAACGATTTGATATCAACCAATTAACCAATTCTGATTAG-3′ primers . The BglII and EcoRI digested cassette was cloned into the unique EcoRI and bglII sites downstream of the renilla cassette in pEGFPT2ARen . This was used as a template for PCR amplification of the entire T2A-renilla kanamycin cassette with primers that added 40 bp homology arms to ORF9 , so as to place the cassette as a fusion to the C terminal residue of ORF9 ( 5′- AGTAGGGCCCGTTCGGCATCAAGAACTGATGCGCGAAAAATG GAC GAGCTGTACAAG -3′ and 5′- TTATACATAATACCGGGTAAACCGTTACTGCGTAATTAACTCGAGTATGATCAGTTATC 3′ Recombinants of pOka BAC containing the cassette were selected based on gain of kanamycin resistance . A second recombination event was induced concordance with ISce induction [51] to remove the kanamycin cassette , and transformants were screened for loss of kanamycin . The resulting pOka BAC containing the ORF9-T2A renilla fusion was subjected to a third induced recombination following transformation with a T2A luciferase-Zeo cassette , amplified using primers to add flanking 40 bp homology arms to enable recombination to the C terminus of ORF63 ( 5′ GGAAAATATCAACATAAAATATATCATCGTAAAAATTCGAGTATGATCAGTTATC 3′ and 5′ GCTCCCGTCATAGCAAATACAAAGACAATTATTAGCGTAATAATGGACGAGCTGTACAAG 3′ . Zeomycin resistant positive transformants were screened for integrity and correct insertion using sequencing . Finally , a kanamycin resistant cassette was inserted into ORF71 to prevent rescue of the ORF63-T2A luciferase cassette by the duplicated gene using a fourth induced recombination . Virus was derived from the BAC on MeWo cells and replicated efficiently as wild type ( data not shown ) . The virus derived from the BAC contained deletion of ORF71 , a fusion of ORF63 to T2A luciferase and a fusion of ORF9 to Renilla . All viruses were cultured and used at less than 20 passages . Infected cell preparations were first treated with mitomycin C ( 0 . 05 mg/ml for 3 hrs ) prior to freezing and subsequently titered on Mewo cells; we routinely achieved viral titres of greater than 1×106 pfu/ml . Cell-free VZV was generated by a rapid freeze ( liquid N2 ) and thaw ( 37°C ) , followed by removal of cellular debris by low speed centrifugation . In comparison to the cell-associated VZV , titres for cell-free virus were reduced by 1–3 logs and we achieved titres of approximately 1×104 pfu/ml . For UV inactivated VZV , cell-free VZV supernatants were treated for 20 min at 150 , 000 J/Cm2 using a Stratalinker UV Crosslinker ( Stratagene ) . For the transcriptome and subsequent confirmatory experiments , 3×104 HEKn cells per well of a 6 well dish were plated out at day 0 and left for 48 hrs , one well was then used to calculate the cell density and other wells were then inoculated with cell-free virus at an m . o . i of 0 . 2 as calculated by viral titration on Mewo cells . The cells were cultured at 34°C , 5% CO2 for 3 days before either maintaining the changing the calcium at [0 . 6 µM] or increasing to [1 . 2 µM] . Parallel wells were used to confirm the percentage of cells infected as measured by flow cytometry . For immunofluorescence , the experiments were carried out on a coverslip in a 24 well plate using 5×103 cells , which were infected with an m . o . i . of 0 . 2 . as above . other details are as above . Infection of nTERTs for PCRs and westerns were carried out in a 6 well plate , with 0 . 25×106 cells left for 24 hrs before infection with an m . o . i . of 0 . 2 . For immunofluorescence , 1×104 nTERTs were plated on a coverslip and infections carried out at an m . o . i . of 0 . 2 for 72 hrs before fixation with 4% PFA . HEKn were seeded onto a de-epidermilised dermis ( DED ) and cultured at the air-liquid interface , as previously described [52] . Rafts were intradermally inoculated with approximately 1×105 infectious units of cell-free virus nine days post-lifting . Mock-injected segments were cultured in parallel as a control . Five days post-infection , all cultures were fixed in 4% PFA and embedded in paraffin . Total RNA was extracted using TRIzol reagent ( Life technologies ) and cDNA libraries were prepared using reagents and protocols supplied with the mRNA seq kit ( Illumina , Essex , UK ) . Briefly , poly-A tailed RNA was purified from 10 µg of total RNA using oligodT beads . The purified RNA was fragmented chemically and cDNA was synthesised using Superscript II ( 25°C for 10 minutes , 42°C for 50 minutes , 70°C for 15 minutes; Life technologies ) primed with random primers supplied in the kit ( Illumina ) . Unique adaptors were ligated to the cDNA and 200 bp fragments were size selected by agarose gel purification . Libraries were validated by using a DNA high sensitivity chip ( Agilent , Cheshire , UK ) and quantified by Qubit analysis using the Quant-iT dsDNA HS Assay ( Life technologies ) . Libraries were sequenced with a 36 bp paired end read using a GAIIx sequencer ( Illumina ) . Each library was loaded onto a sequencing chip at a concentration of 16pM . The library was amplified and the Read 1 sequencing primer was hybridised using Paired end Cluster station reagents version 1 and 2 ( Illumina ) . The paired end module ( PEMx ) was attached to the GAIIx sequencer for Read 2 preparation . Each run was quality controlled by assessment of the Phix sequencing control , loaded at a concentration of 6pM per chip . Solexa sequencing and pipeline analysis was performed by J . Porwisz ( UCL Genomics ) , generating FASTQ files for each sample using GenomeAnalyzer pipeline v1 . 4 ( Illumina ) and CASAVA v1 . 0 ( Illumina ) . Paired end reads were mapped to host and viral genomes ( Homo sapiens ( release 37 ) reference sequence ( GRCh37/hg19 ) ; pOka sequence ( pOka , GenBank reference AB097933 ) ) using BowTie [53] , TopHat [54] , SAMtools [55] . Duplicate reads were then removed using PicardTools ( http://picard . sourceforge . net ) and read counts per gene generated using HTSeq-count ( http://www-huber . embl . de/users/anders/HTSeq ) . R/BioConductor [56] were used to import the mapped count data and the edgeR package used to normalise the data and generate lists of differentially expressed genes . Specifically , a filtering step was applied remove low expression genes with fewer than 1 count per million ( CPM ) in at least 3 samples . Counts were then normalised using a trimmed mean of M-values [57] and fitted to a negative binomial generalised log-linear model ( GLM ) , using empirical Bayes tagwise dispersions to estimate the dispersion parameter for each gene [58] . Differentially expressed genes were identified using GLM likelihood ratio tests applying a FDR significance cut-off of 0 . 01 , unless otherwise stated . The gplots R library was used to construct heatmaps ( http://cran . r-project . org/web/packages/gplots/index . html ) . Functional classification of genes was performed using the DAVID online database ( http://david . abcc . ncifcrf . gov/home . jsp ) [16] . Gene expression heatmaps were generated using the MEV software suite [59] , [60] ( http://www . tm4 . org/mev . html ) . Integrative Genomic Viewer ( IGV ) was used to produce viral genome coverage plots [61] . The Venn diagram in figure 2B was generated using the online tool VENNY ( http://bioinfogp . cnb . csic . es/tools/venny/index . html ) . 3 µg of total RNA was DNaseI treated before being reverse transcribed using MMLV reverse transciptase and random hexamer primers ( Promega , Southhampton , UK ) . qPCR was performed using a Rotorgene 3000 cycler ( Qiagen , Manchester , UK ) using 500 ng cDNA , specific primers [0 . 4 µM] ( Text S1 ) and SYBR green master mix ( Qiagen ) . Each gene was normalised to a housekeeping gene ( GAPDH or RN5S ) and relative expression shown as 2−ΔΔCt [62] . HEKn were fixed in 4% PFA , blocked in 10% Goat serum , washed in permeabilisation buffer ( eBiosciences , Hatfield , UK ) and incubated with the anti-VZV-FITC ( Millipore , Watford , UK ) for 30 min in the dark . Cells were subsequently washed in permeabilisation buffer , resuspended in PBS , processed by FACS Calibur and analysed using FloJo ( v7 . 6 . 5 ) . Cell pellets were lysed in whole cell lysis buffer ( 20 mM HEPES KOH ( pH 7 . 4 ) , 50 mM NaCl , 2%w/v NP40 , 0 . 5%w/v NaDeoxycholate , 0 . 2%w/v SDS , 1 mM NaOrthovanadate , 1 mM EGTA pH 7 , 10 mM NaF , 1 mM PMSF , protease inhibitor cocktail ( Sigma-Aldrich ) . Protein concentration was determined by BCA Assay ( Thermo-Fisher Scientific , Loughborough , UK ) . Samples were added to 4× Gel loading buffer ( Life Technologies ) , DTT ( 0 . 083M ) , heated to 70°C for 5 min . Secreted proteins were concentrated from supernatents using Amicon Ultra 10K filters ( Millipore ) and equal volume of concentrated supernatents were added to 2XSDS loading buffer , heated to 95°C for 5 min . Samples were resolved on a 4–12% Bis-Tris gel ( Life technologies , Paisley , UK ) and transferred to a nitrocellulose membrane and blocked in 5% PVP , 0 . 5% FBS . Membranes were incubated with antibodies ( Text S2 ) and detected by ECL plus ( GE , Buckinghamshire , UK ) . HEKn grown on coverslips were fixed in 4% PFA for 20 min at RT before being washed in PBS , incubated in NH4Cl [10 mM] for 10 min before being permeabilised with 0 . 05% ( w/v ) Triton X-100 on ice for 5 min . Cells were blocked with 3% BSA and incubated with the primary antibody ( 1∶100 ) ( Text S2 ) for 1 hr , followed by the Alexa Fluor secondary antibody ( Life Technologies ) ( 1∶1000 ) for 1 hr . Cells were mounted in Prolong gold ( Life Technologies ) and visualized on a Zeiss Axiovision . Images were analysed using AxioVision Rel . 4 . 8 and ImageJ . Statistical analyses were performed using Prism ( GraphPad Software ) . 5 µM paraffin-embedded sections were processed using conventional techniques . Antigen retrieval was performed by heat-treatment of deparaffinised sections in 10 mM citrate buffer pH 6 . 0 . Sections were treated with 3% hydrogen peroxidase and biotin blocked ( Vector Laboratories , Peterborough , UK ) prior to onset of immunostaining . VZV antigen was amplified using Universal Elite ABC kit in conjunction with M . O . M Biotinylated Anti-Mouse IgG ( Vector Laboratories ) and visualised using fluorophore tyramide amplification reagent ( Perkin Elmer Life Sciences , Buckinghamshire , UK ) . Nuclear staining was visualised by staining with Hoechst 33342 ( Life technologies ) . Sections were mounted in Immunomount ( Thermo-Fisher Scientific , Loughborough , UK ) and images were captured using a Leica epifluorescence microscope . Cells were fixed in 0 . 5% glutaraldehyde in 200 mM sodium cacodylate buffer for 30 min , washed in buffer and secondarily fixed in reduced 1% osmium tetroxide , 1 . 5% potassium ferricyanide for 60 min , washed in water and stained overnight in 0 . 5% Mg Uranyl acetate . The samples were then embedded flat in the dish in Epon resin . Ultrathin sections ( typically 50–70 nm ) were cut parallel to the dish stained with Reynold's lead citrate and examined in a FEI Tecnai electron microscope with CCD camera image acquisition . Plates were fixed at time points post infection with 4% paraformaldehyde and stained by immunohistochemistry using a mixed VZV mAb ( Meridian Life Sciences , Memphis , US ) , followed by and biotin ( Vector labs , Peterborough , UK ) streptavidin ( Jackson ImmunoResearch , PA , US ) amplification . Plaques were visualised using Fast Red TR salt ( Sigma , Dorset , UK ) and images of stained plaques were digitally captured and counted using the ViruSpot Reader ( AID GmbH ) . Keratinocytes infected with VZVLuc were lysed and processed for luciferase activity using the luciferase or dual-luciferase reporter assay system ( Promega ) according to the manufacturer's protocol . vDNA was extracted using the Qiagen DNeasy blood and tissue kit and vDNA copy number was determined by real time PCR for the VZV ORF29 gene , normalized to KRAS ( Text S1 ) using a standard curve .
Varicella zoster virus ( VZV ) causes chickenpox and shingles , which are characterised by the formation of fluid-filled skin lesions . Infectious viral particles present in these lesions are critical for airborne spread to cause chickenpox in non-immune contacts and for infection of nerve ganglia via nerve endings in the skin , a pre-requisite for shingles . Several VZV proteins , although dispensable in laboratory cell-culture , are essential for VZV infection of skin , a finding thought to relate to VZV interaction with a process known as epidermal differentiation . In this , the specialised keratinocyte cells of the outer layer of skin , the epidermis , are continually shed to be replaced by differentiating keratinocytes , which migrate up from lower layers . How VZV interaction with epidermal differentiation leads to the formation of fluid-filled lesions remains unclear . We show using a keratinocyte model of epidermal differentiation that VZV infection alters epidermal differentiation , generating a specific pattern of changes in that is characteristic of blistering and skin shedding diseases . We also identified that the differentiation status of the keratinocytes influences the replication pattern of the viral gene and protein expression , with both increasing as the VZV particles traverses to the uppermost layers of the skin . The findings provide new insights into VZV-host cell interactions .
[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "Methods" ]
[ "medicine", "infectious", "diseases", "developmental", "biology", "biology", "cell", "differentiation" ]
2014
RNA-seq Analysis of Host and Viral Gene Expression Highlights Interaction between Varicella Zoster Virus and Keratinocyte Differentiation
For gene products that must be present in cells at defined concentrations , expression levels must be tightly controlled to ensure robustness against environmental , genetic , and developmental noise . By studying the regulation of the concentration-sensitive Drosophila melanogaster Hox gene Ultrabithorax ( Ubx ) , we found that Ubx enhancer activities respond to both increases in Ubx levels and genetic background . Large , transient increases in Ubx levels are capable of silencing all enhancer input into Ubx transcription , resulting in the complete silencing of this gene . Small increases in Ubx levels , brought about by duplications of the Ubx locus , cause sporadic silencing of subsets of Ubx enhancers . Ubx enhancer silencing can also be induced by outcrossing laboratory stocks to D . melanogaster strains established from wild flies from around the world . These results suggest that enhancer activities are not rigidly determined , but instead are sensitive to genetic background . Together , these findings suggest that enhancer silencing may be used to maintain gene product levels within the correct range in response to natural genetic variation . The transcriptional control of gene expression in eukaryotes is governed by cis-regulatory elements , also known as enhancers , that integrate cell-type and temporal information by binding combinations of transcription factors . Genes that exhibit complex expression patterns are typically controlled by multiple cis-regulatory elements , some of which have overlapping , partially redundant activities [1] , [2] , [3] , [4] . Current estimates suggest that from 10 to 80% of the non-coding DNA of higher eukaryotes is devoted to gene regulation [5] , [6] , [7] , raising the question of how all of this regulatory information is integrated to generate accurate and stereotyped patterns of gene expression in space and time . A third dimension of gene regulation is quantity , which is especially relevant for genes that must be expressed within a narrow range of levels . One possible solution is that enhancers are precisely tuned to generate the appropriate level of transcription that is required in each cell . However , the precision that this type of mechanism demands seems difficult to achieve and especially vulnerable to genetic , environmental , and developmental noise . An alternative solution is that feedback or other regulatory mechanisms exist that modulate enhancer activities in response to the levels of gene product . Although feedback autoregulation is a well-known motif in transcriptional networks [8] , mechanisms that might be used to tune expression levels are not well understood . This problem is particularly challenging for genes that have multiple , partially redundant regulatory inputs . We have begun to study this problem in the fruit fly , Drosophila melanogaster , by analyzing the mechanisms that control the expression of the Hox gene Ultrabithorax ( Ubx ) in the haltere–a dorsal appendage on the third thoracic segment ( T3 ) that helps the fly balance during flight [9] . Although Ubx protein is detected in all cells of the developing haltere imaginal disc , its pattern of expression is not uniform [10] ( Figure 1A ) . Subsets of the complex regulatory input into the Ubx locus can be monitored by examining the expression patterns of Ubx enhancer traps , which exhibit different , overlapping subsets of the Ubx expression pattern ( Figure 1 ) . Ubx-Gal4lac1 , for example , ( monitored with UAS-GFP ) is expressed uniformly throughout the anterior ( A ) compartment of the haltere disc , but only in the distal portion of the posterior ( P ) compartment ( Figure 1B ) . In contrast , Ubx-Gal4LDN is expressed in distal regions ( in both the A and P compartments ) but is not expressed proximally ( Figure 1D ) . Somewhat paradoxically , transient ectopic expression of Ubx , induced either by heat shock or Gal4-mediated expression , resulted in Ubx loss-of-function transfomations that can be visualized both in the adult ( as haltere to wing transformations; [11] ) and in 3rd instar haltere imaginal discs ( as groups of cells that showed a reduction or complete loss of Ubx protein ) [12] ( Figure 2 ) . Thus , a transient pulse of high Ubx protein levels can lead to the complete and heritable silencing of all Ubx expression , implying that Ubx is being silenced by its own gene product . Transient pulses of ectopic Ubx also resulted in the stable silencing of Ubx enhancer traps , including Ubx-Gal4lac1 , Ubx-Gal4M1 , Ubx-Gal4LDN , and Ubx-lacZ166 ( Figure 2 and Table S1 ) . When the absence of Ubx protein was observed , these cells also had no enhancer trap expression ( Figure 2 ) . However , in many cases enhancer trap silencing was observed in cells that had normal Ubx protein levels ( Figure 2 ) . In these cases we suggest that only the enhancers captured by the enhancer trap were silenced , and that other , partially redundant , enhancers in the Ubx locus remained active , resulting in an apparently normal pattern of Ubx expression . We also find , consistent with previous results [12] , that the patches of Ubx-silenced cells in the haltere are clonal events and that the Polycomb system of epigenetic regulators is required for silencing ( Figure S1 and Figure S2 ) . To obtain initial mechanistic insights into Ubx autoregulatory silencing , we carried out experiments that suggest it requires specific DNA binding by Ubx . For these experiments , we monitored the ability of chimeric Hox proteins to induce haltere-to-wing transformations when expressed via the vg-Gal4 driver . Although the more anterior Hox protein Antennapedia ( Antp ) was unable to induce Ubx silencing , transient overexpression of Antp-Ubx chimeric proteins revealed that the Ubx homeodomain and adjacent C-terminal sequences were both necessary and together sufficient to induce robust Ubx silencing ( Figure 3 ) . These findings suggest that Ubx protein , and not Ubx mRNA , is responsible for the induction of silencing . Further , as both the homeodomain and adjacent sequences are implicated in Ubx specificity and DNA binding [13] , [14] , [15] , these results suggest that Ubx triggers silencing by binding to Ubx-specific cis-regulatory elements . Consistently , the Hox protein Abdominal-A ( Abd-A ) , which is very similar to Ubx in both domains , also induced Ubx silencing when transiently expressed during haltere development ( Figure 3 ) . We next tested whether more subtle increases in Ubx levels could also induce silencing . For these experiments , we monitored the expression of Ubx lacZ or Gal4 enhancer traps in flies that had extra copies of the wild type Ubx locus . Ubx-Gal4lac1 and Ubx-Gal4LDN were silenced in groups of haltere cells of 3x Ubx+ and 4x Ubx+ flies ( 100% of 4x Ubx+ haltere discs had at least one group of silenced cells ) ( Figure 4A–4D; Table S1 ) . In these haltere discs , probably because the flies had multiple copies of Ubx+ , the pattern of Ubx protein was invariably wild type ( Figure 4A , 4B , 4D ) . Interestingly , the amount of silencing induced by 4 copies of Ubx was significantly decreased when one of these copies encoded a non-functional Ubx protein ( the Ubx9–22 allele; data not shown ) . This result supports the idea that Ubx protein , not Ubx mRNA , is the inducer of silencing in response to extra copies of the Ubx locus . Ubx-Gal4M1 and Ubx-lacZlac1 responded differently to 4x Ubx+: instead of being silenced in clones , these enhancer traps were no longer expressed in proximal regions of the haltere disc , but distal expression remained unchanged ( Figure 4E , 4F ) . For Ubx-lacZ166 , the levels were strongly reduced in 4x Ubx+ flies compared to 2x Ubx+ flies ( Table S1 ) . Note , however , that Ubx-lacZ166 can be completely silenced in clones in response to hs-Ubx ( Figure S3 and Table S1 ) . Finally , the expression of Ubx-Gal4M3 did not change in the presence of four copies of the Ubx+ locus ( Figure 4G and Table S1 ) . Taken together , these results allow us to make three important conclusions . First , silencing is occurring at the level of Ubx enhancers , not entire Ubx alleles , because different Ubx enhancer traps respond in different ways . Second , silencing can be triggered by the presence of only one or two additional Ubx+ loci , suggesting that less than doubling Ubx levels is sufficient to silence some enhancers . Third , although all Ubx enhancers can be silenced by high Ubx levels , lower Ubx levels result in a range of responses that depend on which enhancer trap , and therefore which subset of Ubx enhancers , is being monitored . Thus , we conclude that different Ubx enhancers are sensitive to different levels of Ubx protein . We also generated flies to monitor two different enhancer trap insertions into the Ubx locus ( Ubx-lacZ166 and Ubx-Gal4lac1 ) at the same time . When silencing was triggered by heat shock-induced Ubx , we observed silencing of both enhancer traps , but at different frequencies: Ubx-Gal4lac1 was silenced to a greater extent than Ubx-lacZ166 ( Figure S3 ) . This finding provides additional support for the idea that individual enhancer traps , and thus different subsets of Ubx enhancers , respond differently to the same increase in Ubx levels . The above results show that epigenetic autoregulatory silencing of Ubx enhancers occurs in response to elevated Ubx levels . Interestingly , increasing the dose of Ubx+ results in smaller halteres [16] , but this size change does not scale linearly with the number of Ubx+ genes . Haltere size is similar to wild type in flies with 3x Ubx+ or 4x Ubx+ , while in flies with 6 copies of Ubx+ , haltere size is greatly reduced ( Figure 5A and Figure S4A ) . These results suggest that haltere size is buffered against increasing doses of the Ubx+ gene . A similar buffering can be observed when Ubx protein levels are quantified in haltere discs from animals with different numbers of Ubx+ genes . When one copy of Ubx is inactivated ( 1x Ubx+ ) , Ubx protein levels are nearly halved ( Figure S4A ) . However , when the Ubx+ complement is doubled ( 4x Ubx+ ) or tripled ( 6x Ubx+ ) only 39% and 60% increases in Ubx protein levels were detected , respectively ( Figure S4A ) . The less-than-expected increases in Ubx levels seen in Ubx duplications is not because they fail to express wild type levels , as they are sufficient to fully rescue a Ubx null mutation , both phenotypically [17] , [18] and with respect to Ubx protein levels ( data not shown ) . Together with the results described above , we suggest that the buffering of Ubx levels and haltere size is due , at least in part , to the epigenetic silencing of Ubx enhancers in response to higher than normal doses of Ubx+ . In wild type animals , we hypothesized that enhancer silencing may be used to ensure uniform Ubx levels in response to naturally occurring genetic variation in the cis- and trans-regulation of Ubx expression . We tested this idea by out-crossing our laboratory Ubx-Gal4lac1 flies to 32 D . melanogaster strains established from wild populations around the world . In our lab stock , less than 5% of haltere discs showed any evidence of Ubx-Gal4lac1 silencing . However , when outcrossed to wild D . melanogaster strains , we frequently observed silencing of Ubx-Gal4lac1 in haltere discs of the F1 generations ( Figure 5 and Table S2 ) . Although the frequency of silencing varied between wild stocks , it was consistent for each wild stock in a statistically significant manner ( Figure 6 ) . Of the 32 stocks crossed to Ubx-Gal4lac1 , 14 resulted in no detectable silencing in the F1 generation , 6 showed weak silencing in the F1 generation , and 12 showed strong silencing in the F1 generation ( Figure 5 and Table S2 ) . Because the amount of silencing can , in some cases , approach 100% ( e . g . Tw2 F1 ) , while 4x Ubx+ resulted in ∼20–30% silencing ( Figure 6 ) , we suggest that differences beyond Ubx levels contribute to silencing in these F1 outcrosses . Genetic variation may , for example , result in differences in the levels or activities of the trans-regulators of Ubx . Silencing was also observed when Ubx-lacZlac1 and Ubx-Gal4LDN were outcrossed to wild populations , demonstrating that this effect is not limited to Ubx-Gal4lac1 ( Figure 5R–5U and Table S1 ) . Despite the silencing of Ubx enhancer traps , the pattern and levels of Ubx protein were similar in the wild stocks , our laboratory stocks , and in their F1 progeny ( Figure S4B ) . We ruled out that the lack of enhancer trap expression in these outcrosses was due to a failure to initiate expression by carrying out a lineage tracing experiment , which demonstrates that Ubx-Gal4lac1 was expressed prior to silencing ( see Materials and Methods ) . We also ruled out that transposon instability ( e . g . hybrid dysgenesis [19] ) was responsible for the loss of enhancer trap expression using several criteria ( see Materials and Methods ) . Most importantly , silencing occurred at the same frequency when the male or female parent was from the wild ( non-laboratory ) stock and the amount of enhancer trap DNA , measured by qPCR , was unchanged between the parental and F2 generations . Further , silencing of enhancer traps in other genes , including Distalless-Gal4 , homothorax-lacZ , and teashirt-lacZ was not observed by crossing these insertions to the same wild strains ( data not shown ) . We postulate that silencing induced in these outcrosses may be due to an incompatibility between the trans-acting factors ( largely derived from the wild stocks ) and cis-regulatory elements ( linked to the monitored Ubx locus of the laboratory stock ) controlling Ubx expression . In support of this idea , when Ubx-Gal4lac1 was further introgressed into weakly or strongly silencing wild stocks , which effectively increases the genetic complement from the wild strain background , an increase in the severity of silencing was observed when compared to the F1 generation ( Figure 6 and Figure S5 ) . We also never observed the complete absence of Ubx protein or haltere-to-wing transformations in any of these outcrosses , arguing that only a subset of enhancer inputs into Ubx is silenced in response to genetic variation . Consistently , individual enhancer traps responded differently when crossed to the same wild strains ( Table S1 ) . Together , these results demonstrate that Ubx enhancer silencing is triggered when Ubx is present at higher than normal levels . When Ubx concentration is especially high ( when Ubx is ectopically expressed via Gal4 or heat-shock promoters ) all enhancer input into Ubx can be silenced , resulting in the complete absence of Ubx expression and haltere-to-wing transformations . Although such high levels of Ubx are not physiological , we also find that Ubx enhancer silencing can be triggered by additional copies of Ubx+ , which in principle results in less than double the amount of Ubx protein . In this case , we find that the expression of some Ubx enhancer traps is clonally silenced ( e . g . Ubx-Gal4lac1 ) , while the expression of other enhancer traps ( e . g . Ubx-lacZ166 ) is reduced . Thus , different Ubx enhancers are differentially sensitive to negative autoregulation; some are shut off by relatively low Ubx levels , while others require high Ubx levels to be silenced . Most remarkably , we found that enhancer silencing can occur simply by varying the genetic background . In Drosophila melanogaster , due in part to its large population size , the frequency of DNA polymorphisms between individuals in the wild is estimated to be as high as 1 in 100 basepairs [20] . Due to these polymorphisms , we imagine that different strains of D . melanogaster , when kept in isolation from each other , may have subtly different ways of regulating Ubx . These may be due to strain-specific differences in the Ubx cis-regulatory elements , in the trans regulators of Ubx expression , or both . Consistent with this idea , it is of interest that gene expression levels , when assayed across entire genomes , show a lot of variability in natural populations [21] , [22] , [23] , [24] , [25] . Although we find that the final Ubx expression pattern and levels are very similar between lab and wild D . melanogaster strains , when two strains are bred together genetic differences may result in fluctuations in the initial Ubx levels . The silencing system described here may function to compensate for these fluctuations and thus ensure that the correct Ubx levels are produced throughout the haltere . In the crosses to wild D . melanogaster strains , we found that the expression of genetically marked Ubx alleles varied tremendously , depending on the genetic background . Extrapolating from these results suggests that there is a lot of previously undetected variability in enhancer activities at the Ubx locus in wild files that would not have been detected using traditional assays . Thus , these results challenge the standard view that a given transcriptional enhancer integrates the same inputs and produces the same outputs , regardless of genetic background . Instead , due to natural genetic variation , the activity of a particular enhancer may vary widely between individuals in wild populations . Additionally , our results show that the activity of an enhancer can even vary among the cells within its expression domain ( e . g . the haltere ) in a single individual . We suggest that plasticity in enhancer activities is essential to compensate for genetic and perhaps environmental variation . Moreover , given that many genes may have multiple , partially redundant enhancers , enhancer silencing may be essential to buffer gene expression levels so that they remain within a narrow , biologically tolerable range . On the other hand , small differences in enhancer activities in flies in the wild may serve as a potential source of phenotypic variation that can be acted upon by natural selection . Since population genetic theory predicts that selection differentials of a small fraction of a percent are seen in natural populations with the effective population size of Drosophila [20] , it is plausible that this variation is functionally significant , perhaps through a subtle influence of haltere morphology on flight performance . The NC2 stocks were obtained from Greg Gibson ( N . C . State University ) ; all other wild stocks were obtained from the Bloomington Stock Center ( Table S2 ) . To show that the lack of expression in these outcrosses was not due to a failure to initiate enhancer trap expression in the wild backgrounds , we carried out a lineage tracing experiment . The genotype of the stock was: Ubx-Gal4lac1 UAS-flp; actin>stop>GFP . The combination of UAS-flp and actin>stop>GFP records the history ( i . e . marks the lineage ) of Gal4 expression . When outcrossed to wild backgrounds , GFP expression was not silenced ( in contrast to when the direct UAS-GFP readout was monitored ) . Together , these results suggest that Ubx-Gal4lac1 was initially activated but then silenced . Hybrid dysgenesis was ruled out as a reason for loss of expression from P transposons by the following tests: 1 ) silencing occurs equally well , regardless of the direction the cross was set up , 2 ) silencing occurs equally well at 18° and 25°C ( while hybrid dysgenesis is suppressed at 18°C ) , 3 ) silencing was not observed for some other transposon insertions ( inside or outside of the Ubx locus ) when crossed to the same wild stocks , 4 ) the miniwhite gene associated with the P element insertions did not lead to a variegated eye phenotype as would be expected for somatic transposon excision , and 5 ) quantitative PCR analysis confirmed that the amount of transposon DNA was the same in the parent ( unsilenced ) and F2 ( silenced ) generations . Finally , enhancer trap expression can be recovered when back-crossed into the laboratory stock background . To measure Ubx protein levels in different genetic backgrounds , we stained haltere discs obtained from uncrowded yw ( 2x Ubx+ ) , yw;If/Cyo;TM2/TM6B ( 1x Ubx+ ) , yw;If/Cyo;DpP5/TM6B ( 3x Ubx+ ) , yw;DpP10x2/CyoGFP;MKRS/TM6B ( 4x Ubx+ ) , yw; DpP10x2/CyoGFP;DpP5/DpP5 ( 6x Ubx+ ) , Hikone-R , Berlin-K , NC2-76 , NC2-80 , yw x NC2-76 F1s , Tw2 , yw x Tw2 F1s , Florida-9 , Reids-2 , and Harwich wandering larvae with anti-Ubx ( FP3 . 38 ) and a fluorescent secondary antibody . Stainings and confocal imaging were done identically and in parallel for ≥8 haltere discs from each genotype . The pixel intensities in identically sized regions of the distal anterior compartments were measured using Adobe Photoshop . This region was quantified because it is a relatively large area that expresses Ubx at uniform levels and gives rise to the main body of the haltere ( the same portion measured in Figure 5A and Figure S4A ) . Similar trends were observed when average pixel intensities for the entire distal haltere were measured . The average intensities for each wild population differed by no more than 16% , suggesting that final Ubx levels are very similar despite differences in genetic background and silencing . To quantify the extent of silencing of the Ubx-Gal4lac1 reporter in response to Ubx+ copy number and outcrosses to wild populations , third instar haltere discs were dissected from wandering larvae of yw122; DpP10x2/CyoGFP; Ubx-Gal4lac1UAS-GFP/TM6B ( 4xUbx+ ) , and the GFP positive , F1 progeny of yw122; If/Cyo; Ubx-Gal4lac1UAS-GFP/TM6B crossed with NC2-80 , NC2-76 , Ber-2 , Tw-2 , and Harwich . GFP positive F3 progeny of yw122; If/Cyo; Ubx-Gal4lac1UAS-GFP/TM6B crossed with NC2-80 and NC2-76 were also dissected . For the outcrosses , we always used females from the wild populations . Haltere discs were fixed , mounted , and imaged for GFP and DAPI on a confocal microscope . Images were made binary in ImageJ . The GFP expressing area relative to the total disc area was measured for each disc , and this value was subtracted from the average GFP expressing area ( relative to total disc size ) of yw122; If/Cyo; Ubx-Gal4lac1UAS-GFP/TM6B haltere discs to yield a ‘% silencing’ value for each disc . Larvae bearing the hs-UbxIa22 transgene [26] were heat-shocked at 37°C for 15–20 minutes 3 or 4 days after egg laying . Larvae were dissected at least 48 hours after heat shock to allow for total dissipation of exogenous Ubx . hs-UbxIa22 larvae that were not heat shocked showed no Ubx silencing . Neutral clones were induced using the same heat shock regime in flies of the genotype yw hsflp; FRT 42D Ub-GFP/FRT 42D; hs-UbxIa22/+ . Ubx-Gal4lac1 [27]; Ubx-lacZlac1 [28]; Ubx-Gal4LDN [29]; Ubx-Gal4M1 [29]; Ubx-lacZ166 [30]; and Ubx-Gal4M3 [29] . Although these lines are hypomorphic mutations of the Ubx locus , this is unlikely to contribute to our results because decreased production of Ubx would , if anything , cause an underestimate of the amount of silencing that occurs at the Ubx locus . 3x Ubx+ flies contain a tandem duplication of the Ubx locus ( Dp ( 3;3 ) P5 ) . 4x Ubx+ flies contain a tandem duplication of a transpositon of the Ubx locus onto the 2nd chromosome ( Dp ( 3;2 ) P10 ) . Further increases in Ubx+ copy number were created by combining these duplications [16] . Ubx9–22 expresses a non-functional Ubx protein due to a ∼1500 bp deletion that removes a splice acceptor site and part of the Ubx homeodomain-encoding exon [31] . Before crossing to enhancer traps , Ubx duplications were introduced into stocks containing marked chromosomes that do not cause silencing ( yw hsflp; If/cyo; Dp ( P5 ) /Tm6B and yw hsflp; Dp ( 3;2 ) P10x2/CyoGFP; MKRS/Tm6B ) . To monitor silencing of Ubx-lacZ166 and Ubx-Gal4lac1 simultaneously ( Figure S3 ) , flies of the genotype , Dp ( 3;2 ) P10x2/heat shock-Ubx; Ubx-lacZ166/Ubx-Gal4lac1 UAS-GFP were given a 15 min . heat shock at 37°C 48 to 96 hrs after egg laying . Imaginal discs were dissected at wandering stage and stained for Ubx , βgal , and GFP . Silencing was not observed in flies of the same genotype without heat shock . FRT101 ph504 FRT2A PcXT109 FRT42D Su ( Z ) 2l . b8 FRT82B ScmD1 FRT42D PclD5 Of these mutations , when analyzed in loss-of-function clones , all but Pcl resulted in repression of Ubx in the haltere ( due to derepression of more posterior Hox genes; data not shown ) and therefore could not be used to assess their role in silencing . UAS-GFP Ubx-Gal4lac1/TM6B UAS-GFP ( X ) ; Ubx-Gal4LDN/TM6B UAS-GFP ( X ) ; Ubx-Gal4M1/TM6B FRT 82B UbxDf ( 109 ) /TM6B hs-UbxIa22/TM6B [26] Ubx9–22/TM6B vg-Gal4 UAS-GFP vg-Gal4 UAS-GFP UAS-flp act>cd2>Gal4 UAS-UbxHA FRT42D Ub-GFP FRT42D Ub-GFP; hs-UbxIa22/Tm6B FRT42D UAS-GFP; FRT42D arm-lacZ; Ubx-Gal4Lac1 hs-Gal4 ( Previously described by [14] UAS-Antp UAS-AUA UAS-UU* ( * refers to a stop codon inserted immediately following the homeodomain ) UAS-AAU UAS-AUU Whole-fly genomic DNA was isolated from the lab stock containing the Ubx-Gal4lac1 enhancer trap ( yw122; If/CyoGFP; Ubx-Gal4lac1 UAS-GFP/TM6B ) and the GFP+ F2 progeny of the Ubx-Gal4lac1 stock crossed to strains Tw2 , NC2-76 , and NC2-80 . Silencing was confirmed to be occurring in these crosses . The F2 progeny were generated by crossing Gal4lac1UAS-GFP F1 males to wild population females , precluding the possibility of recombination between chromosomes of the lab and wild genotypes . Primers were designed to amplify ∼200 bp in the Gal4 and UAS transgenes to determine their relative abundance in each genotype . A ∼200 bp sequence in the 5′UTR of homothorax was amplified to normalize for different amounts of template DNA . PCR amplification was performed in triplicate using Applied Biosystems 7300 Real Time PCR System , and SYBR Green PCR Master Mix . Product dissociation curves were examined to ensure that each primer set only amplified a single product . CT values and amplification curves were consistent with an equal abundance of the Gal4 and UAS sequences in all genotypes . Standard protocols were used with the following primary antibodies: Rabbit anti-β-Gal 1:10 , 000 ( Cappel ) Mouse anti-En 1:10 ( Hybridoma Bank ) Mouse anti-Ubx 1:20 Rat anti-HA 1:100
Gene expression is generally governed by cis-regulatory elements , also called enhancers . For genes whose expression levels must be tightly controlled , enhancer activities must be tightly regulated . In this work , we show that enhancers that control the expression of the Hox gene Ultrabithorax ( Ubx ) in Drosophila are regulated by a negative autoregulatory feedback mechanism . Negative autoregulation can be triggered by less than a two-fold increase in Ubx levels or by varying the genetic background . Together , these data reveal that enhancer activities are not always hardwired , but instead may be sensitive to genetic and environmental variation and , in some cases , to the amount of gene product they regulate . The finding that enhancers are sensitive to genetic background suggests that the regulation of gene expression is more plastic than previously thought and has important implications for how transcription is controlled in vivo .
[ "Abstract", "Introduction", "Results/Discussion", "Materials", "and", "Methods" ]
[ "developmental", "biology/molecular", "development", "molecular", "biology/translational", "regulation", "genetics", "and", "genomics/gene", "expression" ]
2009
Regulation of Ubx Expression by Epigenetic Enhancer Silencing in Response to Ubx Levels and Genetic Variation
Scedosporium apiospermum is part of the Pseudallescheria-Scedosporium complex . Peptidorhamnomannans ( PRMs ) are cell wall glycopeptides present in some fungi , and their structures have been characterized in S . apiospermum , S . prolificans and Sporothrix schenckii . Prior work shows that PRMs can interact with host cells and that the glycopeptides are antigenic . In the present study , three monoclonal antibodies ( mAbs , IgG1 ) to S . apiospermum derived PRM were generated and their effects on S . apiospermum were examined in vitro and in vivo . The mAbs recognized a carbohydrate epitope on PRM . In culture , addition of the PRM mAbs increased S . apiospermum conidia germination and reduced conidial phagocytosis by J774 . 16 macrophages . In a murine infection model , mice treated with antibodies to PRM died prior to control animals . Thus , PRM is involved in morphogenesis and the binding of this glycopeptide by mAbs enhanced the virulence of the fungus . Further insights into the effects of these glycopeptides on the pathobiology of S . apiospermum may lead to new avenues for preventing and treating scedosporiosis . The filamentous and saprophytic fungus Scedosporium apiospermum is an emerging clinically important pathogen that causes localized as well as disseminated infections in both immunocompetent and immunocompromised hosts [1]–[2] . S . apiospermum is an important cause of mycetoma , acquired by traumatic inoculation . Additionally , the fungus can be acquired through inhalation followed by deposition into the lungs or paranasal sinuses , with similar symptoms to those observed in diseases secondary to Aspergillus fumigatus . Dissemination most frequently occurs in immunocompromised individuals and is associated with high mortality rates ( >75% ) [3] . A large number of cases of pseudallescheriosis/scedosporiosis have been reported in children with cystic fibrosis [4] , patients with leukemia [5] and organ transplant recipients [6]–[7] . Despite the rising frequency of S . apiospermum infections , its pathogenesis and the mechanism by which S . apiospermum evades host pulmonary defenses and reaches other organs are poorly understood . Recently , the innate immune response has been shown to be critical for host defense against Pseudallescheria -Scedosporium complex fungi [8] . Importantly , these species are largely resistant to traditional antifungals such as amphotericin B; however , newer triazoles , such as voriconazole , can be therapeutic [3] . Microbial adherence is a prerequisite for colonization and an essential step in the establishment of infection [9] . The composition of the fungal cell surface is of primary importance in the cell response to environmental stimuli and , in this context , glycopeptides are important determinants for many biological activities . Elucidation of the primary structure of surface microbial glycopeptides , especially those that function as virulence determinants , is of great relevance to understanding the pathobiology of a microbe . The mechanisms of adherence and invasion have been studied in several fungal species , including Candida albicans , Histoplasma capsulatum , A . fumigatus , Paracoccidioides brasiliensis , Sporothrix schenckii , Fonsecaea pedrosoi , Trichophyton mentagrophytes and Trichophyton rubrum ( reviewed in [9] ) . However , little is known regarding the adherence and invasion mechanisms for the S . apiospermum/S . apiospermum species complex , although their conidia can attached to and are internalized by HEp 2 cells through a lectin-mediated process involving a peptidorhamnomannan of the fungal cell wall [10] . A complex glycopeptide peptidorhamnomannan ( PRM ) isolated from mycelial forms of S . apiospermum has been characterized chemically and immunologically [11] . S . apiospermum PRM consists of a peptide chain substituted with both O-linked and N-linked glycans . It reacts strongly with antiserum against S . apiospermum mycelium , and this interaction is weakly inhibited by the PRM from S . schenckii or by peptidogalactomannan from A . fumigatus , suggesting that S . apiospermum expresses antigens that are related to S . schenckii peptidopolysaccharide [12] and the major Aspergillus glycopeptide [11] , [13] . To gain a better understanding of PRM function in S . apiospermum , we generated murine monoclonal antibodies ( mAbs ) against PRM . Interestingly , the mAbs promoted conidial germination . Infection of macrophage monolayers with opsonized S . apiospermum conidia resulted in a significant increase in the killing of macrophages and a decrease in phagocytosis in comparison with non-opsonized conidia . Mice that received the mAbs prior to S . apiospermum infection died more rapidly than control animals . These results suggest that mAbs to PRM change the physiology of S . apiospermum cells by altering the kinetics of germination and modifying fungal-host interactions , which dramatically impacts the outcome of disease . S . apiospermum strain HLPB ( formerly Pseudallescheria boydii ) , isolated from a patient with eumycotic mycetoma , was kindly supplied by Dr . Bodo Wanke from Instituto de Pesquisa Evandro Chagas , Fundação Oswaldo Cruz , Rio de Janeiro , Brazil . The isolate was confirmed as S . apiospermum by molecular methods developed by Dr . Kathrin Tintelnot ( Robert Koch-Institut , Berlin , Germany ) . The sequencing of the ITS regions revealed that this strain belongs to clade 4 ( S . apiospermum sensu stricto ) according to the taxonomy proposed by Gilgado et al . [14] . Cells were maintained on potato dextrose ( PD ) agar slants . Fresh cultures were inoculated in PD liquid culture medium and incubated for 7 days at 25°C with shaking . Conidia were grown on Petri dishes containing PD agar medium at 30°C . After 7 days in culture , conidia were obtained after washing the plate surface with phosphate-buffered saline ( PBS- 10 mM NaH2PO4 , 10 mM Na2HPO4 pH 7 . 0 and 150 mM NaCl ) and filtering through gauze to remove hyphal fragments and debris . Conidia were washed three times and counted with a hematocytometer . S . apiospermum clade 5 ( S . apiospermum sensu stricto ) [14] and Scedosporium prolificans strains were kindly supplied by Dr . J . Guarro from Unitat de Microbiologia , Facultat de Medicina e Institut d'Estudis Avançats , Réus , Spain . Cells were maintained on Sabouraud ( SAB - 2% glucose , 1% peptone , 0 . 5% yeast extract ) agar slants . Fresh cultures were inoculated in SAB liquid culture medium and incubated for 7 days at 25°C with orbital shaking . Conidia were grown on Petri dishes containing SAB agar medium at 30°C . After 7 days in culture , conidia were obtained as described for S . apiospermum . Candida albicans SC5314 ( ATCC , MYA-2876 ) , Candida parapsilosis GA1 [15] , and Histoplasma capsulatum G217B ( ATCC , 26032 ) were maintained at −80°C in 35% glycerol . Yeast phases of the Candida species were produced in YPD ( 1% yeast extract , 2% bactopeptone , 2% glucose ) at 30°C and H . capsulatum yeasts were grown in YPD at 37°C . A goat anti-mouse ( GAM ) IgG ( Southern Biotechnology Associates Inc . , Birmingham , AL ) was used as an isotype-matched control in all the experiments . The fluorescence probe 5- ( and 6 ) -carboxytetramethylrhodamine succinimidyl ( NHSRho ) was obtained from Molecular Probes ( Eugene , OR ) . Triton X-100 , fluorescein isothiocyanate ( FITC ) -dextran ( molecular weight , 70 , 000 ) , MTT [3- ( 4 , 5-dimethyl-thiazol-2-yl ) 2 , 5-diphenyl tetrazolium bromide] , and paraformaldehyde were from Sigma-Aldrich ( St . Louis , MO ) . Tetramethyl Rhodamine Isothiocyanate ( TRITC ) was obtained from Southern Biotechnology Associates Inc . SuperBlock buffer in phosphate-buffered saline ( PBS ) and EZ-Link sulfo-N-hydroxysulfosuccinimide – biotin kit were from Pierce ( Rockford , IL ) . The macrophage-like cell line J774 . 16 ( derived from a reticulum cell sarcoma ) was obtained from the ATCC . The J774 . 16 cells were grown with DMEM ( Life Technologies , Carlsbad , CA ) containing 10% fetal calf serum ( Gemini Bio-Products , Woodland , CA ) , 10% NCTC-109 ( Life Technologies ) , 1% nonessential amino acids ( Mediatech , Manassas , VA ) and 1% Penicillin-Streptomycin ( Invitrogen , Carlsbad , CA ) at 37°C in 5% CO2 . The cell counts and viability for all the experiments were determined by trypan blue vital dye exclusion using a hemacytometer . For conidia , this method demonstrated an initial viability of >95% , as confirmed by plating . PRM was produced as described [11] . All murine studies were performed in accordance with the rules and regulations of animal welfare at Federal University of Rio de Janeiro ( UFRJ , RJ , Brazil ) and the Albert Einstein College of Medicine ( Bronx , NY , USA ) . Four 6 week old female BALB/c mice ( from UFRGS , RS , Brazil ) were immunized intraperitoneally with 20 µg of PRM emulsified in complete Freund's adjuvant for the first injection and 20 µg of antigen in incomplete Freund's adjuvant for the subsequent three injections . Injections were spaced at a two-week intervals and the immune response against PRM was monitored by indirect ELISA . Sera were obtained 1 week after the last immunization and analyzed for the presence of antibodies to PRM by ELISA , using 500ng of the antigen per well . The animal whose serum showed the highest level of immunization ( OD value 6 . 5 times higher than the serum of a non-immunized animal ) was boosted intraperitoneally with 50 µg of the antigen without adjuvant 3 days prior to spleen removal and fusion of splenocytes with murine myeloma tumor cells ( SP2/0 ) using polyethylene glycol ( PEG ) . Hybridomas that survived selection in hypoxanthine-aminopterin-thymidine ( HAT ) medium were screened for antibody production by ELISA using 100 ng of the antigen per well . Positive hybridomas were cloned by limiting dilution and cryopreserved . The isotype of the selected mAbs was determined with an isotyping kit ( Sigma-Aldrich ) according to the manufacturer's instructions . The murine mAbs C7 , C11 and F10 of immunoglobulin G1 ( IgG1 ) isotype were selected and used in all assays . Clones were injected into the peritoneal cavity of BALB/c mice previously treated with Pristane to generate ascites and the antibodies to PRM were subsequently purified by protein G affinity chromatography . The purified mAbs were screened to ensure the absence of endotoxin with a Limulus amebocyte assay kit ( BioWhittaker Inc . , Walkersville , MD ) . PRM was added ( 25–50 ng of protein in 50 µL of PBS [0 . 01M; pH 7 . 2] ) per well or 1×106 swollen conidia ( S . apiospermum strain HLPB ( clade 4 ) , S . apiospermum strain clade 5 and S . prolificans ) or yeasts ( C . albicans , C . parapsilosis and H . capsulatum ) in 50 µl PBS per well , followed by incubation for 1h at 37°C and then overnight at 4°C . Plates were washed three times with washing buffer ( 10 mM Tris-buffered saline [TBS] , 0 . 1% Tween 20 [pH 7 . 3] ) and blocked with 1% BSA in PBS ( blocking buffer ) . Serial two-fold dilutions of a 100 µg/mL solution of the different mAbs in blocking buffer were added in duplicate to the wells and incubated at 37°C for 1 h . After three washes , the plates were incubated at 37°C for 1 h with GAM IgG alkaline phosphatase conjugate ( Southern Biotech , Birmingham , AL ) diluted 1∶1 , 000 in blocking buffer at a final volume of 100 µL per well . Plates were washed three times , and then the enzymatic reaction was developed with the addition of pNPP in substrate buffer at 37°C for 30 min . Absorbances were measured on a microplate reader ( Bio-Tek μQuant ) at 405 nm . Immunofluorescence analysis was performed by co-incubating the mAbs with S . apiospermum conidia . In order to assess mAb binding , conidia were incubated in SuperBlock for 1 h at 37°C , washed three times with PBS and incubated with either a mAb to PRM or an isotype-matched control in 100µg/mL in SuperBlock for 1 h at 37°C . The cells were washed and incubated in 100 µl of GAM IgG conjugated with TRITC at a 1∶100 dilution in SuperBlock for 1 h at 37°C . After three washes , cells were suspended in 50 µL of a mounting solution containing 0 . 01 M of N-propylgallate diluted in PBS∶glycerol ( 1∶1 , vol/vol ) . Ten microliters of the suspension was applied to a microscope slide and examined with an Olympus AX70 fluorescence microscope ( Olympus America Inc . , Center Valley , PA ) using a 620-nm filter and a magnification of ×40 . MAbs were biotinylated with a biotin commercial kit , according to the manufacturer's instructions ( Pierce , Rockford , IL , USA ) . ELISA plates were generated as described above , except the concentration of PRM was 25ng/well . After blocking , a constant concentration of the biotinylated mAb was incubated with decreasing concentrations of a different non-biotinylated mAb in blocking buffer for 1 h at 37°C . After washing , avidin conjugated with alkaline phosphatase ( Sigma-Aldrich ) was added , and the preparation was incubated for 1 h at 37°C . Absorbance at 405 nm was recorded after the reaction was developed with pNPP . To remove N-linked glycans , 10 µg of PRM was treated with 100mU of PNGase F ( P0704 , New England Biolabs ) at 37°C for 20 h . ELISA plates were made with 25 ng/well of the treated PRM , the mAbs were applied , and the reaction developed as described above . ELISA plates were made with 25ng/well of purified PRM and the wells were incubated with Proteinase K ( 2 . 5 µg/mL; Sigma-Aldrich ) in 1% SDS solution for 1 h at 4°C [16] . After washing , the mAbs were applied and the reaction developed as described above . Phagocytosis assays were performed as described previously [17] . Briefly , macrophage-like J774 . 16 cells were plated at a concentration of 5×105 cells per well in 24-well cell culture polystyrene plates and grown overnight at 37°C in the presence of 5% CO2 . S . apiospermum conidia were collected after 7 days of growth , washed three times with PBS and 1×106 conidia were incubated with 100 µg/mL of a mAb to PRM , IgG isotype control , or PBS for 1 h at 37°C . After washing , the fungal cells were added to the macrophages at a ratio of 5∶1 ( conidia∶macrophage ) , and the plates were incubated for 1 h at 37°C in the presence of 5% CO2 . Samples were prepared in triplicate . Wells were washed with PBS and fixed with a 40% methanol solution . The numbers of macrophages and conidia were recorded for each field , and at least 200 macrophages were counted . The phagocytosis index was defined as the ratio of the number of intracellular conidia relative to the number of macrophages counted . A second phagocytosis experiment was performed with J774 . 16 cells that were incubated with native PRM ( 100 µg/mL ) 1 h before the interaction with S . apiospermum conidia . To determine the mechanism of conidial engagement with macrophages , additional phagocytosis experiments were performed using fluorescence activated cell sorting ( FACS ) . J774 . 16 cells were incubated with anti-mouse CD11a , anti-mouse CD11b , anti-mouse CD11c , anti-mouse CD14 or anti-mouse CD18 ( Southern Biotechnology Associates Inc . ) antibodies prior to interaction with S . apiospermum conidia . The conidia were incubated in a solution of 0 . 5 mg/mL of FITC in PBS at 37°C for 30 min The conidia were then washed three times with PBS and incubated with treated and control macrophages for 60 min . Samples were washed three times with PBS to remove extracellular conidia . For FACS measurements , cells were suspended in 1 mL PBS and analyzed on a FACS-Calibur™ , equipped with a 5 W argon laser ( Coherent ) tuned to 488 nm , output power 250 mW ( Becton Dickinson , San Jose , CA ) . At least 10 , 000 events were enumerated for each condition [18] . The growth of S . apiospermum conidia in the presence of mAbs was evaluated by incubating the fungus with mAbs to PRM , isotype-matched control mAb , or PBS prior to co-culture with macrophages . Washed conidia were added to wells containing J774 . 16 cells at a ratio of 5∶1 and incubated for 2 h . The cultures were washed with cold PBS , and the macrophages were lysed by adding sterile water . Aliquots were plated into potato dextrose agar plates and incubated at 30°C . The percentage of growth was determined by comparing the number of CFU for S . apiospermum conidia pretreated with mAbs to the number of CFU for untreated conidia . To further explore whether the mAbs affected the intracellular fate of the fungus in macrophages , fusion of phagosomes and lysosomes was evaluated as described [19] . Monolayers of J774 . 16 cells were incubated in fresh non-phenol-red medium with 0 . 5 mg/mL FITC-dextran for 4 h at 37°C in the presence of 5% CO2 . Cells were washed three times with PBS and incubated overnight in medium alone . S . apiospermum conidia were collected , washed , and incubated with 40 µg/mL NHSRho at 4°C for 1 h . Conidia were washed and incubated with 100 µg/mL of mAb to PRM , control mAb or PBS . Conidia were washed , suspended in DMEM , and added to the culture of J774 . 16 cells at a ratio of 5∶1 . The plates were then incubated for 1 h at 37°C in the presence of 5% CO2 . The cells were fixed in 3 . 75% paraformaldehyde for 20 min at room temperature . Cells were observed by phase-contrast and fluorescence microscopy at a magnification of ×400 . In J774 . 16 macrophages , the number of rhodamine-labeled S . apiospermum conidia with co-localization of FITC-dextran and the total number of intracellular labeled conidia for each condition were counted to determine the percentage of phagosomes fused with lysosomes , which were characterized by red fluorescently labeled conidia co-localized with a green fluorescent dextran ring . To evaluate the capacity of S . apiospermum conidia to germinate and survive in acidic media , cells were incubated for 4 h in DMEM at pH 7 . 2 or pH 4 . 0 , and the number of germinated conidia was determined . For live cell imaging , phagocytosis was carried out as described above . Briefly , 5×104 macrophages were plated on polylysine coated coverslip bottom MatTek plates and allowed to adhere overnight . The media was then removed and replaced with fresh media containing S . apiospermum conidia ( S . apiospermum to macrophage ratio of 5∶1 ) . This assay was also performed with conidia opsonized with mAbs F10 , C7 , C11 and irrelevant mAb ( 100 µg/mL ) . Macrophages and conidia were incubated together for 1 h to allow for completion of phagocytosis , washed once with fresh media , replenished with 2 mL feeding media and followed by time-lapse imaging every 10 min . Images were collected at 10× using the Axiovert 200 M inverted microscope and photographed with an AxiocamMR camera controlled by the Axio Vision 4 . 4 software ( Carl Zeiss Micro Imaging , NY ) . This microscope was housed in a Plexiglas box and the temperature was stabilized at 37°C with a forced air heater system . The plate lid was kept in place to prevent evaporation , and 5% CO2 was delivered to a chamber locally at the culture dish . Movie animations were created using ImageJ software [20] . The germination assay was performed as described with minor modifications [21] . S . apiospermum conidia ( 1×105/mL ) were incubated in DMEM in 24-wells plates at 37°C with 100 µg/mL of mAb to PRM , control mAb or PBS . At 2 , 3 , 4 , 8 and 24 h the wells were analyzed and germinated conidia were counted by microscopy . At least 100 conidia per field were counted , and the mean value of three independent counts was calculated . Percent germination was calculated as germinated conidia/total counted conidia ×100 . J774 . 16 cells were plated at 105 cells per well in 96-well polystyrene tissue-culture plates . Conidia were pre-incubated with mAb to PRM , nonspecific IgG , or PBS for 1 h at 37°C prior to addition to the macrophage monolayer . After 1 h of incubation , aliquots from the supernatant were collected at different intervals . Nitric oxide levels were measured using a commercial Griess reagent kit ( Promega ) . Similarly , superoxide dismutase activity was determined using a method that involves generation of superoxide and reduction of the tetrazolium dye MTT to its formazan , which is measured at 570 nm [22] . For survival studies , groups of 6 BALB/c mice ( National Cancer Institute ( NCI ) , Frederick , MD ) were injected intraperitoneally with either 250 µg of a mAb to PRM , an isotype-matched control mAb , or PBS . MAb F10 was also used at 100 and 500 µg per mouse . Two h later , the mice were intratracheally infected with 2 . 5×107 S . apiospermum conidia . A similar model was tested using intravenous inoculation with 1 . 25×106 S . apiospermum conidia . Mice were monitored closely and their survival determined . For survival studies using a model of invasive candidiasis , 6- to 8-week-old A/J ( NCI ) mice were inoculated intraperitoneally with 250 µg of mAb F10 , an IgG isotype-matched control , or PBS 2 h prior to intravenous injection of 1×106 C . albicans yeast cells . Animals were euthanized at day 7 after infection and the kidneys were removed , weighed , homogenized and plated onto YPD agar at 30°C for CFU determinations . Phagocytosis assays were performed as above , and C . albicans yeasts were added to macrophage monolayers ( 5∶1 yeasts∶macrophage ) . Additionally , a second phagocytosis experiment was performed with J774 . 16 cells that were incubated with native PRM ( 100 µg/mL ) 1 h before the addition of yeast cells . Statistical analyses were performed using GraphPad Prism version 5 . 00 for Windows ( GraphPad Software , San Diego CA ) . Unless otherwise noted , a one-way analysis of variance using a Kruskall-Wallis nonparametrical test was used to compare the differences between groups , and individual comparisons of groups were done using a Bonferoni posttest . A t test was used to compare the number of CFU for different groups . A 90–95% confidence interval was determined in all experiments . Survival results were analyzed by a Kaplan-Meyer test to determine the differences between groups . Three IgG1 mAbs , C7 , C11 , and F10 , were generated from a mouse immunized with PRM . Immunofluorescence microscopy revealed that the three mAbs against PRM could bind resting conidia , swollen conidia and hyphae , but only mAb F10 bound germinating conidia , labeling the cell body and the apical portion of germinative tube ( Figure 1A and B ) . Fluorescence images of mAbs C7 ( Figure 1C and D ) and C11 ( Figure 1E and F ) show labeling of swollen conidia . An indirect ELISA was used to quantitatively evaluate mAb binding to PRM . There were variations in binding by the different mAbs and the relative order of reactivity was C7≥C11>F10 ( Figure 1G ) . These mAbs can recognize native PRM ( Figure 1G ) and fixed swollen conidia by ELISA ( Figure 1H ) , suggesting that the mAbs recognize epitopes exposed on the native structure of PRM on the conidia cell surface . Competition ELISA assays showed that the three mAbs to PRM bind extremely close or overlapping epitopes ( Figure 2A ) . Indirect ELISAs using native PRM treated with PNGase F or proteinase K in 1% SDS solution were used in order to analyze which portion of the glycoprotein is recognized by the mAbs . Removal of the carbohydrate from PRM significantly reduced the binding of mAb F10 to PRM ( p<0 . 05 ) ( Figure 2B ) . In contrast , no significant difference was observed when the protein portion was removed , showing that the epitopes do not contain protein moieties . Similar results were observed for mAbs C7 and C11 ( data not shown ) . Indirect ELISAs were performed using different fungal cells to evaluate the specificity of the mAbs . Interestingly , the mAbs to PRM recognized conidial forms of S . apiospermum clade 5 ( Figure S1A ) and S . prolificans ( Figure S1B ) as well as yeast forms of H . capsulatum ( Figure S1C ) , C . albicans ( Figure S1D ) and C . parapsilosis ( Figure S1E ) . Hence , there appears to be conserved mannose-containing structures on the cell surfaces of these fungi . S . apiospermum conidia are effectively phagocytosed by J774 . 16 macrophages . The addition of soluble PRM significantly reduced phagocytosis by 25% ( Figure 3A ) . To investigate the involvement of cellular receptors on conidia uptake , specific blockers were used . A significant reduction in the phagocytosis of S . apiospermum conidia was observed using either anti-CD11b or anti-CD18 antibodies , indicating a requirement for CR3 in the interaction between conidia and macrophages ( Figure 3A ) . Furthermore , mAbs F10 , C7 , and C11 significantly decreased the phagocytosis of conidia compared to conidia incubated with PBS or an isotype-matched IgG control mAb by 31 , 33 and 19% , respectively ( P<0 . 05 ) ( Figure 3B ) , suggesting blocking of phagocytosis by these mAbs . Although phagocytosis was reduced by the mAbs , there were significant increases of 30–40% in the intracellular survival of phagocytosed S . apiospermum when compared with PBS or an isotype-matched control mAb ( Figure 3C ) . Fusion of lysosomes with phagosomes was observed by the detection of FITC-dextran with S . apiospermum conidia within macrophages . The majority of phagosomes containing S . apiospermum opsonized with mAb F10 demonstrated co-localization of FITC-dextran with NHSRho-labeled conidia ( Figure 4A and B ) , which was significantly increased compared to the other conditions examined , including mAb C7 ( Figure 4A and C ) , mAb C11 ( Figure 4A and D ) and irrelevant control mAb ( Figure 4A and E ) . In order to evaluate the capacity of S . apiospermum conidia to germinate and survive in acidic media , cells were incubated in DMEM at pH 7 . 2 or pH 4 . 0 . Interestingly , there was a significant increase ( 127% ) in the number of germinated conidia at pH 4 . 0 , compared to pH 7 . 2 ( Figure 4F ) . In order to evaluate if macrophages were able to eliminate the fungus , S . apiospermum conidia was co-cultured with J774 . 16 cells . We observed that the macrophages could not eradicate the S . apiospermum ( Video S1 ) . Further , S . apiospermum conidia could germinate inside and destroy the macrophages ( Video S1 ) . The same assay was performed with conidia opsonized with mAbs F10 , C7 and C11 , and similar results were achieved ( data not show ) . The metabolic activity in S . apiospermum conidia in the presence of the three mAbs against PRM was examined by MTT reduction assay at 1 , 2 , 3 , 4 , 5 , 6 and 20 h ( Figure 5A ) . The greatest increase in activity was at 6 h , where the increase was 48% , 49% and 51% for F10 , C7 and C11 , respectively , in comparison with controls . There were no differences at 20 h ( p>0 . 05 ) , which is consistent with the germination of all viable conidia in medium alone by this time . The influence of mAbs F10 , C7 and C11 mAbs on conidia germination in vitro was examined . The evaluation was based on visible germinative tube formation . After 4 h of incubation , 77–80% germination was observed in the presence of each of the mAbs to PRM , compared to 59% germination for controls ( Figure 5B and 5C–F ) . Addition of mAbs F10 , C7 and C11 altered superoxide production by macrophages . A decrease in superoxide production occurred in the presence of the mAbs in comparison with the controls ( p<0 . 05 ) ( Figure 6A ) . In contrast , the release of nitric oxide by J774 . 16 cells co-cultured with S . apiospermum conidia was not affected by opsonization ( Figure 6B ) . To determine the effect of the mAbs to PRM in scedosporiosis , mice were treated with mAb to PRM , irrelevant antibody or PBS and then intravenously or intratracheally infected with S . apiospermum conidia . In the intravenous model , administration of 250 µg of mAbs F10 or C7 accelerated disease , resulting in 100% mortality by day 12 ( p<0 . 05 ) ( Figure 7A ) . Although 250 µg of mAbs C11 also enhanced mortality , the difference from controls was not statistically significant . To assess for the possibility of a prozone effect with the dose selected , mice also were treated with 100 or 500 µg of the mAb F10 . Administration of 500 µg of mAb F10 resulted in 100% mortality , but 100 µg was not significantly different from controls ( Figure 7B ) . Intratracheal infection resulted in 100% mortality for each condition examined . MAbs F10 and C7 were able to enhance mortality , but the difference from controls was only significant for mAb F10 ( p<0 . 05 ) ( Figure 7C ) . New medical technologies and therapies have dramatically amplified the numbers of severely immunosuppressed patients , increasing the risk for disease from several opportunistic yeasts and filamentous fungi [23] . In this context , S . apiospermum is emerging as an important human pathogen [23] . The increase in scedosporiasis is especially clinically concerning as numerous in vitro studies have shown that antifungal drugs , such as amphotericin B , nystatin , liposomal nystatin , itraconazole , flucytosine , fluconazole , terbinafine and ketoconazole , have low in vitro activity against fungi of the Scedosporium-Pseudallescheria complex [5] , [24]–[25] . The implementation of antibody-based therapeutics remains largely empirical in part because the factors involved in efficacy are often poorly understood and activation of inflammatory pathways by antibodies may be detrimental [26]–[29] . Although we set out to identify mAbs that would protect against S . apiospermum disease , our panel of mAb to S . apiospermum PRM modified the biology of the fungus to enhance virulence . The mAbs to PRM bound antigen located on the surface of S . apiospermum strain HLPB mycelia and/or conidia . The mAbs also reacted with PRM-like compounds on the cell surface of C . albicans , C . parapsilosis , H . capsulatum , S . apiospermum clade 5 and S . prolificans . S . apiospermum clade 5 and S . prolificans both display surface PRM with only minor differences from S . apiospermum PRM structures [30] . Structural studies on S . apiospermum PRM have shown that α ( 1→2 ) -linked mannosyl units are also located in side chains that have α-mannosyl or α-rhamnosyl terminal non-reducing units [31] , probably located in the N-linked glycan component of the PRM [32] . In the present study , we demonstrated that the carbohydrate portion of the PRM molecule is essential for recognition of the IgG1 mAbs F10 , C7 and C11 , since PNGase F-treatment of PRM ( lacking N-glycans ) significantly reduced mAb binding . The cross-reactivity with the yeasts is presumably related with the presence of α-1 , 2-mannosyl residues on the surface of these fungi . By competition ELISA , the mAbs against PRM competed for PRM binding , but they had distinct labeling patters by immunofluorescence . These results suggested that mAbs F10 , C7 and C11 recognized very close or overlapping epitopes . Changes in the cell surface characteristics of resting conidia during swelling and germination may interfere with mAb recognition . In S . apiospermum , glucosylceramides are detectable on the surface of mycelia and pseudohyphae but not conidial forms , suggesting a differential expression of these glycoconjugates according to the morphological phase of the fungus [33] . Antibodies against glucosylceramides can modify the transition conidia-mycelium in S . apiospermum and the cellular differentiation in C . albicans [33] . Fonsecaea pedrosoi differentially expresses sialylglycoconjugates and sialidase in distinct morphological stages , producing these molecules in conidial and mycelial forms , but not in sclerotic cells , suggesting that the sialic acid expression in F . pedrosoi varies according to the morphological condition [34] . Many virulent strains of H . capsulatum possess α- ( 1 , 3 ) -glucan in the yeast cell wall , although it is absent in the mycelial form [35] . This polysaccharide is likewise present in the yeast phase of two other pathogenic dimorphic fungi , including Paracoccidiodes brasiliensis [36] and Blastomyces dermatitidis [37] . In each of these species , spontaneous variants that have lost their α- ( 1 , 3 ) -glucan have also lost virulence . So , the presence and the native structure of PRM could be different according with the stage of S . apiospermum ( resting conidia , swollen conidia , hyphae ) , which could affect mAb binding . Antibodies can facilitate clearance of fungi from the lungs , bloodstream , or other tissues through a combination of opsonization via the Fc fragment of the antibody and opsonization via classical pathway deposition of C3 [38] . In the present work we decided to investigate the involvement of the mAbs and of the PRM molecule in the phagocytosis of S . apiospermum conidia by J774 . 19 macrophage-like cells . Our results demonstrated the mAbs against PRM reduce the uptake of S . apiospermum conidia and C . albicans yeasts by J774 . 16 cells and opsonized conidia have increased intracellular survival . Previous work from our group using HEp2 cells showed that when the conidial cells of S . apiospermum were pre-incubated with polyclonal antibodies to PRM , the adherence and endocytosis processes were both inhibited in a dose-dependent manner [10] . These results suggested an active participation of the fungus in the interaction process , since HEp2 cells are considered nonprofessional phagocytic cells . In professional phagocytic cells like macrophages , Fc receptors are constitutively active for phagocytosis [39] . Consequently , it is expected that opsonization of yeasts by mAbs facilitates the ingestion of conidia by macrophages . For example , H . capsulatum Hsp60-specific mAbs augment yeast cell phagocytosis by J774 . 16 macrophage-like cells [17] . However , protective mAbs against the glucuronoxylomannan ( GXM ) component of the Cryptococcus neoformans capsular polysaccharide are not always effective mediators of cryptococcal phagocytosis [40] . The mechanism for differential levels of phagocytosis following opsonization by intact IgG is not known . It is possible that , depending on epitope specificities , alterations of the antigen structure may occur on binding that interfere with the interaction between the bound IgG and macrophage Fc receptors . Our in vitro experiments were done in the absence of sera , which does not allow us to directly address the role of complement in the opsonization via classical pathway deposition of C3 . However , the mAbs used in this study were all IgG1 subclass , which does not activate complement well [41] . Other subclasses , especially IgG2a and IgG2b , could contribute to complement-dependent-opsonopahgocytosis of conidia in a milieu with serum . The ability of complement activation by antibodies may be related to their protective effect in infections , as documented in response to encapsulated bacteria [42]–[44] . Interestingly , only 1 of a pair of IgM mAbs to cryptococcal polysaccharide that activated complement in vitro were protective in murine models of cryptococcosis [45] . Furthermore , additional mAbs to cryptococcal polysaccharide suppress rather than enhance binding of C3 to the cryptococcal capsule and this suppressive activity is dependent on the antibody isotype and epitope specificity [46] . In addition to isotype , the epitope specificity influences the biological activity of antibodies [47] . Hence , the lack of protection in our model by the IgG1 mAbs to PRM may be due their weak ability to activate classical pathway deposition of C3 , but it may be also be associated with blocking C3 binding . Although the F10 mAb increased intracellular survival , it also enhanced phagolysosomal fusion , which is often associated with increased killing of microbes within these structures [48] . S . apiospermum conidia , in the presence or absence of mAbs anti-PRM , were able to destroy macrophages . Interestingly , we found that S . apiospermum conidia could readily survive acidic conditions with enhancement of growth . A possible explanation for the observed growth benefit is that S . apiospermum proteolytic enzymes ( metallopeptidases or metal dependent peptidases ) require an acidic pH for optimal activity and these peptidases are key enzymes implicated in microbial metabolism and virulence [1] , [49]–[51] . Surprisingly , mAbs C7 and C11 did not significantly enhance phagosomal fusion with lysosomes , which could be related with their different binding properties . The effect of a mAb depends on several factors , including the targeted antigen , its function , the cell surface density and characteristics of the mAb , including specificity , avidity , and isotype . When a fungal spore germinates , it produces a hypha , which in turn grows by increasing in length through the accumulation of newly formed substances on the hyphal wall [52] . Prevention of some fungal infections presumably requires control of either spore germination and/or hyphal growth . We found that mAbs F10 , C7 and C11 enhanced conidial germination compared to controls , indicating that these mAbs may have accelerated the modification of the inner wall structure . The increased metabolic activity shown by MTT analysis of S . apiospermum conidia and C . albicans yeasts exposed to the mAbs is consistent with the enhancement of cellular processes required for morphogenesis . Reactive oxygen and nitrogen species can impact fungal growth . Activated bronchoalveolar macrophages and neutrophils can kill H . capsulatum by mechanisms dependent on hydrogen peroxide and products of the nitric oxide synthase pathway , whereas fungistasis depends largely on products of the nitric oxide synthase pathway [53] . In cryptococcosis , superoxide dismutase is important because this enzyme can interfere with C . neoformans virulence by affecting the fungus growth inside the macrophages [54] . In this context , we decide to evaluate the profile of superoxide and nitric oxide in S . apiospermum infection in macrophages cells , when the conidia were opsonized by mAbs F10 , C7 and C11 . We found that macrophage superoxide production decreased in the presence of conidia treated with mAbs compared with controls . Although nitric oxide is another important part of the oxidative attack directed against many microbes , the production of nitric oxide was not affected by the mAbs to PRM , in comparison with controls [55]–[56] . MAbs have previously been described to enhance experimental systemic fungal disease , such as mAb 7B6 for H . capsulatum [17] and mAb 13F1 for C . neoformans [45] , [57] . We observed that mAbs F10 and C7 , but not C11 , could enhance the disease in murine models of intravenous and intratracheal infection . Similarly , mice pre-treated with mAb F10 and infected with C . albicans yeasts had higher fungal burdens in the kidneys at 7 days after infection compared to control mice . Additionally , the mAbs to PRM could enhance C . albicans growth and decrease the phagocytosis of C . albicans yeasts by J774 . 16 macrophages relative to controls . In the present study , we demonstrated that mAbs to PRM are either non-protective or disease-enhancing in our S . apiospermum infection models . Hence , administration of mAbs that bind PRM on the surface of S . apiospermum conidia decreases phagocytosis , increases intracellular survival , and increases germination that results in a survival advantage for the fungus during host-pathogen interactions . We additionally found that the mAbs interacted with diverse fungal species and modified the virulence of C . albicans . Hence , antibodies to glycocongugates may significantly impact the pathobiology of many fungi . Further studies are required to gain a more detailed understanding of the antagonist behavior of the mAbs to PRM in S . apiospermum disease and explore the impact of antibody responses to these important cellular structures .
The incidence of fungal infections has increased dramatically over the last 50 years , largely because of the increasing size of the population at risk , which especially includes immunocompromised hosts . Scedosporium apiospermum is a filamentous fungus that causes a variety of infections , ranging from localized disease to life-threatening disseminated infections . Glycoproteins are molecules present in the fungal surface and are comprised of carbohydrate and protein components . They are involved in different important functions in the fungal cell . Monoclonal antibodies can be used as therapeutic agents for infectious disease , but some factors involved in their efficacy are often not well understood . We found that monoclonal antibodies to glycoproteins present in fungal surface can be nonprotective and can even enhance the disease . The administration of these antibodies can affect functions of the fungal cell and the immune cells , resulting in a survival advantage for the fungus during interactions with the host .
[ "Abstract", "Introduction", "Materials", "and", "Methods", "Results", "Discussion" ]
[ "microbiology/cellular", "microbiology", "and", "pathogenesis", "microbiology/immunity", "to", "infections" ]
2010
Monoclonal Antibodies Against Peptidorhamnomannans of Scedosporium apiospermum Enhance the Pathogenicity of the Fungus
Human rabies is an encephalitic disease transmitted by animals infected with lyssaviruses . The most common lyssavirus that causes human infection is rabies virus ( RABV ) , the prototypic member of the genus . The incubation period of RABV in humans varies from few weeks to several months in some instances . During this prodromal period , neither antibodies nor virus is detected . Antibodies , antigen and nucleic acids are detectable only after the onset of encephalitic symptoms , at which point the outcome of the disease is nearly 100% fatal . Hence , the primary intervention for human RABV exposure and subsequent post-exposure prophylaxis relies on testing animals suspected of having rabies . The most widely used diagnostic tests in animals focus on antigen detection , RABV-encoded nucleoprotein ( N protein ) in brain tissues . N protein accumulates in the cytoplasm of infected cells as large and granular inclusions , which are visualized in infected brain tissues by immuno-microscopy using anti-N protein antibodies . In this study , we explored a mass spectrometry ( MS ) based method for N protein detection without the need for any specific antibody reagents or microscopy . The MS-based method described here is unbiased , label-free , requires no amplification and determines any previously sequenced N protein available in the database . The results demonstrate the ability of MS/MS based method for N protein detection and amino acid sequence determination in animal diagnostic samples to obtain RABV variant information . This study demonstrates a potential for future developments of rabies diagnostic tests based on MS platforms . Mass spectrometry ( MS ) based proteomics is a collection of rapidly evolving techniques being utilized as a diagnostic tool for infectious diseases . MALDI-TOF MS ( matrix-assisted laser desorption/ionization–time-of-flight ) is being used in clinical microbiology laboratories to identify an impressive spectrum of bacteria and fungi from infected specimens ( generally enriched by culturing prior to detection ) [1 , 2] . At present , there is no comparable MS-based approach for identification of viruses . As the majority of viruses encode only a few proteins , most of the proteome in diagnostic samples like plasma or tissue samples will be dominated by the host [3] . However , if viral proteins accumulate at high concentration or there was shutdown of host protein synthesis , it might be feasible for direct detection of viral proteins from diagnostic samples . As the primary diagnosis in post-mortem animals for rabies relies on viral protein detection in infected samples , we explored MS as a diagnostic option . In this study , we explored LC-ESI-MS/MS ( liquid chromatography–electrospray ionization tandem mass spectrometry ) for peptide mass and amino acid sequence determination . Rabies is an ancient disease known to humanity for over 5000 years . Initially , clinical signs exhibited by animals such as excessive salivation , aggression , choking or gagging were used to diagnose if the animal was rabid [4] . Once the causative agent of the disease was determined to be a viral infection , diagnosis relied on the ability of brain homogenate from suspect animal to cause infection in naïve animals or cell cultures [5–8] . Both methods required several days to weeks to confirm a RABV infection . The major breakthrough in rabies diagnosis was achieved after the invention of microscopic methods and histostaining , although initial methods still depended on non-specific staining reagents like Sellers stain to identify and differentiate Negri bodies , intra-cytoplasmic inclusions in the brain tissue of infected animals [9] . The inclusions were later characterized as a ribonucleoprotein complex ( RNP ) , predominantly comprised of nucleoprotein ( N protein ) expressed from the viral genome . RABV , the causative agent of rabies , is a bullet-shaped virus belonging to the Lyssavirus genus . Lyssaviruses are in the order Mononegavirales and the family Rhabdoviridae , characterized by a 12 kilobase unsegmented negative-sense RNA genome . The genome encodes five proteins starting with the N protein closest to the 3’ end , followed by the phosphoprotein ( P protein ) , the matrix protein ( M protein ) , the glycoprotein ( G protein ) and the large RNA dependent RNA polymerase ( L protein ) [10] . These proteins are differentially expressed , the genes closer to the 5’ end of positive sense RNA ( or 3’ end of negative sense genome ) are expressed at higher levels compared to downstream genes ( farther from 5’ end ) . The N protein mRNA transcripts are transcribed at higher levels to make it the most abundant viral protein synthesized after RABV infection [11] . N protein along with P and L proteins coat the viral genome to form the RNP , which accumulates in the cytoplasm of infected cells as large or granular inclusions [12] . Once immunological methods were developed , antibodies generated against RABV or RNP , comprised predominantly against the N protein , were utilized for specific detection of RABV antigens ( proteins expressed from RABV genome ) . The most widely used rabies diagnostic method , the direct fluorescent antibody ( DFA ) test or fluorescent antibody test , detects the N protein in brain tissue impressions with polyclonal or monoclonal antibodies ( mAbs ) directly conjugated to fluorescent compounds [13–15] . The DFA test is considered the gold standard in rabies diagnostics for the detection of RABV antigen in animal brain tissues suspected of rabies [15 , 16] . Alternatively , modified chromogenic-based detection methods , such as the direct rapid immunohistochemistry test ( DRIT ) , have also been developed for rabies diagnosis using anti-N mAbs or polyclonal antibodies without the need for fluorescent microscopy [17 , 18] . Non-microscopy based protein detection techniques for rapid , point of care diagnostics , such as the rabies immunochromatographic diagnostic ( RID ) tests , commonly known as lateral flow assay ( LFA ) are available . LFAs have provided mixed results with concerns on specificity and sensitivity of N protein detection [19–21] . The assay still relies on using a combination of antibodies specific to N protein and the ability to capture and detect protein in infected tissue lysates , which can be visualized by a colored band on LFA strips . Currently , these tests are not yet approved for regular diagnostics by the World Health Organization , but are helpful in countries where surveillance is lacking and for epidemiological studies [19] . In this study , we explored MS as an alternative method for rabies N protein detection . MS is an unbiased proteomics approach , non-amplifying , non-sequence specific technique and does not require specific reagents for protein detection [3 , 22] . We demonstrate detection of all RABV encoded proteins in purified or crude infected cell lysates by MS . Additionally , N protein was detected and the amino acid sequence was determined by MS/MS-based peptide fragment mass information from animal diagnostic samples for several RABV variants circulating in the United States ( U . S . ) . BSR ( a clone of Baby Hamster Kidney 21 cells ) or mouse neuroblastoma ( MNA ) cells ( CDC collection ) were cultured in E-MEM supplemented with 10% FBS ( Fetal bovine serum ) containing antibiotics ( Penicillin and Streptomycin ) and antimycotic ( Amphotericin B ) essential vitamins and L-glutamine . BSR cells were infected with the RABV ERA ( Evelyn Rokitnicki Abelseth ) virus strain at 0 . 01 multiplicity of infection for 2–5 days . The media supernatant was harvested , subjected to low speed centrifugation , followed by sucrose density gradient centrifugation to purify ERA virus particles . MNA cells ( T75 flask ) were either mock-infected or infected with RABV CVS-11 ( challenge virus strain , 10 X TC ID50 ) for 24 h , washed with PBS and harvested by centrifugation . The cell pellet was resuspended in 1X NuPAGE LDS sample buffer containing reducing agent ( ThermoFisher ) . Brain samples from animals ( CDC collection ) were submitted by the state public health , state veterinary , and US Department of Agriculture rabies laboratories for confirmatory testing and antigenic typing . Acetone-fixed brain impressions were tested by the standard DFA using the pre-calculated optimal working dilutions of two FITC anti-rabies mAb conjugates ( Millipore Sigma Light Diagnostics and Fujirebio Diagnostics ) as per the National Standard Protocol for rabies diagnosis ( https://www . cdc . gov/rabies/pdf/rabiesdfaspv2 . pdf ) . The two mAbs cocktails have different epitope recognition and affinity/avidity differences are required for DFA confirmatory testing . Similarly , a non-rabies antibody FITC conjugate , negative control reagent ( Millipore Sigma Light Diagnostics ) , which contains the same IgG isotypes as the rabies specific antibody , is used for specificity . All controls ( positive and negative ) demonstrated the expected results . Samples demonstrating the presence of rabies-specific antigen in brain impressions ( typical 4+ sparkling apple-green fluorescent inclusions with both anti-rabies conjugates and no specific fluorescence demonstrated with the non-rabies conjugate ) were reported as positive . Based on the level of N protein specific staining , the samples are classified as 1+ to 4+ distribution , where 4+ demonstrate maximum staining ( https://www . cdc . gov/rabies/pdf/rabiesdfaspv2 . pdf ) . While most samples have either 3+ or 4+ antigen levels , around 5% demonstrate lower levels of N protein staining by DFA . For validation with MS assay , we included samples from 1+ to 4+ antigen distribution . If no specific rabies fluorescence was observed in the impressions , the samples were reported as negative . All the RABV positive samples used in this study were subjected to antigenic typing to obtain variant information . Antigenic typing was performed on DFA positive brain samples by indirect fluorescent antibody tests using a panel of twenty mAbs against the RABV N protein ( anti-N mAbs ) . RABV variants were determined by the demonstration of established reaction patterns of terrestrial and bat RABVs based on the recognition of N-protein epitopes by a panel of twenty CDC anti-N mAbs as previously established [23] . For MS assay , based on the availability , samples with different levels of antigen distribution , infected with different RABV variants that circulate in the U . S . , and from different host species were selected . Purified RABV and lysates from control and RABV infected cells were lysed in 1X NuPAGE LDS sample buffer with reducing agent and boiled at 95°C for 10 min . Infected and uninfected brain tissues ( 26 samples ) were homogenized in 1x PBS ( 100 mg in 150 μl ) followed by boiling with LDS sample buffer at 95°C for 10 min . Proteins were separated in a NuPAGE 4% - 12% Bis-Tris protein gels ( proteins are separated under denaturing and reducing conditions ) using NuPAGE MES running buffer and stained with Imperial protein stain ( ThermoFisher ) . Either entire lane ( 12 slices ) or specific portions of the gel were excised and processed for in-gel tryptic digestion . The amount of tissue homogenates subjected to electrophoresis corresponds to about 0 . 6 μg to 2 μg ( 1 μl– 4 μl ) of the samples . Gel slices were processed as follows . They were cut into 1 mm x 1 mm cubes followed by three washes of 50% acetonitrile , 10 mM ammonium bicarbonate and dried in a SpeedVac concentrator . Gel pieces were reduced for 60 min at 37°C using 10 mM 1 , 4-dithiothreitol in 10 mM ammonium bicarbonate and alkylated at room temperature in the dark using 55 mM iodoacetamide in 10 mM ammonium bicarbonate . Gel pieces were again washed three times with 50% acetonitrile , 10 mM ammonium bicarbonate and dried in a SpeedVac concentrator . Samples were rehydrated with sequencing grade modified trypsin ( Promega ) in 10 mM ammonium bicarbonate and allowed to digest over night at 37°C . The supernatant was collected and gel slices were washed three times with 60% acetonitrile , 10 mM ammonium bicarbonate to extract tryptic peptides . The washes and supernatant were collected , combined , and were dried in a SpeedVac concentrator . About 10%– 25% of tryptic digests obtained from gel slices were subjected for MS analysis . Electrospray ionization ( ESI ) mass spectrometric analysis was performed using a Bruker model maXis ESI-Q-TOF instrument interfaced with a CaptiveSpray ESI spray source ( Bruker Daltonics ) to perform liquid chromatography–tandem mass spectrometry ( LC-MS/MS ) using a U3000 RSLCnano HPLC configured for nl/min flows . The Dionex U-3000 RSLCnano nanobore HPLC was configured with a binary nanoflow ultra-high pressure pump and a ternary high pressure microbore pump . The system used a pulled-loop autosampler configured with a 20 μl sample loop . A desalting trap column ( 0 . 3 mm x 5 mm , 5 μm C18 PepMap 120 Å , Dionex ) was used and the analytical column used was a C18 PepMap ( 0 . 075 mm x 250 mm , 2 μm , 120 Å , Dionex ) . The solvents used were 0 . 1% formic acid in water ( A ) and 80% acetonitrile / 0 . 1% formic acid ( B ) . The gradient was 2%– 55% B in 90 min . The eluent from the analytical column was introduced into the maXis using the Bruker CaptiveSpray source . The source was operated at a spray voltage of 1200 V with a drying gas of nitrogen flowing at 4 liters per min . The capillary temperature was set to 150°C . The mass spectrometer was set to acquire spectra of m/z 50 to 2500 . MS/MS data was acquired in an automated fashion using a dynamic precursor ion selection based on the MS scan with precursor ion active exclusion for 60 s after at least 1 spectrum was acquired for each precursor ion . MS data was acquired at a scan speed of 10 Hz and MS/MS data was acquired at a scan speed of 2 Hz– 10 Hz depending on the intensity of the precursor ion . Total cycle time for acquisition of both MS and MS/MS scans was limited to 2 . 2 s . MS internal calibration was achieved by the use of a lock mass ( HP-1222 , Agilent Technologies ) . The collected data was processed by DataAnalysis ( Bruker Daltonics ) to produce deconvoluted and internally calibrated data and saved as an xml peaklist , which was uploaded to our Proteinscape database ( Bruker Daltonics ) . Proteinscape automatically submitted the peaklist to our in-house MASCOT server for searches against either the Swiss-Prot curated protein Fasta file or a taxonomic filtered data from NCBI’s RefSeq database . The taxonomic filters applied were human and virus . N protein sequences from RABV variants utilized for this study were obtained from Genbank . The sequences were aligned using Clustal Omega software ( https://www . ebi . ac . uk/Tools/msa/clustalo/ ) to obtain consensus . The peptide fragments sequenced by mass spectrometry were highlighted to demonstrate the conservation or differences observed across various RABV variants . To evaluate the potential for mass spectrometric analysis to characterize RABV , purified RABV ERA variant was separated on a protein gel , stained with Imperial protein stain , and subjected to in-gel protein digestion followed by spectrometry ( Fig 1A ) . In the first MS step , based on the experimental peptide mass detected , potential proteins ( with theoretical tryptic peptide mass ) were identified . In the second MS step , certain precursor peptide ions were subjected to low energy collision and fragmentation into product ions , namely “b” and “y” ions ( representing N- and C- terminal fragments respectively ) and masses are determined ( Fig 1C ) . As multiple fragments were generated , by comparison of a panel of “b” and “y” ion fragment masses , it was possible to determine amino acid sequence . Based on the peptide mass fingerprint and fragment mass based amino acid sequence of peptides by MS/MS , all five RABV encoded proteins were identified by MS at expected molecular weight positions in the gel slices ( Fig 1A ) . In addition , based on the amino acid sequence determined peptides ( denoted in red ) , N protein present in the sample was clearly identified as RABV ERA variant ( Fig 1B ) . The amino acid sequence coverage by MS/MS for other proteins and the number of unique peptides and percentage of coverage are provided ( S1A–S1E Fig ) . Thus , with sufficient peptide concentrations , fragment mass guided amino acid sequence determination by MS/MS can differentiate and identify the RABV variant present in the sample . MS has the advantage to detect and perform sequence determination for potential variant typing in a single test . To determine the specificity of MS for RABV detection , MNA cell lysates from mock or RABV infected samples were separated on SDS-PAGE , stained by Imperial protein stain , and the entire lane was excised into 12 slices , subjected to in-gel tryptic digestion and analyzed by MS ( Fig 2 ) . All five RABV proteins were detected specifically in infected cell lysates at expected molecular weight by MS ( as indicated in the gel slices in Fig 2 ) . None of the RABV encoded proteins were detected in any of the 12 slices from the uninfected cell lysate . N protein detection was the highest based on the number of unique peptides and percentage of amino acid sequence coverage , followed by P , M , G and L proteins ( S2A–S2E Fig ) . In addition , based on amino acid sequence determination by MS/MS fragmentation , N , P and G proteins were identified as RABV CVS-11 variant . The number of unique peptides and amino acid sequence coverage are presented in S2F Fig . Rabies diagnosis primarily focuses on detection of antigen ( N protein ) in CNS tissues of animals suspected for rabies . For DFA , touch impressions of brain tissues on slides are tested with anti-N protein specific mAbs or polyclonal Abs . For mass spectrometric detection , tissue homogenates were heat inactivated and separated in a 4%– 12% Bis-Tris protein gel . To limit the number of samples subjected to MS , only the portion of gel corresponding to either N or G proteins ( based on the mobility of N and G protein bands in purified RABV ERA ) were sliced after staining the gel ( Fig 3A ) . N protein was detected by MS in samples 5 and 6 , previously determined positive by DFA . All negative or inconclusive samples were also negative for RABV N protein by MS ( Fig 3B ) . In addition , based on amino acid sequence of unique peptide fragments , both positive samples were correctly identified as either Eastern Raccoon ( E . Raccoon ) or Tadarida brasiliensis ( bat ) variants of RABV ( Fig 3C , described in bioinformatic analysis results section ) . Although gel slices corresponding to the position of G protein mobility were subjected to MS , the G protein was not detected . The limit of detection of RABV N protein was determined by spiking uninfected cell lysate with purified RABV ERA virus . Different amounts of purified ERA virus at 1 . 0 X 107 focus forming unit ( ffu ) / ml ( from 4 μl to 0 . 01 μl ) were added to an equal volume of uninfected cell lysate , and were separated by protein gel followed by staining with Imperial protein stain ( Fig 4A ) . The gel position corresponding to N protein was sliced and subjected to MS . N protein was detected in five subsequent dilutions ( or equivalent to 5 . 0 X 102 ffu viral particles ) based on at least one peptide above the background values ( Fig 4B ) . At higher dilutions , N protein variant information was not obtained by MS/MS fragmentation analysis probably due to low concentration of peptides . This demonstrates the concentration dependence of MS/MS method for detection of N protein and determination of RABV variants . To compare sensitivity of MS for N protein detection with DFA , samples containing different amounts of antigen were tested . Although , DFA is not quantitative , the relative amount of antigen in tissue impressions can be obtained based on the fluorescence intensity and distribution after observation of all microscopic fields . For example , DFA results grade samples from a minimum ( 1 ) to maximum ( 4 ) , with 4+/4+ being maximum fluorescent intensity and distribution , respectively . Generally , anti-N mAbs are titrated to obtain a maximum fluorescent intensity of 4+ . Based on the distribution of antigen in microscopic fields , relative levels of N protein in samples are graded from 1+ to 4+ . To assess the utility of MS to detect N protein from samples with different RABV variants observed in the U . S . from different species of infected animals and different amounts of antigen , a panel of samples previously analyzed by DFA were tested with the MS assay ( S3A and S3B Fig ) . In general , MS positively identified samples that had higher N protein concentrations as determined by DFA ( 3+ or 4+ distribution ) , while samples with lower concentrations ( 1+ or 2+ ) were not detected with the instrument used ( Table 1 ) . In addition , from the amino acid sequence information obtained by MS/MS fragmentation , E . Raccoon and North Central Skunk ( NC Skunk ) variants were correctly identified by general database search in two samples ( Table 1 ) . Of all the samples tested , G protein was detected only in sample 17 by the MS method . Sample–source denotes the CNS tissues of host animals , while RABV variant corresponds to samples from DFA positive CNS tissues determined by antigenic typing . E Raccoon–RABV Raccoon variant observed in Eastern U . S . ; NC Skunk and SC Skunk–corresponds to RABV variants circulating in skunks from either North Central or South Central U . S . , respectively . AZ gray fox and Arctic fox variants of RABV observed in Arizona and Alaska , respectively . DFA–Intensity / distribution–corresponds to the semi-quantitative classification of N protein levels based on distribution of anti-N mAb-FITC conjugate on tissue impressions . Mass spectrometry–Results demonstrate detection of N protein by MS and RABV variant corresponds to identification of RABV variants based on amino acid sequence determination and analysis . MS/MS fragment mass based amino acid sequence determination has the potential to differentiate RABV variants if peptides containing unique sequences for RABV variants are determined . The list of peptides that contained amino acid residues unique to RABV variants are provided in S1 Spreadsheet . Even though several peptides were identified in both ERA and CVS-11 samples due to higher concentration of N protein in purified virus or experimentally infected samples , only certain peptides could differentiate RABV variants . For ERA , in the peptide VNNQVVSLKPEIIVDQHEYK , H was unique ( bold and underlined ) compared to other variants ( Fig 5 ) . Similarly , in the peptide TDVDGNWALTGGMELTR , aspartic acid ( D ) was unique to CVS-11 N protein sequence . In the subsequent analysis , RABV variants were determined from infected brain tissues corresponding to E . Raccoon , T . brasiliensis ( Fig 3C ) and NC Skunk RABV variants ( Table 1 ) , while variant information for other positive samples by MS was not obtained by default search results . To improve variant identification , we performed a manual search of peptide fragments based on the Clustal alignment of different N protein sequences for RABV variants used in the study ( Fig 5 ) . As the amino acid sequence identity ranged from 94%– 98% ( Fig 6 ) , unless peptides containing differences in amino acid sequences are ionized and detected , RABV variant information could not be determined . Identification of RABV E . Raccoon variant in sample 5 was based on amino acid sequence derived from the peptide “DPTIPEHASLVGLLLSYLYR” ( Fig 7 , S4 Fig ) . In this peptide , amino acid residues in position 4 ( "I” ) and position 5 ( “P” ) were unique to E . Raccoon variant . Two additional unique peptides “ELQDYEAAELTK” and “KPSISLGK” for E . Raccoon variant were identified in sample 11 , in which “D” in amino acid position 4 and “S” in amino acid positions 3 and 5 were present only in this N protein sequence . Interestingly , in one of the peptides derived from NC Skunk variant , “QINLTAGEAILYFFHK” , the highlighted “G” in amino acid position 7 is unique and it replaces either a “K” or a “R” in other variants . Since the peptides are generated by proteolytic cleavage with trypsin which cleaves after”K” or “R” residues , this peptide is truncated in other variants leading to this longer peptide only being detected in the NC Skunk variant . Based on the peptide analysis from MS/MS results , South Central ( SC ) Skunk and Arctic Fox RABV variants were identified . Details of amino acid sequences of peptides utilized for RABV variant identification are provided in S1 Spreadsheet . Only the Arizona ( AZ ) gray fox variant was not resolved by MS/MS due to the absence of unique peptides . The list of all peptides derived from each sample is provided in a spreadsheet ( S2 Spreadsheet ) . Based on this list , a set of 10 peptides ( Fig 5 , underlined and boxed ) detected in tissue samples and frequency of detection ( not considering MS results from purified ERA and infected cell lysate CVS-11 ) were presented in Table 2 . Although , not directly comparable , one of the peptides at the N terminus “VNNQVVSLKPEIIVDQHEYK” encompasses the target for the recently validated LN34 real time RT-PCR [24] . Rabies is a zoonotic disease transmitted by the bite of a lyssavirus infected animal . Due to the lack of specific anti-virals or therapeutics , rabies is considered to have one of the highest case fatality rates for any of the infectious agents after symptom onset [16] . Unfortunately , human rabies diagnosis is not available during the pre-symptomatic phase , which encompasses the time from virus exposure to the establishment of viral replication in the brain [25] . The testing of suspected rabid animals for RABV infection is important to initiate post-exposure prophylaxis for rabies disease prevention in humans who have been bitten . Current rabies diagnostic reagents have been selected by comparison studies to be broadly reactive to highly conserved lyssavirus epitopes , and can be used to detect all of the RABV and lyssaviruses to date albeit at different levels of sensitivity . With new lyssaviruses being discovered , it will be necessary to have reagents that can detect N protein without compromising the specificity of detection . In addition , since the assay depends on the use of antibodies , variations in reagent batches and lots due to purification and conjugation procedures might affect the functionality of the assay . Due to the high expression level of N protein and the characteristic inclusions formed in the cytoplasm of infected cells , microscopy based methods are extremely reliable for rabies diagnosis . Specifically , the DFA is considered the gold standard for rabies diagnostics in animals [16] . Several modifications to detection methods , primary antibodies , and experimental procedures have been developed over the years to detect N protein . Two studies have attempted to determine metabolomics changes as a measure during the initial stages of rabies , however , it still requires additional data to be considered for routine diagnosis [26 , 27] . Non-immunomicroscopy based detection of N protein has utilized rapid , point-of-care platforms , including lateral flow assays [19 , 21 , 28] . These assays rely on two different antibodies that react with N protein , one for antigen capture and the other for detection in diagnostics . Although several products are currently in the market , the results are mixed . The specificity and sensitivity varies with different kits including the limits of detection . The variability in sensitivity raises an important concern with rapid tests and the potential incidence of false negatives [19] , that could result in inappropriate recommendations regarding the need for PEP . Although some reagents and protocols demonstrated a high level of concordance with the reference technique DFA [21] and could be utilized for resource limited areas , these assays requires additional confirmatory testing . Direct detection and sequencing of proteins as a diagnostic for viral infection has not been widely attempted . As each protein has unique amino acid composition and peptide mass fingerprint , it can be used for the specific detection of target proteins . In addition , amino acid sequence of peptides can be determined by MS/MS fragment mass analysis , which further improves the confidence level for protein identifications . In this study , we first demonstrated the ability to detect and obtain RABV variant information for all five RABV encoded proteins by MS in purified RABV particles ( Fig 1 and S1 Fig ) . The specificity of MS was next demonstrated using in vitro infected or uninfected cell culture lysates . RABV expressed proteins were detected only from the infected lysate and three of the five proteins had unique sequence information to classify as CVS-11 variant ( Fig 2 and S2 Fig ) . RABV encompasses different variants , primarily based on the adaptation for efficient replication in host reservoir species . These RABV variants have distinct differences in amino acid sequences of encoded proteins as identified in seven major terrestrial wildlife hosts in the U . S . and territories . These include raccoons ( E . Raccoon , in East ? ? ) , Skunks ( in NC and SC variants ) , foxes ( AZ Gray , Texas Gray and Arctic [Alaska] ) , mongoose ( in Puerto Rico ) , and at least 14 RABV variants associated with different bat species which are ubiquitous in the U . S . In this study , we demonstrate detection of N protein in clinical specimens from major RABV variants observed in the U . S . The two major observations obtained from this study are , the ability ( 1 ) to detect N protein from crude tissue preparation without any amplification or N protein specific reagents and ( 2 ) to obtain amino acid sequence information for further confirmation and identification of RABV variants . Samples that were classified as 3–4+ antigen distribution by DFA , were predominantly positive by MS , while samples with lower N protein content were not sensitive enough for detection using the Bruker maXis QTOF instrument . Of the 18 DFA positive samples tested , N protein was detected by MS in 11 samples ( 61% sensitivity ) . As majority of samples have higher levels of antigen , the MS assay described in this study would have higher levels of sensitivity in actual field samples . In addition to confirmation of N protein in tissue samples , all but one of the RABV variants were determined based on amino acid sequence information . The current N protein detection method by immunomicroscopy requires two separate tests for RABV variant determination . Once RABV N proteins are confirmed by DFA , a panel of 20 anti-N mAbs that bind differentially to RABV variants are tested by IFA . Based on the pattern of reactivity of these anti-N mAbs and comparison with established terrestrial and bat variant patterns , the RABV variant is identified . In spite of high level of sequence identity exhibited by N protein , amino acid sequencing of unique peptides were able to differentiate RABV variants ( Fig 5 ) . This is particularly significant as only changes in nucleotides that results in amino acid change ( non-synonymous substitutions ) are detected in MS , compared to both synonymous and non-synonymous substitutions obtained by DNA sequencing . Based on MS/MS results , high-resolution peptide sequence analysis that can differentiate E . Raccoon , T . brasiliensis , NC Skunk , SC Skunk and Arctic Fox RABV variants were identified ( Fig 5 , Fig 7 , S1 Spreadsheet ) . Thus , our results demonstrate for the first time N protein detection and sequencing by MS/MS directly from RABV infected animal CNS tissue samples . Although we also attempted to detect G protein in infected tissues , except for one sample , it was not detected by MS . The detection of N and not G protein , further substantiates the current diagnostic methods focus on N protein detection due to higher level of expression followed by RABV infection . The MS method was successful in identifying several major RABV variants circulating in the U . S . and from several different host animal species . The results from this study provide information on peptides derived from the N protein that are readily ionizable and detectable by MS . Peptides conserved across all RABV variants or containing unique sequences for differentiation of variants could be employed for either species- or variant- specific MS-based assays ( S1 Spreadsheet ) . Furthermore the development of a PRM ( parallel reaction monitoring ) type target based assay , would allow for multiplexing since the MS instrumentation can be programmed to scan for dozens of potential targets and for the selective quantification of proteins in samples [29 , 30] . While this study focused on RABV variants that circulate in the U . S . , MS analysis of RABV variants or non-rabies lyssaviruses observed in other countries will be helpful to generate a peptide database for species and variant specific detections . In this study , we have explored MS as an alternative method and obtained preliminary data on the feasibility for rabies N protein detection . We will further explore the possibility to detect N protein directly from brain suspensions , instead of separating by protein gels using PRM methods to improve the sensitivity of detection compared to DFA . Although , MS-based assays may not be cost effective at present , it could be an additional antigen/protein detection method relying not on antibodies and microscopy at reference laboratories . With advancement in MS and increased use in clinical laboratories , it offers a next generation technology platform for exploring rabies antigen detection . Another major advantage of recent “-omics” technologies are identification of multiple pathogens or pathogen discovery from a single assay . As the MS-based assay is unbiased and does not need specific reagents , it can be employed for pathogen identification for samples with unknown etiology ( negative for RABV , but exhibiting neurological symptoms ) by “characterization of proteomics” similar to “metagenomics” approach .
Although rabies is almost always fatal after the symptom onset phase , it can be prevented by timely administration of post-exposure prophylaxis ( PEP ) , which involves passive antibody transfer and vaccination . One of the primary laboratory confirmatory tests for RABV infection is antigen detection , directed against the RABV encoded N protein using anti-N protein specific antibodies , in central nervous system ( CNS ) tissue samples of animals . This immuno-microscopy based detection utilizes either fluorescent tags ( direct detection ) or chromogenic substrates ( indirect ) in brain impressions from animals in which rabies is suspected . In this study , we explored the detection of N protein by a novel mass spectrometry ( MS ) based method that is label-free and does not require target amplification . The MS method specifically detected N protein in brain tissue and identified RABV variants based on amino acid sequence information . To our knowledge , this is the first report of an N protein detection method that does not utilize either antibodies or microscopy . This method provides an alternative platform for the development of future rabies diagnostic tests .
[ "Abstract", "Introduction", "Methods", "Results", "Discussion" ]
[ "sequencing", "techniques", "medicine", "and", "health", "sciences", "immune", "physiology", "tissue", "proteins", "pathology", "and", "laboratory", "medicine", "pathogens", "immunology", "tropical", "diseases", "microbiology", "vertebrates", "animals", "mammals", "amino", "acid", "sequence", "analysis", "viruses", "rabies", "raccoons", "rna", "viruses", "neglected", "tropical", "diseases", "molecular", "biology", "techniques", "antibodies", "research", "and", "analysis", "methods", "rabies", "virus", "sequence", "analysis", "immune", "system", "proteins", "infectious", "diseases", "zoonoses", "bioinformatics", "proteins", "medical", "microbiology", "microbial", "pathogens", "biological", "databases", "molecular", "biology", "biochemistry", "lyssavirus", "eukaryota", "sequence", "databases", "database", "and", "informatics", "methods", "viral", "pathogens", "physiology", "biology", "and", "life", "sciences", "viral", "diseases", "amniotes", "protein", "sequencing", "organisms" ]
2018
Novel mass spectrometry based detection and identification of variants of rabies virus nucleoprotein in infected brain tissues